diff maaslin-4450aa4ecc84/src/lib/Misc.R @ 1:a87d5a5f2776

Uploaded the version running on the prod server

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+#####################################################################################
+#Copyright (C) <2012>
+#
+#Permission is hereby granted, free of charge, to any person obtaining a copy of
+#this software and associated documentation files (the "Software"), to deal in the
+#Software without restriction, including without limitation the rights to use, copy,
+#modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
+#and to permit persons to whom the Software is furnished to do so, subject to
+#the following conditions:
+#
+#The above copyright notice and this permission notice shall be included in all copies
+#or substantial portions of the Software.
+#
+#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+#INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+#PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+#HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+#OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+#SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#
+# This file is a component of the MaAsLin (Multivariate Associations Using Linear Models), 
+# authored by the Huttenhower lab at the Harvard School of Public Health
+# (contact Timothy Tickle, [e-mail]).
+#####################################################################################
+
+### Modified Code
+### This code is from the package agricolae by Felipe de Mendiburu
+### Modifications here are minimal and allow one to use the p.values from the post hoc comparisons
+### Authors do not claim credit for this solution only needed to modify code to use the output.
+kruskal <- function (y, trt, alpha = 0.05, p.adj = c("none", "holm", "hochberg", 
+    "bonferroni", "BH", "BY", "fdr"), group = TRUE, main = NULL) 
+{
+    dfComparisons=NULL
+    dfMeans=NULL
+    dntStudent=NULL
+    dLSD=NULL
+    dHMean=NULL
+    name.y <- paste(deparse(substitute(y)))
+    name.t <- paste(deparse(substitute(trt)))
+    p.adj <- match.arg(p.adj)
+    junto <- subset(data.frame(y, trt), is.na(y) == FALSE)
+    N <- nrow(junto)
+    junto[, 1] <- rank(junto[, 1])
+    means <- tapply.stat(junto[, 1], junto[, 2], stat = "sum")
+    sds <- tapply.stat(junto[, 1], junto[, 2], stat = "sd")
+    nn <- tapply.stat(junto[, 1], junto[, 2], stat = "length")
+    means <- data.frame(means, replication = nn[, 2])
+    names(means)[1:2] <- c(name.t, name.y)
+    ntr <- nrow(means)
+    nk <- choose(ntr, 2)
+    DFerror <- N - ntr
+    rs <- 0
+    U <- 0
+    for (i in 1:ntr) {
+        rs <- rs + means[i, 2]^2/means[i, 3]
+        U <- U + 1/means[i, 3]
+    }
+    S <- (sum(junto[, 1]^2) - (N * (N + 1)^2)/4)/(N - 1)
+    H <- (rs - (N * (N + 1)^2)/4)/S
+#    cat("\nStudy:", main)
+#    cat("\nKruskal-Wallis test's\nTies or no Ties\n")
+#    cat("\nValue:", H)
+#    cat("\ndegrees of freedom:", ntr - 1)
+    p.chisq <- 1 - pchisq(H, ntr - 1)
+#    cat("\nPvalue chisq  :", p.chisq, "\n\n")
+    DFerror <- N - ntr
+    Tprob <- qt(1 - alpha/2, DFerror)
+    MSerror <- S * ((N - 1 - H)/(N - ntr))
+    means[, 2] <- means[, 2]/means[, 3]
+#    cat(paste(name.t, ",", sep = ""), " means of the ranks\n\n")
+    dfMeans=data.frame(row.names = means[, 1], means[, -1])
+    if (p.adj != "none") {
+#        cat("\nP value adjustment method:", p.adj)
+        a <- 1e-06
+        b <- 1
+        for (i in 1:100) {
+            x <- (b + a)/2
+            xr <- rep(x, nk)
+            d <- p.adjust(xr, p.adj)[1] - alpha
+            ar <- rep(a, nk)
+            fa <- p.adjust(ar, p.adj)[1] - alpha
+            if (d * fa < 0) 
+                b <- x
+            if (d * fa > 0) 
+                a <- x
+        }
+        Tprob <- qt(1 - x/2, DFerror)
+    }
+    nr <- unique(means[, 3])
+    if (group) {
+        Tprob <- qt(1 - alpha/2, DFerror)
+#        cat("\nt-Student:", Tprob)
+#        cat("\nAlpha    :", alpha)
+        dntStudent=Tprob
+        dAlpha=alpha
+        if (length(nr) == 1) {
+            LSD <- Tprob * sqrt(2 * MSerror/nr)
+#            cat("\nLSD      :", LSD, "\n")
+            dLSD=LSD
+        }
+        else {
+            nr1 <- 1/mean(1/nn[, 2])
+            LSD1 <- Tprob * sqrt(2 * MSerror/nr1)
+#            cat("\nLSD      :", LSD1, "\n")
+            dLSD =LSD1
+#            cat("\nHarmonic Mean of Cell Sizes ", nr1)
