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6
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1 c_strDir <- file.path(getwd( ),"..")
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2
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3 source(file.path(c_strDir,"lib","Constants.R"))
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4 source(file.path(c_strDir,"lib","Utility.R"))
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5 source(file.path(c_strDir,"lib","AnalysisModules.R"))
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6
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7 # General setup
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8 covX1 = c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
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9 covX2 = c(144.4, 245.9, 141.9, 253.3, 144.7, 244.1, 150.7, 245.2, 160.1)
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10 covX3 = as.factor(c(1,2,3,1,2,3,1,2,3))
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11 covX4 = as.factor(c(1,1,1,1,2,2,2,2,2))
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12 covX5 = as.factor(c(1,2,1,2,1,2,1,2,1))
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13 covY = c(.26, .31, .25, .50, .36, .40, .52, .28, .38)
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14 frmeTmp = data.frame(Covariate1=covX1, Covariate2=covX2, Covariate3=covX3, Covariate4=covX4, Covariate5=covX5, adCur= covY)
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15 iTaxon = 6
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16
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17 lsCov1 = list()
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18 lsCov1$name = "Covariate1"
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19 lsCov1$orig = "Covariate1"
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20 lsCov1$taxon = "adCur"
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21 lsCov1$data = covY
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22 lsCov1$factors = "Covariate1"
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23 lsCov1$metadata = covX1
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24 vdCoef = c()
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25 vdCoef["(Intercept)"]=round(0.0345077486,5)
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26 vdCoef["Covariate1"]= round(0.0052097355,5)
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27 vdCoef["Covariate2"]= round(0.0005806568,5)
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28 vdCoef["Covariate32"]=round(-0.1333421874,5)
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29 vdCoef["Covariate33"]=round(-0.1072006419,5)
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30 vdCoef["Covariate42"]=round(0.0849198280,5)
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31 lsCov1$value = c(Covariate1=round(0.005209736,5))
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32 lsCov1$std = round(0.0063781728,5)
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33 lsCov1$allCoefs = vdCoef
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34 lsCov2 = list()
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35 lsCov2$name = "Covariate2"
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36 lsCov2$orig = "Covariate2"
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37 lsCov2$taxon = "adCur"
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38 lsCov2$data = covY
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39 lsCov2$factors = "Covariate2"
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40 lsCov2$metadata = covX2
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41 lsCov2$value = c(Covariate2=round(0.0005806568,5))
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42 lsCov2$std = round(0.0006598436,5)
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43 lsCov2$allCoefs = vdCoef
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44 lsCov3 = list()
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45 lsCov3$name = "Covariate3"
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46 lsCov3$orig = "Covariate32"
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47 lsCov3$taxon = "adCur"
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48 lsCov3$data = covY
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49 lsCov3$factors = "Covariate3"
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50 lsCov3$metadata = covX3
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51 lsCov3$value = c(Covariate32=round(-0.1333422,5))
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52 lsCov3$std = round(0.0895657826,5)
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53 lsCov3$allCoefs = vdCoef
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54 lsCov4 = list()
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55 lsCov4$name = "Covariate3"
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56 lsCov4$orig = "Covariate33"
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57 lsCov4$taxon = "adCur"
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58 lsCov4$data = covY
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59 lsCov4$factors = "Covariate3"
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60 lsCov4$metadata = covX3
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61 lsCov4$value = c(Covariate33=round(-0.1072006,5))
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62 lsCov4$std = round(0.0792209541,5)
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63 lsCov4$allCoefs = vdCoef
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64 lsCov5 = list()
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65 lsCov5$name = "Covariate4"
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66 lsCov5$orig = "Covariate42"
