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

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author george-weingart
date Sun, 08 Feb 2015 23:08:38 -0500
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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
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6 #Test Utilities
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7 context("Test funcGetLMResults")
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8 context("Test funcGetStepPredictors")
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9
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10 context("Test funcMakeContrasts")
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11 covX1 = c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
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12 covX2 = c(144.4, 245.9, 141.9, 253.3, 144.7, 244.1, 150.7, 245.2, 160.1)
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13 covX3 = as.factor(c(1,2,3,1,2,3,1,2,3))
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14 covX4 = as.factor(c(1,1,1,1,2,2,2,2,2))
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15 covX5 = as.factor(c(1,2,1,2,1,2,1,2,1))
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16 covY = c(.26, .31, .25, .50, .36, .40, .52, .28, .38)
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17 frmeTmp = data.frame(Covariate1=covX1, Covariate2=covX2, Covariate3=covX3, Covariate4=covX4, Covariate5=covX5, adCur= covY)
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18 iTaxon = 6
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19 #Add in updating QC errors
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20 #Add in random covariates
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21 strFormula = "adCur ~ Covariate1"
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22 strRandomFormula = NULL
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23 lsSig = list()
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24 lsSig[[1]] = list()
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25 lsSig[[1]]$name = "Covariate1"
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26 lsSig[[1]]$orig = "Covariate1"
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27 lsSig[[1]]$taxon = "adCur"
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28 lsSig[[1]]$data = covY
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29 lsSig[[1]]$factors = "Covariate1"
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30 lsSig[[1]]$metadata = covX1
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31 vdCoef = c(Covariate1=0.6)
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32 lsSig[[1]]$value = vdCoef
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33 lsSig[[1]]$std = sd(covX1)
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34 lsSig[[1]]$allCoefs = vdCoef
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35 ret1 = funcMakeContrasts(strFormula=strFormula, strRandomFormula=strRandomFormula, frmeTmp=frmeTmp, iTaxon=iTaxon,
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36 functionContrast=function(x,adCur,dfData)
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37 {
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38 retList = list()
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39 ret = cor.test(as.formula(paste("~",x,"+ adCur")), data=dfData, method="spearman", na.action=c_strNA_Action)
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40 #Returning rho for the coef in a named vector
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41 vdCoef = c()
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42 vdCoef[[x]]=ret$estimate
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43 retList[[1]]=list(p.value=ret$p.value,SD=sd(dfData[[x]]),name=x,coef=vdCoef)
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44 return(retList)
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45 }, lsQCCounts=list())
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46 ret1$adP = round(ret1$adP,5)
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47 test_that("1. Test that the funcMakeContrasts works on a continuous variable.",{
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48 expect_equal(ret1,list(adP=round(c(0.09679784),5),lsSig=lsSig,lsQCCounts=list()))})
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49
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50 strFormula = "adCur ~ Covariate1 + Covariate2"
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51 strRandomFormula = NULL
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52 lsSig = list()
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53 lsSig[[1]] = list()
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54 lsSig[[1]]$name = "Covariate1"
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55 lsSig[[1]]$orig = "Covariate1"
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56 lsSig[[1]]$taxon = "adCur"
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57 lsSig[[1]]$data = covY
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58 lsSig[[1]]$factors = "Covariate1"
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59 lsSig[[1]]$metadata = covX1
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60 vdCoef = c(Covariate1=0.6)
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61 lsSig[[1]]$value = vdCoef
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62 lsSig[[1]]$std = sd(covX1)
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63 lsSig[[1]]$allCoefs = vdCoef
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64 lsSig[[2]] = list()
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65 lsSig[[2]]$name = "Covariate2"
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66 lsSig[[2]]$orig = "Covariate2"
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67 lsSig[[2]]$taxon = "adCur"
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68 lsSig[[2]]$data = covY
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69 lsSig[[2]]$factors = "Covariate2"
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70 lsSig[[2]]$metadata = covX2
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71 vdCoef = c(Covariate2=0.46666667)
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72 lsSig[[2]]$value = vdCoef
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73 lsSig[[2]]$std = sd(covX2)
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74 lsSig[[2]]$allCoefs = vdCoef
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75 ret1 = funcMakeContrasts(strFormula=strFormula, strRandomFormula=strRandomFormula, frmeTmp=frmeTmp, iTaxon=iTaxon,
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76 functionContrast=function(x,adCur,dfData)
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77 {
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78 retList = list()
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79 ret = cor.test(as.formula(paste("~",x,"+ adCur")), data=dfData, method="spearman", na.action=c_strNA_Action)
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80 #Returning rho for the coef in a named vector
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81 vdCoef = c()
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82 vdCoef[[x]]=ret$estimate
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83 retList[[1]]=list(p.value=ret$p.value,SD=sd(dfData[[x]]),name=x,coef=vdCoef)
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84 return(retList)
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85 }, lsQCCounts=list())
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86 ret1$adP = round(ret1$adP,5)
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87 test_that("Test that the funcMakeContrasts works on 2 continuous variables.",{
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88 expect_equal(ret1,list(adP=round(c(0.09679784,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))})
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89
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90 strFormula = "adCur ~ Covariate4"
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91 strRandomFormula = NULL
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92 lsSig = list()
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93 lsSig[[1]] = list()
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94 lsSig[[1]]$name = "Covariate4"
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95 lsSig[[1]]$orig = "Covariate42"
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96 lsSig[[1]]$taxon = "adCur"
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97 lsSig[[1]]$data = covY
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98 lsSig[[1]]$factors = "Covariate4"
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99 lsSig[[1]]$metadata = covX4 #update
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100 vdCoef = c(Covariate42=NA)
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101 lsSig[[1]]$value = vdCoef
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102 lsSig[[1]]$std = sd(covX4) #update
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103 lsSig[[1]]$allCoefs = vdCoef
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104 # Get return
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105 rets = funcMakeContrasts(strFormula=strFormula, strRandomFormula=strRandomFormula, frmeTmp=frmeTmp, iTaxon=iTaxon,
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106 functionContrast=function(x,adCur,dfData)
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107 {
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108 retList = list()
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109 lmodKW = kruskal(adCur,dfData[[x]],group=FALSE,p.adj="holm")
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110 asLevels = levels(dfData[[x]])
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111 # The names of the generated comparisons, sometimes the control is first sometimes it is not so
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112 # We will just check which is in the names and use that
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113 asComparisons = row.names(lmodKW$comparisons)
