Merge branch 'master' into seq-along

Author: davidruegamer

R/FDboost.R

@@ -582,7 +582,7 @@ FDboost <- function(formula,          ### response ~ xvars
   allCovs <- unique(c(nameid, all.vars(formula)))
   if(length(allCovs) > 1){
     data <- data[allCovs[!allCovs %in% c(yname, nameyind)] ]
-    if( any(is.na(names(data))) ) data <- data[ !is.na(names(data)) ]
+    if( anyNA(names(data)) ) data <- data[ !is.na(names(data)) ]
   }else{
     data <- list(NULL)  # <SB> intercept-model without covariates
   } 
@@ -636,7 +636,7 @@ FDboost <- function(formula,          ### response ~ xvars
         stopifnot(all(length(response) == sapply(time, length)) & length(response) == length(id)) else
           stopifnot(length(response) == length(time) & length(response) == length(id))
 
-      if(any(is.na(response))) warning("For non-grid observations the response should not contain missing values.")
+      if(anyNA(response)) warning("For non-grid observations the response should not contain missing values.")
       if( !all(sort(unique(id)) == seq_along(unique(id))) ) stop("id has to be integers 1, 2, 3,..., N.")
 
       nr <- length(response) # total number of observations
@@ -1045,7 +1045,7 @@ FDboost <- function(formula,          ### response ~ xvars
         }
         # meanY <- sapply(1:nc, function(i) offsetFun(responseInter[,i], 1*!is.na(responseInter[,i])))
 
-        if( is.null(meanY) ||  any(is.na(meanY)) ){
+        if( is.null(meanY) || anyNA(meanY) ){
           warning("Mean offset cannot be computed by family@offset(). Use a weighted mean instead.")
           meanY <- c()
           for(i in 1:nc){

R/baselearners.R

@@ -124,11 +124,11 @@ integrationWeights <- function(X1, xind, id = NULL){
   }
 
   # taking into account missing values
-  if(any(is.na(X1))){
+  if(anyNA(X1)){
     Lneu <- sapply(1:nrow(X1), function(i){
       x <- X1[i,]
 
-      if(!any(is.na(x))){
+      if(!anyNA(x)){
         l <- L[i, ] # no missing values in curve i
       }else{
         xindL <- xind # lower

R/factorize.R

@@ -335,7 +335,7 @@ NULL
 #'
 predict.FDboost_fac <- function(object, newdata = NULL, which = NULL, ...) {
   w <- object$which(which)
-  if(any(is.na(w)))
+  if(anyNA(w))
     stop("Don't know 'which' base-learner is meant.")
   names(w) <- names(object$baselearner)[w]
   drop(sapply(w,
@@ -349,7 +349,7 @@ predict.FDboost_fac <- function(object, newdata = NULL, which = NULL, ...) {
 #' @rdname predict.FDboost_fac
 plot.FDboost_fac <- function(x, which = NULL, main = NULL, ...) {
   w <- x$which(which, usedonly = TRUE)
-  if(any(is.na(w)))
+  if(anyNA(w))
     stop(paste("Don't know which base-learner is meant by:",
          which[which.min(is.na(w))]))
   if(is.null(main))

R/hmatrix.R

@@ -82,7 +82,7 @@ hmatrix <- function(time, id, x, argvals=1:ncol(x),
   x <- matrix(x, ncol=ncol(x), nrow=nrow(x))  
 
   #### check argvals and x
-  if( any(duplicated(argvals)) ){
+  if(anyDuplicated(argvals) > 0){
     stop("argvals contains duplicates.")
   } 
   if( is.unsorted(argvals) ){

R/utilityFunctions.R

@@ -1178,7 +1178,7 @@ reweightData <- function(data, argvals, vars,
     my_temp_idvars <- temp_idvars
     i <- 1
     ## add 0.1^1 to duplicates, 0.1^1 + 0.1^2 = 0.11 to triplicates, ...
-    while(any(duplicated(my_index_long))){ # loop until no more duplicates in the data  
+    while(anyDuplicated(my_index_long) > 0){ # loop until no more duplicates in the data
       my_temp_idvars[duplicated(my_index_long)] <- my_temp_idvars[duplicated(my_index_long)] + 0.1^i
       my_index_long[duplicated(my_index_long)] <- my_index_long[duplicated(my_index_long)] + 0.1^i
       i <- i + 1