Mercurial > repos > george-weingart > maaslin
annotate maaslin-4450aa4ecc84/src/lib/AnalysisModules.R @ 1:a87d5a5f2776
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author | george-weingart |
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date | Sun, 08 Feb 2015 23:08:38 -0500 |
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1 ##################################################################################### |
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2 #Copyright (C) <2012> |
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3 # |
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4 #Permission is hereby granted, free of charge, to any person obtaining a copy of |
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5 #this software and associated documentation files (the "Software"), to deal in the |
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6 #Software without restriction, including without limitation the rights to use, copy, |
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7 #modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, |
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8 #and to permit persons to whom the Software is furnished to do so, subject to |
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9 #the following conditions: |
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10 # |
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11 #The above copyright notice and this permission notice shall be included in all copies |
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12 #or substantial portions of the Software. |
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13 # |
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14 #THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, |
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15 #INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A |
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16 #PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT |
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17 #HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION |
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18 #OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE |
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19 #SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
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20 # |
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21 # This file is a component of the MaAsLin (Multivariate Associations Using Linear Models), |
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22 # authored by the Huttenhower lab at the Harvard School of Public Health |
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23 # (contact Timothy Tickle, ttickle@hsph.harvard.edu). |
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24 ##################################################################################### |
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25 |
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26 inlinedocs <- function( |
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27 ##author<< Curtis Huttenhower <chuttenh@hsph.harvard.edu> and Timothy Tickle <ttickle@hsph.harvard.edu> |
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28 ##description<< Allows one to plug in new modules to perform analysis (univariate or multivariate), regularization, and data (response) transformation. |
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29 ) { return( pArgs ) } |
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30 |
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31 # Libraries |
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32 suppressMessages(library( agricolae, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE)) |
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33 # Needed for the pot-hoc Kruskal wallis comparisons |
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34 suppressMessages(library( penalized, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE)) |
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35 # Needed for stepAIC |
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36 suppressMessages(library( MASS, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE)) |
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37 # Needed for na action behavior |
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38 suppressMessages(library( gam, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE)) |
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39 # Needed for boosting |
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40 suppressMessages(library( gbm, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE)) |
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41 # Needed for LASSO |
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42 suppressMessages(library( glmnet, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE)) |
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43 # Needed for mixed models |
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44 #suppressMessages(library( lme4, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE)) |
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45 suppressMessages(library( nlme, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE)) |
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46 |
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47 # Needed for zero inflated models |
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48 #suppressMessages(library( MCMCglmm, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE)) |
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49 suppressMessages(library( pscl, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE)) |
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50 suppressMessages(library( gamlss, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE)) |
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51 # Do not use #suppressMessages(library( glmmADMB, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE)) |
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52 |
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53 fAddBack = TRUE |
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54 dUnevenMax = .9 |
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55 |
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56 |
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57 ### Helper functions |
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58 # OK |
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59 funcMakeContrasts <- function |
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60 ### Makes univariate contrasts of all predictors in the model formula with the response. |
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61 (strFormula, |
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62 ### lm style string defining reponse and predictors |
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63 strRandomFormula, |
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64 ### mixed model string defining the fixed covariates |
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65 frmeTmp, |
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66 ### The data frame to find predictor data in |
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67 iTaxon, |
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68 ### Taxon |
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69 functionContrast, |
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70 ### functionContrast The univariate test to perform |
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71 lsQCCounts |
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72 ### QC info |
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73 ){ |
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74 #TODO are we updating the QCCounts? |
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75 lsSig = list() |
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76 ### Holds all the significance results from the tests |
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77 adP = c() |
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78 ### Holds the p-values |
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79 sCurDataName = names(frmeTmp)[iTaxon] |
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80 ### The name of the taxon (data row) that is being associated (always assumed to be numeric) |
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81 #Get test comparisons (predictor names from formula string) |
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82 asComparisons = unique(c(funcFormulaStrToList(strFormula),funcFormulaStrToList(strRandomFormula))) |
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83 |
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84 #Change metadata in formula to univariate comparisons |
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85 for(sComparison in asComparisons) |
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86 { |
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87 # Metadata values |
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88 vxTest = frmeTmp[[sComparison]] |
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89 |
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90 # Get the levels in the comparison |
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91 # Can ignore the first level because it is the reference level |
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92 asLevels = sComparison |
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93 if(is.factor(vxTest)){asLevels = levels(vxTest)[2:length(vxTest)]} |
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94 |
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95 lasComparisonResults = functionContrast(x=sComparison, adCur=frmeTmp[[sCurDataName]], dfData=frmeTmp) |
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96 for(asComparison in lasComparisonResults) |
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97 { |
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98 if( is.na( asComparison$p.value ) ) { next } |
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99 # Get pvalue |
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100 adP = c(adP, asComparison$p.value) |
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101 # Get SD, if not available, give SD of covariate |
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102 dSTD = asComparison$SD |
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103 # TODO Is using sd on factor and binary data correct? |
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104 if(is.na(dSTD) || is.null(dSTD)){dSTD = sd(vxTest)} |
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105 |
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106 lsSig[[length( lsSig ) + 1]] <- list( |
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107 #Current metadata name (string column name) ok |
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108 name = sComparison, |
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109 #Current metadatda name (string, as a factor level if existing as such) ok |
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110 orig = asComparison$name, |
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111 #Taxon feature name (string) ok |
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112 taxon = colnames( frmeTmp )[iTaxon], |
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113 #Taxon data / response (double vector) ok |
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114 data = frmeTmp[,iTaxon], |
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115 #Name of column ok |
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116 factors = sComparison, |
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117 #Metadata values (metadata as a factor or raw numeric) ok |
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118 metadata = vxTest, |
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119 #Current coefficient value (named coef value with level name (from coefs) ok |
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120 value = asComparison$coef, |
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121 #Standard deviation (numeric) ok |
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122 std = dSTD, |
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123 #Model coefficients (output from coefs with intercept) ok |
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124 allCoefs = asComparison$coef) |
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125 } |
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126 } |
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127 return(list(adP=adP, lsSig=lsSig, lsQCCounts=lsQCCounts)) |
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128 ### Returns a list of p-value, standard deviation, and comparison which produced the p-value |
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129 } |
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130 |
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131 #Ok |
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132 funcGetStepPredictors <- function |
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133 ### Retrieve the predictors of the reduced model after stepwise model selection |
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134 (lmod, |
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135 ### Linear model resulting from step-wise model selection |
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136 frmeTmp, |
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137 strLog |
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138 ### File to document logging |
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139 ){ |
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140 #Document |
