cv.pengls {pengls}

R Documentation

Peform cross-validation pengls

Description

Peform cross-validation pengls

Usage

cv.pengls(
  data,
  glsSt,
  xNames,
  outVar,
  corMat,
  nfolds,
  foldid,
  cvType = "blocked",
  lambdas,
  transFun = "identity",
  transFunArgs = list(),
  ...
)

Arguments

data	A data matrix or data frame
glsSt	a covariance structure, as supplied to nlme::gls as "correlation"
xNames	names of the regressors in data
outVar	name of the outcome variable in data
corMat	a starting value for th correlation matrix. Taken to be a diagonal matrix if missing
nfolds	an integer, the number of folds used in cv.glmnet to find lambda
foldid	An optional vector deffining the fold
cvType	A character vector defining the type of cross-validation. Either "random" or "blocked", ignored if foldid is provided
lambdas	an optional lambda sequence
transFun	a transformation function to apply to predictions and outcome in the cross-validation
transFunArgs	Additional arguments passed onto transFun
...	passed onto glmnet::glmnet

Value

A list with components

lambda	The series of lambdas
cvm	The vector of mean R2's
cvsd	The standard error of R2 at the maximum
cvOpt	The R2 according to the 1 standard error rule
coefs	The matrix of coefficients for every lambda value
lambda.min	Lambda value with maximal R2
lambda.1se	Smallest lambda value within 1 standard error from the maximum
foldid	The folds
glsSt	The nlme correlation object

Examples

library(nlme)
library(BiocParallel)
n <- 50 #Sample size
p <- 100 #Number of features
g <- 10 #Size of the grid
#Generate grid
Grid <- expand.grid("x" = seq_len(g), "y" = seq_len(g))
# Sample points from grid without replacement
GridSample <- Grid[sample(nrow(Grid), n, replace = FALSE),]
#Generate outcome and regressors
b <- matrix(rnorm(p*n), n , p)
a <- rnorm(n, mean = b %*% rbinom(p, size = 1, p = 0.2)) #20% signal
#Compile to a matrix
df <- data.frame("a" = a, "b" = b, GridSample)
# Define the correlation structure (see ?nlme::gls), with initial nugget 0.5 and range 5
corStruct = corGaus(form = ~ x + y, nugget = TRUE, value = c("range" = 5, "nugget" = 0.5))
#Fit the pengls model, for simplicity for a simple lambda
register(MulticoreParam(3)) #Prepare multithereading
penglsFitCV = cv.pengls(data = df, outVar = "a",
xNames = grep(names(df), pattern = "b", value =TRUE),
glsSt = corStruct, nfolds = 5)
penglsFitCV$lambda.1se #Lambda for 1 standard error rule
penglsFitCV$cvOpt #Corresponding R2
coef(penglsFitCV)
penglsFitCV$foldid #The folds used

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