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TestScript.R
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require(microbenchmark)
require(penalizedcpp)
require(penalized)
data(nki70)
# Function which compares two penfit objects
compPenfitObj <- function(fit1, fit2, i = 0) {
passed = T
# penalized
pen = all.equal(fit1@penalized, fit2@penalized, check.names = T, check.attributes = T)
if (!isTRUE(pen)){ warning(paste("Difference detected comparing penalized slot:", pen)); passed = F}
# unpenalized
upen = all.equal(fit1@unpenalized, fit2@unpenalized, check.names = T, check.attributes = T)
if (!isTRUE(upen)){ warning(paste("Difference detected comparing unpenalized slot:", upen)); passed = F}
# residuals
res = all.equal(fit1@residuals, fit2@residuals, check.names = T, check.attributes = T)
if (!isTRUE(res)){ warning(paste("Difference detected comparing residuals slot:", res)); passed = F}
# fitted
fitted = all.equal(fit1@fitted, fit2@fitted, check.names = T, check.attributes = T)
if (!isTRUE(fitted)){ warning(paste("Difference detected comparing fitted slot:", fitted)); passed = F}
# lin.pred
lp = all.equal([email protected], [email protected], check.names = T, check.attributes = T)
if (!isTRUE(lp)){ warning(paste("Difference detected comparing lin.pred slot:", lp)); passed = F}
# loglik
loglik = all.equal(fit1@loglik, fit2@loglik, check.names = T, check.attributes = T)
if (!isTRUE(loglik)){ warning(paste("Difference detected comparing loglik slot:", loglik)); passed = F}
# penalty
penalty = all.equal(fit1@penalty, fit2@penalty, check.names = T, check.attributes = T)
if (!isTRUE(penalty)){ warning(paste("Difference detected comparing penalty slot:", penalty)); passed = F}
# iterations
iter = all.equal(fit1@iterations, fit2@iterations, check.names = T, check.attributes = T)
if (!isTRUE(iter)){ warning(paste("Difference detected comparing iterations slot:", iter)); passed = F}
# converged
converged = all.equal(fit1@converged, fit2@converged, check.names = T, check.attributes = T)
if (!isTRUE(converged)){ warning(paste("Difference detected comparing converged slot:", converged)); passed = F}
# model
model = all.equal(fit1@model, fit2@model, check.names = T, check.attributes = T)
if (!isTRUE(model)){ warning(paste("Difference detected comparing model slot:", model)); passed = F}
# lambda1
l1 = all.equal(fit1@lambda1, fit2@lambda1, check.names = T, check.attributes = T)
if (!isTRUE(l1)){ warning(paste("Difference detected comparing lambda1 slot:", l1)); passed = F}
# lambda2
l2 = all.equal(fit1@lambda2, fit2@lambda2, check.names = T, check.attributes = T)
if (!isTRUE(l2)){ warning(paste("Difference detected comparing lambda2 slot:", l2)); passed = F}
# weights
weights = all.equal(fit1@weights, fit2@weights, check.names = T, check.attributes = T)
if (!isTRUE(weights)){ warning(paste("Difference detected comparing weights slot:", weights)); passed = F}
return(passed)
}
# Function which compares output of opt/prof functions
compareCVOutput <- function(fit1, fit2) {
# lambda
lambda = all.equal(fit1$lambda, fit2$lambda, check.names = T, check.attributes = T)
if (!isTRUE(lambda)) warning(paste("Difference detected comparing lambda element:", lambda))
# cvl
cvl = all.equal(fit1$cvl, fit2$cvl, check.names = T, check.attributes = T)
if (!isTRUE(cvl)) warning(paste("Difference detected comparing cvl element:", cvl))
# fold
fold = all.equal(fit1$fold, fit2$fold, check.names = T, check.attributes = T)