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141 funcWrite( "#model", strLog ) |
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142 funcWrite( lmod$fit, strLog ) |
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143 #TODO funcWrite( lmod$train.error, strLog ) |
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144 #TODO funcWrite( lmod$Terms, strLog ) |
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145 funcWrite( "#summary-gbm", strLog ) |
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146 funcWrite( summary(lmod), strLog ) |
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147 |
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148 #Get Names from coefficients |
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149 asStepCoefsFactors = coefficients(lmod) |
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150 astrCoefNames = setdiff(names(asStepCoefsFactors[as.vector(!is.na(asStepCoefsFactors))==TRUE]),"(Intercept)") |
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151 asStepCoefsFactors = unique(as.vector(sapply(astrCoefNames,funcCoef2Col, frmeData=frmeTmp))) |
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152 |
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153 if(length(asStepCoefsFactors)<1){ return(NA) } |
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154 return( asStepCoefsFactors ) |
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155 ### Vector of string predictor names |
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156 } |
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157 |
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158 funcGetUnivariateResults <- function |
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159 ### Reduce the list of list of results to the correct format |
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160 ( llmod, |
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161 ### The list of univariate models |
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162 frmeData, |
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163 ### Data analysis is performed on |
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164 liTaxon, |
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165 ### The response id |
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166 dSig, |
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167 ### Significance level for q-values |
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168 adP, |
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169 ### List of pvalues from all associations performed |
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170 lsSig, |
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171 ### List of information from the lm containing, metadata name, metadatda name (as a factor level if existing as such), Taxon feature name, Taxon data / response, All levels, Metadata values, Current coeficient value, Standard deviation, Model coefficients |
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172 strLog, |
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173 ### File to which to document logging |
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174 lsQCCounts, |
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175 ### Records of counts associated with quality control |
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176 lastrCols, |
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177 ### Predictors used in the association |
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178 asSuppressCovariates=c() |
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179 ### Vector of covariates to suppress and not give results for |
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180 ){ |
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181 adP = c() |
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182 lsSig = list() |
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183 for(lmod in llmod) |
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184 { |
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185 adP = c(adP,lmod$adP) |
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186 lsSig = c(lsSig,lmod$lsSig) |
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187 } |
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188 return(list(adP=adP, lsSig=lsSig, lsQCCounts=llmod[length(llmod)]$lsQCCounts)) |
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189 } |
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190 |
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191 # OK |
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192 funcGetLMResults <- function |
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193 ### Reduce the lm object return to just the data needed for further analysis |
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194 ( llmod, |
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195 ### The result from a linear model |
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196 frmeData, |
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197 ### Data analysis is performed on |
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198 liTaxon, |
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199 ### The response id |
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200 dSig, |
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201 ### Significance level for q-values |
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202 adP, |
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203 ### List of pvalues from all associations performed |
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204 lsSig, |
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205 ### List of information from the lm containing, metadata name, metadatda name (as a factor level if existing as such), Taxon feature name, Taxon data / response, All levels, Metadata values, Current coeficient value, Standard deviation, Model coefficients |
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206 strLog, |
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207 ### File to which to document logging |
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208 lsQCCounts, |
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209 ### Records of counts associated with quality control |
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210 lastrCols, |
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211 ### Predictors used in the association |
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212 asSuppressCovariates=c() |
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213 ### Vector of covariates to suppress and not give results for |
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214 ){ |
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215 ilmodIndex = 0 |
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216 for( lmod in llmod ) |
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217 { |
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218 ilmodIndex = ilmodIndex + 1 |
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219 lmod = llmod[[ ilmodIndex ]] |
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220 iTaxon = liTaxon[[ ilmodIndex ]] |
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221 astrCols = lastrCols[[ ilmodIndex ]] |
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222 |
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223 #Exclude none and errors |
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224 if( !is.na( lmod ) && ( class( lmod ) != "try-error" ) ) |
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225 { |
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226 #holds the location of the pvlaues if an lm, if lmm is detected this will be changed |
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227 iPValuePosition = 4 |
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228 |
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229 #Get the column name of the iTaxon index |
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230 #frmeTmp needs to be what? |
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231 strTaxon = colnames( frmeData )[iTaxon] |
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232 #Get summary information from the linear model |
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233 lsSum = try( summary( lmod ) ) |
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234 #The following can actually happen when the stranger regressors return broken results |
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235 if( class( lsSum ) == "try-error" ) |
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236 { |
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237 next |
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238 } |
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239 |
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240 #Write summary information to log file |
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241 funcWrite( "#model", strLog ) |
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242 funcWrite( lmod, strLog ) |
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243 funcWrite( "#summary", strLog ) |
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244 #Unbelievably, some of the more unusual regression methods crash out when _printing_ their results |
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245 try( funcWrite( lsSum, strLog ) ) |
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246 |
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247 #Get the coefficients |
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248 #This should work for linear models |
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249 frmeCoefs <- try( coefficients( lsSum ) ) |
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250 |
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251 if( ( class(frmeCoefs ) == "try-error" ) || is.null( frmeCoefs ) ) |
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252 { |
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253 adCoefs = try( coefficients( lmod )) |
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254 if(class( adCoefs ) == "try-error") |
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255 { |
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256 adCoefs = coef(lmod) |
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257 } |
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258 frmeCoefs <- NA |
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259 } else { |
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260 if( class( frmeCoefs ) == "list" ) |
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261 { |
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262 frmeCoefs <- frmeCoefs$count |
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263 } |
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264 adCoefs = frmeCoefs[,1] |
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265 } |
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266 |
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267 #Go through each coefficient |
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268 astrRows <- names( adCoefs ) |
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269 |
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270 ##lmm |
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271 if( is.null( astrRows ) ) |
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272 { |
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273 astrRows = rownames( lsSum$tTable ) |
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274 frmeCoefs = lsSum$tTable |
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275 iPValuePosition = 5 |
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276 adCoefs = frmeCoefs[,1] |
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277 } |
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278 |
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279 for( iMetadata in 1:length( astrRows ) ) |
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280 { |
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281 #Current coef which is being evaluated |
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282 strOrig = astrRows[ iMetadata ] |
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283 #Skip y interscept |
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284 if( strOrig %in% c("(Intercept)", "Intercept", "Log(theta)") ) { next } |
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285 #Skip suppressed covariates |
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286 if( funcCoef2Col( strOrig, frmeData ) %in% asSuppressCovariates){ next } |
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287 |
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288 #Extract pvalue and std in standard model |
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289 dP = frmeCoefs[ strOrig, iPValuePosition ] |
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290 dStd = frmeCoefs[ strOrig, 2 ] |
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291 |
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292 #Attempt to extract the pvalue and std in mixed effects summary |
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293 #Could not get the pvalue so skip result |
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294 if( is.nan( dP ) || is.na( dP ) || is.null( dP ) ) { next } |
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295 |
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296 dCoef = adCoefs[ iMetadata ] |
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297 |
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298 #Setting adMetadata |
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299 #Metadata values |
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300 strMetadata = funcCoef2Col( strOrig, frmeData, astrCols ) |
