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diff maaslin-4450aa4ecc84/src/test-AnalysisModules/test-AnalysisModules.R @ 1:a87d5a5f2776

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Uploaded the version running on the prod server
author george-weingart
date Sun, 08 Feb 2015 23:08:38 -0500
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/maaslin-4450aa4ecc84/src/test-AnalysisModules/test-AnalysisModules.R	Sun Feb 08 23:08:38 2015 -0500
@@ -0,0 +1,788 @@
+c_strDir <- file.path(getwd( ),"..")
+
+source(file.path(c_strDir,"lib","Constants.R"))
+source(file.path(c_strDir,"lib","Utility.R"))
+
+#Test Utilities
+context("Test funcGetLMResults")
+context("Test funcGetStepPredictors")
+
+context("Test funcMakeContrasts")
+covX1 = c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
+covX2 = c(144.4, 245.9, 141.9, 253.3, 144.7, 244.1, 150.7, 245.2, 160.1)
+covX3 = as.factor(c(1,2,3,1,2,3,1,2,3))
+covX4 = as.factor(c(1,1,1,1,2,2,2,2,2))
+covX5 = as.factor(c(1,2,1,2,1,2,1,2,1))
+covY = c(.26,  .31,  .25,  .50,  .36,  .40,  .52,  .28,  .38)
+frmeTmp = data.frame(Covariate1=covX1, Covariate2=covX2, Covariate3=covX3, Covariate4=covX4, Covariate5=covX5, adCur= covY)
+iTaxon = 6
+#Add in updating QC errors
+#Add in random covariates
+strFormula = "adCur ~ Covariate1"
+strRandomFormula = NULL
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate1"
+lsSig[[1]]$orig = "Covariate1"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = covY
+lsSig[[1]]$factors = "Covariate1"
+lsSig[[1]]$metadata = covX1
+vdCoef = c(Covariate1=0.6)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(covX1)
+lsSig[[1]]$allCoefs = vdCoef
+ret1 = funcMakeContrasts(strFormula=strFormula, strRandomFormula=strRandomFormula, frmeTmp=frmeTmp, iTaxon=iTaxon,
+    functionContrast=function(x,adCur,dfData)
+    {
+      retList = list()
+      ret = cor.test(as.formula(paste("~",x,"+ adCur")), data=dfData, method="spearman", na.action=c_strNA_Action)
+      #Returning rho for the coef in a named vector
+      vdCoef = c()
+      vdCoef[[x]]=ret$estimate
+      retList[[1]]=list(p.value=ret$p.value,SD=sd(dfData[[x]]),name=x,coef=vdCoef)
+      return(retList)
+    }, lsQCCounts=list())
+ret1$adP = round(ret1$adP,5)
+test_that("1. Test that the funcMakeContrasts works on a continuous variable.",{
+  expect_equal(ret1,list(adP=round(c(0.09679784),5),lsSig=lsSig,lsQCCounts=list()))})
+
+strFormula = "adCur ~ Covariate1 + Covariate2"
+strRandomFormula = NULL
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate1"
+lsSig[[1]]$orig = "Covariate1"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = covY
+lsSig[[1]]$factors = "Covariate1"
+lsSig[[1]]$metadata = covX1
+vdCoef = c(Covariate1=0.6)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(covX1)
+lsSig[[1]]$allCoefs = vdCoef
+lsSig[[2]] = list()
+lsSig[[2]]$name = "Covariate2"
+lsSig[[2]]$orig = "Covariate2"
+lsSig[[2]]$taxon = "adCur"
+lsSig[[2]]$data = covY
+lsSig[[2]]$factors = "Covariate2"
+lsSig[[2]]$metadata = covX2
+vdCoef = c(Covariate2=0.46666667)
+lsSig[[2]]$value = vdCoef
+lsSig[[2]]$std = sd(covX2)
+lsSig[[2]]$allCoefs = vdCoef
+ret1 = funcMakeContrasts(strFormula=strFormula, strRandomFormula=strRandomFormula, frmeTmp=frmeTmp, iTaxon=iTaxon,
+    functionContrast=function(x,adCur,dfData)
+    {
+      retList = list()
+      ret = cor.test(as.formula(paste("~",x,"+ adCur")), data=dfData, method="spearman", na.action=c_strNA_Action)
+      #Returning rho for the coef in a named vector
+      vdCoef = c()
+      vdCoef[[x]]=ret$estimate
+      retList[[1]]=list(p.value=ret$p.value,SD=sd(dfData[[x]]),name=x,coef=vdCoef)
+      return(retList)
+    }, lsQCCounts=list())
+ret1$adP = round(ret1$adP,5)
+test_that("Test that the funcMakeContrasts works on 2 continuous variables.",{
+  expect_equal(ret1,list(adP=round(c(0.09679784,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))})
+
+strFormula = "adCur ~ Covariate4"
+strRandomFormula = NULL
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate4"
+lsSig[[1]]$orig = "Covariate42"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = covY
+lsSig[[1]]$factors = "Covariate4"
+lsSig[[1]]$metadata = covX4 #update
+vdCoef = c(Covariate42=NA)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(covX4) #update