if (!isTRUE(fold)) warning(paste("Difference detected comparing cvl element:", fold))
# predictions
fold = all.equal(fit1$fold, fit2$fold, check.names = T, check.attributes = T)
if (!isTRUE(fold)) warning(paste("Difference detected comparing cvl element:", fold))
# fullfit
if ( (is.list(fit1$fullfit) != is.list(fit2$fullfit)) | (length(fit1$fullfit) != length(fit2$fullfit)) )
{
warning("Mismatch between fullfit types or length")
} else if (is.list(fit1$fullfit)) {
for (i in 1:length(fit1$fullfit))
{
pass = compPenfitObj(fit1$fullfit[[i]], fit2$fullfit[[i]])
if (!pass) print(paste("Failures occured at fullfit index", i))
}
} else {
compPenfitObj(fit1$fullfit, fit2$fullfit)
}
}
# ***** COMPARE RESULTS *****
# Cox model w/ lasso
result1 = penalized:::penalized(Surv(time, event)~strata(ER), penalized = nki70[,8:76],
data = nki70, standardize=TRUE, lambda1 = 0.5)
result2 = penalizedcpp:::penalized(Surv(time, event)~strata(ER), penalized = nki70[,8:76],
data = nki70, standardize=TRUE, lambda1 = 0.5)
compPenfitObj(result1, result2)
# Cox model w/ ridge
result1 = penalized:::penalized(Surv(time, event)~strata(ER), penalized = nki70[,8:76],
data = nki70, standardize=TRUE, lambda2 = 0.5)
result2 = penalizedcpp:::penalized(Surv(time, event)~strata(ER), penalized = nki70[,8:76],
data = nki70, standardize=TRUE, lambda2 = 0.5)
compPenfitObj(result1, result2)
# Cox model w/ l1 penalty optimization
set.seed(42);
result1 = penalized:::optL1(Surv(time, event)~strata(ER), penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5)
set.seed(42);
result2 = penalizedcpp:::optL1(Surv(time, event)~strata(ER), penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5)
compareCVOutput(result1, result2)
# Cox model w/ l1 profile
set.seed(42);
result1 = penalized:::profL1(Surv(time, event)~strata(ER), penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5)
set.seed(42);
result2 = penalizedcpp:::profL1(Surv(time, event)~strata(ER), penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5)
compareCVOutput(result1, result2)
# Cox model w/ both l1 and l2 penalty and n < p
result1 = penalized:::penalized(Surv(nki70[1:20,1], nki70[1:20,2]), penalized = nki70[1:20,8:77], data = nki70,
lambda1 = .001, lambda2 = .002, fusedl = F)
result2 = penalizedcpp:::penalized(Surv(nki70[1:20,1], nki70[1:20,2]), penalized = nki70[1:20,8:77], data = nki70,
lambda1 = .001, lambda2 = .002, fusedl = F)
compPenfitObj(result1, result2)
# Logistic regression w/ lasso
result1 = penalized:::penalized(ER, penalized = nki70[,8:76], data=nki70, lambda1=3)
result2 = penalizedcpp:::penalized(ER, penalized = nki70[,8:76], data=nki70, lambda1=3)
compPenfitObj(result1, result2)
# Logistic regression w/ ridge
result1 = penalized:::penalized(ER, penalized = nki70[,8:76], data=nki70, lambda2=3)
result2 = penalizedcpp:::penalized(ER, penalized = nki70[,8:76], data=nki70, lambda2=3)
compPenfitObj(result1, result2)
# Logistic regression w/ l1 penalty optimization
set.seed(42);
result1 = penalized:::optL1(ER, penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5)
set.seed(42);
result2 = penalizedcpp:::optL1(ER, penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5)
compareCVOutput(result1, result2)
# Logistic regression w/ l1 profile
set.seed(42);
result1 = penalized:::profL1(ER, penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5)
set.seed(42);
result2 = penalizedcpp:::profL1(ER, penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5)