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301 if( is.na( strMetadata ) ) |
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302 { |
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303 if( substring( strOrig, nchar( strOrig ) - 1 ) == "NA" ) { next } |
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304 c_logrMaaslin$error( "Unknown coefficient: %s", strOrig ) |
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305 } |
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306 if( substring( strOrig, nchar( strMetadata ) + 1 ) == "NA" ) { next } |
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307 adMetadata <- frmeData[, strMetadata ] |
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308 |
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309 # Store (factor level modified) p-value |
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310 # Store general results for each coef |
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311 adP <- c( adP, dP ) |
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312 |
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313 # Append to the list of information about associations |
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314 lsSig[[ length( lsSig ) + 1 ]] <- list( |
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315 # Current metadata name |
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316 name = strMetadata, |
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317 # Current metadatda name (as a factor level if existing as such) |
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318 orig = strOrig, |
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319 # Taxon feature name |
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320 taxon = strTaxon, |
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321 # Taxon data / response |
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322 data = frmeData[, iTaxon ], |
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323 # All levels |
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324 factors = c( strMetadata ), |
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325 # Metadata values |
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326 metadata = adMetadata, |
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327 # Current coeficient value |
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328 value = dCoef, |
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329 # Standard deviation |
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330 std = dStd, |
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331 # Model coefficients |
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332 allCoefs = adCoefs ) |
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333 } |
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334 } |
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335 } |
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336 return( list( adP = adP, lsSig = lsSig, lsQCCounts = lsQCCounts ) ) |
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337 ### List containing a list of pvalues, a list of significant data per association, and a list of QC data |
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338 } |
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339 |
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340 funcGetZeroInflatedResults <- function |
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341 ### Reduce the lm object return to just the data needed for further analysis |
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342 ( llmod, |
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343 ### The result from a linear model |
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344 frmeData, |
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345 ### Data analysis is performed on |
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346 liTaxon, |
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347 ### The response id |
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348 dSig, |
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349 ### Significance level for q-values |
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350 adP, |
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351 ### List of pvalues from all associations performed |
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352 lsSig, |
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353 ### List of information from the lm containing, metadata name, metadatda name (as a factor level if existing as such), Taxon feature name, Taxon data / response, All levels, Metadata values, Current coeficient value, Standard deviation, Model coefficients |
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354 strLog, |
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355 ### File to which to document logging |
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356 lsQCCounts, |
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357 ### Records of counts associated with quality control |
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358 lastrCols, |
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359 ### Predictors used in the association |
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360 asSuppressCovariates=c() |
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361 ### Vector of covariates to suppress and not give results for |
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362 ){ |
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363 ilmodIndex = 0 |
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364 for(lmod in llmod) |
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365 { |
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366 ilmodIndex = ilmodIndex + 1 |
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367 lmod = llmod[[ilmodIndex]] |
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368 iTaxon = liTaxon[[ilmodIndex]] |
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369 astrCols = lastrCols[[ilmodIndex]] |
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370 |
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371 #Exclude none and errors |
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372 if( !is.na( lmod ) && ( class( lmod ) != "try-error" ) ) |
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373 { |
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374 #holds the location of the pvlaues if an lm, if lmm is detected this will be changed |
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375 iPValuePosition = 4 |
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376 |
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377 #Get the column name of the iTaxon index |
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378 #frmeTmp needs to be what? |
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379 strTaxon = colnames( frmeData )[iTaxon] |
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380 |
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381 #Write summary information to log file |
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382 funcWrite( "#model", strLog ) |
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383 funcWrite( lmod, strLog ) |
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384 |
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385 #Get the coefficients |
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386 #This should work for linear models |
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387 frmeCoefs <- summary(lmod) |
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388 if(! is.null( frmeCoefs$coefficients$count ) ) # Needed for zeroinfl |
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389 { |
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390 frmeCoefs = frmeCoefs$coefficients$count |
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391 } |
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392 adCoefs = frmeCoefs[,1] |
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393 names(adCoefs) = row.names(frmeCoefs) |
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394 |
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395 funcWrite( "#Coefs", strLog ) |
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396 funcWrite( frmeCoefs, strLog ) |
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397 |
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398 #Go through each coefficient |
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399 astrRows <- row.names( frmeCoefs ) |
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400 |
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401 for( iMetadata in 1:length( astrRows ) ) |
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402 { |
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403 #Current coef which is being evaluated |
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404 strOrig = astrRows[iMetadata] |
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405 #Skip y interscept |
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406 if( strOrig %in% c("(Intercept)", "Intercept", "Log(theta)") ) { next } |
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407 #Skip suppressed covariates |
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408 if( funcCoef2Col(strOrig,frmeData) %in% asSuppressCovariates){next} |
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409 |
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410 #Extract pvalue and std in standard model |
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411 dP = frmeCoefs[strOrig, iPValuePosition] |
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412 if(is.nan(dP)){next} |
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413 dStd = frmeCoefs[strOrig,2] |
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414 |
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415 dCoef = adCoefs[iMetadata] |
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416 |
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417 #Setting adMetadata |
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418 #Metadata values |
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419 strMetadata = funcCoef2Col( strOrig, frmeData, astrCols ) |
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420 if( is.na( strMetadata ) ) |
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421 { |
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422 if( substring( strOrig, nchar( strOrig ) - 1 ) == "NA" ) { next } |
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423 c_logrMaaslin$error( "Unknown coefficient: %s", strOrig ) |
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424 } |
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425 if( substring( strOrig, nchar( strMetadata ) + 1 ) == "NA" ) { next } |
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426 adMetadata <- frmeData[,strMetadata] |
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427 |
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428 #Store (factor level modified) p-value |
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429 #Store general results for each coef |
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430 adP <- c(adP, dP) |
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431 lsSig[[length( lsSig ) + 1]] <- list( |
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432 #Current metadata name |
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433 name = strMetadata, |
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434 #Current metadatda name (as a factor level if existing as such) |
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435 orig = strOrig,# |
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436 #Taxon feature name |
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437 taxon = strTaxon, |
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438 #Taxon data / response |
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439 data = frmeData[,iTaxon], |
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440 #All levels |
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441 factors = c(strMetadata), |
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442 #Metadata values |
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443 metadata = adMetadata, |
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444 #Current coeficient value |
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445 value = dCoef, |
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446 #Standard deviation |
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447 std = dStd, |
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448 #Model coefficients |
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449 allCoefs = adCoefs) |
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450 } |
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451 } |
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452 } |
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453 return(list(adP=adP, lsSig=lsSig, lsQCCounts=lsQCCounts)) |
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454 ### List containing a list of pvalues, a list of significant data per association, and a list of QC data |
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455 } |
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456 |
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457 oldfuncGetZeroInflatedResults <- function |
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458 ### Reduce the lm object return to just the data needed for further analysis |
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459 ( llmod, |
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460 ### The result from a linear model |
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461 frmeData, |
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462 ### Data analysis is performed on |
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463 liTaxon, |
