cv.glmTLP: Cross-validation for glmTLP

Description Usage Arguments Details Value Author(s) References Examples

View source: R/cvglmTLP.R

Description

Does k-fold cross-validation for glmTLP, produces a plot, and returns a value for lambda with pre-specified tau.

Usage

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cv.glmTLP(x, y, family=c("gaussian","binomial","poisson","multinomial","cox","mgaussian"),
nfolds = 10, weights, offset=NULL, lambda, tau = 0.3, 
nlambda=100, penalty.factor = rep(1, nvars), 
 lambda.min.ratio=ifelse(nobs<nvars,1e-3,1e-4),
standardize=TRUE,intercept=TRUE,dfmax=nvars+1,
pmax=min(dfmax*2+20,nvars), lower.limits=-Inf,upper.limits=Inf,
standardize.response=FALSE, maxIter=100, Tol=1e-4)

Arguments

x

x matrix as in glmnet.

y

response variable. Quantitative for family="gaussian", or family="poisson" (non-negative counts). For family="binomial" should be either a factor with two levels, or a two-column matrix of counts or proportions (the second column is treated as the target class; for a factor, the last level in alphabetical order is the target class). For family="multinomial", can be a nc>=2 level factor, or a matrix with nc columns of counts or proportions. For either "binomial" or "multinomial", if y is presented as a vector, it will be coerced into a factor. For family="cox", y should be a two-column matrix with columns named 'time' and 'status'. The latter is a binary variable, with '1' indicating death, and '0' indicating right censored. The function Surv() in package survival produces such a matrix. For family="mgaussian", y is a matrix of quantitative responses.

family

Response type (see above)

nfolds

number of folds - default is 10. Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is nfolds=3

weights

Observation weights; defaults to 1 per observation

offset

Offset vector (matrix) as in glmnet

lambda

Optional user-supplied lambda sequence; default is NULL, and glmTLP chooses its own sequence

tau

Tuning parameter.

nlambda

The number of lambda values - default is 100.

penalty.factor

Separate penalty factors can be applied to each coefficient. This is a number that multiplies lambda to allow differential shrinkage. Can be 0 for some variables, which implies no shrinkage, and that variable is always included in the model. Default is 1 for all variables (and implicitly infinity for variables listed in exclude). Note: the penalty factors are internally rescaled to sum to nvars, and the lambda sequence will reflect this change.

lambda.min.ratio

Smallest value for lambda, as a fraction of lambda.max, the (data derived) entry value (i.e. the smallest value for which all coefficients are zero). The default depends on the sample size nobs relative to the number of variables nvars. If nobs > nvars, the default is 0.0001, close to zero. If nobs < nvars, the default is 0.01. A very small value of lambda.min.ratio will lead to a saturated fit in the nobs < nvars case. This is undefined for "binomial" and "multinomial" models, and glmnet will exit gracefully when the percentage deviance explained is almost 1.

standardize

Logical flag for x variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is standardize=TRUE. If variables are in the same units already, you might not wish to standardize. See details below for y standardization with family="gaussian".

intercept

Should intercept(s) be fitted (default=TRUE) or set to zero (FALSE)

dfmax

Limit the maximum number of variables in the model. Useful for very large nvars, if a partial path is desired.

pmax

Limit the maximum number of variables ever to be nonzero

lower.limits

Vector of lower limits for each coefficient; default -Inf. Each of these must be non-positive. Can be presented as a single value (which will then be replicated), else a vector of length nvars

upper.limits

Vector of upper limits for each coefficient; default Inf. See lower.limits

standardize.response

This is for the family="mgaussian" family, and allows the user to standardize the response variables

maxIter

Maximum iteration for TLP.

Tol

Tolerance.

Details

The function runs glmTLP nfolds+1 times; the first to get the lambda sequence, and then the remainder to compute the fit with each of the folds omitted. The error is accumulated, and the average error and standard deviation over the folds is computed. Note that cv.glmnet does NOT search for values for tau. A specific value should be supplied, else tau= 0.3 is assumed by default.

Value

an object of class "cv.glmnet" is returned, which is a list with the ingredients of the cross-validation fit. Although the implementation is different, we try to mimic returning as "cv.glment" in a popular package glmnet such that users can use truncated lasso as using elastic net.

lambda

the values of lambda used in the fits.

cvm

The mean cross-validated error - a vector of length length(lambda).

cvsd

estimate of standard error of cvm.

cvup

upper curve = cvm+cvsd.

cvlo

lower curve = cvm-cvsd.

nzero

number of non-zero coefficients at each lambda.

name

a text string indicating type of measure (for plotting purposes).

glmnet.fit

a fitted glmnet object for the full data.

lambda.min

value of lambda that gives minimum cvm.

lambda.1se

largest value of lambda such that error is within 1 standard error of the minimum.

fit.preval

if keep=TRUE, this is the array of prevalidated fits. Some entries can be NA, if that and subsequent values of lambda are not reached for that fold

foldid

if keep=TRUE, the fold assignments used

Author(s)

Chong Wu
Maintainer: Chong Wu <[email protected]>

References

Xiaotong Shen , Wei Pan and Yunzhang Zhu (2012) Likelihood-Based Selection and Sharp Parameter Estimation, Journal of the American Statistical Association, 107:497, 223-232

Examples

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	data("QuickStartExample")
	fit = cv.glmTLP(x,y,tau = 1,nfolds = 2, lambda = c(0.1,05)) 
	#We set nflods and lambda just to speed it up 
	# and pass the CRAN check. You should either use
	# the default setting or search a larger space.

glmtlp documentation built on May 2, 2019, 6:36 a.m.