compareCVOutput(result1, result2)
# Logistic regression model w/ both l1 and l2 penalty and n < p
result1 = penalized:::penalized(nki70[1:20,5], penalized = nki70[1:20,8:77], data = nki70,
lambda1 = .001, lambda2 = .002, fusedl = F)
result2 = penalizedcpp:::penalized(nki70[1:20,5], penalized = nki70[1:20,8:77], data = nki70,
lambda1 = .001, lambda2 = .002, fusedl = F)
compPenfitObj(result1, result2)
# Linear model w/ lasso
result1 = penalized:::penalized(time, penalized = nki70[,8:76], data=nki70, lambda1=3)
result2 = penalizedcpp:::penalized(time, penalized = nki70[,8:76], data=nki70, lambda1=3)
compPenfitObj(result1, result2)
# Linear model w/ ridge
result1 = penalized:::penalized(time, penalized = nki70[,8:76], data=nki70, lambda2=3)
result2 = penalizedcpp:::penalized(time, penalized = nki70[,8:76], data=nki70, lambda2=3)
compPenfitObj(result1, result2)
# Linear model w/ l1 penalty optimization
set.seed(42);
result1 = penalized:::optL1(time, penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5)
set.seed(42);
result2 = penalizedcpp:::optL1(time, penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5)
compareCVOutput(result1, result2)
# Linear model w/ l1 profile
set.seed(42);
result1 = penalized:::profL1(time, penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5)
set.seed(42);
result2 = penalizedcpp:::profL1(time, penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5)
compareCVOutput(result1, result2)
# Linear model w/ both l1 and l2 penalty and n < p
result1 = penalized:::penalized(nki70[1:20,1], penalized = nki70[1:20,8:77], data = nki70,
lambda1 = .001, lambda2 = .002, fusedl = F)
result2 = penalizedcpp:::penalized(nki70[1:20,1], penalized = nki70[1:20,8:77], data = nki70,
lambda1 = .001, lambda2 = .002, fusedl = F)
compPenfitObj(result1, result2)
# High dimensional analysis data set
# http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36000
#source("http://bioconductor.org/biocLite.R")
#biocLite("affy")
library(affy)
setwd("/Users/matthewlueder/desktop/exprDat/")
eset <- justRMA()
exprs <- exprs(eset)
set.seed(1234)
CR_event <- sample( c(0,1,2), 76, replace=TRUE )
hdim <- cbind( nki70[1:76,1:2], CR_event, nki70[1:76,3:length(nki70)], t(exprs))
# H-Dim Cox model w/ lasso
result1 = penalized:::penalized(Surv(time, event), penalized = hdim[,9:54753],
data = hdim, standardize=TRUE, lambda1 = 3)
result2 = penalizedcpp:::penalized(Surv(time, event), penalized = hdim[,9:54753],
data = hdim, standardize=TRUE, lambda1 = 3)
compPenfitObj(result1, result2)
# H-Dim competing risks model w/ lasso
result1 = penalized:::penalized(Surv(time, CR_event == 1), penalized = hdim[,9:54753],
data = hdim, standardize=TRUE, lambda1 = 3)
result2 = penalizedcpp:::penalized(Surv(time, CR_event == 1), penalized = hdim[,9:54753],
data = hdim, standardize=TRUE, lambda1 = 3)
compPenfitObj(result1, result2)
# ***** COMPARE SPEED *****
# Cox model w/ lasso
microbenchmark(
penalized:::penalized(Surv(time, event)~strata(ER), penalized = nki70[,8:76],
data = nki70, standardize=TRUE, lambda1 = 0.5),
penalizedcpp:::penalized(Surv(time, event)~strata(ER), penalized = nki70[,8:76],
data = nki70, standardize=TRUE, lambda1 = 0.5)
)
# min lq mean median uq max neval
# 554.3666 584.2260 699.3707 712.1897 745.1892 1304.951 100
# 224.7664 233.5997 264.8149 244.1310 253.3629 470.378 100
# Cox model w/ ridge
microbenchmark(
penalized:::penalized(Surv(time, event)~strata(ER), penalized = nki70[,8:76],
data = nki70, standardize=TRUE, lambda2 = 0.5),
