refactor: useless argument for cv.glmnet

R/rsimilarity.R

@@ -53,7 +53,7 @@
 #' @importFrom stats lm predict complete.cases cor as.formula
 #' @export
 rsimilarity <- function(Rn, predictors, newpredictors, FUN=invRMSE, power=0.5, symmetrize=mean, model="glmnet",
-		args_glmnet=list(alpha=0.5, s="lambda.min", lower.limits=0), verbose = TRUE, seed=NULL){
+		args_glmnet=list(s="lambda.min", lower.limits=0), verbose = TRUE, seed=NULL){
 
 	# Checks inputs
 	if(inherits(Rn, "units")) Rn <- units::drop_units(Rn)
@@ -112,7 +112,7 @@ rsimilarity <- function(Rn, predictors, newpredictors, FUN=invRMSE, power=0.5, s
 		if(is.character(args_glmnet[["s"]])){s <- glm_similarity[[args_glmnet[["s"]]]]}else{s <- args_glmnet[["s"]]}
 		similarity <- as.vector(stats::predict(glm_similarity, newx=as.matrix(newpredictors), s=s))
 		coef <- as.matrix(coef(glm_similarity, s=s))[-1,]
-		# Infinite similarity of predictors leads to infinite predicted similarity (instead of NA) 
+		# Infinite similarity of predictors leads to infinite predicted similarity (instead of NA)
 		inf_similarity <- apply(newpredictors, MARGIN=1, FUN = function(x) any(is.infinite(x)))
 		if(any(inf_similarity)) similarity[inf_similarity] <- Inf
 		# Compute R2 manually
@@ -139,7 +139,7 @@ rsimilarity <- function(Rn, predictors, newpredictors, FUN=invRMSE, power=0.5, s
 		coef <- as.matrix(coef(lm_similarity))[-1,]
 		r2 <- summary(lm_similarity)$r.squared
 		pvalue <- summary(lm_similarity)$coefficients[-1, "Pr(>|t|)"]
-		# Infinite similarity of predictors leads to infinite predicted similarity (instead of NA) 
+		# Infinite similarity of predictors leads to infinite predicted similarity (instead of NA)
 		inf_similarity <- apply(newpredictors, MARGIN=1, FUN = function(x) any(is.infinite(x)))
 		if(any(inf_similarity)) similarity[inf_similarity] <- Inf
 		# Indicators of the distribution of the weights