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114 #Get the comparison with the control
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115 for(sLevel in asLevels[2:length(asLevels)])
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116 {
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117 sComparison = intersect(c(paste(asLevels[1],sLevel,sep=" - "),paste(sLevel,asLevels[1],sep=" - ")),asComparisons)
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118 #Returning NA for the coef in a named vector
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119 vdCoef = c()
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120 vdCoef[[paste(x,sLevel,sep="")]]=NA
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121 retList[[length(retList)+1]]=list(p.value=lmodKW$comparisons[sComparison,"p.value"],SD=NA,name=paste(x,sLevel,sep=""),coef=vdCoef)
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122 }
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123 return(retList)
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124 }, lsQCCounts=list())
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125 rets$adP=round(rets$adP,digits=5)
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126 test_that("Test that the funcMakeContrasts works on 1 factor covariate with 2 levels.",{
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127 expect_equal(rets,list(adP=round(c(0.24434),5),lsSig=lsSig,lsQCCounts=list()))})
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128
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129 strFormula = "adCur ~ Covariate3"
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130 strRandomFormula = NULL
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131 lsSig = list()
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132 lsSig[[1]] = list()
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133 lsSig[[1]]$name = "Covariate3"
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134 lsSig[[1]]$orig = "Covariate32"
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135 lsSig[[1]]$taxon = "adCur"
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136 lsSig[[1]]$data = covY
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137 lsSig[[1]]$factors = "Covariate3"
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138 lsSig[[1]]$metadata = covX3 #update
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139 vdCoef = c(Covariate32=NA)
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140 lsSig[[1]]$value = vdCoef
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141 lsSig[[1]]$std = sd(covX3) #update
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142 lsSig[[1]]$allCoefs = vdCoef
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143 lsSig[[2]] = list()
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144 lsSig[[2]]$name = "Covariate3"
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145 lsSig[[2]]$orig = "Covariate33"
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146 lsSig[[2]]$taxon = "adCur"
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147 lsSig[[2]]$data = covY
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148 lsSig[[2]]$factors = "Covariate3"
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149 lsSig[[2]]$metadata = covX3 #update
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150 vdCoef = c(Covariate33=NA)
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151 lsSig[[2]]$value = vdCoef
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152 lsSig[[2]]$std = sd(covX3) #update
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153 lsSig[[2]]$allCoefs = vdCoef
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154 ret1 = funcMakeContrasts(strFormula=strFormula, strRandomFormula=strRandomFormula, frmeTmp=frmeTmp, iTaxon=iTaxon,
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155 functionContrast=function(x,adCur,dfData)
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156 {
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157 retList = list()
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158 lmodKW = kruskal(adCur,dfData[[x]],group=FALSE,p.adj="holm")
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159 asLevels = levels(dfData[[x]])
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160 # The names of the generated comparisons, sometimes the control is first sometimes it is not so
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161 # We will just check which is in the names and use that
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162 asComparisons = row.names(lmodKW$comparisons)
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163 #Get the comparison with the control
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164 for(sLevel in asLevels[2:length(asLevels)])
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165 {
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166 sComparison = intersect(c(paste(asLevels[1],sLevel,sep=" - "),paste(sLevel,asLevels[1],sep=" - ")),asComparisons)
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167 #Returning NA for the coef in a named vector
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168 vdCoef = c()
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169 vdCoef[[paste(x,sLevel,sep="")]]=NA
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170 retList[[length(retList)+1]]=list(p.value=lmodKW$comparisons[sComparison,"p.value"],SD=NA,name=paste(x,sLevel,sep=""),coef=vdCoef)
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171 }
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172 return(retList)
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173 }, lsQCCounts=list())
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174 ret1$adP = round(ret1$adP,5)
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175 test_that("Test that the funcMakeContrasts works on 1 factor covariate with 3 levels.",{
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176 expect_equal(ret1,list(adP=c(1.0,1.0),lsSig=lsSig,lsQCCounts=list()))})
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177
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178 strFormula = "adCur ~ Covariate4 + Covariate5"
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179 strRandomFormula = NULL
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180 lsSig = list()
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181 lsSig[[1]] = list()
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182 lsSig[[1]]$name = "Covariate4"
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183 lsSig[[1]]$orig = "Covariate42"
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184 lsSig[[1]]$taxon = "adCur"
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185 lsSig[[1]]$data = covY
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186 lsSig[[1]]$factors = "Covariate4"
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187 lsSig[[1]]$metadata = covX4 #update
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188 vdCoef = c(Covariate42=NA)
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189 lsSig[[1]]$value = vdCoef
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190 lsSig[[1]]$std = sd(covX4) #update
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191 lsSig[[1]]$allCoefs = vdCoef
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192 lsSig[[2]] = list()
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193 lsSig[[2]]$name = "Covariate5"
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194 lsSig[[2]]$orig = "Covariate52"
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195 lsSig[[2]]$taxon = "adCur"
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196 lsSig[[2]]$data = covY
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197 lsSig[[2]]$factors = "Covariate5"
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198 lsSig[[2]]$metadata = covX5 #update
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199 vdCoef = c(Covariate52=NA)
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200 lsSig[[2]]$value = vdCoef
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201 lsSig[[2]]$std = sd(covX5) #update
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202 lsSig[[2]]$allCoefs = vdCoef
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203 ret1 = funcMakeContrasts(strFormula=strFormula, strRandomFormula=strRandomFormula, frmeTmp=frmeTmp, iTaxon=iTaxon,
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204 functionContrast=function(x,adCur,dfData)
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205 {
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206 retList = list()
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207 lmodKW = kruskal(adCur,dfData[[x]],group=FALSE,p.adj="holm")
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208 asLevels = levels(dfData[[x]])
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209 # The names of the generated comparisons, sometimes the control is first sometimes it is not so
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210 # We will just check which is in the names and use that
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211 asComparisons = row.names(lmodKW$comparisons)
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212 #Get the comparison with the control
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213 for(sLevel in asLevels[2:length(asLevels)])
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214 {
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215 sComparison = intersect(c(paste(asLevels[1],sLevel,sep=" - "),paste(sLevel,asLevels[1],sep=" - ")),asComparisons)
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216 #Returning NA for the coef in a named vector
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217 vdCoef = c()
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218 vdCoef[[paste(x,sLevel,sep="")]]=NA
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219 retList[[length(retList)+1]]=list(p.value=lmodKW$comparisons[sComparison,"p.value"],SD=NA,name=paste(x,sLevel,sep=""),coef=vdCoef)
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220 }