+lsSig[[1]]$allCoefs = vdCoef
+# Get return
+rets = funcMakeContrasts(strFormula=strFormula, strRandomFormula=strRandomFormula, frmeTmp=frmeTmp, iTaxon=iTaxon,
+    functionContrast=function(x,adCur,dfData)
+    {
+      retList = list()
+      lmodKW = kruskal(adCur,dfData[[x]],group=FALSE,p.adj="holm")
+      asLevels = levels(dfData[[x]])
+      # The names of the generated comparisons, sometimes the control is first sometimes it is not so
+      # We will just check which is in the names and use that
+      asComparisons = row.names(lmodKW$comparisons)
+      #Get the comparison with the control
+      for(sLevel in asLevels[2:length(asLevels)])
+      {
+        sComparison = intersect(c(paste(asLevels[1],sLevel,sep=" - "),paste(sLevel,asLevels[1],sep=" - ")),asComparisons)
+        #Returning NA for the coef in a named vector
+        vdCoef = c()
+        vdCoef[[paste(x,sLevel,sep="")]]=NA
+        retList[[length(retList)+1]]=list(p.value=lmodKW$comparisons[sComparison,"p.value"],SD=NA,name=paste(x,sLevel,sep=""),coef=vdCoef)
+      }
+      return(retList)
+    }, lsQCCounts=list())
+rets$adP=round(rets$adP,digits=5)
+test_that("Test that the funcMakeContrasts works on 1 factor covariate with 2 levels.",{
+  expect_equal(rets,list(adP=round(c(0.24434),5),lsSig=lsSig,lsQCCounts=list()))})
+
+strFormula = "adCur ~ Covariate3"
+strRandomFormula = NULL
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate3"
+lsSig[[1]]$orig = "Covariate32"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = covY
+lsSig[[1]]$factors = "Covariate3"
+lsSig[[1]]$metadata = covX3 #update
+vdCoef = c(Covariate32=NA)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(covX3) #update
+lsSig[[1]]$allCoefs = vdCoef
+lsSig[[2]] = list()
+lsSig[[2]]$name = "Covariate3"
+lsSig[[2]]$orig = "Covariate33"
+lsSig[[2]]$taxon = "adCur"
+lsSig[[2]]$data = covY
+lsSig[[2]]$factors = "Covariate3"
+lsSig[[2]]$metadata = covX3 #update
+vdCoef = c(Covariate33=NA)
+lsSig[[2]]$value = vdCoef
+lsSig[[2]]$std = sd(covX3) #update
+lsSig[[2]]$allCoefs = vdCoef
+ret1 = funcMakeContrasts(strFormula=strFormula, strRandomFormula=strRandomFormula, frmeTmp=frmeTmp, iTaxon=iTaxon,
+    functionContrast=function(x,adCur,dfData)
+    {
+      retList = list()
+      lmodKW = kruskal(adCur,dfData[[x]],group=FALSE,p.adj="holm")
+      asLevels = levels(dfData[[x]])
+      # The names of the generated comparisons, sometimes the control is first sometimes it is not so
+      # We will just check which is in the names and use that
+      asComparisons = row.names(lmodKW$comparisons)
+      #Get the comparison with the control
+      for(sLevel in asLevels[2:length(asLevels)])
+      {
+        sComparison = intersect(c(paste(asLevels[1],sLevel,sep=" - "),paste(sLevel,asLevels[1],sep=" - ")),asComparisons)
+        #Returning NA for the coef in a named vector
+        vdCoef = c()
+        vdCoef[[paste(x,sLevel,sep="")]]=NA
+        retList[[length(retList)+1]]=list(p.value=lmodKW$comparisons[sComparison,"p.value"],SD=NA,name=paste(x,sLevel,sep=""),coef=vdCoef)
+      }
+      return(retList)
+    }, lsQCCounts=list())
+ret1$adP = round(ret1$adP,5)
+test_that("Test that the funcMakeContrasts works on 1 factor covariate with 3 levels.",{
+  expect_equal(ret1,list(adP=c(1.0,1.0),lsSig=lsSig,lsQCCounts=list()))})
+
+strFormula = "adCur ~ Covariate4 + Covariate5"
+strRandomFormula = NULL
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate4"
+lsSig[[1]]$orig = "Covariate42"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = covY
+lsSig[[1]]$factors = "Covariate4"
+lsSig[[1]]$metadata = covX4 #update
+vdCoef = c(Covariate42=NA)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(covX4) #update
+lsSig[[1]]$allCoefs = vdCoef
+lsSig[[2]] = list()
+lsSig[[2]]$name = "Covariate5"
+lsSig[[2]]$orig = "Covariate52"
+lsSig[[2]]$taxon = "adCur"
+lsSig[[2]]$data = covY
+lsSig[[2]]$factors = "Covariate5"
+lsSig[[2]]$metadata = covX5 #update
+vdCoef = c(Covariate52=NA)
+lsSig[[2]]$value = vdCoef
+lsSig[[2]]$std = sd(covX5) #update
+lsSig[[2]]$allCoefs = vdCoef
+ret1 = funcMakeContrasts(strFormula=strFormula, strRandomFormula=strRandomFormula, frmeTmp=frmeTmp, iTaxon=iTaxon,
+    functionContrast=function(x,adCur,dfData)