penalizedcpp:::penalized(Surv(time, event)~strata(ER), penalized = nki70[,8:76],
data = nki70, standardize=TRUE, lambda2 = 0.5)
)
# min lq mean median uq max neval
#30.26409 37.73726 40.98312 39.27207 40.28391 221.2033 100
#29.74334 35.88285 40.34717 36.99988 38.36700 214.6791 100
# Cox model w/ l1 penalty optimization
microbenchmark(
penalized:::optL1(Surv(time, event)~strata(ER), penalized = nki70[,8:77], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5),
penalizedcpp:::optL1(Surv(time, event)~strata(ER), penalized = nki70[,8:77], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5),
times = 10
)
# min lq mean median uq max neval
# 1533.1250 1762.368 2410.892 1977.835 2845.067 4991.450 10
# 970.0893 1028.945 1324.902 1078.799 1116.879 3121.993 10
# Cox model w/ l1 profile
microbenchmark(
penalized:::profL1(Surv(time, event)~strata(ER), penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5),
penalizedcpp:::profL1(Surv(time, event)~strata(ER), penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5),
times = 10
)
# Cox model w/ both l1 and l2 penalty and n < p
microbenchmark(
penalized:::penalized(Surv(nki70[1:20,1], nki70[1:20,2]), penalized = nki70[1:20,8:77], data = nki70,
lambda1 = .001, lambda2 = .002, fusedl = F),
penalizedcpp:::penalized(Surv(nki70[1:20,1], nki70[1:20,2]), penalized = nki70[1:20,8:77], data = nki70,
lambda1 = .001, lambda2 = .002, fusedl = F)
)
# Logistic regression w/ lasso
microbenchmark(
penalized:::penalized(ER, penalized = nki70[,8:76], data=nki70, lambda1=3),
penalizedcpp:::penalized(ER, penalized = nki70[,8:76], data=nki70, lambda1=3)
)
# Logistic regression w/ ridge
microbenchmark(
penalized:::penalized(ER, penalized = nki70[,8:76], data=nki70, lambda2=3),
penalizedcpp:::penalized(ER, penalized = nki70[,8:76], data=nki70, lambda2=3)
)
# Logistic regression w/ l1 penalty optimization
microbenchmark(
penalized:::optL1(ER, penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5),
penalizedcpp:::optL1(ER, penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5),
times = 20
)
# Linear model w/ lasso
microbenchmark(
penalized:::penalized(time, penalized = nki70[,8:76], data=nki70, lambda1=3),
penalizedcpp:::penalized(time, penalized = nki70[,8:76], data=nki70, lambda1=3)
)
# Linear model w/ l1 profile
microbenchmark(
penalized:::profL1(time, penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5),
penalizedcpp:::profL1(time, penalized = nki70[,8:76], unpenalized = nki70[,77],
data = nki70, standardize=TRUE, fold = 5),
times = 10
)
# Linear model w/ both l1 and l2 penalty and n < p
microbenchmark(
penalized:::penalized(nki70[1:20,1], penalized = nki70[1:20,8:77], data = nki70,
lambda1 = .001, lambda2 = .002, fusedl = F),
penalizedcpp:::penalized(nki70[1:20,1], penalized = nki70[1:20,8:77], data = nki70,
lambda1 = .001, lambda2 = .002, fusedl = F)
)
# H-Dim Cox model w/ lasso
microbenchmark(
penalized:::penalized(Surv(time, event), penalized = hdim[,9:54753],
data = hdim, standardize=TRUE, lambda1 = 3),
penalizedcpp:::penalized(Surv(time, event), penalized = hdim[,9:54753],
data = hdim, standardize=TRUE, lambda1 = 3),
times = 5
)
# H-Dim competing risks model w/ lasso
microbenchmark(
penalized:::penalized(Surv(time, CR_event == 1), penalized = hdim[,9:54753],
data = hdim, standardize=TRUE, lambda1 = 3),
penalizedcpp:::penalized(Surv(time, CR_event == 1), penalized = hdim[,9:54753],
data = hdim, standardize=TRUE, lambda1 = 3),
times = 5
)