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221 return(retList)
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222 }, lsQCCounts=list())
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223 ret1$adP = round(ret1$adP,5)
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224 test_that("1. Test that the funcMakeContrasts works on 2 factor covariate with 2 levels.",{
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225 expect_equal(ret1,list(adP=round(c(0.24434,0.655852),5),lsSig=lsSig,lsQCCounts=list()))})
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226
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227
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228 #Test Model selection
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229
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230
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231 context("Test funcBoostModel")
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232 context("Test funcForwardModel")
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233 context("Test funcBackwardsModel")
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234
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235
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236 #Test Univariates
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237 context("Test funcSpearman")
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238 strFormula = "adCur ~ Covariate1"
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239 adCur = c(.26, .31, .25, .50, .36, .40, .52, .28, .38)
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240 x = c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
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241 frmeTmp = data.frame(Covariate1=x, adCur=adCur)
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242 iTaxon = 2
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243 lsQCCounts = list()
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244 lsSig = list()
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245 lsSig[[1]] = list()
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246 lsSig[[1]]$name = "Covariate1"
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247 lsSig[[1]]$orig = "Covariate1"
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248 lsSig[[1]]$taxon = "adCur"
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249 lsSig[[1]]$data = adCur
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250 lsSig[[1]]$factors = "Covariate1"
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251 lsSig[[1]]$metadata = x
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252 vdCoef = c(Covariate1=0.6)
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253 lsSig[[1]]$value = vdCoef
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254 lsSig[[1]]$std = sd(x)
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255 lsSig[[1]]$allCoefs = vdCoef
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256 ret1 = funcSpearman(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsQCCounts=lsQCCounts,strRandomFormula=NULL)
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257 ret1$adP = round(ret1$adP,5)
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258 test_that("Test that the spearman test has the correct results for 1 covariate.",{
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259 expect_equal(ret1,list(adP=round(c(0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
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260 })
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261
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262 strFormula = "adCur ~ Covariate1 + Covariate2"
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263 frmeTmp = data.frame(Covariate1=x, Covariate2=x, adCur=adCur)
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264 iTaxon = 3
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265 lsSig = list()
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266 lsSig[[1]] = list()
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267 lsSig[[1]]$name = "Covariate1"
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268 lsSig[[1]]$orig = "Covariate1"
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269 lsSig[[1]]$taxon = "adCur"
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270 lsSig[[1]]$data = adCur
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271 lsSig[[1]]$factors = "Covariate1"
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272 lsSig[[1]]$metadata = x
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273 vdCoef = c(Covariate1=0.6)
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274 lsSig[[1]]$value = vdCoef
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275 lsSig[[1]]$std = sd(x)
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276 lsSig[[1]]$allCoefs = vdCoef
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277 lsSig[[2]] = list()
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278 lsSig[[2]]$name = "Covariate2"
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279 lsSig[[2]]$orig = "Covariate2"
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280 lsSig[[2]]$taxon = "adCur"
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281 lsSig[[2]]$data = adCur
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282 lsSig[[2]]$factors = "Covariate2"
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283 lsSig[[2]]$metadata = x
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284 vdCoef = c(Covariate2=0.6)
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285 lsSig[[2]]$value = vdCoef
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286 lsSig[[2]]$std = sd(x)
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287 lsSig[[2]]$allCoefs = vdCoef
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288 lsQCCounts = list()
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289 ret1 = funcSpearman(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsQCCounts=lsQCCounts,strRandomFormula=NULL)
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290 ret1$adP = round(ret1$adP,5)
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291 test_that("Test that the spearman test has the correct results for 2 covariates.",{
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292 expect_equal(ret1,list(adP=round(c(0.09679784,0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
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293 })
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294
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295
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296 context("Test funcWilcoxon")
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297 strFormula = "adCur ~ Covariate1"
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298 x = c(TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,FALSE,FALSE)
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299 frmeTmp = data.frame(Covariate1=x, adCur=adCur)
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300 iTaxon = 2
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301 lsSig = list()
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302 lsSig[[1]] = list()
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303 lsSig[[1]]$name = "Covariate1"
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304 lsSig[[1]]$orig = "Covariate1"
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305 lsSig[[1]]$taxon = "adCur"
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306 lsSig[[1]]$data = adCur
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307 lsSig[[1]]$factors = "Covariate1"
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308 lsSig[[1]]$metadata = x
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309 vdCoef = c(Covariate1=13)
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310 lsSig[[1]]$value = vdCoef
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311 lsSig[[1]]$std = sd(x)
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312 lsSig[[1]]$allCoefs = vdCoef
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313 lsQCCounts = list()
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314 ret1 = funcWilcoxon(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsQCCounts=lsQCCounts,strRandomFormula=NULL)
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315 ret1$adP = round(ret1$adP,5)
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316 test_that("Test that the wilcoxon test has the correct results for 1 covariate.",{
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317 expect_equal(ret1,list(adP=round(c(0.55555556),5),lsSig=lsSig,lsQCCounts=list()))
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318 })
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319
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320
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321 context("Test funcKruskalWallis")
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322 strFormula = "adCur ~ Covariate1"
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323 x = as.factor(c("one","two","three","one","one","three","two","three","two"))
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324 frmeTmp = data.frame(Covariate1=x, adCur=adCur)
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325 iTaxon = 2
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326 lsSig = list()
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327 lsSig[[1]] = list()
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328 lsSig[[1]]$name = "Covariate1"
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329 lsSig[[1]]$orig = "Covariate1three"
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330 lsSig[[1]]$taxon = "adCur"
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331 lsSig[[1]]$data = adCur
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332 lsSig[[1]]$factors = "Covariate1"
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333 lsSig[[1]]$metadata = x
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334 vdCoef = c(Covariate1three=NA)
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335 lsSig[[1]]$value = vdCoef
