Mercurial > repos > george-weingart > maaslin
annotate maaslin-4450aa4ecc84/src/test-AnalysisModules/test-AnalysisModules.R @ 1:a87d5a5f2776
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author | george-weingart |
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date | Sun, 08 Feb 2015 23:08:38 -0500 |
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rev | line source |
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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$adP,5) |
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428 test_that("Test that the funcMakeContrasts works on 1 factor covariate with 2 levels.",{ |
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429 expect_equal(ret1,list(adP=round(c(0.2857143),5),lsSig=lsSig,lsQCCounts=list())) |
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430 expect_equal(ret2,list(adP=round(c(0.2857143),5),lsSig=lsSig,lsQCCounts=list())) |
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431 }) |
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432 lsSig[[2]] = list() |
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433 lsSig[[2]]$name = "Covariate5" |
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434 lsSig[[2]]$orig = "Covariate5" |
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435 lsSig[[2]]$taxon = "adCur" |
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436 lsSig[[2]]$data = adCur |
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437 lsSig[[2]]$factors = "Covariate5" |
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438 lsSig[[2]]$metadata = frmeTmp[["Covariate5"]] |
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439 vdCoef = c(Covariate5=8) |
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440 lsSig[[2]]$value = vdCoef |
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441 lsSig[[2]]$std = sd(frmeTmp[["Covariate5"]]) |
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442 lsSig[[2]]$allCoefs = vdCoef |
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443 ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate4 + Covariate5",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula=NULL) |
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444 ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate4 + 1|Covariate5") |
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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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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]]$allCoefs = vdCoef |
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466 lsSig[[2]] = list() |
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467 lsSig[[2]]$name = "Covariate1" |
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468 lsSig[[2]]$orig = "Covariate1" |
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469 lsSig[[2]]$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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475 lsSig[[2]]$std = sd(frmeTmp[["Covariate1"]]) |
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476 lsSig[[2]]$allCoefs = vdCoef |
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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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483 test_that("Test that the funcMakeContrasts works on 1 factor covariate with 2 levels and a continuous variable.",{ |
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484 expect_equal(ret1,list(adP=round(c(0.2857143,0.09679784),5),lsSig=lsSig,lsQCCounts=list())) |
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485 expect_equal(ret2,list(adP=round(c(0.2857143,0.09679784),5),lsSig=lsSig,lsQCCounts=list())) |
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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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488 lsSig = list() |
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489 lsSig[[1]] = list() |
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490 lsSig[[1]]$name = "Covariate3" |
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491 lsSig[[1]]$orig = "Covariate32" |
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492 lsSig[[1]]$taxon = "adCur" |
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493 lsSig[[1]]$data = adCur |
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494 lsSig[[1]]$factors = "Covariate3" |
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495 lsSig[[1]]$metadata = frmeTmp[["Covariate3"]] |
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496 vdCoef = c(Covariate32=NA) |
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497 lsSig[[1]]$value = vdCoef |
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498 lsSig[[1]]$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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504 lsSig[[2]]$data = adCur |
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505 lsSig[[2]]$factors = "Covariate3" |
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506 lsSig[[2]]$metadata = frmeTmp[["Covariate3"]] |
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507 vdCoef = c(Covariate33=NA) |
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508 lsSig[[2]]$value = vdCoef |
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509 lsSig[[2]]$std = sd(frmeTmp[["Covariate3"]]) |
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510 lsSig[[2]]$allCoefs = vdCoef |
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511 lsSig[[3]] = list() |
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512 lsSig[[3]]$name = "Covariate1" |
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513 lsSig[[3]]$orig = "Covariate1" |
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514 lsSig[[3]]$taxon = "adCur" |
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515 lsSig[[3]]$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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518 vdCoef = c(Covariate1=0.6) |
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519 lsSig[[3]]$value = vdCoef |
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520 lsSig[[3]]$std = sd(frmeTmp[["Covariate1"]]) |
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521 lsSig[[3]]$allCoefs = vdCoef |
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522 lsSig[[4]] = list() |
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523 lsSig[[4]]$name = "Covariate2" |
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524 lsSig[[4]]$orig = "Covariate2" |
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525 lsSig[[4]]$taxon = "adCur" |
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526 lsSig[[4]]$data = adCur |
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527 lsSig[[4]]$factors = "Covariate2" |
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528 lsSig[[4]]$metadata = frmeTmp[["Covariate2"]] |
