opt1D: Parallelized repeated tuning of Lasso or Ridge penalty...

In pensim: Simulation of High-Dimensional Data and Parallelized Repeated Penalized Regression

Parallelized repeated tuning of Lasso or Ridge penalty parameter

Description

This function is a wrapper to the optL1 and optL2 functions of the penalized R package, useful for parallelized repeated tuning of the penalty parameters.

Usage

opt1D(nsim = 50, nprocessors = 1, setpen = "L1", cl = NULL, ...)

Arguments

nsim	Number of times to repeat the simulation (around 50 is suggested)
nprocessors	An integer number of processors to use.
setpen	Either "L1" (Lasso) or "L2" (Ridge) penalty
cl	Optional cluster object created with the makeCluster() function of the parallel package. If this is not set, pensim calls makeCluster(nprocessors, type="SOCK"). Setting this parameter can enable parallelization in more diverse scenarios than multi-core desktops; see the documentation for the parallel package. Note that if cl is user-defined, this function will not automatically run parallel::stopCluster() to shut down the cluster.
...	arguments passed on to optL1 or optL2 function of the penalized R package

Details

This function sets up a SNOW (Simple Network of Workstations) "sock" cluster to parallelize the task of repeated tunings the L1 or L2 penalty parameter. Tuning of the penalty parameters is done by the optL1 or optL2 functions of the penalized R package.

Value

Returns a matrix with the following columns:

L1 (or L2)	optimized value of the penalty parameter
cvl	optimized cross-validated likelihood
coef_1, coef_2, ..., coef_n	argmax coefficients for the model with this value of the tuning parameter

The matrix contains one row for each repeat of the regression.

Note

Depends on the R packages: penalized, parallel, rlecuyer

Author(s)

Levi Waldron et al.

References

Waldron L, Pintilie M, Tsao M-S, Shepherd FA, Huttenhower C*, Jurisica I*: Optimized application of penalized regression methods to diverse genomic data. Bioinformatics 2011, 27:3399-3406. (*equal contribution)

See Also

optL1, optL2

Examples

data(beer.exprs)
data(beer.survival)

##select just 100 genes to speed computation:
set.seed(1)
beer.exprs.sample <- beer.exprs[sample(1:nrow(beer.exprs), 100),]

gene.quant <- apply(beer.exprs.sample, 1, quantile, probs = 0.75)
dat.filt <- beer.exprs.sample[gene.quant > log2(100),]
gene.iqr <- apply(dat.filt, 1, IQR)
dat.filt <- as.matrix(dat.filt[gene.iqr > 0.5,])
dat.filt <- t(dat.filt)

##define training and test sets
set.seed(1)
trainingset <- sample(rownames(dat.filt), round(nrow(dat.filt) / 2))
testset <-
  rownames(dat.filt)[!rownames(dat.filt) %in% trainingset]

dat.training <- data.frame(dat.filt[trainingset, ])
pheno.training <- beer.survival[trainingset, ]

library(survival)
surv.training <- Surv(pheno.training$os, pheno.training$status)

dat.test <- data.frame(dat.filt[testset, ])
all.equal(colnames(dat.training), colnames(dat.test))
pheno.test <- beer.survival[testset, ]
surv.test <- Surv(pheno.test$os, pheno.test$status)

##ideally nsim should be on the order of 50,  but this slows computation
##50x without parallelization.
set.seed(1)
output <-
  pensim::opt1D(
    nsim = 1,
    nprocessors = 1,
    setpen = "L2",
    response = surv.training,
    penalized = dat.training,
    fold = 3,
    positive = FALSE,
    standardize = TRUE,
    minlambda2 = 1,
    maxlambda2 = 100
  )

cc <- output[which.max(output[, "cvl"]),-(1:2)]  #coefficients
sum(abs(cc) > 0)  #count non-zero coefficients

preds.training <- as.matrix(dat.training) %*% cc
preds.training.median <- median(preds.training)
preds.training.dichot <-
  ifelse(preds.training > preds.training.median, "high risk", "low risk")
preds.training.dichot <-
  factor(preds.training.dichot[, 1], levels = c("low risk", "high risk"))
preds.test <- as.matrix(dat.test) %*% cc
preds.test.dichot <-
  ifelse(preds.test > preds.training.median, "high risk", "low risk")
preds.test.dichot <-
  factor(preds.test.dichot[, 1], levels = c("low risk", "high risk"))

coxphfit.training <- coxph(surv.training ~ preds.training.dichot)
survfit.training <- survfit(surv.training ~ preds.training.dichot)
summary(coxphfit.training)
coxphfit.test <- coxph(surv.test ~ preds.test.dichot)
survfit.test <- survfit(surv.test ~ preds.test.dichot)
summary(coxphfit.test)

(p.training <-
    signif(summary(coxphfit.training)$logtest[3], 2))  #likelihood ratio test
(hr.training <- signif(summary(coxphfit.training)$conf.int[1], 2))
(hr.lower.training <- summary(coxphfit.training)$conf.int[3])
(hr.upper.training <- summary(coxphfit.training)$conf.int[4])
par(mfrow = c(1, 2))
plot(
  survfit.training,
  col = c("black", "red"),
  conf.int = FALSE,
  xlab = "Months",
  main = "TRAINING",
  ylab = "Overall survival"
)
xmax <- par("usr")[2] - 50
text(
  x = xmax,
  y = 0.4,
  lab = paste("HR=", hr.training),
  pos = 2
)
text(
  x = xmax,
  y = 0.3,
  lab = paste("p=", p.training, "", sep = ""),
  pos = 2
)
tmp <- summary(preds.training.dichot)
text(
  x = c(xmax, xmax),
  y = c(0.2, 0.1),
  lab = paste(tmp, names(tmp)),
  col = 1:2,
  pos = 2
)
(p.test <-
    signif(summary(coxphfit.test)$logtest[3], 2))  #likelihood ratio test
(hr.test <- signif(summary(coxphfit.test)$conf.int[1], 2))
(hr.lower.test <- summary(coxphfit.test)$conf.int[3])
(hr.upper.test <- summary(coxphfit.test)$conf.int[4])
plot(
  survfit.test,
  col = c("black", "red"),
  conf.int = FALSE,
  xlab = "Months",
  main = "TEST"
)
text(
  x = xmax,
  y = 0.4,
  lab = paste("HR=", hr.test),
  pos = 2
)
text(
  x = xmax,
  y = 0.3,
  lab = paste("p=", p.test, "", sep = ""),
  pos = 2
)
tmp <- summary(preds.test.dichot)
text(
  x = c(xmax, xmax),
  y = c(0.2, 0.1),
  lab = paste(tmp, names(tmp)),
  col = 1:2,
  pos = 2
)

pensim documentation built on Dec. 9, 2022, 1:10 a.m.