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464 ### The response id |
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465 dSig, |
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466 ### Significance level for q-values |
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467 adP, |
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468 ### List of pvalues from all associations performed |
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469 lsSig, |
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470 ### List of information from the lm containing, metadata name, metadatda name (as a factor level if existing as such), Taxon feature name, Taxon data / response, All levels, Metadata values, Current coeficient value, Standard deviation, Model coefficients |
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471 strLog, |
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472 ### File to which to document logging |
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473 lsQCCounts, |
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474 ### Records of counts associated with quality control |
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475 lastrCols, |
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476 ### Predictors used in the association |
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477 asSuppressCovariates=c() |
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478 ### Vector of covariates to suppress and not give results for |
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479 ){ |
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480 ilmodIndex = 0 |
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481 for(lmod in llmod) |
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482 { |
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483 ilmodIndex = ilmodIndex + 1 |
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484 lmod = llmod[[ilmodIndex]] |
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485 iTaxon = liTaxon[[ilmodIndex]] |
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486 astrCols = lastrCols[[ilmodIndex]] |
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487 |
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488 #Exclude none and errors |
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489 if( !is.na( lmod ) && ( class( lmod ) != "try-error" ) ) |
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490 { |
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491 #holds the location of the pvlaues if an lm, if lmm is detected this will be changed |
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492 iPValuePosition = 4 |
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493 |
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494 #Get the column name of the iTaxon index |
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495 #frmeTmp needs to be what? |
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496 strTaxon = colnames( frmeData )[iTaxon] |
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497 |
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498 #Write summary information to log file |
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499 funcWrite( "#model", strLog ) |
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500 funcWrite( lmod, strLog ) |
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501 |
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502 #Get the coefficients |
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503 #This should work for linear models |
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504 frmeCoefs <- summary(lmod) |
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505 adCoefs = frmeCoefs[,1] |
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506 names(adCoefs) = row.names(frmeCoefs) |
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507 |
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508 #Go through each coefficient |
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509 astrRows <- row.names( frmeCoefs ) |
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510 |
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511 for( iMetadata in 1:length( astrRows ) ) |
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512 { |
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513 #Current coef which is being evaluated |
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514 strOrig = astrRows[iMetadata] |
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515 #Skip y interscept |
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516 if( strOrig %in% c("(Intercept)", "Intercept", "Log(theta)") ) { next } |
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517 #Skip suppressed covariates |
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518 if( funcCoef2Col(strOrig,frmeData) %in% asSuppressCovariates){next} |
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519 |
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520 #Extract pvalue and std in standard model |
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521 dP = frmeCoefs[strOrig, iPValuePosition] |
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522 dStd = frmeCoefs[strOrig,2] |
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523 |
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524 dCoef = adCoefs[iMetadata] |
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525 |
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526 #Setting adMetadata |
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527 #Metadata values |
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528 strMetadata = funcCoef2Col( strOrig, frmeData, astrCols ) |
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529 if( is.na( strMetadata ) ) |
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530 { |
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531 if( substring( strOrig, nchar( strOrig ) - 1 ) == "NA" ) { next } |
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532 c_logrMaaslin$error( "Unknown coefficient: %s", strOrig ) |
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533 } |
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534 if( substring( strOrig, nchar( strMetadata ) + 1 ) == "NA" ) { next } |
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535 adMetadata <- frmeData[,strMetadata] |
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536 |
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537 #Store (factor level modified) p-value |
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538 #Store general results for each coef |
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539 adP <- c(adP, dP) |
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540 lsSig[[length( lsSig ) + 1]] <- list( |
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541 #Current metadata name |
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542 name = strMetadata, |
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543 #Current metadatda name (as a factor level if existing as such) |
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544 orig = strOrig,# |
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545 #Taxon feature name |
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546 taxon = strTaxon, |
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547 #Taxon data / response |
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548 data = frmeData[,iTaxon], |
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549 #All levels |
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550 factors = c(strMetadata), |
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551 #Metadata values |
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552 metadata = adMetadata, |
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553 #Current coeficient value |
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554 value = dCoef, |
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555 #Standard deviation |
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556 std = dStd, |
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557 #Model coefficients |
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558 allCoefs = adCoefs) |
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559 } |
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560 } |
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561 } |
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562 return(list(adP=adP, lsSig=lsSig, lsQCCounts=lsQCCounts)) |
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563 ### List containing a list of pvalues, a list of significant data per association, and a list of QC data |
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564 } |
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565 |
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566 |
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567 notfuncGetZeroInflatedResults <- function |
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568 ### Reduce the lm object return to just the data needed for further analysis |
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569 ( llmod, |
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570 ### The result from a linear model |
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571 frmeData, |
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572 ### Data analysis is performed on |
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573 liTaxon, |
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574 ### The response id |
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575 dSig, |
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576 ### Significance level for q-values |
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577 adP, |
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578 ### List of pvalues from all associations performed |
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579 lsSig, |
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580 ### List of information from the lm containing, metadata name, metadatda name (as a factor level if existing as such), Taxon feature name, Taxon data / response, All levels, Metadata values, Current coeficient value, Standard deviation, Model coefficients |
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581 strLog, |
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582 ### File to which to document logging |
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583 lsQCCounts, |
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584 ### Records of counts associated with quality control |
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585 lastrCols, |
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586 ### Predictors used in the association |
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587 asSuppressCovariates=c() |
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588 ### Vector of covariates to suppress and not give results for |
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589 ){ |
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590 ilmodIndex = 0 |
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591 for(lmod in llmod) |
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592 { |
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593 ilmodIndex = ilmodIndex + 1 |
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594 lmod = llmod[[ilmodIndex]] |
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595 iTaxon = liTaxon[[ilmodIndex]] |
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596 astrCols = lastrCols[[ilmodIndex]] |
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597 |
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598 #Exclude none and errors |
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599 if( !is.na( lmod ) && ( class( lmod ) != "try-error" ) ) |
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600 { |
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601 #holds the location of the pvlaues if an lm, if lmm is detected this will be changed |
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602 iPValuePosition = 4 |
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603 |
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604 #Get the column name of the iTaxon index |
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605 #frmeTmp needs to be what? |
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606 strTaxon = colnames( frmeData )[iTaxon] |
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607 |
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608 #Write summary information to log file |
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609 funcWrite( "#model", strLog ) |
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610 funcWrite( lmod, strLog ) |
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611 |
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612 #Get the coefficients |
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613 #This should work for linear models |
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614 frmeCoefs <- summary(lmod) |
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615 frmeCoefs = frmeCoefs$coefficients$count |
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616 funcWrite( "#Coefs", strLog ) |
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617 funcWrite( frmeCoefs, strLog ) |
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618 |
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619 adCoefs = frmeCoefs[,1] |
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620 names(adCoefs) = row.names(frmeCoefs) |
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621 |
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622 #Go through each coefficient |
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623 astrRows <- row.names( frmeCoefs ) |
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624 |
