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1
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2 ########################################################
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3 #
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4 # creation date : 08/01/16
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5 # last modification : 23/06/16
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6 # author : Dr Nicolas Beaume
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7 # owner : IRRI
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8 #
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9 ########################################################
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10
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31
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11 # compute correlation
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4
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12 predictionCorrelation <- function(prediction, obs) {
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13 return(cor(prediction, obs, method = "pearson"))
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14 }
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15
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31
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16 # compute r2 by computing the classic formula
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17 # compare the sum of square difference from target to prediciton
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18 # to the sum of square difference from target to the mean of the target
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19 r2 <- function(target, prediction) {
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20 sst <- sum((target-mean(target))^2)
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21 ssr <- sum((target-prediction)^2)
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22 return(1-ssr/sst)
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23 }
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24
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25 ################################## main function ###########################
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26
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27 evaluatePredictions <- function(data, prediction=NULL, traitValue=NULL, doR2=F,
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31
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28 folds=NULL, classes=NULL, doCor=F) {
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29 eval <- NULL
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31
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30 # case of r2
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31 if(doR2) {
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32 eval <- c(eval, list(r2=NULL))
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33 }
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34 if(doCor) {
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35 eval <- c(eval, list(cor=NULL))
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36 }
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37 for (i in 1:length(folds)) {
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38 train <- unlist(folds[-i])
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39 test <- folds[[i]]
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40 if(doR2) {
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41 if(!is.null(traitValue)) {
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42 eval$r2 <- c(eval$r2, r2(traitValue[test], prediction[[i]]))
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43 } else {
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44 eval$r2 <- c(eval$r2, NA)
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45 }
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46 }
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31
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47 # case of correlation
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48 if(doCor) {
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49 if(!is.null(traitValue)) {
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50 eval$cor <- c(eval$cor, cor(traitValue[test], prediction[[i]]))
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51 } else {
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52 eval$cor <- c(eval$cor, NA)
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53 }
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54 }
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55 }
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31
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56 # print output
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57 print(eval)
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58 if(doR2) {
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59 print(paste("mean r2 : ", mean(eval$r2), "standard deviation :", sd(eval$r2)))
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60 }
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61 if(doCor) {
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62 print(paste("mean correlation : ", mean(eval$cor), "standard deviation :", sd(eval$cor)))
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63 }
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64 }
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65 ############################ main #############################
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66
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31
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67 # load arguments
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4
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68 cmd <- commandArgs(trailingOnly = T)
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69 source(cmd[1])
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70 # set which evaluation are used
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71 if(as.integer(doR2) == 1) {
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72 doR2 <- T
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73 }
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74 if(as.integer(doCor) == 1) {
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75 doCor <- T
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76 }
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77 # load genotype & phenotype
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78 con = file(genotype)
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79 genotype <- readLines(con = con, n = 1, ok=T)
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80 close(con)
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81 genotype <- read.table(genotype, sep="\t",h=T)
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82 phenotype <- read.table(phenotype, sep="\t",h=T)[,1]
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83 # load prediction
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84 con = file(prediction)
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85 prediction <- readLines(con = con, n = 1, ok=T)
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86 close(con)
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87 prediction <- readRDS(prediction)
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88 # load folds
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89 con = file(folds)
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90 folds <- readLines(con = con, n = 1, ok=T)
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91 close(con)
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92 folds <- readRDS(folds)
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31
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93 # evaluation
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4
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94 evaluatePredictions(data=genotype, prediction=prediction, traitValue=phenotype, doR2=doR2,
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95 folds=folds, doCor=doCor) |
