Prediction with nearest shrunken centroid classifier

Predict from a nearest shrunken centroid fit.

Usage

pam.predict(pam.intcv.model, pred.obj, pred.obj.group.id)

Arguments

pam.intcv.model: a PAM classifier built with pam.intcv.
pred.obj: dataset to have its sample group predicted. The dataset must have rows as probes and columns as samples. It must have an equal number of probes as the dataset being trained.
pred.obj.group.id: a vector of sample-group labels for each sample of the dataset to be predicted. It must have an equal length to the number of samples as pred.obj.

Value

a list of 3 elements:

pred: predicted sample group for each sample
mc: a predicted misclassification error rate (external validation)
prob: predicted probability for each sample

References

T. Hastie, R. Tibshirani, Balasubramanian Narasimhan and Gil Chu (2014). pamr: Pam: prediction analysis for microarrays. R package version 1.55. https://CRAN.R-project.org/package=pamr

Examples

set.seed(101)
biological.effect <- estimate.biological.effect(uhdata = uhdata.pl)
ctrl.genes <- unique(rownames(uhdata.pl))[grep("NC", unique(rownames(uhdata.pl)))]
biological.effect.nc <- biological.effect[!rownames(biological.effect) %in% ctrl.genes, ]
group.id <- substr(colnames(biological.effect.nc), 7, 7)

biological.effect.train.ind <- colnames(biological.effect.nc)[c(sample(which(group.id == "E"), size = 64),
                                          sample(which(group.id == "V"), size = 64))]
biological.effect.test.ind <- colnames(biological.effect.nc)[!colnames(biological.effect.nc) %in% biological.effect.train.ind]

biological.effect.nc.tr <- biological.effect.nc[, biological.effect.train.ind]
biological.effect.nc.te <- biological.effect.nc[, biological.effect.test.ind]

pam.int <- pam.intcv(X = biological.effect.nc.tr,
                     y = substr(colnames(biological.effect.nc.tr), 7, 7),
                     kfold = 5, seed = 1)
#> 12345678910111213141516171819202122232425262728293012Fold 1 :123456789101112131415161718192021222324252627282930
#> Fold 2 :123456789101112131415161718192021222324252627282930
#> Fold 3 :123456789101112131415161718192021222324252627282930
#> Fold 4 :123456789101112131415161718192021222324252627282930
#> Fold 5 :123456789101112131415161718192021222324252627282930
#> 1      id             E-score V-score
#>  [1,] A_25_P00010987 0.2963  -0.2963
#>  [2,] A_25_P00010987 0.2384  -0.2384
#>  [3,] A_25_P00012126 -0.2247 0.2247 
#>  [4,] A_25_P00010987 0.2165  -0.2165
#>  [5,] A_25_P00010987 0.211   -0.211 
#>  [6,] A_25_P00012126 -0.2081 0.2081 
#>  [7,] A_25_P00012126 -0.2077 0.2077 
#>  [8,] A_25_P00012126 -0.2061 0.2061 
#>  [9,] A_25_P00010987 0.2018  -0.2018
#> [10,] A_25_P00012126 -0.1937 0.1937 
#> [11,] A_25_P00012126 -0.1929 0.1929 
#> [12,] A_25_P00012126 -0.1875 0.1875 
#> [13,] A_25_P00012126 -0.1859 0.1859 
#> [14,] A_25_P00010987 0.181   -0.181 
#> [15,] A_25_P00012126 -0.1739 0.1739 
#> [16,] A_25_P00010987 0.1707  -0.1707
#> [17,] A_25_P00012126 -0.1664 0.1664 
#> [18,] A_25_P00010987 0.1601  -0.1601
#> [19,] A_25_P00014849 -0.1545 0.1545 
#> [20,] A_25_P00010987 0.1504  -0.1504
#> [21,] A_25_P00010987 0.1453  -0.1453
#> [22,] A_25_P00014849 -0.1383 0.1383 
#> [23,] A_25_P00012150 0.126   -0.126 
#> [24,] A_25_P00012150 0.1241  -0.1241
#> [25,] A_25_P00014849 -0.1206 0.1206 
#> [26,] A_25_P00014849 -0.1124 0.1124 
#> [27,] A_25_P00014849 -0.1105 0.1105 
#> [28,] A_25_P00012150 0.1079  -0.1079
#> [29,] A_25_P00014849 -0.1054 0.1054 
#> [30,] A_25_P00012150 0.1051  -0.1051
#> [31,] A_25_P00014849 -0.0998 0.0998 
#> [32,] A_25_P00012150 0.0988  -0.0988
#> [33,] A_25_P00012150 0.0985  -0.0985
#> [34,] A_25_P00012150 0.0938  -0.0938
#> [35,] A_25_P00014849 -0.0867 0.0867 
#> [36,] A_25_P00012150 0.0862  -0.0862
#> [37,] A_25_P00014849 -0.0828 0.0828 
#> [38,] A_25_P00012150 0.0767  -0.0767
#> [39,] A_25_P00012150 0.058   -0.058 
#> [40,] A_25_P00014849 -0.0575 0.0575 
#> [41,] A_25_P00011017 -0.0541 0.0541 
#> [42,] A_25_P00011017 -0.0365 0.0365 
#> [43,] A_25_P00011017 -0.0361 0.0361 
#> [44,] A_25_P00010683 -0.0317 0.0317 
#> [45,] A_25_P00012138 0.0263  -0.0263
#> [46,] A_25_P00012697 0.0166  -0.0166
#> [47,] A_25_P00012138 0.0115  -0.0115
#> [48,] A_25_P00010683 -0.0114 0.0114 
#> [49,] A_25_P00012138 0.0094  -0.0094
#> [50,] A_25_P00010132 0.0079  -0.0079
#> [51,] A_25_P00015907 -0.0023 0.0023 
#> [52,] A_25_P00011017 -0.0017 0.0017 
#> [53,] A_25_P00012970 -0.0017 0.0017 
#> [54,] A_25_P00010683 -3e-04  3e-04  
#> 

pam.pred <- pam.predict(pam.intcv.model = pam.int,
                        pred.obj = biological.effect.nc.te,
                        pred.obj.group.id = substr(colnames(biological.effect.nc.te), 7, 7))
pam.int$mc
#> [1] 0.1171875
pam.pred$mc
#> [1] 0.125