pengls: Iterative estimation of penalised generalised least squares

In sthawinke/pengls: Fit Penalised Generalised Least Squares models

Iterative estimation of penalised generalised least squares

Description

Iterative estimation of penalised generalised least squares

Usage

pengls(
  data,
  glsSt,
  xNames,
  outVar,
  corMat,
  lambda,
  foldid,
  exclude = NULL,
  maxIter = 30,
  tol = 0.05,
  verbose = FALSE,
  scale = FALSE,
  center = FALSE,
  optControl = lmeControl(opt = "optim", maxIter = 500, msVerbose = verbose, msMaxIter =
    500, niterEM = 1000, msMaxEval = 1000),
  nfolds = 10,
  penalty.factor = c(0, rep(1, length(xNames))),
  ...
)

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 the correlation matrix. Taken to be a diagonal matrix if missing
lambda	The penalty value for glmnet. If missing, the optimal value of vanilla glmnet without autocorrelation component is used
foldid	An optional vector defining the fold
exclude	indices of predictors to be excluded from intercept + xNames
maxIter	maximum number of iterations between glmnet and gls
tol	A convergence tolerance
verbose	a boolean, should output be printed?
scale, center	booleans, should regressors be scaled to zero mean and variance 1? Defaults to TRUE
optControl	control arguments, passed onto nlme::gls' control argument
nfolds	an integer, the number of folds used in cv.glmnet to find lambda
penalty.factor	passed onto glmnet:glmnet. The first entry is zero by default for the intercept, which is not shrunk
...	passed onto glmnet::glmnet

Value

A list with components

glmnet	The glmnet fit, which can be manipulated as such
gls	A list with info on the estimated correlation matrix
iter	The iterations needed
conv	A boolean, indicating whether the iteration between mean model and covariance estimation converged
xNames,data,glsSt,outVar	As provided
lambda	The lambda penalty paraneter used

See Also

cv.pengls

Examples

### Example 1: spatial data
# Define the dimensions of the data
library(nlme)
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
penglsFit <- pengls(data = df, outVar = "a", xNames = grep(names(df), pattern = "b", value =TRUE),
glsSt = corStruct, nfolds = 5)

### Example 2: timecourse data
dfTime <- data.frame("a" = a, "b" = b, "t" = seq_len(n))
dfTime$a[-1] = dfTime$a[-n]*0.25 #Some temporal signal
corStructTime <- corAR1(form = ~ t, value = 0.5)
penglsFitTime <- pengls(data = dfTime, outVar = "a",
xNames = grep(names(dfTime), pattern = "b", value =TRUE),
glsSt = corStructTime, nfolds = 5)

sthawinke/pengls documentation built on July 2, 2023, 7:27 a.m.