update to match CRAN requirements

Author: davidruegamer

NAMESPACE

@@ -119,8 +119,10 @@ importFrom(stats,na.omit)
 importFrom(stats,predict)
 importFrom(stats,quantile)
 importFrom(stats,sd)
+importFrom(stats,setNames)
 importFrom(stats,terms.formula)
 importFrom(stats,variable.names)
 importFrom(utils,getS3method)
 importFrom(utils,packageDescription)
+importFrom(utils,relist)
 importFrom(zoo,na.locf)

R/FDboost-package.R

@@ -14,7 +14,7 @@
 #' Details on the functional regression models that can be fitted with \pkg{FDboost}
 #' can be found in Brockhaus et al. (2015, 2017, 2018) and Ruegamer et al. (2018).  
 #' A hands-on tutorial for the package can be found 
-#' in Brockhaus, Ruegamer and Greven (2020), see \url{https://doi.org/10.18637/jss.v094.i10}.
+#' in Brockhaus, Ruegamer and Greven (2020), see <doi:10.18637/jss.v094.i10>.
 #' For density-on-scalar regression models see Maier et al. (2021).
 #' 
 #' Using component-wise gradient boosting as fitting procedure, \pkg{FDboost} relies on 

R/FDboost.R

@@ -431,8 +431,8 @@
 #' @import methods Matrix mboost
 #' @importFrom grDevices heat.colors rgb
 #' @importFrom graphics abline barplot contour legend lines matplot par persp plot points
-#' @importFrom utils getS3method packageDescription
-#' @importFrom stats approx as.formula coef complete.cases fitted formula lm median model.matrix model.weights na.omit predict quantile sd terms.formula variable.names 
+#' @importFrom utils relist getS3method packageDescription
+#' @importFrom stats setNames approx as.formula coef complete.cases fitted formula lm median model.matrix model.weights na.omit predict quantile sd terms.formula variable.names 
 #' @importFrom gamboostLSS GaussianLSS GaussianMu GaussianSigma make.grid cvrisk.mboostLSS mboostLSS_fit
 #' @importFrom stabs stabsel stabsel_parameters 
 #' @importFrom splines bs splineDesign
@@ -471,8 +471,8 @@ FDboost <- function(formula,          ### response ~ xvars
   }
 
   ## check formulas
-  if(class(try(id)) == "try-error") stop("id must either be NULL or a formula object.")
-  if(missing(timeformula) || class(try(timeformula)) == "try-error") 
+  if(inherits(try(id), "try-error")) stop("id must either be NULL or a formula object.")
+  if(missing(timeformula) || inherits(try(timeformula),  "try-error")) 
     stop("timeformula must either be NULL or a formula object.")
     stopifnot(class(formula) == "formula")
   if(!is.null(timeformula)) stopifnot(class(timeformula) == "formula")
@@ -817,7 +817,7 @@ FDboost <- function(formula,          ### response ~ xvars
     }else{
       bl_df <- vector("list", length(tmp))
       bl_df[equalBrackets] <- lapply(tmp[equalBrackets], function(x) try(get_df(x)))
-      bl_df <- unlist(bl_df[equalBrackets & (!sapply(bl_df, class) %in% "try-error")])
+      bl_df <- unlist(bl_df[equalBrackets & (!sapply(bl_df, function(x) inherits(x,  "try-error")))])
       #print(bl_df)
 
       if( !is.null(bl_df) && any(abs(bl_df - bl_df[1]) > .Machine$double.eps * 10^10) ){
@@ -1066,7 +1066,7 @@ FDboost <- function(formula,          ### response ~ xvars
                             silent = offset_control$silent )
         }
 
-        if(any(class(modOffset) == "try-error")){
+        if(inherits(modOffset, "try-error")){
           warning(paste("Could not fit the smooth offset by adaptive splines (default), use a simple spline expansion with 5 df instead.",
                         if(offset_control$cyclic) "This offset is not cyclic!"))
           if(round(length(time)/2) < 8) warning("Most likely because of too few time-points.")
@@ -1121,7 +1121,7 @@ FDboost <- function(formula,          ### response ~ xvars
                             silent = offset_control$silent )
         }
 
-        if(any(class(modOffset) == "try-error")){
+        if(inherits(modOffset, "try-error")){
           warning(paste("Could not fit the smooth offset by adaptive splines (default), use a simple spline expansion with 5 df instead.",
                         if(offset_control$cyclic) "This offset is not cyclic!"))
           if(round(length(time)/2) < 8) warning("Most likely because of too few time-points.")