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625 for( iMetadata in 1:length( astrRows ) ) |
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626 { |
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627 #Current coef which is being evaluated |
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628 strOrig = astrRows[iMetadata] |
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629 |
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630 #Skip y interscept |
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631 if( strOrig %in% c("(Intercept)", "Intercept", "Log(theta)") ) { next } |
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632 #Skip suppressed covariates |
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633 if( funcCoef2Col(strOrig,frmeData) %in% asSuppressCovariates){next} |
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634 |
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635 #Extract pvalue and std in standard model |
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636 dP = frmeCoefs[strOrig, iPValuePosition] |
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637 if(is.nan(dP)){ next } |
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638 dStd = frmeCoefs[strOrig,2] |
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639 dCoef = adCoefs[iMetadata] |
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640 |
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641 #Setting adMetadata |
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642 #Metadata values |
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643 strMetadata = funcCoef2Col( strOrig, frmeData, astrCols ) |
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644 if( is.na( strMetadata ) ) |
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645 { |
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646 if( substring( strOrig, nchar( strOrig ) - 1 ) == "NA" ) { next } |
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647 c_logrMaaslin$error( "Unknown coefficient: %s", strOrig ) |
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648 } |
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649 if( substring( strOrig, nchar( strMetadata ) + 1 ) == "NA" ) { next } |
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650 adMetadata <- frmeData[,strMetadata] |
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651 |
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652 #Store (factor level modified) p-value |
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653 #Store general results for each coef |
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654 adP <- c(adP, dP) |
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655 lsSig[[length( lsSig ) + 1]] <- list( |
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656 #Current metadata name |
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657 name = strMetadata, |
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658 #Current metadatda name (as a factor level if existing as such) |
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659 orig = strOrig,# |
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660 #Taxon feature name |
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661 taxon = strTaxon, |
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662 #Taxon data / response |
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663 data = frmeData[,iTaxon], |
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664 #All levels |
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665 factors = c(strMetadata), |
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666 #Metadata values |
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667 metadata = adMetadata, |
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668 #Current coeficient value |
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669 value = dCoef, |
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670 #Standard deviation |
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671 std = dStd, |
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672 #Model coefficients |
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673 allCoefs = adCoefs) |
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674 } |
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675 } |
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676 } |
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677 return(list(adP=adP, lsSig=lsSig, lsQCCounts=lsQCCounts)) |
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678 ### List containing a list of pvalues, a list of significant data per association, and a list of QC data |
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679 } |
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680 |
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681 |
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682 ### Options for variable selection |
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683 # OK |
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684 funcBoostModel <- function( |
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685 ### Perform model selection / regularization with boosting |
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686 strFormula, |
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687 ### The formula of the full model before boosting |
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688 frmeTmp, |
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689 ### The data on which to perform analysis |
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690 adCur, |
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691 ### The response data |
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692 lsParameters, |
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693 ### User controlled parameters needed specific to boosting |
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694 lsForcedParameters = NULL, |
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695 ### Force these predictors to be in the model |
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696 strLog |
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697 ### File to which to document logging |
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698 ){ |
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699 funcWrite( c("#Boost formula", strFormula), strLog ) |
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700 lmod = try( gbm( as.formula( strFormula ), data=frmeTmp, distribution="laplace", verbose=FALSE, n.minobsinnode=min(10, round(0.1 * nrow( frmeTmp ) ) ), n.trees=1000 ) ) |
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701 #TODO# lmod = try( gbm( as.formula( strFormula ), data=frmeTmp, distribution="gaussian", verbose=FALSE, n.minobsinnode=min(10, round(0.1 * nrow( frmeTmp ) ) ), n.trees=1000 ) ) |
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702 |
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703 astrTerms <- c() |
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704 if( !is.na( lmod ) && ( class( lmod ) != "try-error" ) ) |
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705 { |
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706 #Get boosting summary results |
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707 lsSum <- summary( lmod, plotit = FALSE ) |
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708 |
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709 #Document |
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710 funcWrite( "#model-gbm", strLog ) |
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711 funcWrite( lmod$fit, strLog ) |
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712 funcWrite( lmod$train.error, strLog ) |
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713 funcWrite( lmod$Terms, strLog ) |
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714 funcWrite( "#summary-gbm", strLog ) |
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715 funcWrite( lsSum, strLog ) |
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716 |
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717 # Uneven metadata |
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718 vstrUneven = c() |
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719 # Kept metadata |
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720 vstrKeepMetadata = c() |
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721 |
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722 #Select model predictors |
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723 #Check the frequency of selection and skip if not selected more than set threshold dFreq |
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724 for( strMetadata in lmod$var.names ) |
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725 { |
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726 #Get the name of the metadata |
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727 strTerm <- funcCoef2Col( strMetadata, frmeTmp, c(astrMetadata, astrGenetics) ) |
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728 |
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729 #Add back in uneven metadata |
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730 if(fAddBack) |
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731 { |
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732 ldMetadata = frmeTmp[[strMetadata]] |
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733 if(length(which(table(ldMetadata)/length(ldMetadata)>dUnevenMax))>0) |
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734 { |
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735 astrTerms <- c(astrTerms, strTerm) |
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736 vstrUneven = c(vstrUneven,strMetadata) |
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737 next |
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738 } |
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739 } |
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740 |
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741 #If the selprob is less than a certain frequency, skip |
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742 dSel <- lsSum$rel.inf[which( lsSum$var == strMetadata )] / 100 |
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743 if( is.na(dSel) || ( dSel <= lsParameters$dFreq ) ){ next } |
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744 #TODO# if( is.na(dSel) || ( dSel < lsParameters$dFreq ) ){ next } |
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745 |
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746 #If you should ignore the metadata, continue |
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747 if( is.null( strTerm ) ) { next } |
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748 |
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749 #If you cant find the metadata name, write |
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750 if( is.na( strTerm ) ) |
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751 { |
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752 c_logrMaaslin$error( "Unknown coefficient: %s", strMetadata ) |
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753 next |
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754 } |
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755 |
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756 #Collect metadata names |
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757 astrTerms <- c(astrTerms, strTerm) |
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758 vstrKeepMetadata = c(vstrKeepMetadata,strTerm) |
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759 } |
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760 } else { astrTerms = lsForcedParameters } |
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761 |
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762 # funcBoostInfluencePlot(vdRelInf=lsSum$rel.inf, sFeature=lsParameters$sBugName, vsPredictorNames=lsSum$var, vstrKeepMetadata=vstrKeepMetadata, vstrUneven=vstrUneven) |
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763 |
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764 return(unique(c(astrTerms,lsForcedParameters))) |
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765 ### Return a vector of predictor names to use in a reduced model |
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766 } |
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767 |
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768 #Glmnet default is to standardize the variables. |
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769 #used as an example for implementation |
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770 #http://r.789695.n4.nabble.com/estimating-survival-times-with-glmnet-and-coxph-td4614225.html |
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771 funcPenalizedModel <- function( |
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772 ### Perform penalized regularization for variable selection |
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773 strFormula, |
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774 ### The formula of the full model before boosting |
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775 frmeTmp, |
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776 ### The data on which to perform analysis |
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777 adCur, |
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778 ### The response data |
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779 lsParameters, |
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780 ### User controlled parameters needed specific to boosting |
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781 lsForcedParameters = NULL, |
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782 ### Force these predictors to be in the model |
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783 strLog |