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336 lsSig[[1]]$std = sd(x)
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337 lsSig[[1]]$allCoefs = vdCoef
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338 lsSig[[2]] = list()
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339 lsSig[[2]]$name = "Covariate1"
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340 lsSig[[2]]$orig = "Covariate1two"
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341 lsSig[[2]]$taxon = "adCur"
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342 lsSig[[2]]$data = adCur
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343 lsSig[[2]]$factors = "Covariate1"
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344 lsSig[[2]]$metadata = x
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345 vdCoef = c(Covariate1two=NA)
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346 lsSig[[2]]$value = vdCoef
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347 lsSig[[2]]$std = sd(x)
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348 lsSig[[2]]$allCoefs = vdCoef
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349 lsQCCounts = list()
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350 ret1 = funcKruskalWallis(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsQCCounts=lsQCCounts,strRandomFormula=NULL)
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351 ret1$adP = round(ret1$adP,5)
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352 test_that("Test that the Kruskal Wallis (Nonparameteric anova) has the correct results for 1 covariate.",{
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353 expect_equal(ret1,list(adP=c(1.0,1.0),lsSig=lsSig,lsQCCounts=list()))
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354 })
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355
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356
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357 context("test funcDoUnivariate")
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358 covX1 = c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
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359 covX2 = c(144.4, 245.9, 141.9, 253.3, 144.7, 244.1, 150.7, 245.2, 160.1)
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360 covX3 = as.factor(c(1,2,3,1,2,3,1,2,3))
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361 covX4 = as.factor(c(1,1,1,1,2,2,2,2,2))
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362 covX5 = as.factor(c(1,2,1,2,1,2,1,2,1))
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363 covX6 = as.factor(c("one","two","three","one","one","three","two","three","two"))
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364 covY = c(.26, .31, .25, .50, .36, .40, .52, .28, .38)
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365 frmeTmp = data.frame(Covariate1=covX1, Covariate2=covX2, Covariate3=covX3, Covariate4=covX4, Covariate5=covX5, Covariate6=covX6, adCur= covY)
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366 iTaxon = 7
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367 # 1 cont answer
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368 lsSig = list()
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369 lsSig[[1]] = list()
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370 lsSig[[1]]$name = "Covariate1"
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371 lsSig[[1]]$orig = "Covariate1"
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372 lsSig[[1]]$taxon = "adCur"
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373 lsSig[[1]]$data = adCur
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374 lsSig[[1]]$factors = "Covariate1"
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375 lsSig[[1]]$metadata = frmeTmp[["Covariate1"]]
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376 vdCoef = c(Covariate1=0.6)
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377 lsSig[[1]]$value = vdCoef
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378 lsSig[[1]]$std = sd(frmeTmp[["Covariate1"]])
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379 lsSig[[1]]$allCoefs = vdCoef
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380 lsHistory = list(adP=c(), lsSig=c(),lsQCCounts=list())
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381 ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate1",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula=NULL)
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382 ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate1")
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383 ret1$adP = round(ret1$adP,5)
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384 ret2$adP = round(ret2$adP,5)
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385 print("ret1")
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386 print(ret1)
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387 print("list(adP=round(c(0.09679784),5),lsSig=lsSig,lsQCCounts=list())")
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388 print(list(adP=round(c(0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
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389 test_that("2. Test that the funcMakeContrasts works on a continuous variable.",{
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390 expect_equal(ret1,list(adP=round(c(0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
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391 expect_equal(ret2,list(adP=round(c(0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
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392 })
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393 lsSig[[2]] = list()
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394 lsSig[[2]]$name = "Covariate2"
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395 lsSig[[2]]$orig = "Covariate2"
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396 lsSig[[2]]$taxon = "adCur"
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397 lsSig[[2]]$data = adCur
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398 lsSig[[2]]$factors = "Covariate2"
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399 lsSig[[2]]$metadata = frmeTmp[["Covariate2"]]
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400 vdCoef = c(Covariate2=0.46666667)
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401 lsSig[[2]]$value = vdCoef
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402 lsSig[[2]]$std = sd(frmeTmp[["Covariate2"]])
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403 lsSig[[2]]$allCoefs = vdCoef
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404 ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate1 + Covariate2",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory,strRandomFormula=NULL)
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405 ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate1 + 1|Covariate2")
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406 ret1$adP = round(ret1$adP,5)
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407 ret2$adP = round(ret2$adP,5)
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408 test_that("Test that the funcMakeContrasts works on 2 continuous variables.",{
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409 expect_equal(ret1,list(adP=round(c(0.09679784,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
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410 expect_equal(ret2,list(adP=round(c(0.09679784,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
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411 })
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412 lsSig = list()
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413 lsSig[[1]] = list()
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414 lsSig[[1]]$name = "Covariate4"
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415 lsSig[[1]]$orig = "Covariate4"
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416 lsSig[[1]]$taxon = "adCur"
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417 lsSig[[1]]$data = adCur
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418 lsSig[[1]]$factors = "Covariate4"
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419 lsSig[[1]]$metadata = frmeTmp[["Covariate4"]]
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420 vdCoef = c(Covariate4=5)
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421 lsSig[[1]]$value = vdCoef
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422 lsSig[[1]]$std = sd(frmeTmp[["Covariate4"]])
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423 lsSig[[1]]$allCoefs = vdCoef
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424 ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate4",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory,strRandomFormula=NULL)
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425 ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate4")
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426 ret1$adP = round(ret1$adP,5)
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427 ret2$adP = round(ret2$adP,5)
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428 test_that("Test that the funcMakeContrasts works on 1 factor covariate with 2 levels.",{
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429 expect_equal(ret1,list(adP=round(c(0.2857143),5),lsSig=lsSig,lsQCCounts=list()))
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430 expect_equal(ret2,list(adP=round(c(0.2857143),5),lsSig=lsSig,lsQCCounts=list()))
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parents:
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431 })
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432 lsSig[[2]] = list()
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433 lsSig[[2]]$name = "Covariate5"
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434 lsSig[[2]]$orig = "Covariate5"
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parents:
diff changeset
435 lsSig[[2]]$taxon = "adCur"
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parents:
diff changeset
436 lsSig[[2]]$data = adCur
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parents:
