cv.dLDA {MGSDA} | R Documentation |
Cross-validation for MGSDA
Description
Chooses optimal tuning parameter lambda for function dLDA based on the m-fold cross-validation mean squared error
Usage
cv.dLDA(Xtrain, Ytrain, lambdaval = NULL, nl = 100, msep = 5, eps = 1e-6,
l_min_ratio = ifelse(n<p,0.1,0.0001),myseed=NULL,prior=TRUE,rho=1)
Arguments
Xtrain |
A Nxp data matrix; N observations on the rows and p features on the columns |
Ytrain |
A N vector containing the group labels. Should be coded as 1,2,...,G, where G is the number of groups |
lambdaval |
Optional user-supplied sequence of tuning parameters; the default value is NULL and |
nl |
Number of lambda values; the default value is 50 |
msep |
Number of cross-validation folds; the default value is 5 |
eps |
Tolerance level for the convergence of the optimization algorithm; the default value is 1e-6 |
l_min_ratio |
Smallest value for lambda, as a fraction of |
myseed |
Optional specification of random seed for generating the folds; the default value is NULL. |
prior |
A logical indicating whether to put larger weights to the groups of larger size; the default value is TRUE. |
rho |
A scalar that ensures the objective function is bounded from below; the default value is 1. |
Value
lambdaval |
The sequence of tuning parameters used |
error_mean |
The mean cross-validated number of misclassified observations - a vector of length |
error_se |
The standard error associated with each value of |
lambda_min |
The value of tuning parameter that has the minimal mean cross-validation error |
f |
The mean cross-validated number of non-zero features - a vector of length |
Author(s)
Irina Gaynanova
References
I.Gaynanova, J.Booth and M.Wells (2016). "Simultaneous sparse estimation of canonical vectors in the p>>N setting", JASA, 111(514), 696-706.
Examples
### Example 1
n=10
p=100
G=3
ytrain=rep(1:G,each=n)
set.seed(1)
xtrain=matrix(rnorm(p*n*G),n*G,p)
# find optimal tuning parameter
out.cv=cv.dLDA(xtrain,ytrain)
# find V
V=dLDA(xtrain,ytrain,lambda=out.cv$lambda_min)
# number of non-zero features
sum(rowSums(V)!=0)