From 5fa8edf1be860c5b106ff7486b85f4431b3d74ef Mon Sep 17 00:00:00 2001
From: Ajinkya Kokandakar <ajinkyakokandakar@gmail.com>
Date: Fri, 26 Apr 2024 10:52:15 -0500
Subject: [PATCH] Replaced Matrix::rBind with base function rbind, removed
import statement for rBind, changed docstrings
---
NAMESPACE | 1 -
R/pls.R | 11 +++++------
R/templateApproach.R | 2 +-
3 files changed, 6 insertions(+), 8 deletions(-)
diff --git a/NAMESPACE b/NAMESPACE
index d8da47e..4b37428 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -20,7 +20,6 @@ export(rLmer)
importFrom(Matrix,Cholesky)
importFrom(Matrix,Diagonal)
importFrom(Matrix,bdiag)
-importFrom(Matrix,rBind)
importFrom(Matrix,sparse.model.matrix)
importFrom(lme4,findbars)
importFrom(lme4,nobars)
diff --git a/R/pls.R b/R/pls.R
index 5b2c253..9da53b3 100644
--- a/R/pls.R
+++ b/R/pls.R
@@ -1,5 +1,4 @@
##' @importMethodsFrom Matrix t %*% crossprod diag tcrossprod solve determinant update
-##' @importFrom Matrix bdiag rBind Diagonal Cholesky sparse.model.matrix
##' @importFrom lme4 findbars nobars subbars
NULL
@@ -341,9 +340,9 @@ Zsection <- function(grp,mm) {
## # eg: if m = 2, nrow(Jt) = 10, we want the order:
## # 1,11,2,12,3,13,...,20
## rinds <- as.vector(matrix(seq_len(m*nrow(Jt)), nrow=m, byrow=TRUE))
-## # rBind products of Jt and a diagonal matrix
+## # rbind products of Jt and a diagonal matrix
## # for each column, then rearrange rows.
-## do.call(rBind,lapply(seq_len(m), function(j) Jt %*% Diagonal(x=mm[,j])))[rinds,]
+## do.call(rbind,lapply(seq_len(m), function(j) Jt %*% Diagonal(x=mm[,j])))[rinds,]
## }
@@ -459,7 +458,7 @@ blockLambdat <- function(nl, nc) {
##' The basic idea of this function is to call \code{\link{Zsection}} and
##' \code{\link{blockLambdat}} once for each random effects term (ie.
##' each list element in \code{grps} and \code{mms}). The results of
-##' \code{\link{Zsection}} for each term are \code{rBind}ed together.
+##' \code{\link{Zsection}} for each term are \code{rbind}ed together.
##' The results of \code{\link{blockLambdat}} are \code{bdiag}ed
##' together, unless all terms have only a single column ('predictor')
##' in which case a diagonal matrix is created directly.
@@ -478,8 +477,8 @@ blockLambdat <- function(nl, nc) {
mkRanefRepresentation <- function(grps, mms) {
# compute transposed random effects model
# matrix, Zt (Class="dgCMatrix"), by
- # rBinding the sections for each term.
- ll <- list(Zt = do.call(rBind, mapply(Zsection, grps, mms)))
+ # rbinding the sections for each term.
+ ll <- list(Zt = do.call(rbind, mapply(Zsection, grps, mms)))
# number of levels in each grouping factor
nl <- sapply(grps, function(g) length(levels(g)))
# number of columns in each model matrix
diff --git a/R/templateApproach.R b/R/templateApproach.R
index 07e2d1e..25d36f1 100644
--- a/R/templateApproach.R
+++ b/R/templateApproach.R
@@ -59,7 +59,7 @@ mkZtSection <- function(grp,mm) {
##' @export
mkZt <- function(grp,mm){
ZtSections <- mapply(mkZtSection, grp, mm, SIMPLIFY=FALSE)
- do.call(rBind, ZtSections)
+ do.call(rbind, ZtSections)
}
##' Make a single template for a relative covariance factor