diff changeset
437 lsSig[[2]]$factors = "Covariate5"
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parents:
diff changeset
438 lsSig[[2]]$metadata = frmeTmp[["Covariate5"]]
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parents:
diff changeset
439 vdCoef = c(Covariate5=8)
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diff changeset
440 lsSig[[2]]$value = vdCoef
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parents:
diff changeset
441 lsSig[[2]]$std = sd(frmeTmp[["Covariate5"]])
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442 lsSig[[2]]$allCoefs = vdCoef
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diff changeset
443 ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate4 + Covariate5",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula=NULL)
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diff changeset
444 ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate4 + 1|Covariate5")
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diff changeset
445 ret3 = funcDoUnivariate(strFormula="adCur ~ Covariate4",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate5")
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446 ret1$adP = round(ret1$adP,5)
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447 ret2$adP = round(ret2$adP,5)
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448 ret3$adP = round(ret3$adP,5)
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449 test_that("2. Test that the funcMakeContrasts works on 2 factor covariate with 2 levels.",{
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diff changeset
450 expect_equal(ret1,list(adP=round(c(0.2857143,0.73016),5),lsSig=lsSig,lsQCCounts=list()))
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451 expect_equal(ret2,list(adP=round(c(0.2857143,0.73016),5),lsSig=lsSig,lsQCCounts=list()))
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452 expect_equal(ret3,list(adP=round(c(0.2857143,0.73016),5),lsSig=lsSig,lsQCCounts=list()))
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453 })
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454 lsSig = list()
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455 lsSig[[1]] = list()
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456 lsSig[[1]]$name = "Covariate4"
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457 lsSig[[1]]$orig = "Covariate4"
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458 lsSig[[1]]$taxon = "adCur"
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459 lsSig[[1]]$data = adCur
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460 lsSig[[1]]$factors = "Covariate4"
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461 lsSig[[1]]$metadata = frmeTmp[["Covariate4"]]
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462 vdCoef = c(Covariate4=5)
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463 lsSig[[1]]$value = vdCoef
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464 lsSig[[1]]$std = sd(frmeTmp[["Covariate4"]])
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465 lsSig[[1]]$allCoefs = vdCoef
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466 lsSig[[2]] = list()
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parents:
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467 lsSig[[2]]$name = "Covariate1"
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468 lsSig[[2]]$orig = "Covariate1"
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parents:
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469 lsSig[[2]]$taxon = "adCur"
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470 lsSig[[2]]$data = adCur
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471 lsSig[[2]]$factors = "Covariate1"
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472 lsSig[[2]]$metadata = frmeTmp[["Covariate1"]]
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473 vdCoef = c(Covariate1=0.6)
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474 lsSig[[2]]$value = vdCoef
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parents:
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475 lsSig[[2]]$std = sd(frmeTmp[["Covariate1"]])
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476 lsSig[[2]]$allCoefs = vdCoef
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diff changeset
477 ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate4 + Covariate1",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula=NULL)
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478 ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate4 + 1|Covariate1")
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479 ret3 = funcDoUnivariate(strFormula="adCur ~ Covariate4",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate1")
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480 ret1$adP = round(ret1$adP,5)
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481 ret2$adP = round(ret2$adP,5)
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482 ret3$adP = round(ret3$adP,5)
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parents:
diff changeset
483 test_that("Test that the funcMakeContrasts works on 1 factor covariate with 2 levels and a continuous variable.",{
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
484 expect_equal(ret1,list(adP=round(c(0.2857143,0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
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parents:
diff changeset
485 expect_equal(ret2,list(adP=round(c(0.2857143,0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
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diff changeset
486 expect_equal(ret3,list(adP=round(c(0.2857143,0.09679784),5),lsSig=lsSig,lsQCCounts=list()))
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487 })
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george-weingart
parents:
diff changeset
488 lsSig = list()
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diff changeset
489 lsSig[[1]] = list()
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parents:
diff changeset
490 lsSig[[1]]$name = "Covariate3"
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parents:
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491 lsSig[[1]]$orig = "Covariate32"
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492 lsSig[[1]]$taxon = "adCur"
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parents:
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493 lsSig[[1]]$data = adCur
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parents:
diff changeset
494 lsSig[[1]]$factors = "Covariate3"
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parents:
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495 lsSig[[1]]$metadata = frmeTmp[["Covariate3"]]
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diff changeset
496 vdCoef = c(Covariate32=NA)
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497 lsSig[[1]]$value = vdCoef
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diff changeset
498 lsSig[[1]]$std = sd(frmeTmp[["Covariate3"]])
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499 lsSig[[1]]$allCoefs = vdCoef
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500 lsSig[[2]] = list()
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501 lsSig[[2]]$name = "Covariate3"
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502 lsSig[[2]]$orig = "Covariate33"
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503 lsSig[[2]]$taxon = "adCur"
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george-weingart
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504 lsSig[[2]]$data = adCur
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505 lsSig[[2]]$factors = "Covariate3"
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506 lsSig[[2]]$metadata = frmeTmp[["Covariate3"]]
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diff changeset
507 vdCoef = c(Covariate33=NA)
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508 lsSig[[2]]$value = vdCoef
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parents:
diff changeset
509 lsSig[[2]]$std = sd(frmeTmp[["Covariate3"]])
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george-weingart
parents:
diff changeset
510 lsSig[[2]]$allCoefs = vdCoef
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diff changeset
511 lsSig[[3]] = list()
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george-weingart
parents:
diff changeset
512 lsSig[[3]]$name = "Covariate1"
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parents:
diff changeset
513 lsSig[[3]]$orig = "Covariate1"
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514 lsSig[[3]]$taxon = "adCur"
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diff changeset
515 lsSig[[3]]$data = adCur
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516 lsSig[[3]]$factors = "Covariate1"
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517 lsSig[[3]]$metadata = frmeTmp[["Covariate1"]]
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diff changeset
518 vdCoef = c(Covariate1=0.6)
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diff changeset
519 lsSig[[3]]$value = vdCoef
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parents:
diff changeset
520 lsSig[[3]]$std = sd(frmeTmp[["Covariate1"]])
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george-weingart
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diff changeset
521 lsSig[[3]]$allCoefs = vdCoef
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diff changeset
522 lsSig[[4]] = list()
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george-weingart
parents:
diff changeset
523 lsSig[[4]]$name = "Covariate2"
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diff changeset
524 lsSig[[4]]$orig = "Covariate2"
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george-weingart
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525 lsSig[[4]]$taxon = "adCur"
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george-weingart
parents:
diff changeset
526 lsSig[[4]]$data = adCur
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diff changeset
527 lsSig[[4]]$factors = "Covariate2"
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diff changeset