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67 lsCov5$taxon = "adCur"
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68 lsCov5$data = covY
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69 lsCov5$factors = "Covariate4"
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70 lsCov5$metadata = covX4
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71 lsCov5$value = c(Covariate42=round(0.08491983,5))
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72 lsCov5$std = round(0.0701018621,5)
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73 lsCov5$allCoefs = vdCoef
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74
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75 context("Test funcClean")
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76
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77 context("Test funcBugHybrid")
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78 # multiple covariates, one call lm
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79 aiMetadata = c(1:5)
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80 aiData = c(iTaxon)
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81 dFreq = 0.5 / length( aiMetadata )
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82 dSig = 0.25
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83 dMinSamp = 0.1
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84 adP = c()
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85 lsSig = list()
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86 funcReg = NA
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87 funcAnalysis = funcLM
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88 funcGetResult = funcGetLMResults
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89 lsData = list(frmeData=frmeTmp, aiMetadata=aiMetadata, aiData=aiData, lsQCCounts=list())
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90 lsData$astrMetadata = names(frmeTmp)[aiMetadata]
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91
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92 adPExpected = round(c(0.4738687,0.4436566,0.4665972,0.5378693,0.3124672),5)
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93 QCExpected = list(iLms=numeric(0))
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94 lsSigExpected = list()
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95 lsSigExpected[[1]] = lsCov1
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96 lsSigExpected[[2]] = lsCov2
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97 lsSigExpected[[3]] = lsCov3
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98 lsSigExpected[[4]] = lsCov4
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99 lsSigExpected[[5]] = lsCov5
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100 expectedReturn = list(adP=adPExpected,lsSig=lsSigExpected,lsQCCounts=QCExpected)
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101 receivedReturn = funcBugHybrid(iTaxon=iTaxon,frmeData=frmeTmp,lsData=lsData,aiMetadata=aiMetadata,dFreq=dFreq,dSig=dSig,dMinSamp=dMinSamp,adP=adP,lsSig=lsSig, strLog=NA,funcReg=funcReg,lsNonPenalizedPredictors=NULL,funcAnalysis=funcAnalysis,lsRandomCovariates=NULL,funcGetResult=funcGetResult)
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102 receivedReturn$adP = round(receivedReturn$adP,5)
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103
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104 vCoefs=receivedReturn$lsSig[[1]]$allCoefs
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105 vCoefs[1]=round(vCoefs[1],5)
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106 vCoefs[2]=round(vCoefs[2],5)
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107 vCoefs[3]=round(vCoefs[3],5)
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108 vCoefs[4]=round(vCoefs[4],5)
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109 vCoefs[5]=round(vCoefs[5],5)
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110 vCoefs[6]=round(vCoefs[6],5)
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111 receivedReturn$lsSig[[1]]$allCoefs=vCoefs
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112 receivedReturn$lsSig[[2]]$allCoefs=vCoefs
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113 receivedReturn$lsSig[[3]]$allCoefs=vCoefs
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114 receivedReturn$lsSig[[4]]$allCoefs=vCoefs
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115 receivedReturn$lsSig[[5]]$allCoefs=vCoefs
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116 vValue=c()
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117 vValue[receivedReturn$lsSig[[1]]$orig]=round(receivedReturn$lsSig[[1]]$value[[1]],5)
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118 receivedReturn$lsSig[[1]]$value=vValue
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119 vValue=c()
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120 vValue[receivedReturn$lsSig[[2]]$orig]=round(receivedReturn$lsSig[[2]]$value[[1]],5)
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121 receivedReturn$lsSig[[2]]$value=vValue
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122 vValue=c()
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123 vValue[receivedReturn$lsSig[[3]]$orig]=round(receivedReturn$lsSig[[3]]$value[[1]],5)
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124 receivedReturn$lsSig[[3]]$value=vValue
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125 vValue=c()
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126 vValue[receivedReturn$lsSig[[4]]$orig]=round(receivedReturn$lsSig[[4]]$value[[1]],5)
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127 receivedReturn$lsSig[[4]]$value=vValue
