Commit ⋅ rauschenberger/starnet

#' for the base and meta learners, respectively.
#' 
#' @examples
#' \dontshow{
#' if(!grepl('SunOS',Sys.info()['sysname'])){
#' set.seed(1)
#' n <- 50; p <- 100
#' y <- rnorm(n=n)
#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
#' object <- starnet(y=y,X=X,family="gaussian")
#' object <- starnet(y=y,X=X,family="gaussian")}}
#' \donttest{
#' set.seed(1)
#' n <- 50; p <- 100
#' y <- rnorm(n=n)
#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
#' object <- starnet(y=y,X=X,family="gaussian")}
#' 
starnet <- function(y,X,family="gaussian",nalpha=21,alpha=NULL,nfolds=10,foldid=NULL,type.measure="deviance",alpha.meta=1,penalty.factor=NULL,intercept=NULL,upper.limit=NULL,unit.sum=NULL,...){
  

@@ -316,12 +324,21 @@

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#' and \code{none} (intercept-only model).
#' 
#' @examples
#' \dontshow{
#' if(!grepl('SunOS',Sys.info()['sysname'])){
#' set.seed(1)
#' n <- 50; p <- 100
#' y <- rnorm(n=n)
#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
#' object <- starnet(y=y,X=X)
#' y_hat <- predict(object,newx=X[c(1),,drop=FALSE])}}
#' \donttest{
#' set.seed(1)
#' n <- 50; p <- 100
#' y <- rnorm(n=n)
#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
#' object <- starnet(y=y,X=X)
#' y_hat <- predict(object,newx=X[c(1),,drop=FALSE])
#' y_hat <- predict(object,newx=X[c(1),,drop=FALSE])}
#' 
predict.starnet <- function(object,newx,type="response",nzero=NULL,...){
  if(length(list(...))!=0){warning("Ignoring argument.",call.=FALSE)}

@@ -384,12 +401,21 @@

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#' respectively.
#' 
#' @examples
#' \dontshow{
#' if(!grepl('SunOS',Sys.info()['sysname'])){
#' set.seed(1)
#' n <- 50; p <- 100
#' y <- rnorm(n=n)
#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
#' object <- starnet(y=y,X=X)
#' coef <- coef(object)}}
#' \donttest{
#' set.seed(1)
#' n <- 50; p <- 100
#' y <- rnorm(n=n)
#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
#' object <- starnet(y=y,X=X)
#' coef <- coef(object)
#' coef <- coef(object)}
#' 
coef.starnet <- function(object,nzero=NULL,...){
  if(length(list(...))!=0){warning("Ignoring argument.",call.=FALSE)}

@@ -446,12 +472,21 @@

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#' Vector containing intercept and slopes from the meta learner.
#' 
#' @examples
#' \dontshow{
#' if(!grepl('SunOS',Sys.info()['sysname'])){
#' set.seed(1)
#' n <- 50; p <- 100
#' y <- rnorm(n=n)
#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
#' object <- starnet(y=y,X=X)
#' weights(object)}}
#' \donttest{
#' set.seed(1)
#' n <- 50; p <- 100
#' y <- rnorm(n=n)
#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
#' object <- starnet(y=y,X=X)
#' weights(object)
#' weights(object)}
#' 
weights.starnet <- function(object,...){
  if(length(list(...))!=0){warning("Ignoring argument.",call.=FALSE)}

@@ -480,12 +515,21 @@

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#' Prints "stacked gaussian/binomial/poisson elastic net".
#' 
#' @examples
#' \dontshow{
#' if(!grepl('SunOS',Sys.info()['sysname'])){
#' set.seed(1)
#' n <- 50; p <- 100
#' y <- rnorm(n=n)
#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
#' object <- starnet(y=y,X=X)
#' print(object)
#' print(object)}}
#' \donttest{
#' set.seed(1)
#' n <- 50; p <- 100
#' y <- rnorm(n=n)
#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
#' object <- starnet(y=y,X=X)
#' print(object)}
#' 
print.starnet <- function(x,...){
  cat(paste0("stacked \"",x$info$family,"\" elastic net"),"\n")

@@ -533,12 +577,13 @@

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#' with the maximum number of non-zero coefficients shown in the column name.
#' 
#' @examples
#' \dontshow{
#' if(!grepl('SunOS',Sys.info()['sysname'])){
#' set.seed(1)
#' n <- 50; p <- 20
#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
#' y <- rnorm(n=n,mean=rowSums(X[,1:20]))
#' \dontshow{
#' loss <- cv.starnet(y=y,X=X,nfolds.ext=2,nfolds.int=3)}
#' loss <- cv.starnet(y=y,X=X,nfolds.ext=2,nfolds.int=3)}}
#' \donttest{
#' loss <- cv.starnet(y=y,X=X)}
#' 

Files		•	•	•	Coverage
R/functions.R	374	188	0	186	50.27%
Project Totals (1 files)	374	188	0	186	50.27%