528 lsSig[[4]]$metadata = frmeTmp[["Covariate2"]]
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george-weingart
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diff changeset
529 vdCoef = c(Covariate2=0.46666667)
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george-weingart
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diff changeset
530 lsSig[[4]]$value = vdCoef
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george-weingart
parents:
diff changeset
531 lsSig[[4]]$std = sd(frmeTmp[["Covariate2"]])
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diff changeset
532 lsSig[[4]]$allCoefs = vdCoef
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parents:
diff changeset
533 ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate3 + Covariate1 + Covariate2",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula=NULL)
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george-weingart
parents:
diff changeset
534 ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate3 + 1|Covariate1 + 1|Covariate2")
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george-weingart
parents:
diff changeset
535 ret3 = funcDoUnivariate(strFormula="adCur ~ Covariate3 + Covariate1",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate2")
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george-weingart
parents:
diff changeset
536 ret1$adP = round(ret1$adP,5)
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george-weingart
parents:
diff changeset
537 ret2$adP = round(ret2$adP,5)
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george-weingart
parents:
diff changeset
538 ret3$adP = round(ret3$adP,5)
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george-weingart
parents:
diff changeset
539 test_that("Test that the funcMakeContrasts works on 1 factor covariate with 3 levels and 2 continuous variables.",{
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
540 expect_equal(ret1,list(adP=round(c(1.0,1.0,0.09679784,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
541 expect_equal(ret2,list(adP=round(c(1.0,1.0,0.09679784,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
542 expect_equal(ret3,list(adP=round(c(1.0,1.0,0.09679784,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
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george-weingart
parents:
diff changeset
543 })
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george-weingart
parents:
diff changeset
544 lsSig = list()
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george-weingart
parents:
diff changeset
545 lsSig[[1]] = list()
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george-weingart
parents:
diff changeset
546 lsSig[[1]]$name = "Covariate4"
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george-weingart
parents:
diff changeset
547 lsSig[[1]]$orig = "Covariate4"
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george-weingart
parents:
diff changeset
548 lsSig[[1]]$taxon = "adCur"
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george-weingart
parents:
diff changeset
549 lsSig[[1]]$data = adCur
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george-weingart
parents:
diff changeset
550 lsSig[[1]]$factors = "Covariate4"
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george-weingart
parents:
diff changeset
551 lsSig[[1]]$metadata = frmeTmp[["Covariate4"]]
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george-weingart
parents:
diff changeset
552 vdCoef = c(Covariate4=5)
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george-weingart
parents:
diff changeset
553 lsSig[[1]]$value = vdCoef
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george-weingart
parents:
diff changeset
554 lsSig[[1]]$std = sd(frmeTmp[["Covariate4"]])
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george-weingart
parents:
diff changeset
555 lsSig[[1]]$allCoefs = vdCoef
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george-weingart
parents:
diff changeset
556 lsSig[[2]] = list()
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george-weingart
parents:
diff changeset
557 lsSig[[2]]$name = "Covariate2"
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george-weingart
parents:
diff changeset
558 lsSig[[2]]$orig = "Covariate2"
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george-weingart
parents:
diff changeset
559 lsSig[[2]]$taxon = "adCur"
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george-weingart
parents:
diff changeset
560 lsSig[[2]]$data = adCur
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george-weingart
parents:
diff changeset
561 lsSig[[2]]$factors = "Covariate2"
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parents:
diff changeset
562 lsSig[[2]]$metadata = frmeTmp[["Covariate2"]]
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george-weingart
parents:
diff changeset
563 vdCoef = c(Covariate2=0.46666667)
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george-weingart
parents:
diff changeset
564 lsSig[[2]]$value = vdCoef
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george-weingart
parents:
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565 lsSig[[2]]$std = sd(frmeTmp[["Covariate2"]])
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566 lsSig[[2]]$allCoefs = vdCoef
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567 ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate4 + Covariate2",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula=NULL)
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diff changeset
568 ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate4 + 1|Covariate2")
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diff changeset
569 ret3 = funcDoUnivariate(strFormula= "adCur ~ Covariate4",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate2")
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570 ret1$adP = round(ret1$adP,5)
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571 ret2$adP = round(ret2$adP,5)
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572 ret3$adP = round(ret3$adP,5)
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573 test_that("3. Test that the funcMakeContrasts works on 2 factor covariate with 2 levels and a continuous variable.",{
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574 expect_equal(ret1,list(adP=round(c(0.2857143,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
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575 expect_equal(ret2,list(adP=round(c(0.2857143,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
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576 expect_equal(ret3,list(adP=round(c(0.2857143,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
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577 })
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578 lsSig = list()
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579 lsSig[[1]] = list()
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580 lsSig[[1]]$name = "Covariate4"
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581 lsSig[[1]]$orig = "Covariate4"
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582 lsSig[[1]]$taxon = "adCur"
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583 lsSig[[1]]$data = adCur
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584 lsSig[[1]]$factors = "Covariate4"
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585 lsSig[[1]]$metadata = frmeTmp[["Covariate4"]]
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586 vdCoef = c(Covariate4=5)
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587 lsSig[[1]]$value = vdCoef
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588 lsSig[[1]]$std = sd(frmeTmp[["Covariate4"]])
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589 lsSig[[1]]$allCoefs = vdCoef
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590 lsSig[[2]] = list()
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591 lsSig[[2]]$name = "Covariate3"
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592 lsSig[[2]]$orig = "Covariate32"
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593 lsSig[[2]]$taxon = "adCur"
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594 lsSig[[2]]$data = adCur
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595 lsSig[[2]]$factors = "Covariate3"
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596 lsSig[[2]]$metadata = frmeTmp[["Covariate3"]]
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597 vdCoef = c(Covariate32=NA)
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598 lsSig[[2]]$value = vdCoef
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599 lsSig[[2]]$std = sd(frmeTmp[["Covariate3"]])
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600 lsSig[[2]]$allCoefs = vdCoef
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601 lsSig[[3]] = list()
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602 lsSig[[3]]$name = "Covariate3"
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603 lsSig[[3]]$orig = "Covariate33"
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604 lsSig[[3]]$taxon = "adCur"
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605 lsSig[[3]]$data = adCur
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606 lsSig[[3]]$factors = "Covariate3"
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607 lsSig[[3]]$metadata = frmeTmp[["Covariate3"]]
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608 vdCoef = c(Covariate33=NA)
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609 lsSig[[3]]$value = vdCoef
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610 lsSig[[3]]$std = sd(frmeTmp[["Covariate3"]])
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611 lsSig[[3]]$allCoefs = vdCoef
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612 lsSig[[4]] = list()
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613 lsSig[[4]]$name = "Covariate2"
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614 lsSig[[4]]$orig = "Covariate2"
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615 lsSig[[4]]$taxon = "adCur"
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616 lsSig[[4]]$data = adCur
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617 lsSig[[4]]$factors = "Covariate2"