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529 vdCoef = c(Covariate2=0.46666667) |
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530 lsSig[[4]]$value = vdCoef |
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531 lsSig[[4]]$std = sd(frmeTmp[["Covariate2"]]) |
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532 lsSig[[4]]$allCoefs = vdCoef |
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533 ret1 = funcDoUnivariate(strFormula="adCur ~ Covariate3 + Covariate1 + Covariate2",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula=NULL) |
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534 ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate3 + 1|Covariate1 + 1|Covariate2") |
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535 ret3 = funcDoUnivariate(strFormula="adCur ~ Covariate3 + Covariate1",frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate2") |
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536 ret1$adP = round(ret1$adP,5) |
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537 ret2$adP = round(ret2$adP,5) |
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538 ret3$adP = round(ret3$adP,5) |
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539 test_that("Test that the funcMakeContrasts works on 1 factor covariate with 3 levels and 2 continuous variables.",{ |
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540 expect_equal(ret1,list(adP=round(c(1.0,1.0,0.09679784,0.21252205),5),lsSig=lsSig,lsQCCounts=list())) |
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541 expect_equal(ret2,list(adP=round(c(1.0,1.0,0.09679784,0.21252205),5),lsSig=lsSig,lsQCCounts=list())) |
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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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543 }) |
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544 lsSig = list() |
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545 lsSig[[1]] = list() |
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546 lsSig[[1]]$name = "Covariate4" |
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547 lsSig[[1]]$orig = "Covariate4" |
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548 lsSig[[1]]$taxon = "adCur" |
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549 lsSig[[1]]$data = adCur |
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550 lsSig[[1]]$factors = "Covariate4" |
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551 lsSig[[1]]$metadata = frmeTmp[["Covariate4"]] |
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552 vdCoef = c(Covariate4=5) |
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553 lsSig[[1]]$value = vdCoef |
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554 lsSig[[1]]$std = sd(frmeTmp[["Covariate4"]]) |
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555 lsSig[[1]]$allCoefs = vdCoef |
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556 lsSig[[2]] = list() |
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557 lsSig[[2]]$name = "Covariate2" |
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558 lsSig[[2]]$orig = "Covariate2" |
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559 lsSig[[2]]$taxon = "adCur" |
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560 lsSig[[2]]$data = adCur |
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561 lsSig[[2]]$factors = "Covariate2" |
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562 lsSig[[2]]$metadata = frmeTmp[["Covariate2"]] |
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563 vdCoef = c(Covariate2=0.46666667) |
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564 lsSig[[2]]$value = vdCoef |
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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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568 ret2 = funcDoUnivariate(strFormula=NULL,frmeTmp=frmeTmp,iTaxon=iTaxon, lsHistory=lsHistory, strRandomFormula="adCur ~ 1|Covariate4 + 1|Covariate2") |
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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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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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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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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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669 #}) |
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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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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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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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689 test_that("Test that the neg binomial regression has the correct results for 1 covariate.",{ |
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690 expect_equal(funcBinomialMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet) |
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691 }) |
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692 #Test for correct call for 2 covariates |
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693 strFormula = "adCur ~ Covariate1 + Covariate2" |
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694 iTaxon = 5 |
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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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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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700 #strFormula = "adCur ~ Covariate1" |
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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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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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705 #}) |
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706 ##Test for correct call with 1 random and 2 fixed covariates |
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707 #strFormula = "adCur ~ Covariate1 + Covariate2" |
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708 #strRandomFormula = "~1|FCovariate3" |
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709 #lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=binomial(link=logit), data=frmeTmp) |
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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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712 #}) |
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713 ##Test for correct call with 2 random and 1 fixed covariates |
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714 #strFormula = "adCur ~ Covariate1" |