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128 vValue=c()
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129 vValue[receivedReturn$lsSig[[5]]$orig]=round(receivedReturn$lsSig[[5]]$value[[1]],5)
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130 receivedReturn$lsSig[[5]]$value=vValue
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131 receivedReturn$lsSig[[1]]$std=round(receivedReturn$lsSig[[1]]$std,5)
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132 receivedReturn$lsSig[[2]]$std=round(receivedReturn$lsSig[[2]]$std,5)
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133 receivedReturn$lsSig[[3]]$std=round(receivedReturn$lsSig[[3]]$std,5)
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134 receivedReturn$lsSig[[4]]$std=round(receivedReturn$lsSig[[4]]$std,5)
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135 receivedReturn$lsSig[[5]]$std=round(receivedReturn$lsSig[[5]]$std,5)
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136 test_that("funcBugHybrid works with the lm option with multiple covariates.",{expect_equal(receivedReturn,expectedReturn)})
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137
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138
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139 # single covariate, single call lm
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140 aiMetadata = c(1)
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141 dFreq = 0.5 / length( aiMetadata )
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142 lsData$astrMetadata = names(frmeTmp)[aiMetadata]
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143 adPExpected = round(c(0.1081731),5)
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144 QCExpected = list(iLms=numeric(0))
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145 lsSigExpected = list()
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146 lsSigExpected[[1]] = lsCov1
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147 lsSigExpected[[1]]$std=round(0.005278468,5)
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148 vdCoef = c()
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149 vdCoef["(Intercept)"]=round(-0.102410716,5)
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150 vdCoef["Covariate1"]= round(0.009718095,5)
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151 lsSigExpected[[1]]$allCoefs= vdCoef
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152 lsSigExpected[[1]]$value = c(Covariate1=round(0.009718095,5))
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153
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154 expectedReturn = list(adP=adPExpected,lsSig=lsSigExpected,lsQCCounts=QCExpected)
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155 receivedReturn = funcBugHybrid(iTaxon=iTaxon,frmeData=frmeTmp,lsData=lsData,aiMetadata=aiMetadata,dFreq=dFreq,dSig=dSig,dMinSamp=dMinSamp,adP=adP,lsSig=lsSig, strLog=NA,funcReg=funcReg,lsNonPenalizedPredictors=NULL,funcAnalysis=funcAnalysis,lsRandomCovariates=NULL,funcGetResult=funcGetResult)
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156 receivedReturn$adP = round(receivedReturn$adP,5)
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157
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158 vCoefs=receivedReturn$lsSig[[1]]$allCoefs
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159 vCoefs[1]=round(vCoefs[1],5)
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160 vCoefs[2]=round(vCoefs[2],5)
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161 receivedReturn$lsSig[[1]]$allCoefs=vCoefs
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162 vValue=c()
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163 vValue[receivedReturn$lsSig[[1]]$orig]=round(receivedReturn$lsSig[[1]]$value[[1]],5)
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164 receivedReturn$lsSig[[1]]$value=vValue
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165 receivedReturn$lsSig[[1]]$std=round(0.005278468,5)
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166 test_that("funcBugHybrid works with the lm option with 1 covariates.",{expect_equal(receivedReturn,expectedReturn)})
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167
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168
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169 # multiple covariate, single call univariate
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170 funcReg = NA
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171 funcAnalysis = funcDoUnivariate
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172 funcGetResult = NA
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173 aiMetadata = c(3,1,2)
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174 dFreq = 0.5 / length( aiMetadata )
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175 lsData$astrMetadata = names(frmeTmp)[aiMetadata]
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176 adPExpected = round(c(1.0,1.0,0.09679784,0.21252205),5)
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177 QCExpected = list(iLms=numeric(0))
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178 lsSigExpected = list()
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179 lsCov1 = list()
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180 lsCov1$name = "Covariate3"
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181 lsCov1$orig = "Covariate32"
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182 lsCov1$taxon = "adCur"
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183 lsCov1$data = covY
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184 lsCov1$factors = "Covariate3"
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185 lsCov1$metadata = frmeTmp[["Covariate3"]]