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618 lsSig[[4]]$metadata = frmeTmp[["Covariate2"]]
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619 vdCoef = c(Covariate2=0.46666667)
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620 lsSig[[4]]$value = vdCoef
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621 lsSig[[4]]$std = sd(frmeTmp[["Covariate2"]])
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622 lsSig[[4]]$allCoefs = vdCoef
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623 ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate4 + Covariate3 + Covariate2",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula=NULL)
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624 ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate4 +1|Covariate3 + 1|Covariate2")
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625 ret1$adP = round(ret1$adP,5)
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626 ret2$adP = round(ret2$adP,5)
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627 test_that("Test that the funcMakeContrasts works on 1 factor covariate with 2 levels , 1 factor with 3 levels, and a continuous variable.",{
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628 expect_equal(ret1,list(adP=round(c(0.2857143,1.0,1.0,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
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629 expect_equal(ret2,list(adP=round(c(0.2857143,1.0,1.0,0.21252205),5),lsSig=lsSig,lsQCCounts=list()))
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630 })
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631
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632 #Test multivariates
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633 context("Test funcLasso")
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634
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635
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636 context("Test funcLM")
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parents:
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637 #This test just makes sure the statistical method is being called correctly for one covariate with the correct return
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638 strFormula = "adCur ~ Covariate1"
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639 strRandomFormula = NULL
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640 x = c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
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641 x2 = c(34.2, 32.5, 22.4, 43, 3.25, 6.4, 7, 87, 9)
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642 xf1 = c(1,1,2,2,1,2,1,1,2)
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643 xf2 = c(1,1,1,1,2,2,2,2,2)
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644 frmeTmp = data.frame(Covariate1=x, Covariate2=x2, FCovariate3=xf1, FCovariate4=xf2, adCur=adCur)
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645 iTaxon = 5
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646 lmRet = lm(as.formula(strFormula), data=frmeTmp, na.action = c_strNA_Action)
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diff changeset
647 test_that("Test that the lm has the correct results for 1 covariate.",{
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648 expect_equal(funcLM(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
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649 })
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650 #Test for correct call for 2 covariates
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651 strFormula = "adCur ~ Covariate1 + Covariate2"
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652 lmRet = lm(as.formula(strFormula), data=frmeTmp, na.action = c_strNA_Action)
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diff changeset
653 test_that("Test that the lm has the correct results for 2 covariates.",{
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654 expect_equal(funcLM(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
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655 })
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656 ##Test for correct call with 1 random and one fixed covariate
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657 #strFormula = "adCur ~ Covariate1"
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658 #strRandomFormula = "~1|FCovariate3"
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659 #lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=gaussian(link="identity"), data=frmeTmp)
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660 #test_that("Test that the lm has the correct results for 1 random and one fixed covariate.",{
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661 # expect_equal(funcLM(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
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662 #})
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663 ##Test for correct call with 1 random and 2 fixed covariates
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664 #strFormula = "adCur ~ Covariate1 + Covariate2"
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665 #strRandomFormula = "~1|FCovariate3"
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666 #lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=gaussian(link="identity"), data=frmeTmp)
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667 #test_that("Test that the lm has the correct results for 1 random and 2 fixed covariates.",{
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668 # expect_equal(funcLM(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
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diff changeset
669 #})
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parents:
diff changeset
670 ##Test for correct call with 2 random and 1 fixed covariates
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671 #strFormula = "adCur ~ Covariate1"
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672 #strRandomFormula = "~1|FCovariate4+1|FCovariate3"
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673 #lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=gaussian(link="identity"), data=frmeTmp)
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674 #test_that("Test that the lm has the correct results for 2 random and 1 fixed covariates.",{
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675 # expect_equal(funcLM(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
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676 #})
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677
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678
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679 context("Test funcBinomialMult")
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680 strFormula = "adCur ~ Covariate1"
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681 strRandomFormula = NULL
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682 x = c(44.4, 45.9, 41.9, 53.3, 44.7, 44.1, 50.7, 45.2, 60.1)
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683 x2 = c(34.2, 32.5, 22.4, 43, 3.25, 6.4, 7, 87, 9)
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diff changeset
684 xf1 = c(1,1,2,2,1,2,1,1,2)
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685 xf2 = c(1,1,1,1,2,2,2,2,2)
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diff changeset
686 frmeTmp = data.frame(Covariate1=x, Covariate2=x2, FCovariate3=xf1, FCovariate4=xf2, adCur=adCur)
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687 iTaxon = 5
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688 lmRet = glm(as.formula(strFormula), family=binomial(link=logit), data=frmeTmp, na.action=c_strNA_Action)
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diff changeset
689 test_that("Test that the neg binomial regression has the correct results for 1 covariate.",{
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diff changeset
690 expect_equal(funcBinomialMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
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691 })
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george-weingart
parents:
diff changeset
692 #Test for correct call for 2 covariates
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parents:
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693 strFormula = "adCur ~ Covariate1 + Covariate2"
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parents:
diff changeset
694 iTaxon = 5
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diff changeset
695 lmRet = glm(as.formula(strFormula), family=binomial(link=logit), data=frmeTmp, na.action=c_strNA_Action)
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696 test_that("Test that the neg binomial regression has the correct results for 2 covariates.",{
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diff changeset
697 expect_equal(funcBinomialMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
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698 })
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699 ##Test for correct call with 1 random and one fixed covariate
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parents:
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700 #strFormula = "adCur ~ Covariate1"
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parents:
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701 #strRandomFormula = "~1|FCovariate3"
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702 #lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=binomial(link=logit), data=frmeTmp)
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parents:
diff changeset
703 #test_that("Test that the lm has the correct results for 1 random and one fixed covariate.",{
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704 # expect_equal(funcBinomialMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
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parents:
diff changeset
705 #})
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parents:
diff changeset
706 ##Test for correct call with 1 random and 2 fixed covariates
