Multivariate Cluster Elastic Net

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Documentation for package ‘mcen’ version 1.2.1

• DESCRIPTION file.

Help Pages

beta_adjust	Adjusts the value of the coefficients to account for the scaling of x and y.
beta_adjust_bin	Adjusts the value of the binomial coefficients to account for the scaling of x.
bin_horse	The workhorse function for the binomial updates in mcen. It uses IRWLS glmnet updates to solve the regression problem.
CalcHorseBin	Creates the the working response for all responses for glmnet binomial family
CalcHorseEBin	Creates the probabilities and working response for the glmnet update for a given response with a binomial family
cluster	Wrapper function for different clustering methods
cluster.vals	Returns the cluster values from a cv.mcen object.
coef.cv.mcen	Returns the coefficients from the cv.mcen object with the smallest cross-validation error.
coef.mcen	Returns the coefficients from an mcen object.
cv.mcen	Cross validation for mcen function
get_best_cvm	Gets the index position for the model with the smallest cross-validation error.
matrix_multiply	matrix multiply
mcen	Fits an MCEN model
mcen.init	Provides initial estimates for the mcen functionF
mcen_bin_workhorse	Calculates cluster assignment and coefficient estimates for a binomial mcen.
mcen_workhorse	Estimates the clusters and provides the coefficients for an mcen object
predict.cv.mcen	Makes predictions from the model with the smallest cross-validation error.
predict.mcen	predictions from a mcen model
pred_eval	Calculates the out of sample likelihood for an mcen object
pred_eval.mbinom_mcen	Evaluates prediction error for multiple binomial responses.
pred_eval.mgauss_mcen	Calculates the prediction error for a mgauss_mcen object.
print.cv.mcen	Prints nice output for a cv.mcen object.
print.mcen	Prints nice output for an mcen object.
randomly_assign	randomly assign n samples to k groups
SetEq	SetEq test set equivalence of two clustering sets
squared_error	Calculates sum of squared error between two vectors or matrices
vl_binom	Calculates out of sample error on the binomial likelihood