+    {
+      retList = list()
+      lmodKW = kruskal(adCur,dfData[[x]],group=FALSE,p.adj="holm")
+      asLevels = levels(dfData[[x]])
+      # The names of the generated comparisons, sometimes the control is first sometimes it is not so
+      # We will just check which is in the names and use that
+      asComparisons = row.names(lmodKW$comparisons)
+      #Get the comparison with the control
+      for(sLevel in asLevels[2:length(asLevels)])
+      {
+        sComparison = intersect(c(paste(asLevels[1],sLevel,sep=" - "),paste(sLevel,asLevels[1],sep=" - ")),asComparisons)
+        #Returning NA for the coef in a named vector
+        vdCoef = c()
+        vdCoef[[paste(x,sLevel,sep="")]]=NA
+        retList[[length(retList)+1]]=list(p.value=lmodKW$comparisons[sComparison,"p.value"],SD=NA,name=paste(x,sLevel,sep=""),coef=vdCoef)
+      }
+      return(retList)
+    }, lsQCCounts=list())
+ret1$adP = round(ret1$adP,5)
+test_that("1. Test that the funcMakeContrasts works on 2 factor covariate with 2 levels.",{
+  expect_equal(ret1,list(adP=round(c(0.24434,0.655852),5),lsSig=lsSig,lsQCCounts=list()))})
+
+
+#Test Model selection
+
+
+context("Test funcBoostModel")
+context("Test funcForwardModel")
+context("Test funcBackwardsModel")
+
+
+#Test Univariates
+context("Test funcSpearman")
+strFormula = "adCur ~ Covariate1"
+adCur = c(.26,  .31,  .25,  .50,  .36,  .40,  .52,  .28,  .38)
+x = c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
+frmeTmp = data.frame(Covariate1=x, adCur=adCur)
+iTaxon = 2
+lsQCCounts = list()
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate1"
+lsSig[[1]]$orig = "Covariate1"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = adCur
+lsSig[[1]]$factors = "Covariate1"
+lsSig[[1]]$metadata = x
+vdCoef = c(Covariate1=0.6)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(x)
+lsSig[[1]]$allCoefs = vdCoef
+ret1 = funcSpearman(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsQCCounts=lsQCCounts,strRandomFormula=NULL)
+ret1$adP = round(ret1$adP,5)
+test_that("Test that the spearman test has the correct results for 1 covariate.",{
+  expect_equal(ret1,list(adP=round(c(0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
+})
+
+strFormula = "adCur ~ Covariate1 + Covariate2"
+frmeTmp = data.frame(Covariate1=x, Covariate2=x, adCur=adCur)
+iTaxon = 3
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate1"
+lsSig[[1]]$orig = "Covariate1"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = adCur
+lsSig[[1]]$factors = "Covariate1"
+lsSig[[1]]$metadata = x
+vdCoef = c(Covariate1=0.6)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(x)
+lsSig[[1]]$allCoefs = vdCoef
+lsSig[[2]] = list()
+lsSig[[2]]$name = "Covariate2"
+lsSig[[2]]$orig = "Covariate2"
+lsSig[[2]]$taxon = "adCur"
+lsSig[[2]]$data = adCur 
+lsSig[[2]]$factors = "Covariate2"
+lsSig[[2]]$metadata = x
+vdCoef = c(Covariate2=0.6)
+lsSig[[2]]$value = vdCoef
+lsSig[[2]]$std = sd(x)
+lsSig[[2]]$allCoefs = vdCoef
+lsQCCounts = list()
+ret1 = funcSpearman(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsQCCounts=lsQCCounts,strRandomFormula=NULL)
+ret1$adP = round(ret1$adP,5)
+test_that("Test that the spearman test has the correct results for 2 covariates.",{
+  expect_equal(ret1,list(adP=round(c(0.09679784,0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
+})
+
+
+context("Test funcWilcoxon")
+strFormula = "adCur ~ Covariate1"
+x = c(TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE)
+frmeTmp = data.frame(Covariate1=x, adCur=adCur)
+iTaxon = 2
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate1"
+lsSig[[1]]$orig = "Covariate1"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = adCur
+lsSig[[1]]$factors = "Covariate1"
+lsSig[[1]]$metadata = x
+vdCoef = c(Covariate1=13)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(x)
+lsSig[[1]]$allCoefs = vdCoef
+lsQCCounts = list()
+ret1 = funcWilcoxon(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsQCCounts=lsQCCounts,strRandomFormula=NULL)
+ret1$adP = round(ret1$adP,5)
+test_that("Test that the wilcoxon test has the correct results for 1 covariate.",{
+  expect_equal(ret1,list(adP=round(c(0.55555556),5),lsSig=lsSig,lsQCCounts=list()))
+})
+
+
+context("Test funcKruskalWallis")
+strFormula = "adCur ~ Covariate1"