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784 ### File to which to document logging |
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785 ){ |
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786 #Convert the data frame to a model matrix |
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787 mtrxDesign = model.matrix(as.formula(strFormula), data=frmeTmp) |
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788 |
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789 #Cross validate the lambda |
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790 cvRet = cv.glmnet(x=mtrxDesign,y=adCur,alpha=lsParameters$dPAlpha) |
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791 |
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792 #Perform lasso |
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793 glmnetMod = glmnet(x=mtrxDesign,y=adCur,family=lsParameters$family,alpha=lsParameters$dPAlpha,lambda=cvRet$lambda.min) |
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794 |
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795 #Get non zero beta and return column names for covariate names. |
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796 ldBeta = glmnetMod$beta[,which.max(glmnetMod$dev.ratio)] |
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797 ldBeta = names(ldBeta[which(abs(ldBeta)>0)]) |
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798 return(sapply(ldBeta,funcCoef2Col,frmeData=frmeTmp)) |
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799 } |
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800 |
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801 # OK |
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802 funcForwardModel <- function( |
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803 ### Perform model selection with forward stepwise selection |
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804 strFormula, |
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805 ### lm style string defining reposne and predictors |
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806 frmeTmp, |
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807 ### Data on which to perform analysis |
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808 adCur, |
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809 ### Response data |
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810 lsParameters, |
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811 ### User controlled parameters needed specific to boosting |
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812 lsForcedParameters = NULL, |
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813 ### Force these predictors to be in the model |
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814 strLog |
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815 ### File to which to document logging |
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816 ){ |
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817 funcWrite( c("#Forward formula", strFormula), strLog ) |
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818 |
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819 strNullFormula = "adCur ~ 1" |
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820 if(!is.null(lsForcedParameters)) |
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821 { |
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822 strNullFormula = paste( "adCur ~", paste( sprintf( "`%s`", lsForcedParameters ), collapse = " + " )) |
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823 } |
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824 lmodNull <- try( lm(as.formula( strNullFormula ), data=frmeTmp)) |
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825 lmodFull <- try( lm(as.formula( strFormula ), data=frmeTmp )) |
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826 if(!("try-error" %in% c(class( lmodNull ),class( lmodFull )))) |
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827 { |
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828 lmod = stepAIC(lmodNull, scope=list(lower=lmodNull,upper=lmodFull), direction="forward", trace=0) |
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829 return(funcGetStepPredictors(lmod, frmeTmp, strLog)) |
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830 } |
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831 return( lsForcedParameters ) |
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832 ### Return a vector of predictor names to use in a reduced model or NA on error |
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833 } |
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834 |
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835 # OK |
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836 # Select model with backwards selection |
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837 funcBackwardsModel <- function( |
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838 ### Perform model selection with backwards stepwise selection |
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839 strFormula, |
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840 ### lm style string defining reponse and predictors |
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841 frmeTmp, |
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842 ### Data on which to perform analysis |
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843 adCur, |
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844 ### Response data |
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845 lsParameters, |
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846 ### User controlled parameters needed specific to boosting |
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847 lsForcedParameters = NULL, |
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848 ### Force these predictors to be in the model |
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849 strLog |
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850 ### File to which to document logging |
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851 ){ |
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852 funcWrite( c("#Backwards formula", strFormula), strLog ) |
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853 |
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854 strNullFormula = "adCur ~ 1" |
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855 if(!is.null(lsForcedParameters)) |
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856 { |
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857 strNullFormula = paste( "adCur ~", paste( sprintf( "`%s`", lsForcedParameters ), collapse = " + " )) |
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858 } |
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859 |
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860 lmodNull <- try( lm(as.formula( strNullFormula ), data=frmeTmp)) |
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861 lmodFull <- try( lm(as.formula( strFormula ), data=frmeTmp )) |
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862 |
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863 if(! class( lmodFull ) == "try-error" ) |
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864 { |
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865 lmod = stepAIC(lmodFull, scope=list(lower=lmodNull, upper=lmodFull), direction="backward") |
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866 return(funcGetStepPredictors(lmod, frmeTmp, strLog)) |
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867 } else { |
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868 return( lsForcedParameters ) } |
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869 ### Return a vector of predictor names to use in a reduced model or NA on error |
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870 } |
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871 |
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872 ### Analysis methods |
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873 ### Univariate options |
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874 |
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875 # Sparse Dir. Model |
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876 #TODO# Implement in sfle |
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877 |
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878 # Tested |
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879 # Correlation |
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880 # NOTE: Ignores the idea of random and fixed covariates |
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881 funcSpearman <- function( |
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882 ### Perform multiple univariate comparisons producing spearman correlations for association |
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883 strFormula, |
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884 ### lm style string defining reponse and predictors, for mixed effects models this holds the fixed variables |
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885 frmeTmp, |
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886 ### Data on which to perform analysis |
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887 iTaxon, |
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888 ### Index of the response data |
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889 lsQCCounts, |
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890 ### List recording anything important to QC |
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891 strRandomFormula = NULL |
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892 ### Has the formula for random covariates |
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893 ){ |
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894 return(funcMakeContrasts(strFormula=strFormula, strRandomFormula=strRandomFormula, frmeTmp=frmeTmp, iTaxon=iTaxon, |
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895 functionContrast=function(x,adCur,dfData) |
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896 { |
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897 retList = list() |
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898 ret = cor.test(as.formula(paste("~",x,"+ adCur")), data=dfData, method="spearman", na.action=c_strNA_Action) |
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899 #Returning rho for the coef in a named vector |
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900 vdCoef = c() |
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901 vdCoef[[x]]=ret$estimate |
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902 retList[[1]]=list(p.value=ret$p.value,SD=sd(dfData[[x]]),name=x,coef=vdCoef) |
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903 return(retList) |
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904 }, lsQCCounts)) |
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905 ### List of contrast information, pvalue, contrast and std per univariate test |
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906 } |
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907 |
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908 # Tested |
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909 # Wilcoxon (T-Test) |
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910 # NOTE: Ignores the idea of random and fixed covariates |
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911 funcWilcoxon <- function( |
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912 ### Perform multiple univariate comparisons performing wilcoxon tests on discontinuous data with 2 levels |
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913 strFormula, |
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914 ### lm style string defining reponse and predictors, for mixed effects models this holds the fixed variables |
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915 frmeTmp, |
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916 ### Data on which to perform analysis |
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917 iTaxon, |
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918 ### Index of the response data |
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919 lsQCCounts, |
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920 ### List recording anything important to QC |
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921 strRandomFormula = NULL |
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922 ### Has the formula for random covariates |
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923 ){ |
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924 return(funcMakeContrasts(strFormula=strFormula, strRandomFormula=strRandomFormula, frmeTmp=frmeTmp, iTaxon=iTaxon, |
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925 functionContrast=function(x,adCur,dfData) |
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926 { |
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927 retList = list() |
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928 ret = wilcox.test(as.formula(paste("adCur",x,sep=" ~ ")), data=dfData, na.action=c_strNA_Action) |
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929 #Returning NA for the coef in a named vector |
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930 vdCoef = c() |
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931 vdCoef[[x]]=ret$statistic |
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932 retList[[1]]=list(p.value=ret$p.value,SD=sd(dfData[[x]]),name=x,coef=vdCoef) |
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933 return(retList) |
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934 }, lsQCCounts)) |
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935 ### List of contrast information, pvalue, contrast and std per univariate test |
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936 } |
