Vignette of the pengls package

Stijn Hawinkel

1 Introduction

This vignette demonstrates the use of the pengls package for high-dimensional data with spatial or temporal autocorrelation. It consists of an iterative loop around the nlme and glmnet packages. Currently, only continuous outcomes and \(R^2\) and MSE as performance measure are implemented.

2 Installation instuctions

The pengls package is available from BioConductor, and can be installed as follows:

library(BiocManager)
install("pengls")

Once installed, it can be loaded and version info printed.

suppressPackageStartupMessages(library(pengls))
cat("pengls package version", as.character(packageVersion("pengls")), "\n")
## pengls package version 1.8.0

3 Illustration

3.1 Spatial autocorrelation

We first create a toy dataset with spatial coordinates.

library(nlme)
n <- 25 #Sample size
p <- 50 #Number of features
g <- 15 #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 = 0.25), sd = 0.1) #25% signal
#Compile to a matrix
df <- data.frame("a" = a, "b" = b, GridSample)

The pengls method requires prespecification of a functional form for the autocorrelation. This is done through the corStruct objects defined by the nlme package. We specify a correlation decaying as a Gaussian curve with distance, and with a nugget parameter. The nugget parameter is a proportion that indicates how much of the correlation structure explained by independent errors; the rest is attributed to spatial autocorrelation. The starting values are chosen as reasonable guesses; they will be overwritten in the fitting process.

# 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))

Finally the model is fitted with a single outcome variable and large number of regressors, with the chosen covariance structure and for a prespecified penalty parameter \(\lambda=0.2\).

#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, lambda = 0.2, verbose = TRUE)
## Starting iterations...
## Iteration 1 
## Iteration 2

Standard extraction functions like print(), coef() and predict() are defined for the new “pengls” object.

penglsFit
## pengls model with correlation structure: corGaus 
##  and 15 non-zero coefficients
penglsCoef <- coef(penglsFit)
penglsPred <- predict(penglsFit)

3.2 Temporal autocorrelation

The method can also account for temporal autocorrelation by defining another correlation structure from the nlme package, e.g. autocorrelation structure of order 1:

set.seed(354509)
n <- 100 #Sample size
p <- 10 #Number of features
#Generate outcome and regressors
b <- matrix(rnorm(p*n), n , p)
a <- rnorm(n, mean = b %*% rbinom(p, size = 1, p = 0.25), sd = 0.1) #25% signal
#Compile to a matrix
dfTime <- data.frame("a" = a, "b" = b, "t" = seq_len(n))
corStructTime <- corAR1(form = ~ t, value = 0.5)

The fitting command is similar, this time the \(\lambda\) parameter is found through cross-validation of the naive glmnet (for full cross-validation , see below). We choose \(\alpha=0.5\) this time, fitting an elastic net model.

penglsFitTime <- pengls(data = dfTime, outVar = "a", verbose = TRUE,
xNames = grep(names(dfTime), pattern = "b", value =TRUE),
glsSt = corStructTime, nfolds = 5, alpha = 0.5)
## Fitting naieve model...
## Starting iterations...
## Iteration 1 
## Iteration 2

Show the output

penglsFitTime
## pengls model with correlation structure: corAR1 
##  and 2 non-zero coefficients

3.3 Penalty parameter and cross-validation

The pengls package also provides cross-validation for finding the optimal \(\lambda\) value. If the tuning parameter \(\lambda\) is not supplied, the optimal \(\lambda\) according to cross-validation with the naive glmnet function (the one that ignores dependence) is used. Hence we recommend to use the following function to use cross-validation. Multithreading is supported through the BiocParallel package :

library(BiocParallel)
register(MulticoreParam(3)) #Prepare multithereading
nfolds <- 3 #Number of cross-validation folds

The function is called similarly to cv.glmnet:

penglsFitCV <- cv.pengls(data = df, outVar = "a", xNames = grep(names(df), pattern = "b", value =TRUE), glsSt = corStruct, nfolds = nfolds)

Check the result:

penglsFitCV
## Cross-validated pengls model with correlation structure: corGaus 
##  and 1 non-zero coefficients.
##  3 fold cross-validation yielded an estimated R2 of -0.1708484 .

By default, the 1 standard error is used to determine the optimal value of \(\lambda\) :

penglsFitCV$lambda.1se #Lambda for 1 standard error rule
## [1] 0.9602137
penglsFitCV$cvOpt #Corresponding R2
## [1] -0.1708484

Extract coefficients and fold IDs.

head(coef(penglsFitCV))
## [1] 0 0 0 0 0 0
penglsFitCV$foldid #The folds used
##  85 194 118 142  86  26 175 116   3 202 131 204 132  73 148 115 203  79 109 130 
##   3   1   3   1   3   3   1   3   2   1   3   1   3   3   3   3   1   2   2   3 
##  82  98 218  91 197 
##   2   3   1   2   1

By default, blocked cross-validation is used, but random cross-validation is also available (but not recommended for timecourse or spatial data). First we illustrate the different ways graphically, again using the timecourse example:

set.seed(5657)
randomFolds <- makeFolds(nfolds = nfolds, dfTime, "random", "t")
blockedFolds <- makeFolds(nfolds = nfolds, dfTime, "blocked", "t")
plot(dfTime$t, randomFolds, xlab ="Time", ylab ="Fold")
points(dfTime$t, blockedFolds, col = "red")
legend("topleft", legend = c("random", "blocked"), pch = 1, col = c("black", "red"))

To perform random cross-validation

penglsFitCVtime <- cv.pengls(data = dfTime, outVar = "a", xNames = grep(names(dfTime), pattern = "b", value =TRUE), glsSt = corStructTime, nfolds = nfolds, cvType = "random")

To negate baseline differences at different timepoints, it may be useful to center or scale the outcomes in the cross validation. For instance for centering only:

penglsFitCVtimeCenter <- cv.pengls(data = dfTime, outVar = "a", xNames = grep(names(dfTime), pattern = "b", value =TRUE), glsSt = corStructTime, nfolds = nfolds, cvType = "blocked", transFun = function(x) x-mean(x))
penglsFitCVtimeCenter$cvOpt #Better performance
## [1] 0.9949127

Alternatively, the mean squared error (MSE) can be used as loss function, rather than the default \(R^2\):

penglsFitCVtime <- cv.pengls(data = dfTime, outVar = "a", xNames = grep(names(dfTime), pattern = "b", value =TRUE), glsSt = corStructTime, nfolds = nfolds, loss =  "MSE")

4 Session info

sessionInfo()
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] BiocParallel_1.36.0 nlme_3.1-163        pengls_1.8.0       
## 
## loaded via a namespace (and not attached):
##  [1] cli_3.6.1         knitr_1.44        rlang_1.1.1       xfun_0.40        
##  [5] jsonlite_1.8.7    htmltools_0.5.6.1 sass_0.4.7        glmnet_4.1-8     
##  [9] rmarkdown_2.25    grid_4.3.1        evaluate_0.22     jquerylib_0.1.4  
## [13] fastmap_1.1.1     yaml_2.3.7        foreach_1.5.2     compiler_4.3.1   
## [17] codetools_0.2-19  Rcpp_1.0.11       lattice_0.22-5    digest_0.6.33    
## [21] R6_2.5.1          parallel_4.3.1    splines_4.3.1     shape_1.4.6      
## [25] bslib_0.5.1       Matrix_1.6-1.1    tools_4.3.1       iterators_1.0.14 
## [29] survival_3.5-7    cachem_1.0.8