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186 vdCoef = c(Covariate32=NA)
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187 lsCov1$value = vdCoef
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188 lsCov1$std = sd(frmeTmp[["Covariate3"]])
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189 lsCov1$allCoefs = vdCoef
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190 lsCov2 = list()
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191 lsCov2$name = "Covariate3"
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192 lsCov2$orig = "Covariate33"
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193 lsCov2$taxon = "adCur"
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194 lsCov2$data = covY
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195 lsCov2$factors = "Covariate3"
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196 lsCov2$metadata = frmeTmp[["Covariate3"]]
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197 vdCoef = c(Covariate33=NA)
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198 lsCov2$value = vdCoef
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199 lsCov2$std = sd(frmeTmp[["Covariate3"]])
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200 lsCov2$allCoefs = vdCoef
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201 lsCov3 = list()
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202 lsCov3$name = "Covariate1"
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203 lsCov3$orig = "Covariate1"
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204 lsCov3$taxon = "adCur"
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205 lsCov3$data = covY
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206 lsCov3$factors = "Covariate1"
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207 lsCov3$metadata = frmeTmp[["Covariate1"]]
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208 vdCoef = c(Covariate1=0.6)
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209 lsCov3$value = vdCoef
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210 lsCov3$std = sd(frmeTmp[["Covariate1"]])
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211 lsCov3$allCoefs = vdCoef
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212 lsCov4 = list()
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213 lsCov4$name = "Covariate2"
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214 lsCov4$orig = "Covariate2"
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215 lsCov4$taxon = "adCur"
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216 lsCov4$data = covY
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217 lsCov4$factors = "Covariate2"
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218 lsCov4$metadata = frmeTmp[["Covariate2"]]
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219 vdCoef = c(Covariate2=0.46666667)
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220 lsCov4$value = vdCoef
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221 lsCov4$std = sd(frmeTmp[["Covariate2"]])
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222 lsCov4$allCoefs = vdCoef
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223
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224 lsSigExpected = list()
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225 lsSigExpected[[1]] = lsCov1
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226 lsSigExpected[[2]] = lsCov2
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227 lsSigExpected[[3]] = lsCov3
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228 lsSigExpected[[4]] = lsCov4
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229
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230 expectedReturn = list(adP=adPExpected,lsSig=lsSigExpected,lsQCCounts=QCExpected)
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231 receivedReturn = funcBugHybrid(iTaxon=iTaxon,frmeData=frmeTmp,lsData=lsData,aiMetadata=aiMetadata,dFreq=dFreq,dSig=dSig,dMinSamp=dMinSamp,adP=adP,lsSig=lsSig, strLog=NA,funcReg=funcReg,lsNonPenalizedPredictors=NULL,funcAnalysis=funcAnalysis,lsRandomCovariates=NULL,funcGetResult=funcGetResult)
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232 receivedReturn$adP = round(receivedReturn$adP,5)
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233 test_that("funcBugHybrid works with the univariate option with 3 covariates.",{expect_equal(receivedReturn,expectedReturn)})
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234
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235
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236 # single covariate, single call univariate
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237 funcReg = NA
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238 funcAnalysis = funcDoUnivariate
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239 funcGetResult = NA
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240 aiMetadata = c(1)
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241 dFreq = 0.5 / length( aiMetadata )
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242 lsData$astrMetadata = names(frmeTmp)[aiMetadata]
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243 adPExpected = round(c(0.09679784),5)
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244 QCExpected = list(iLms=numeric(0))
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245 lsSigExpected = list()
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246 lsSigExpected[[1]] = lsCov3
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247
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248 expectedReturn = list(adP=adPExpected,lsSig=lsSigExpected,lsQCCounts=QCExpected)