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937 |
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938 # Tested |
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939 # Kruskal.Wallis (Nonparameteric anova) |
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940 # NOTE: Ignores the idea of random and fixed covariates |
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941 funcKruskalWallis <- function( |
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942 ### Perform multiple univariate comparisons performing Kruskal wallis rank sum tests on discontuous data with more than 2 levels |
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943 strFormula, |
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944 ### lm style string defining reponse and predictors, for mixed effects models this holds the fixed variables |
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945 frmeTmp, |
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946 ### Data on which to perform analysis |
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947 iTaxon, |
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948 ### Index of the response data |
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949 lsQCCounts, |
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950 ### List recording anything important to QC |
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951 strRandomFormula = NULL |
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952 ### Has the formula for random covariates |
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953 ){ |
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954 return(funcMakeContrasts(strFormula=strFormula, strRandomFormula=strRandomFormula, frmeTmp=frmeTmp, iTaxon=iTaxon, |
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955 functionContrast=function(x,adCur,dfData) |
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956 { |
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957 retList = list() |
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958 lmodKW = kruskal(adCur,dfData[[x]],group=FALSE,p.adj="holm") |
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959 |
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960 asLevels = levels(dfData[[x]]) |
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961 # The names of the generated comparisons, sometimes the control is first sometimes it is not so |
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962 # We will just check which is in the names and use that |
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963 asComparisons = row.names(lmodKW$comparisons) |
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964 #Get the comparison with the control |
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965 for(sLevel in asLevels[2:length(asLevels)]) |
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966 { |
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967 sComparison = intersect(c(paste(asLevels[1],sLevel,sep=" - "),paste(sLevel,asLevels[1],sep=" - ")),asComparisons) |
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968 #Returning NA for the coef in a named vector |
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969 vdCoef = c() |
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970 vdCoef[[paste(x,sLevel,sep="")]]=lmodKW$comparisons[sComparison,"Difference"] |
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971 # vdCoef[[paste(x,sLevel,sep="")]]=NA |
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972 retList[[length(retList)+1]]=list(p.value=lmodKW$comparisons[sComparison,"p.value"],SD=1.0,name=paste(x,sLevel,sep=""),coef=vdCoef) |
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973 } |
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974 return(retList) |
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975 }, lsQCCounts)) |
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976 ### List of contrast information, pvalue, contrast and std per univariate test |
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977 } |
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978 |
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979 # Tested |
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980 # NOTE: Ignores the idea of random and fixed covariates |
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981 funcDoUnivariate <- function( |
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982 ### Perform multiple univariate comparisons producing spearman correlations for association |
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983 strFormula, |
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984 ### lm style string defining reponse and predictors, for mixed effects models this holds the fixed variables |
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985 frmeTmp, |
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986 ### Data on which to perform analysis |
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987 iTaxon, |
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988 ### Index of the response data |
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989 lsHistory, |
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990 ### List recording p-values, association information, and QC counts |
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991 strRandomFormula = NULL, |
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992 ### Has the formula for random covariates |
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993 fZeroInflate = FALSE |
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994 ){ |
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995 if(fZeroInflate) |
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996 { |
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997 throw("There are no zero-inflated univariate models to perform your analysis.") |
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998 } |
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999 |
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1000 # Get covariates |
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1001 astrCovariates = unique(c(funcFormulaStrToList(strFormula),funcFormulaStrToList(strRandomFormula))) |
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1002 |
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1003 # For each covariate |
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1004 for(sCovariate in astrCovariates) |
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1005 { |
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1006 ## Check to see if it is discrete |
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1007 axData = frmeTmp[[sCovariate]] |
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1008 lsRet = NA |
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1009 if(is.factor(axData) || is.logical(axData)) |
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1010 { |
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1011 ## If discrete check how many levels |
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1012 lsDataLevels = levels(axData) |
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1013 ## If 2 levels do wilcoxon test |
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1014 if(length(lsDataLevels) < 3) |
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1015 { |
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1016 lsRet = funcWilcoxon(strFormula=paste("adCur",sCovariate,sep=" ~ "), frmeTmp=frmeTmp, iTaxon=iTaxon, lsQCCounts=lsHistory$lsQCCounts) |
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1017 } else { |
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1018 ## If 3 or more levels do kruskal wallis test |
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1019 lsRet = funcKruskalWallis(strFormula=paste("adCur",sCovariate,sep=" ~ "), frmeTmp=frmeTmp, iTaxon=iTaxon, lsQCCounts=lsHistory$lsQCCounts) |
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1020 } |
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1021 } else { |
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1022 ## If not discrete do spearman test (list(adP=adP, lsSig=lsSig, lsQCCounts=lsQCCounts)) |
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1023 lsRet = funcSpearman(strFormula=paste("adCur",sCovariate,sep=" ~ "), frmeTmp=frmeTmp, iTaxon=iTaxon, lsQCCounts=lsHistory$lsQCCounts) |
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1024 } |
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1025 lsHistory[["adP"]] = c(lsHistory[["adP"]], lsRet[["adP"]]) |
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1026 lsHistory[["lsSig"]] = c(lsHistory[["lsSig"]], lsRet[["lsSig"]]) |
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1027 lsHistory[["lsQCCounts"]] = lsRet[["lsQCCounts"]] |
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1028 } |
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1029 return(lsHistory) |
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1030 } |
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1031 |
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1032 ### Multivariate |
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1033 |
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1034 # Tested |
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1035 funcLM <- function( |
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1036 ### Perform vanilla linear regression |
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1037 strFormula, |
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1038 ### lm style string defining reponse and predictors, for mixed effects models this holds the fixed variables |
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1039 frmeTmp, |
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1040 ### Data on which to perform analysis |
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1041 iTaxon, |
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1042 ### Index of the response data |
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1043 lsHistory, |
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1044 ### List recording p-values, association information, and QC counts |
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1045 strRandomFormula = NULL, |
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1046 ### Has the formula for random covariates |
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1047 fZeroInflated = FALSE |
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1048 ### Turns on the zero inflated model |
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1049 ){ |
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1050 adCur = frmeTmp[,iTaxon] |
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1051 if(fZeroInflated) |
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1052 { |
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1053 return(try(gamlss(formula=as.formula(strFormula), family=BEZI, data=frmeTmp))) # gamlss |
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1054 } else { |
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1055 if(!is.null(strRandomFormula)) |
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1056 { |
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1057 return(try(glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=gaussian(link="identity"), data=frmeTmp))) |
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1058 #lme4 package but does not have pvalues for the fixed variables (have to use a mcmcsamp/pvals.fnc function which are currently disabled) |
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1059 #return(try( lmer(as.formula(strFormula), data=frmeTmp, na.action=c_strNA_Action) )) |
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1060 } else { |
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1061 return(try( lm(as.formula(strFormula), data=frmeTmp, na.action=c_strNA_Action) )) |
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1062 } |
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1063 } |
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1064 ### lmod result object from lm |
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1065 } |
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1066 |
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1067 # Tested |
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1068 funcBinomialMult <- function( |
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1069 ### Perform linear regression with negative binomial link |
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1070 strFormula, |
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1071 ### lm style string defining reponse and predictors, for mixed effects models this holds the fixed variables |
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1072 frmeTmp, |
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1073 ### Data on which to perform analysis |
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1074 iTaxon, |
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1075 ### Index of the response data |
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1076 lsHistory, |
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1077 ### List recording p-values, association information, and QC counts |
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1078 strRandomFormula = NULL, |
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1079 ### Has the formula for random covariates |
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1080 fZeroInflated = FALSE |
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1081 ### Turns on the zero inflated model |
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1082 ){ |
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1083 adCur = frmeTmp[,iTaxon] |
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1084 if(fZeroInflated) |
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1085 { |
