[R] understanding the parameters of 'spca' function of 'elasticnet' package
Hi all, i am trying to use the 'spca' function of 'elasticnet' package. But i am confused with the parameter 'para'. Basically the parameter 'para' depends on the parameter 'sparse'. If sparse=penalty, para is a vector of 1-norm penalty parameters. If sparse=varnum, para deï¬nes the number of sparse loadings to be obtained. As my understanding, if i use sparse=penalty i have to set the amount of variance for each component in a vector. But how could i know the amount of variance in this case? If i use sparse='varnum', i have to set the number of features i want to extract from each component in a vector. But i dont have these numbers even because i dont know how many key features are present in each component. Thanks -- Sent from my Android device with K-9 Mail. Please excuse my brevity. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] understanding the parameters of 'spca' function of 'elasticnet' package
Hi all, i am trying to use the 'spca' function of 'elasticnet' package. But i am confused with the parameter 'para'. Basically the parameter 'para' depends on the parameter 'sparse'. If sparse=penalty, para is a vector of 1-norm penalty parameters. If sparse=varnum, para defines the number of sparse loadings to be obtained. As my understanding, if i use sparse=penalty i have to set the amount of variance for each component in a vector. But how could i know the amount of variance in this case? If i use sparse='varnum', i have to set the number of features i want to extract from each component in a vector. But i dont have these numbers even because i dont know how many key features are present in each component. Thanks __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