R/methods.R

@@ -1284,7 +1284,7 @@ coef.FDboost <- function(object, raw = FALSE, which = NULL,
 
     ## short names for the terms, if shortnames() does not work, use the original names
     shrtlbls <- try(unlist(lapply(names(object$baselearner), shortnames)))
-    if(class(shrtlbls)=="try-error") shrtlbls <- names(object$baselearner) 
+    if(inherits(shrtlbls, "try-error")) shrtlbls <- names(object$baselearner) 
 
     ###### just return the data that is used for the prediction
     if(returnData){
@@ -1697,14 +1697,14 @@ plot.FDboost <- function(x, raw = FALSE, rug = TRUE, which = NULL,
       rm(temp)
     }
 
-    if(class(terms)!="list") terms <- list(terms)
+    if(!inherits(terms,"list")) terms <- list(terms)
     if(mstop(x) > 0 && length(which) == 1 && which == 0) terms[[1]] <- offset
     if(length(which) == 1 && length(terms[[1]]) == 1 && terms[[1]] == 0){ terms[[1]] <- rep(0, l=length(x$yind)) }
 
     #if(length(which)==1 && !any(class(x)=="FDboostLong")) terms <- list(terms) 
 
     shrtlbls <- try(coef(x, which=which, computeCoef=FALSE))# get short names
-    if(class(shrtlbls) == "try-error"){
+    if(inherits(shrtlbls, "try-error")){
       shrtlbls <- names(x$baselearner)[which[which!=0]]
       if(0 %in% which) shrtlbls <- c("offset", which)
     }

man/FDboost-package.Rd

@@ -82,7 +82,8 @@ par(opar)
 fac <- factorize(m)
 
 vi <- as.data.frame(varimp(fac$cov))
-lattice::barchart(variable ~ reduction, group = blearner, vi, stack = TRUE)
+# if(require(lattice))
+#   barchart(variable ~ reduction, group = blearner, vi, stack = TRUE)
 
 cbind(d^2, sort(vi$reduction, decreasing = TRUE)[1:3])
 
@@ -162,11 +163,13 @@ for(w in 1:length(wch)) {
                                 main = names(fac$resp$baselearner[wch[w]]))
 
   lines(sort(griddata$t), 
-        ratio*predict(fac2$resp, which = wch[w])[order(griddata$t)], col = cols[w], lty = 2)
+        ratio*predict(fac2$resp, which = wch[w])[order(griddata$t)], 
+        col = cols[w], lty = 2)
   plot(fac$cov, which = wch[w], 
        main = names(fac$cov$baselearner[wch[w]]))
   this_x <- fac2$cov$model.frame(which = wch[w])[[1]][[1]]
-    lines(sort(this_x), 1/ratio*predict(fac2$cov, which = wch[w])[order(this_x)], col = cols[w], lty = 1)
+    lines(sort(this_x), 1/ratio*predict(fac2$cov, which = wch[w])[order(this_x)], 
+          col = cols[w], lty = 1)
 }
 par(opar)
 

man/factorize.Rd

@@ -65,7 +65,8 @@ par(opar)
 fac <- factorize(m)
 
 vi <- as.data.frame(varimp(fac$cov))
-lattice::barchart(variable ~ reduction, group = blearner, vi, stack = TRUE)
+# if(require(lattice))
+#   barchart(variable ~ reduction, group = blearner, vi, stack = TRUE)
 
 cbind(d^2, vi$reduction[c(1:2, 10)])
 

tests/factorize_test_irregular.R

@@ -10,6 +10,8 @@
 \usepackage{authblk}
 \usepackage[left=25mm, right=25mm, top=20mm, bottom=20mm]{geometry}
 %\usepackage[nolists]{endfloat}
+%\usepackage{mathspec}
+\usepackage{dsfont}
 \usepackage{bbm}
 
 %\VignetteEngine{knitr::knitr}
@@ -211,7 +213,7 @@ In the continuous case, the sum is proportional to the integral numerically, if
 Thus, using \texttt{bbsc} is suitable in this case, as well.
 Second, we must specify the B-spline basis in \texttt{bbsc} appropriately. 
 The continuous case is straightforward, e.g., by using cubic B-splines.
-In our discrete case, a suitable (unconstrained) basis is $(\mathbbm{1}_{\{1\}}, \ldots, \mathbbm{1}_{\{12\}}) \in L^2 ( \delta)^{12}$, where $\mathbbm{1}_{A}$ denotes the indicator function of $A \in \mathcal{A}$.
+In our discrete case, a suitable (unconstrained) basis is $(\mathds{1}_{\{1\}}, \ldots, \mathds{1}_{\{12\}}) \in L^2 ( \delta)^{12}$, where $\mathds{1}_{A}$ denotes the indicator function of $A \in \mathcal{A}$.
 This results in the identity matrix as design matrix.
 In \texttt{bbs()} (or \texttt{bbsc()}, which yields the corresponding constrained basis), this can be achieved using \texttt{degree = 1} with knots equal to $\mathcal{T}$.