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1086 return(try(zeroinfl(as.formula(strFormula), data=frmeTmp, dist="negbin"))) # pscl |
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1087 # return(try(gamlss(as.formula(strFormula), family=ZINBI, data=frmeTmp))) # pscl |
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1088 } else { |
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1089 if(!is.null(strRandomFormula)) |
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1090 { |
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1091 throw("This analysis flow is not completely developed, please choose an option other than negative bionomial with random covariates") |
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1092 #TODO need to estimate the theta |
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1093 #return(try(glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=negative.binomial(theta = 2, link=log), data=frmeTmp))) |
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1094 #lme4 package but does not have pvalues for the fixed variables (have to use a mcmcsamp/pvals.fnc function which are currently disabled) |
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1095 } else { |
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1096 return(try( glm.nb(as.formula(strFormula), data=frmeTmp, na.action=c_strNA_Action ))) |
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1097 } |
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1098 } |
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1099 ### lmod result object from lm |
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1100 } |
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1101 |
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1102 # Tested |
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1103 funcQuasiMult <- function( |
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1104 ### Perform linear regression with quasi-poisson link |
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1105 strFormula, |
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1106 ### lm style string defining reponse and predictors, for mixed effects models this holds the fixed variables |
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1107 frmeTmp, |
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1108 ### Data on which to perform analysis |
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1109 iTaxon, |
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1110 ### Index of the response data |
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1111 lsHistory, |
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1112 ### List recording p-values, association information, and QC counts |
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1113 strRandomFormula = NULL, |
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1114 ### Has the formula for random covariates |
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1115 fZeroInflated = FALSE |
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1116 ### Turns on a zero infalted model |
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1117 ){ |
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1118 adCur = frmeTmp[,iTaxon] |
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1119 if(fZeroInflated) |
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1120 { |
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1121 # return(try(gamlss(formula=as.formula(strFormula), family=ZIP, data=frmeTmp))) # gamlss |
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1122 return(try(zeroinfl(as.formula(strFormula), data=frmeTmp, dist="poisson"))) # pscl |
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1123 } else { |
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1124 #Check to see if | is in the model, if so use a lmm otherwise the standard glm is ok |
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1125 if(!is.null(strRandomFormula)) |
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1126 { |
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1127 return(try(glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family= quasipoisson, data=frmeTmp))) |
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1128 #lme4 package but does not have pvalues for the fixed variables (have to use a mcmcsamp/pvals.fnc function which are currently disabled) |
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1129 #return(try ( glmer(as.formula(strFormula), data=frmeTmp, family=quasipoisson, na.action=c_strNA_Action) )) |
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1130 } else { |
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1131 return(try( glm(as.formula(strFormula), family=quasipoisson, data=frmeTmp, na.action=c_strNA_Action) )) |
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1132 } |
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1133 } |
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1134 ### lmod result object from lm |
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1135 } |
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1136 |
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1137 ### Transformations |
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1138 # Tested |
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1139 funcArcsinSqrt <- function( |
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1140 # Transform data with arcsin sqrt transformation |
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1141 aData |
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1142 ### The data on which to perform the transformation |
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1143 ){ |
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1144 return(asin(sqrt(aData))) |
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1145 ### Transformed data |
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1146 } |
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1147 |
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1148 funcSquareSin <- function( |
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1149 # Transform data with square sin transformation |
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1150 # Opposite of the funcArcsinSqrt |
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1151 aData |
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1152 ### The data on which to perform the transformation |
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1153 ){ |
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1154 return(sin(aData)^2) |
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1155 ### Transformed data |
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1156 } |
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1157 |
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1158 # Tested |
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1159 funcNoTransform <-function( |
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1160 ### Pass data without transform |
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1161 aData |
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1162 ### The data on which to perform the transformation |
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1163 ### Only given here to preserve the pattern, not used. |
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1164 ){ |
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1165 return(aData) |
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1166 ### Transformed data |
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1167 } |
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1168 |
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1169 funcGetAnalysisMethods <- function( |
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1170 ### Returns the appropriate functions for regularization, analysis, data transformation, and analysis object inspection. |
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1171 ### This allows modular customization per analysis step. |
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1172 ### To add a new method insert an entry in the switch for either the selection, transform, or method |
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1173 ### Insert them by using the pattern optparse_keyword_without_quotes = function_in_AnalysisModules |
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1174 ### Order in the return listy is currently set and expected to be selection, transforms/links, analysis method |
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1175 ### none returns null |
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1176 sModelSelectionKey, |
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1177 ### Keyword defining the method of model selection |
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1178 sTransformKey, |
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1179 ### Keyword defining the method of data transformation |
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1180 sMethodKey, |
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1181 ### Keyword defining the method of analysis |
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1182 fZeroInflated = FALSE |
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1183 ### Indicates if using zero inflated models |
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1184 ){ |
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1185 lRetMethods = list() |
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1186 #Insert selection methods here |
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1187 lRetMethods[[c_iSelection]] = switch(sModelSelectionKey, |
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1188 boost = funcBoostModel, |
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1189 penalized = funcPenalizedModel, |
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1190 forward = funcForwardModel, |
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1191 backward = funcBackwardsModel, |
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1192 none = NA) |
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1193 |
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1194 #Insert transforms |
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1195 lRetMethods[[c_iTransform]] = switch(sTransformKey, |
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1196 asinsqrt = funcArcsinSqrt, |
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1197 none = funcNoTransform) |
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1198 |
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1199 #Insert untransform |
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1200 lRetMethods[[c_iUnTransform]] = switch(sTransformKey, |
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1201 asinsqrt = funcNoTransform, |
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1202 none = funcNoTransform) |
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1203 |
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1204 #Insert analysis |
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1205 lRetMethods[[c_iAnalysis]] = switch(sMethodKey, |
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1206 neg_binomial = funcBinomialMult, |
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1207 quasi = funcQuasiMult, |
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1208 univariate = funcDoUnivariate, |
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1209 lm = funcLM, |
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1210 none = NA) |
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1211 |
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1212 # If a univariate method is used it is required to set this to true |
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1213 # For correct handling. |
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1214 lRetMethods[[c_iIsUnivariate]]=sMethodKey=="univariate" |
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1215 |
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1216 #Insert method to get results |
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1217 if(fZeroInflated) |
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1218 { |
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1219 lRetMethods[[c_iResults]] = switch(sMethodKey, |
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1220 neg_binomial = funcGetZeroInflatedResults, |
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1221 quasi = funcGetZeroInflatedResults, |
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1222 univariate = funcGetUnivariateResults, |
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1223 lm = funcGetZeroInflatedResults, |
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1224 none = NA) |
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1225 } else { |
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1226 lRetMethods[[c_iResults]] = switch(sMethodKey, |
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1227 neg_binomial = funcGetLMResults, |
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1228 quasi = funcGetLMResults, |
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1229 univariate = funcGetUnivariateResults, |
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1230 lm = funcGetLMResults, |
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1231 none = NA) |
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1232 } |
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1233 |
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1234 return(lRetMethods) |
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1235 ### Returns a list of functions to be passed for regularization, data transformation, analysis, |
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1236 ### and custom analysis results introspection functions to pull from return objects data of interest |
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1237 } |