Commit ⋅ rauschenberger/palasso

#' n <- 50; p <- 20
#' y <- rbinom(n=n,size=1,prob=0.5)
#' X <- lapply(1:2,function(x) matrix(rnorm(n*p),nrow=n,ncol=p))
#' object <- palasso(y=y,X=X,family="binomial",standard=TRUE)
#' object <- palasso(y=y,X=X,family="binomial")
#' 
palasso <- function(y=y,X=X,max=10,...){
    

@@ -575,7 +575,7 @@

#' Use \link[palasso]{palasso} to fit the paired lasso.
#' 
#' @examples
#' \donttest{set.seed(1)
#' \dontrun{set.seed(1)
#' n <- 30; p <- 40
#' X <- matrix(rpois(n*p,lambda=3),nrow=n,ncol=p)
#' x <- palasso:::.prepare(X)

@@ -154,7 +154,7 @@

    cat("\n")
    
    # non-zero weights
    weights <- weights.palasso(object)
    weights <- weights.palasso(object,model=model)
    name <- colnames(weights)
    number <- colSums(weights!=0)
    cat("non-zero weights:",paste(number,name,collapse=", "),"\n\n")

@@ -163,13 +163,13 @@

    x <- subset.palasso(x=object,model=model)
    id <- list()
    id$min <- which(x$lambda==x$lambda.min)
    id$ose <- which(x$lambda==x$lambda.1se)
    frame <- matrix(NA,nrow=2,ncol=3)
    #id$ose <- which(x$lambda==x$lambda.1se)
    frame <- matrix(NA,nrow=length(id),ncol=3)
    frame[,1] <- vapply(id,function(i) x$lambda[i],numeric(1))
    frame[,2] <- vapply(id,function(i) x$nzero[i],integer(1))
    frame[,3] <- vapply(id,function(i) x$cvm[i],numeric(1))
    rownames(frame) <- c("min","1se")
    colnames(frame) <- c("lambda","nzero",names(x$name))
    rownames(frame) <- names(id) #c("min","1se")
    colnames(frame) <- c("lambda","nzero",x$name) # was names(x$name)
    base::print(round(frame,digits=2))
    return(invisible(NULL))
}

Files		•	•	•	Coverage
R	830	265	0	565	31.93%
Project Totals (3 files)	830	265	0	565	31.93%

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575	575		#' Use \link[palasso]{palasso} to fit the paired lasso.
576	576		#'
577	577		#' @examples
578		-	#' \donttest{set.seed(1)
	578	+	#' \dontrun{set.seed(1)
579	579		#' n <- 30; p <- 40
580	580		#' X <- matrix(rpois(n*p,lambda=3),nrow=n,ncol=p)
581	581		#' x <- palasso:::.prepare(X)

154	154		cat("\n")
155	155
156	156		# non-zero weights
157		-	weights <- weights.palasso(object)
	157	+	weights <- weights.palasso(object,model=model)
158	158		name <- colnames(weights)
159	159		number <- colSums(weights!=0)
160	160		cat("non-zero weights:",paste(number,name,collapse=", "),"\n\n")

163	163		x <- subset.palasso(x=object,model=model)
164	164		id <- list()
165	165		id$min <- which(x$lambda==x$lambda.min)
166		-	id$ose <- which(x$lambda==x$lambda.1se)
167		-	frame <- matrix(NA,nrow=2,ncol=3)
	166	+	#id$ose <- which(x$lambda==x$lambda.1se)
	167	+	frame <- matrix(NA,nrow=length(id),ncol=3)
168	168		frame[,1] <- vapply(id,function(i) x$lambda[i],numeric(1))
169	169		frame[,2] <- vapply(id,function(i) x$nzero[i],integer(1))
170	170		frame[,3] <- vapply(id,function(i) x$cvm[i],numeric(1))
171		-	rownames(frame) <- c("min","1se")
172		-	colnames(frame) <- c("lambda","nzero",names(x$name))
	171	+	rownames(frame) <- names(id) #c("min","1se")
	172	+	colnames(frame) <- c("lambda","nzero",x$name) # was names(x$name)
173	173		base::print(round(frame,digits=2))
174	174		return(invisible(NULL))
175	175		}

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50	50		#' n <- 50; p <- 20
51	51		#' y <- rbinom(n=n,size=1,prob=0.5)
52	52		#' X <- lapply(1:2,function(x) matrix(rnorm(n*p),nrow=n,ncol=p))
53		-	#' object <- palasso(y=y,X=X,family="binomial",standard=TRUE)
	53	+	#' object <- palasso(y=y,X=X,family="binomial")
54	54		#'
55	55		palasso <- function(y=y,X=X,max=10,...){
56	56

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