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parents:
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707 #strFormula = "adCur ~ Covariate1 + Covariate2"
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708 #strRandomFormula = "~1|FCovariate3"
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diff changeset
709 #lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=binomial(link=logit), data=frmeTmp)
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parents:
diff changeset
710 #test_that("Test that the lm has the correct results for 1 random and 2 fixed covariates.",{
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711 # expect_equal(funcBinomialMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
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parents:
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712 #})
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parents:
diff changeset
713 ##Test for correct call with 2 random and 1 fixed covariates
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parents:
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714 #strFormula = "adCur ~ Covariate1"
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parents:
diff changeset
715 #strRandomFormula = "~1|FCovariate4+1|FCovariate3"
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716 #lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=binomial(link=logit), data=frmeTmp)
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717 #test_that("Test that the lm has the correct results for 2 random and 1 fixed covariates.",{
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718 # expect_equal(funcBinomialMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
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719 #})
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parents:
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720
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parents:
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721
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parents:
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722 context("Test funcQuasiMult")
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parents:
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723 strFormula = "adCur ~ Covariate1"
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parents:
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724 strRandomFormula = NULL
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parents:
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725 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)
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726 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)
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727 xf1 = c(1,1,2,2,1,1,2,2,2,1,1,2,2,1,1,2,2,2)
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
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728 xf2 = c(1,1,1,1,2,2,2,2,2,1,1,1,1,2,2,2,2,2)
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
729 frmeTmp = data.frame(Covariate1=x, Covariate2=x2, FCovariate3=xf1, FCovariate4=xf2, adCur=adCur)
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parents:
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730 iTaxon = 5
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parents:
diff changeset
731 lmRet = glm(as.formula(strFormula), family=quasipoisson, data=frmeTmp, na.action=c_strNA_Action)
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
732 test_that("Test that the quasi poisson has the correct results for 1 covariate.",{
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
733 expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
734 })
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parents:
diff changeset
735 #Test for correct call for 2 covariates
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parents:
diff changeset
736 strFormula = "adCur ~ Covariate1 + Covariate2"
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
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737 iTaxon = 5
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
738 lmRet = glm(as.formula(strFormula), family=quasipoisson, data=frmeTmp, na.action=c_strNA_Action)
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
739 test_that("Test that the quasi poisson has the correct results for 2 covariates.",{
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
740 expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
741 })
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george-weingart
parents:
diff changeset
742 ##Test for correct call with 1 random and one fixed covariate
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parents:
diff changeset
743 #strFormula = "adCur ~ Covariate1"
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parents:
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744 #strRandomFormula = "~1|FCovariate3"
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
745 #lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=quasipoisson, data=frmeTmp)
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
746 #test_that("Test that the lm has the correct results for 1 random and one fixed covariate.",{
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
747 # expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
748 #})
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
749 ##Test for correct call with 1 random and 2 fixed covariates
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
750 #strFormula = "adCur ~ Covariate1 + Covariate2"
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
751 #strRandomFormula = "~1|FCovariate3"
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
752 #lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=quasipoisson, data=frmeTmp)
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george-weingart
parents:
diff changeset
753 #test_that("Test that the lm has the correct results for 1 random and 2 fixed covariates.",{
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
754 # expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
755 #})
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george-weingart
parents:
diff changeset
756 ##Test for correct call with 2 random and 1 fixed covariates
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parents:
diff changeset
757 #strFormula = "adCur ~ Covariate1"
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parents:
diff changeset
758 #strRandomFormula = "~1|FCovariate4+1|FCovariate3"
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parents:
diff changeset
759 #lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=quasipoisson, data=frmeTmp)
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george-weingart
parents:
diff changeset
760 #test_that("Test that the lm has the correct results for 2 random and 1 fixed covariates.",{
a87d5a5f2776 Uploaded the version running on the prod server
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parents:
diff changeset
761 # expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet)
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parents:
diff changeset
762 #})
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parents:
diff changeset
763
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george-weingart
parents:
diff changeset
764
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george-weingart
parents:
diff changeset
765 #Test transforms
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parents:
diff changeset
766 context("Test funcNoTransform")
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parents:
diff changeset
767 aTest1 = c(NA)
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parents:
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768 aTest2 = c(NULL)
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parents:
diff changeset
769 aTest3 = c(0.5,1.4,2.4,3332.4,0.0,0.0000003)
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parents:
diff changeset
770 aTest4 = c(0.1)
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parents:
diff changeset
771 test_that("Test that no transform does not change the data.",{
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george-weingart
parents:
diff changeset
772 expect_equal(funcNoTransform(aTest1), aTest1)
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george-weingart
parents:
diff changeset
773 expect_equal(funcNoTransform(aTest2), aTest2)
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george-weingart
parents:
diff changeset
774 expect_equal(funcNoTransform(aTest3), aTest3)
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george-weingart
parents:
diff changeset
775 expect_equal(funcNoTransform(aTest4), aTest4)
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george-weingart
parents:
diff changeset
776 })
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george-weingart
parents:
diff changeset
777
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
778
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george-weingart
parents:
diff changeset
779 context("Test funcArcsinSqrt")
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george-weingart
parents:
diff changeset
780 aTest1 = c(NA)
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
781 aTest2 = c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0)
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george-weingart
parents:
diff changeset
782 aTest3 = c(0.000001)
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george-weingart
parents:
diff changeset
783 test_that("Test that funcArcsinSqrt performs the transform correctly.",{
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
784 expect_equal(funcArcsinSqrt(NA), as.numeric(NA))
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
785 expect_equal(funcArcsinSqrt(aTest1), asin(sqrt(aTest1)))
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
786 expect_equal(funcArcsinSqrt(aTest2), asin(sqrt(aTest2)))
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
787 expect_equal(funcArcsinSqrt(aTest3), asin(sqrt(aTest3)))
a87d5a5f2776 Uploaded the version running on the prod server
george-weingart
parents:
diff changeset
788 })