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249 receivedReturn = funcBugHybrid(iTaxon=iTaxon,frmeData=frmeTmp,lsData=lsData,aiMetadata=aiMetadata,dFreq=dFreq,dSig=dSig,dMinSamp=dMinSamp,adP=adP,lsSig=lsSig, strLog=NA,funcReg=funcReg,lsNonPenalizedPredictors=NULL,funcAnalysis=funcAnalysis,lsRandomCovariates=NULL,funcGetResult=funcGetResult)
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250 receivedReturn$adP = round(receivedReturn$adP,5)
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251 test_that("funcBugHybrid works with the univariate option with 1 covariates.",{expect_equal(receivedReturn,expectedReturn)})
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252
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253
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254 context("Test funcBugs")
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255 #One LM run
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256 frmeData=frmeTmp
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257 aiMetadata=c(1)
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258 aiData=c(iTaxon)
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259 strData=NA
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260 dFreq= 0.5 / length( aiMetadata )
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261 dSig=0.25
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262 dMinSamp=0.1
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263 strDirOut=NA
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264 funcReg=NA
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265 lsNonPenalizedPredictors=NULL
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266 lsRandomCovariates=NULL
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267 funcAnalysis=funcLM
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268 funcGetResults=funcGetLMResults
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269 fDoRPlot=FALSE
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270 lsData = list(frmeData=frmeData, aiMetadata=aiMetadata, aiData=aiData, lsQCCounts=list())
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271 lsData$astrMetadata = names(frmeTmp)[aiMetadata]
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272 QCExpected = list(iLms=numeric(0))
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273
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274 expectedReturn = list(aiReturnBugs=aiData,lsQCCounts=QCExpected)
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275 receivedReturn = funcBugs(frmeData=frmeData, lsData=lsData, aiMetadata=aiMetadata, aiData=aiData, strData=strData, dFreq=dFreq, dSig=dSig, dMinSamp=dMinSamp,strDirOut=strDirOut, funcReg=funcReg,lsNonPenalizedPredictors=lsNonPenalizedPredictors,funcAnalysis=funcAnalysis,lsRandomCovariates=lsRandomCovariates,funcGetResults=funcGetResults,fDoRPlot=fDoRPlot)
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276
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277 test_that("funcBugs works with the lm option with 1 covariate.",{expect_equal(receivedReturn,expectedReturn)})
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278
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279 #multiple LM run
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280 frmeData=frmeTmp
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281 aiMetadata=c(1:5)
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282 aiData=c(iTaxon)
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283 strData=NA
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284 dFreq= 0.5 / length( aiMetadata )
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285 dSig=0.25
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286 dMinSamp=0.1
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287 strDirOut=NA
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288 funcReg=NA
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289 lsNonPenalizedPredictors=NULL
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290 lsRandomCovariates=NULL
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291 funcAnalysis=funcLM
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292 funcGetResults=funcGetLMResults
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293 fDoRPlot=FALSE
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294 lsData = list(frmeData=frmeData, aiMetadata=aiMetadata, aiData=aiData, lsQCCounts=list())
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295 lsData$astrMetadata = names(frmeTmp)[aiMetadata]
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296 QCExpected = list(iLms=numeric(0))
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297
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298 expectedReturn = list(aiReturnBugs=aiData,lsQCCounts=QCExpected)
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299 receivedReturn = funcBugs(frmeData=frmeData, lsData=lsData, aiMetadata=aiMetadata, aiData=aiData, strData=strData, dFreq=dFreq, dSig=dSig, dMinSamp=dMinSamp,strDirOut=strDirOut, funcReg=funcReg,lsNonPenalizedPredictors=lsNonPenalizedPredictors,funcAnalysis=funcAnalysis,lsRandomCovariates=lsRandomCovariates,funcGetResults=funcGetResults,fDoRPlot=fDoRPlot)
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300
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301 print("START START")
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302 print(expectedReturn)
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303 print("RECEIVED")
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304 print(receivedReturn)
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305 print("STOP STOP")
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306
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307 test_that("funcBugs works with the lm option with multiple covariates.",{expect_equal(receivedReturn,expectedReturn)}) |