+x = as.factor(c("one","two","three","one","one","three","two","three","two"))
+frmeTmp = data.frame(Covariate1=x, adCur=adCur)
+iTaxon = 2
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate1"
+lsSig[[1]]$orig = "Covariate1three"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = adCur
+lsSig[[1]]$factors = "Covariate1"
+lsSig[[1]]$metadata = x
+vdCoef = c(Covariate1three=NA)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(x)
+lsSig[[1]]$allCoefs = vdCoef
+lsSig[[2]] = list()
+lsSig[[2]]$name = "Covariate1"
+lsSig[[2]]$orig = "Covariate1two"
+lsSig[[2]]$taxon = "adCur"
+lsSig[[2]]$data = adCur
+lsSig[[2]]$factors = "Covariate1"
+lsSig[[2]]$metadata = x
+vdCoef = c(Covariate1two=NA)
+lsSig[[2]]$value = vdCoef
+lsSig[[2]]$std = sd(x)
+lsSig[[2]]$allCoefs = vdCoef
+lsQCCounts = list()
+ret1 = funcKruskalWallis(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsQCCounts=lsQCCounts,strRandomFormula=NULL)
+ret1$adP = round(ret1$adP,5)
+test_that("Test that the Kruskal Wallis (Nonparameteric anova) has the correct results for 1 covariate.",{
+  expect_equal(ret1,list(adP=c(1.0,1.0),lsSig=lsSig,lsQCCounts=list()))
+})
+
+
+context("test funcDoUnivariate")
+covX1 = c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
+covX2 = c(144.4, 245.9, 141.9, 253.3, 144.7, 244.1, 150.7, 245.2, 160.1)
+covX3 = as.factor(c(1,2,3,1,2,3,1,2,3))
+covX4 = as.factor(c(1,1,1,1,2,2,2,2,2))
+covX5 = as.factor(c(1,2,1,2,1,2,1,2,1))
+covX6 = as.factor(c("one","two","three","one","one","three","two","three","two"))
+covY = c(.26,  .31,  .25,  .50,  .36,  .40,  .52,  .28,  .38)
+frmeTmp = data.frame(Covariate1=covX1, Covariate2=covX2, Covariate3=covX3, Covariate4=covX4, Covariate5=covX5, Covariate6=covX6, adCur= covY)
+iTaxon = 7
+# 1 cont answer
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate1"
+lsSig[[1]]$orig = "Covariate1"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = adCur
+lsSig[[1]]$factors = "Covariate1"
+lsSig[[1]]$metadata = frmeTmp[["Covariate1"]]
+vdCoef = c(Covariate1=0.6)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(frmeTmp[["Covariate1"]])
+lsSig[[1]]$allCoefs = vdCoef
+lsHistory = list(adP=c(), lsSig=c(),lsQCCounts=list())
+ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate1",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula=NULL)
+ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate1")
+ret1$adP = round(ret1$adP,5)
+ret2$adP = round(ret2$adP,5)
+print("ret1")
+print(ret1)
+print("list(adP=round(c(0.09679784),5),lsSig=lsSig,lsQCCounts=list())")
+print(list(adP=round(c(0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
+test_that("2. Test that the funcMakeContrasts works on a continuous variable.",{
+  expect_equal(ret1,list(adP=round(c(0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
+  expect_equal(ret2,list(adP=round(c(0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
+})
+lsSig[[2]] = list()
+lsSig[[2]]$name = "Covariate2"
+lsSig[[2]]$orig = "Covariate2"
+lsSig[[2]]$taxon = "adCur"
+lsSig[[2]]$data = adCur
+lsSig[[2]]$factors = "Covariate2"
+lsSig[[2]]$metadata = frmeTmp[["Covariate2"]]
+vdCoef = c(Covariate2=0.46666667)
+lsSig[[2]]$value = vdCoef
+lsSig[[2]]$std = sd(frmeTmp[["Covariate2"]])
+lsSig[[2]]$allCoefs = vdCoef
+ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate1 + Covariate2",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory,strRandomFormula=NULL)
+ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate1 + 1|Covariate2")
+ret1$adP = round(ret1$adP,5)
+ret2$adP = round(ret2$adP,5)
+test_that("Test that the funcMakeContrasts works on 2 continuous variables.",{
+  expect_equal(ret1,list(adP=round(c(0.09679784,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
+  expect_equal(ret2,list(adP=round(c(0.09679784,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
+})
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate4"
+lsSig[[1]]$orig = "Covariate4"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = adCur
+lsSig[[1]]$factors = "Covariate4"
+lsSig[[1]]$metadata = frmeTmp[["Covariate4"]]
+vdCoef = c(Covariate4=5)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(frmeTmp[["Covariate4"]])
+lsSig[[1]]$allCoefs = vdCoef
+ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate4",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory,strRandomFormula=NULL)
+ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate4")
+ret1$adP = round(ret1$adP,5)
+ret2$adP = round(ret2$adP,5)
+test_that("Test that the funcMakeContrasts works on 1 factor covariate with 2 levels.",{
+  expect_equal(ret1,list(adP=round(c(0.2857143),5),lsSig=lsSig,lsQCCounts=list()))
+  expect_equal(ret2,list(adP=round(c(0.2857143),5),lsSig=lsSig,lsQCCounts=list()))
+})
+lsSig[[2]] = list()
+lsSig[[2]]$name = "Covariate5"
+lsSig[[2]]$orig = "Covariate5"
+lsSig[[2]]$taxon = "adCur"
+lsSig[[2]]$data = adCur
+lsSig[[2]]$factors = "Covariate5"
+lsSig[[2]]$metadata = frmeTmp[["Covariate5"]]
+vdCoef = c(Covariate5=8)
+lsSig[[2]]$value = vdCoef
+lsSig[[2]]$std = sd(frmeTmp[["Covariate5"]])
+lsSig[[2]]$allCoefs = vdCoef
+ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate4 + Covariate5",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula=NULL)
+ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate4 + 1|Covariate5")
+ret3 = funcDoUnivariate(strFormula="adCur ~ Covariate4",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate5")
+ret1$adP = round(ret1$adP,5)
+ret2$adP = round(ret2$adP,5)
+ret3$adP = round(ret3$adP,5)
+test_that("2. Test that the funcMakeContrasts works on 2 factor covariate with 2 levels.",{
+  expect_equal(ret1,list(adP=round(c(0.2857143,0.73016),5),lsSig=lsSig,lsQCCounts=list()))
+  expect_equal(ret2,list(adP=round(c(0.2857143,0.73016),5),lsSig=lsSig,lsQCCounts=list()))
+  expect_equal(ret3,list(adP=round(c(0.2857143,0.73016),5),lsSig=lsSig,lsQCCounts=list()))
+})
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate4"
+lsSig[[1]]$orig = "Covariate4"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = adCur
+lsSig[[1]]$factors = "Covariate4"
+lsSig[[1]]$metadata = frmeTmp[["Covariate4"]]
+vdCoef = c(Covariate4=5)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(frmeTmp[["Covariate4"]])
+lsSig[[1]]$allCoefs = vdCoef
+lsSig[[2]] = list()
+lsSig[[2]]$name = "Covariate1"
+lsSig[[2]]$orig = "Covariate1"
+lsSig[[2]]$taxon = "adCur"
+lsSig[[2]]$data = adCur
+lsSig[[2]]$factors = "Covariate1"
+lsSig[[2]]$metadata = frmeTmp[["Covariate1"]]
+vdCoef = c(Covariate1=0.6)
+lsSig[[2]]$value = vdCoef
+lsSig[[2]]$std = sd(frmeTmp[["Covariate1"]])
+lsSig[[2]]$allCoefs = vdCoef
+ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate4 + Covariate1",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula=NULL)
+ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate4 + 1|Covariate1")
+ret3 = funcDoUnivariate(strFormula="adCur ~ Covariate4",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate1")
+ret1$adP = round(ret1$adP,5)
+ret2$adP = round(ret2$adP,5)
+ret3$adP = round(ret3$adP,5)
+test_that("Test that the funcMakeContrasts works on 1 factor covariate with 2 levels and a continuous variable.",{
+  expect_equal(ret1,list(adP=round(c(0.2857143,0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
+  expect_equal(ret2,list(adP=round(c(0.2857143,0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
+  expect_equal(ret3,list(adP=round(c(0.2857143,0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
+})
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate3"
+lsSig[[1]]$orig = "Covariate32"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = adCur
+lsSig[[1]]$factors = "Covariate3"
+lsSig[[1]]$metadata = frmeTmp[["Covariate3"]]
+vdCoef = c(Covariate32=NA)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(frmeTmp[["Covariate3"]])
+lsSig[[1]]$allCoefs = vdCoef
+lsSig[[2]] = list()
+lsSig[[2]]$name = "Covariate3"
+lsSig[[2]]$orig = "Covariate33"
+lsSig[[2]]$taxon = "adCur"
+lsSig[[2]]$data = adCur
+lsSig[[2]]$factors = "Covariate3"
+lsSig[[2]]$metadata = frmeTmp[["Covariate3"]]
+vdCoef = c(Covariate33=NA)
+lsSig[[2]]$value = vdCoef
+lsSig[[2]]$std = sd(frmeTmp[["Covariate3"]])
+lsSig[[2]]$allCoefs = vdCoef
+lsSig[[3]] = list()
+lsSig[[3]]$name = "Covariate1"
+lsSig[[3]]$orig = "Covariate1"
+lsSig[[3]]$taxon = "adCur"
+lsSig[[3]]$data = adCur
+lsSig[[3]]$factors = "Covariate1"
+lsSig[[3]]$metadata = frmeTmp[["Covariate1"]]
+vdCoef = c(Covariate1=0.6)
+lsSig[[3]]$value = vdCoef
+lsSig[[3]]$std = sd(frmeTmp[["Covariate1"]])
+lsSig[[3]]$allCoefs = vdCoef