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316	324		#' and \code{none} (intercept-only model).
317	325		#'
318	326		#' @examples
	327	+	#' \dontshow{
	328	+	#' if(!grepl('SunOS',Sys.info()['sysname'])){
	329	+	#' set.seed(1)
	330	+	#' n <- 50; p <- 100
	331	+	#' y <- rnorm(n=n)
	332	+	#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
	333	+	#' object <- starnet(y=y,X=X)
	334	+	#' y_hat <- predict(object,newx=X[c(1),,drop=FALSE])}}
	335	+	#' \donttest{
319	336		#' set.seed(1)
320	337		#' n <- 50; p <- 100
321	338		#' y <- rnorm(n=n)
322	339		#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
323	340		#' object <- starnet(y=y,X=X)
324		-	#' y_hat <- predict(object,newx=X[c(1),,drop=FALSE])
	341	+	#' y_hat <- predict(object,newx=X[c(1),,drop=FALSE])}
325	342		#'
326	343		predict.starnet <- function(object,newx,type="response",nzero=NULL,...){
327	344		if(length(list(...))!=0){warning("Ignoring argument.",call.=FALSE)}

384	401		#' respectively.
385	402		#'
386	403		#' @examples
	404	+	#' \dontshow{
	405	+	#' if(!grepl('SunOS',Sys.info()['sysname'])){
	406	+	#' set.seed(1)
	407	+	#' n <- 50; p <- 100
	408	+	#' y <- rnorm(n=n)
	409	+	#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
	410	+	#' object <- starnet(y=y,X=X)
	411	+	#' coef <- coef(object)}}
	412	+	#' \donttest{
387	413		#' set.seed(1)
388	414		#' n <- 50; p <- 100
389	415		#' y <- rnorm(n=n)
390	416		#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
391	417		#' object <- starnet(y=y,X=X)
392		-	#' coef <- coef(object)
	418	+	#' coef <- coef(object)}
393	419		#'
394	420		coef.starnet <- function(object,nzero=NULL,...){
395	421		if(length(list(...))!=0){warning("Ignoring argument.",call.=FALSE)}

446	472		#' Vector containing intercept and slopes from the meta learner.
447	473		#'
448	474		#' @examples
	475	+	#' \dontshow{
	476	+	#' if(!grepl('SunOS',Sys.info()['sysname'])){
	477	+	#' set.seed(1)
	478	+	#' n <- 50; p <- 100
	479	+	#' y <- rnorm(n=n)
	480	+	#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
	481	+	#' object <- starnet(y=y,X=X)
	482	+	#' weights(object)}}
	483	+	#' \donttest{
449	484		#' set.seed(1)
450	485		#' n <- 50; p <- 100
451	486		#' y <- rnorm(n=n)
452	487		#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
453	488		#' object <- starnet(y=y,X=X)
454		-	#' weights(object)
	489	+	#' weights(object)}
455	490		#'
456	491		weights.starnet <- function(object,...){
457	492		if(length(list(...))!=0){warning("Ignoring argument.",call.=FALSE)}

480	515		#' Prints "stacked gaussian/binomial/poisson elastic net".
481	516		#'
482	517		#' @examples
	518	+	#' \dontshow{
	519	+	#' if(!grepl('SunOS',Sys.info()['sysname'])){
483	520		#' set.seed(1)
484	521		#' n <- 50; p <- 100
485	522		#' y <- rnorm(n=n)
486	523		#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
487	524		#' object <- starnet(y=y,X=X)
488		-	#' print(object)
	525	+	#' print(object)}}
	526	+	#' \donttest{
	527	+	#' set.seed(1)
	528	+	#' n <- 50; p <- 100
	529	+	#' y <- rnorm(n=n)
	530	+	#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
	531	+	#' object <- starnet(y=y,X=X)
	532	+	#' print(object)}
489	533		#'
490	534		print.starnet <- function(x,...){
491	535		cat(paste0("stacked \"",x$info$family,"\" elastic net"),"\n")

533	577		#' with the maximum number of non-zero coefficients shown in the column name.
534	578		#'
535	579		#' @examples
	580	+	#' \dontshow{
	581	+	#' if(!grepl('SunOS',Sys.info()['sysname'])){
536	582		#' set.seed(1)
537	583		#' n <- 50; p <- 20
538	584		#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
539	585		#' y <- rnorm(n=n,mean=rowSums(X[,1:20]))
540		-	#' \dontshow{
541		-	#' loss <- cv.starnet(y=y,X=X,nfolds.ext=2,nfolds.int=3)}
	586	+	#' loss <- cv.starnet(y=y,X=X,nfolds.ext=2,nfolds.int=3)}}
542	587		#' \donttest{
543	588		#' loss <- cv.starnet(y=y,X=X)}
544	589		#'

83	83		#' for the base and meta learners, respectively.
84	84		#'
85	85		#' @examples
	86	+	#' \dontshow{
	87	+	#' if(!grepl('SunOS',Sys.info()['sysname'])){
86	88		#' set.seed(1)
87	89		#' n <- 50; p <- 100
88	90		#' y <- rnorm(n=n)
89	91		#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
90		-	#' object <- starnet(y=y,X=X,family="gaussian")
	92	+	#' object <- starnet(y=y,X=X,family="gaussian")}}
	93	+	#' \donttest{
	94	+	#' set.seed(1)
	95	+	#' n <- 50; p <- 100
	96	+	#' y <- rnorm(n=n)
	97	+	#' X <- matrix(rnorm(n*p),nrow=n,ncol=p)
	98	+	#' object <- starnet(y=y,X=X,family="gaussian")}
91	99		#'
92	100		starnet <- function(y,X,family="gaussian",nalpha=21,alpha=NULL,nfolds=10,foldid=NULL,type.measure="deviance",alpha.meta=1,penalty.factor=NULL,intercept=NULL,upper.limit=NULL,unit.sum=NULL,...){
93	101

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