+            dHMean=nr1
+        }
+#        cat("\nMeans with the same letter are not significantly different\n")
+#        cat("\nGroups, Treatments and mean of the ranks\n")
+        output <- order.group(means[, 1], means[, 2], means[, 
+            3], MSerror, Tprob, std.err = sqrt(MSerror/means[, 
+            3]))
+        dfComparisons=order.group(means[, 1], means[, 2], means[, 
+            3], MSerror, Tprob, std.err = sqrt(MSerror/means[, 
+            3]))
+    }
+    if (!group) {
+        comb <- combn(ntr, 2)
+        nn <- ncol(comb)
+        dif <- rep(0, nn)
+        LCL <- dif
+        UCL <- dif
+        pvalue <- dif
+        sdtdif <- dif
+        for (k in 1:nn) {
+            i <- comb[1, k]
+            j <- comb[2, k]
+            if (means[i, 2] < means[j, 2]) {
+                comb[1, k] <- j
+                comb[2, k] <- i
+            }
+            dif[k] <- abs(means[i, 2] - means[j, 2])
+            sdtdif[k] <- sqrt(S * ((N - 1 - H)/(N - ntr)) * (1/means[i, 
+                3] + 1/means[j, 3]))
+            pvalue[k] <- 2 * round(1 - pt(dif[k]/sdtdif[k], DFerror), 
+                6)
+        }
+        if (p.adj != "none") 
+            pvalue <- round(p.adjust(pvalue, p.adj), 6)
+        LCL <- dif - Tprob * sdtdif
+        UCL <- dif + Tprob * sdtdif
+        sig <- rep(" ", nn)
+        for (k in 1:nn) {
+            if (pvalue[k] <= 0.001) 
+                sig[k] <- "***"
+            else if (pvalue[k] <= 0.01) 
+                sig[k] <- "**"
+            else if (pvalue[k] <= 0.05) 
+                sig[k] <- "*"
+            else if (pvalue[k] <= 0.1) 
+                sig[k] <- "."
+        }
+        tr.i <- means[comb[1, ], 1]
+        tr.j <- means[comb[2, ], 1]
+        dfComparisons <- data.frame(Difference = dif, p.value = pvalue, 
+            sig, LCL, UCL)
+        rownames(dfComparisons) <- paste(tr.i, tr.j, sep = " - ")
+#        cat("\nComparison between treatments mean of the ranks\n\n")
+#        print(output)
+        dfMeans <- data.frame(trt = means[, 1], means = means[, 
+            2], M = "", N = means[, 3])
+    }
+#    invisible(output)
+     invisible(list(study=main,test="Kruskal-Wallis test",value=H,df=(ntr - 1),chisq.p.value=p.chisq,p.adj.method=p.adj,ntStudent=dntStudent,alpha=alpha,LSD=dLSD,Harmonic.mean=dHMean,comparisons=dfComparisons,means=dfMeans))
+}
+
+### This function is NOT original code but is from the gamlss package.
+### It is written here in an effort to over write the gamlss object summary method
+### so that I can return information of interest.
+estimatesgamlss<-function (object, Qr, p1, coef.p, 
+                           est.disp , df.r, 
+                           digits = max(3, getOption("digits") - 3),
+                           covmat.unscaled , ...)
+{
+  #covmat.unscaled <- chol2inv(Qr$qr[p1, p1, drop = FALSE])
+  dimnames(covmat.unscaled) <- list(names(coef.p), names(coef.p))
+  covmat <- covmat.unscaled #in glm is=dispersion * covmat.unscaled, but here is already multiplied by the dispersion
+  var.cf <- diag(covmat)
+  s.err <- sqrt(var.cf)
+  tvalue <- coef.p/s.err
+  dn <- c("Estimate", "Std. Error")
+  if (!est.disp) 
+  {
+    pvalue <- 2 * pnorm(-abs(tvalue))
+    coef.table <- cbind(coef.p, s.err, tvalue, pvalue)
+    dimnames(coef.table) <- list(names(coef.p), c(dn, "z value","Pr(>|z|)"))
+  } else if (df.r > 0) {
+    pvalue <- 2 * pt(-abs(tvalue), df.r)
+    coef.table <- cbind(coef.p, s.err, tvalue, pvalue)
+    dimnames(coef.table) <- list(names(coef.p), c(dn, "t value","Pr(>|t|)"))
+  } else {
+    coef.table <- cbind(coef.p, Inf)
+    dimnames(coef.table) <- list(names(coef.p), dn)
+  }
+  return(coef.table)
+}
+
+### This function is NOT original code but is from the gamlss package.
+### It is written here in an effort to over write the gamlss object summary method
+### so that I can return information of interest.
+summary.gamlss<- function (object, type = c("vcov", "qr"), save = FALSE, ...) 
+{
+  return(as.data.frame(estimatesgamlss(object=object,Qr=object$mu.qr, p1=1:(object$mu.df-object$mu.nl.df), 
+    coef.p=object$mu.coefficients[object$mu.qr$pivot[1:(object$mu.df-object$mu.nl.df)]], 
+    est.disp =TRUE, df.r=(object$noObs - object$mu.df),
+    covmat.unscaled=chol2inv(object$mu.qr$qr[1:(object$mu.df-object$mu.nl.df), 1:(object$mu.df-object$mu.nl.df), drop = FALSE]) )) )
+}
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