+lsSig[[4]] = list()
+lsSig[[4]]$name = "Covariate2"
+lsSig[[4]]$orig = "Covariate2"
+lsSig[[4]]$taxon = "adCur"
+lsSig[[4]]$data = adCur
+lsSig[[4]]$factors = "Covariate2"
+lsSig[[4]]$metadata = frmeTmp[["Covariate2"]]
+vdCoef = c(Covariate2=0.46666667)
+lsSig[[4]]$value = vdCoef
+lsSig[[4]]$std = sd(frmeTmp[["Covariate2"]])
+lsSig[[4]]$allCoefs = vdCoef
+ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate3 + Covariate1 + Covariate2",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula=NULL)
+ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate3 + 1|Covariate1 + 1|Covariate2")
+ret3 = funcDoUnivariate(strFormula="adCur ~ Covariate3 + Covariate1",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate2")
+ret1$adP = round(ret1$adP,5)
+ret2$adP = round(ret2$adP,5)
+ret3$adP = round(ret3$adP,5)
+test_that("Test that the funcMakeContrasts works on 1 factor covariate with 3 levels and 2 continuous variables.",{
+  expect_equal(ret1,list(adP=round(c(1.0,1.0,0.09679784,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
+  expect_equal(ret2,list(adP=round(c(1.0,1.0,0.09679784,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
+  expect_equal(ret3,list(adP=round(c(1.0,1.0,0.09679784,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
+})
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate4"
+lsSig[[1]]$orig = "Covariate4"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = adCur
+lsSig[[1]]$factors = "Covariate4"
+lsSig[[1]]$metadata = frmeTmp[["Covariate4"]]
+vdCoef = c(Covariate4=5)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(frmeTmp[["Covariate4"]])
+lsSig[[1]]$allCoefs = vdCoef
+lsSig[[2]] = list()
+lsSig[[2]]$name = "Covariate2"
+lsSig[[2]]$orig = "Covariate2"
+lsSig[[2]]$taxon = "adCur"
+lsSig[[2]]$data = adCur
+lsSig[[2]]$factors = "Covariate2"
+lsSig[[2]]$metadata = frmeTmp[["Covariate2"]]
+vdCoef = c(Covariate2=0.46666667)
+lsSig[[2]]$value = vdCoef
+lsSig[[2]]$std = sd(frmeTmp[["Covariate2"]])
+lsSig[[2]]$allCoefs = vdCoef
+ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate4 + Covariate2",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula=NULL)
+ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate4 + 1|Covariate2")
+ret3 = funcDoUnivariate(strFormula= "adCur ~ Covariate4",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate2")
+ret1$adP = round(ret1$adP,5)
+ret2$adP = round(ret2$adP,5)
+ret3$adP = round(ret3$adP,5)
+test_that("3. Test that the funcMakeContrasts works on 2 factor covariate with 2 levels and a continuous variable.",{
+  expect_equal(ret1,list(adP=round(c(0.2857143,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
+  expect_equal(ret2,list(adP=round(c(0.2857143,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
+  expect_equal(ret3,list(adP=round(c(0.2857143,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
+})
+lsSig = list()
+lsSig[[1]] = list()
+lsSig[[1]]$name = "Covariate4"
+lsSig[[1]]$orig = "Covariate4"
+lsSig[[1]]$taxon = "adCur"
+lsSig[[1]]$data = adCur
+lsSig[[1]]$factors = "Covariate4"
+lsSig[[1]]$metadata = frmeTmp[["Covariate4"]]
+vdCoef = c(Covariate4=5)
+lsSig[[1]]$value = vdCoef
+lsSig[[1]]$std = sd(frmeTmp[["Covariate4"]])
+lsSig[[1]]$allCoefs = vdCoef
+lsSig[[2]] = list()
+lsSig[[2]]$name = "Covariate3"
+lsSig[[2]]$orig = "Covariate32"
+lsSig[[2]]$taxon = "adCur"
+lsSig[[2]]$data = adCur
+lsSig[[2]]$factors = "Covariate3"
+lsSig[[2]]$metadata = frmeTmp[["Covariate3"]]
+vdCoef = c(Covariate32=NA)
+lsSig[[2]]$value = vdCoef
+lsSig[[2]]$std = sd(frmeTmp[["Covariate3"]])
+lsSig[[2]]$allCoefs = vdCoef
+lsSig[[3]] = list()
+lsSig[[3]]$name = "Covariate3"
+lsSig[[3]]$orig = "Covariate33"
+lsSig[[3]]$taxon = "adCur"
+lsSig[[3]]$data = adCur
+lsSig[[3]]$factors = "Covariate3"
+lsSig[[3]]$metadata = frmeTmp[["Covariate3"]]
+vdCoef = c(Covariate33=NA)
+lsSig[[3]]$value = vdCoef
+lsSig[[3]]$std = sd(frmeTmp[["Covariate3"]])
+lsSig[[3]]$allCoefs = vdCoef
+lsSig[[4]] = list()
+lsSig[[4]]$name = "Covariate2"
+lsSig[[4]]$orig = "Covariate2"
+lsSig[[4]]$taxon = "adCur"
+lsSig[[4]]$data = adCur
+lsSig[[4]]$factors = "Covariate2"
+lsSig[[4]]$metadata = frmeTmp[["Covariate2"]]
+vdCoef = c(Covariate2=0.46666667)
+lsSig[[4]]$value = vdCoef
+lsSig[[4]]$std = sd(frmeTmp[["Covariate2"]])