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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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720 |
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721 |
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722 context("Test funcQuasiMult") |
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723 strFormula = "adCur ~ Covariate1" |
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724 strRandomFormula = NULL |
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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) |
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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) |
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729 frmeTmp = data.frame(Covariate1=x, Covariate2=x2, FCovariate3=xf1, FCovariate4=xf2, adCur=adCur) |
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730 iTaxon = 5 |
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731 lmRet = glm(as.formula(strFormula), family=quasipoisson, data=frmeTmp, na.action=c_strNA_Action) |
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732 test_that("Test that the quasi poisson has the correct results for 1 covariate.",{ |
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733 expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet) |
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734 }) |
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735 #Test for correct call for 2 covariates |
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736 strFormula = "adCur ~ Covariate1 + Covariate2" |
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737 iTaxon = 5 |
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738 lmRet = glm(as.formula(strFormula), family=quasipoisson, data=frmeTmp, na.action=c_strNA_Action) |
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739 test_that("Test that the quasi poisson has the correct results for 2 covariates.",{ |
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740 expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet) |
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741 }) |
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742 ##Test for correct call with 1 random and one fixed covariate |
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743 #strFormula = "adCur ~ Covariate1" |
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744 #strRandomFormula = "~1|FCovariate3" |
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745 #lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=quasipoisson, data=frmeTmp) |
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746 #test_that("Test that the lm has the correct results for 1 random and one fixed covariate.",{ |
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747 # expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet) |
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748 #}) |
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749 ##Test for correct call with 1 random and 2 fixed covariates |
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750 #strFormula = "adCur ~ Covariate1 + Covariate2" |
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751 #strRandomFormula = "~1|FCovariate3" |
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752 #lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=quasipoisson, data=frmeTmp) |
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753 #test_that("Test that the lm has the correct results for 1 random and 2 fixed covariates.",{ |
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754 # expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet) |
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755 #}) |
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756 ##Test for correct call with 2 random and 1 fixed covariates |
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757 #strFormula = "adCur ~ Covariate1" |
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758 #strRandomFormula = "~1|FCovariate4+1|FCovariate3" |
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759 #lmRet = glmmPQL(fixed=as.formula(strFormula), random=as.formula(strRandomFormula), family=quasipoisson, data=frmeTmp) |
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760 #test_that("Test that the lm has the correct results for 2 random and 1 fixed covariates.",{ |
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761 # expect_equal(funcQuasiMult(strFormula=strFormula,frmeTmp=frmeTmp,iTaxon=iTaxon,lsHistory=lsHistory,strRandomFormula=strRandomFormula),lmRet) |
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762 #}) |
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763 |
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764 |
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765 #Test transforms |
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766 context("Test funcNoTransform") |
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767 aTest1 = c(NA) |
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768 aTest2 = c(NULL) |
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769 aTest3 = c(0.5,1.4,2.4,3332.4,0.0,0.0000003) |
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770 aTest4 = c(0.1) |
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771 test_that("Test that no transform does not change the data.",{ |
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772 expect_equal(funcNoTransform(aTest1), aTest1) |
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773 expect_equal(funcNoTransform(aTest2), aTest2) |
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774 expect_equal(funcNoTransform(aTest3), aTest3) |
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775 expect_equal(funcNoTransform(aTest4), aTest4) |
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776 }) |
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777 |
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778 |
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779 context("Test funcArcsinSqrt") |
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780 aTest1 = c(NA) |
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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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782 aTest3 = c(0.000001) |
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783 test_that("Test that funcArcsinSqrt performs the transform correctly.",{ |
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784 expect_equal(funcArcsinSqrt(NA), as.numeric(NA)) |
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785 expect_equal(funcArcsinSqrt(aTest1), asin(sqrt(aTest1))) |
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786 expect_equal(funcArcsinSqrt(aTest2), asin(sqrt(aTest2))) |
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787 expect_equal(funcArcsinSqrt(aTest3), asin(sqrt(aTest3))) |
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788 }) |