+lsSig[[4]]$allCoefs = vdCoef
+ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate4 + Covariate3 + Covariate2",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula=NULL)
+ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate4 +1|Covariate3 + 1|Covariate2") 
+ret1$adP = round(ret1$adP,5)
+ret2$adP = round(ret2$adP,5)
+test_that("Test that the funcMakeContrasts works on 1 factor covariate with 2 levels , 1 factor with 3 levels, and a continuous variable.",{
+  expect_equal(ret1,list(adP=round(c(0.2857143,1.0,1.0,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
+  expect_equal(ret2,list(adP=round(c(0.2857143,1.0,1.0,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
+})
+
+#Test multivariates
+context("Test funcLasso")
+
+
+context("Test funcLM")
+#This test just makes sure the statistical method is being called correctly for one covariate with the correct return
+strFormula = "adCur ~ Covariate1"
+strRandomFormula = NULL
+x = c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
+x2 = c(34.2, 32.5, 22.4, 43, 3.25, 6.4, 7, 87, 9)
+xf1 = c(1,1,2,2,1,2,1,1,2)
+xf2 = c(1,1,1,1,2,2,2,2,2)
+frmeTmp = data.frame(Covariate1=x, Covariate2=x2, FCovariate3=xf1, FCovariate4=xf2, adCur=adCur)
+iTaxon = 5
+lmRet = lm(as.formula(strFormula), data=frmeTmp, na.action = c_strNA_Action)
+test_that("Test that the lm has the correct results for 1 covariate.",{
+  expect_equal(funcLM(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+})
+#Test for correct call for 2 covariates
+strFormula = "adCur ~ Covariate1 + Covariate2"
+lmRet = lm(as.formula(strFormula), data=frmeTmp, na.action = c_strNA_Action)
+test_that("Test that the lm has the correct results for 2 covariates.",{
+  expect_equal(funcLM(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+})
+##Test for correct call with 1 random and one fixed covariate
+#strFormula = "adCur ~ Covariate1"
+#strRandomFormula = "~1|FCovariate3"
+#lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=gaussian(link="identity"), data=frmeTmp)
+#test_that("Test that the lm has the correct results for 1 random and one fixed covariate.",{
+#  expect_equal(funcLM(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+#})
+##Test for correct call with 1 random and 2 fixed covariates
+#strFormula = "adCur ~ Covariate1 + Covariate2"
+#strRandomFormula = "~1|FCovariate3"
+#lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=gaussian(link="identity"), data=frmeTmp)
+#test_that("Test that the lm has the correct results for 1 random and 2 fixed covariates.",{
+#  expect_equal(funcLM(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+#})
+##Test for correct call with 2 random and 1 fixed covariates
+#strFormula = "adCur ~ Covariate1"
+#strRandomFormula = "~1|FCovariate4+1|FCovariate3"
+#lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=gaussian(link="identity"), data=frmeTmp)
+#test_that("Test that the lm has the correct results for 2 random and 1 fixed covariates.",{
+#  expect_equal(funcLM(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+#})
+
+
+context("Test funcBinomialMult")
+strFormula = "adCur ~ Covariate1"
+strRandomFormula = NULL
+x = c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
+x2 = c(34.2, 32.5, 22.4, 43, 3.25, 6.4, 7, 87, 9)
+xf1 = c(1,1,2,2,1,2,1,1,2)
+xf2 = c(1,1,1,1,2,2,2,2,2)
+frmeTmp = data.frame(Covariate1=x, Covariate2=x2, FCovariate3=xf1, FCovariate4=xf2, adCur=adCur)
+iTaxon = 5
+lmRet = glm(as.formula(strFormula), family=binomial(link=logit), data=frmeTmp, na.action=c_strNA_Action)
+test_that("Test that the neg binomial regression has the correct results for 1 covariate.",{
+  expect_equal(funcBinomialMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+})
+#Test for correct call for 2 covariates
+strFormula = "adCur ~ Covariate1 + Covariate2"
+iTaxon = 5
+lmRet = glm(as.formula(strFormula), family=binomial(link=logit), data=frmeTmp, na.action=c_strNA_Action)
+test_that("Test that the neg binomial regression has the correct results for 2 covariates.",{
+  expect_equal(funcBinomialMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+})
+##Test for correct call with 1 random and one fixed covariate
+#strFormula = "adCur ~ Covariate1"
+#strRandomFormula = "~1|FCovariate3"
+#lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=binomial(link=logit), data=frmeTmp)
+#test_that("Test that the lm has the correct results for 1 random and one fixed covariate.",{
+#  expect_equal(funcBinomialMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+#})
+##Test for correct call with 1 random and 2 fixed covariates
+#strFormula = "adCur ~ Covariate1 + Covariate2"
+#strRandomFormula = "~1|FCovariate3"
+#lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=binomial(link=logit), data=frmeTmp)
+#test_that("Test that the lm has the correct results for 1 random and 2 fixed covariates.",{
+#  expect_equal(funcBinomialMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+#})
+##Test for correct call with 2 random and 1 fixed covariates
+#strFormula = "adCur ~ Covariate1"
+#strRandomFormula = "~1|FCovariate4+1|FCovariate3"
+#lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=binomial(link=logit), data=frmeTmp)
+#test_that("Test that the lm has the correct results for 2 random and 1 fixed covariates.",{
+#  expect_equal(funcBinomialMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+#})
+
+
+context("Test funcQuasiMult")
+strFormula = "adCur ~ Covariate1"
+strRandomFormula = NULL
+x = c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1,44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
+x2 = c(34.2, 32.5, 22.4, 43, 3.25, 6.4, 7, 87, 9,34.2, 32.5, 22.4, 43, 3.25, 6.4, 7, 87, 9)
+xf1 = c(1,1,2,2,1,1,2,2,2,1,1,2,2,1,1,2,2,2)
+xf2 = c(1,1,1,1,2,2,2,2,2,1,1,1,1,2,2,2,2,2)
+frmeTmp = data.frame(Covariate1=x, Covariate2=x2, FCovariate3=xf1, FCovariate4=xf2, adCur=adCur)
+iTaxon = 5
+lmRet = glm(as.formula(strFormula), family=quasipoisson, data=frmeTmp, na.action=c_strNA_Action)
+test_that("Test that the quasi poisson has the correct results for 1 covariate.",{
+  expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+})
+#Test for correct call for 2 covariates
+strFormula = "adCur ~ Covariate1 + Covariate2"
+iTaxon = 5
+lmRet = glm(as.formula(strFormula), family=quasipoisson, data=frmeTmp, na.action=c_strNA_Action)
+test_that("Test that the quasi poisson has the correct results for 2 covariates.",{
+  expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+})
+##Test for correct call with 1 random and one fixed covariate
+#strFormula = "adCur ~ Covariate1"
+#strRandomFormula = "~1|FCovariate3"
+#lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=quasipoisson, data=frmeTmp)
+#test_that("Test that the lm has the correct results for 1 random and one fixed covariate.",{
+#  expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+#})
+##Test for correct call with 1 random and 2 fixed covariates
+#strFormula = "adCur ~ Covariate1 + Covariate2"
+#strRandomFormula = "~1|FCovariate3"
+#lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=quasipoisson, data=frmeTmp)
+#test_that("Test that the lm has the correct results for 1 random and 2 fixed covariates.",{
+#  expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+#})
+##Test for correct call with 2 random and 1 fixed covariates
+#strFormula = "adCur ~ Covariate1"
+#strRandomFormula = "~1|FCovariate4+1|FCovariate3"
+#lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=quasipoisson, data=frmeTmp)
+#test_that("Test that the lm has the correct results for 2 random and 1 fixed covariates.",{
+#  expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
+#})
+
+
+#Test transforms
+context("Test funcNoTransform")
+aTest1 = c(NA)
+aTest2 = c(NULL)
+aTest3 = c(0.5,1.4,2.4,3332.4,0.0,0.0000003)
+aTest4 = c(0.1)
+test_that("Test that no transform does not change the data.",{
+  expect_equal(funcNoTransform(aTest1), aTest1)
+  expect_equal(funcNoTransform(aTest2), aTest2)
+  expect_equal(funcNoTransform(aTest3), aTest3)
+  expect_equal(funcNoTransform(aTest4), aTest4)
+})
+
+
+context("Test funcArcsinSqrt")
+aTest1 = c(NA)
+aTest2 = c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)
+aTest3 = c(0.000001)
+test_that("Test that funcArcsinSqrt performs the transform correctly.",{
+  expect_equal(funcArcsinSqrt(NA), as.numeric(NA))
+  expect_equal(funcArcsinSqrt(aTest1), asin(sqrt(aTest1)))
+  expect_equal(funcArcsinSqrt(aTest2), asin(sqrt(aTest2)))
+  expect_equal(funcArcsinSqrt(aTest3), asin(sqrt(aTest3)))
+})
\ No newline at end of file