glmTLP: Fit a GLM with truncated lasso regularization

Description Usage Arguments Details Value Author(s) References Examples

View source: R/glmTLP.R

Description

Fit a generalized linear model via penalized maximum likelihood. The regularization path is computed for the truncated lasso at a grid of values for the regularization parameter lambda. Can deal with all shapes of data, including very large sparse data matrices. Fit linear, logistic and multinomial, poisson, and Cox regression models.

Usage

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glmTLP(x, y, family=c("gaussian","binomial","poisson","multinomial","cox","mgaussian"),
    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

input matrix, of dimension nobs x nvars; each row is an observation vector. Can be in sparse matrix format (inherit from class "sparseMatrix" as in package Matrix; not yet available for family="cox")

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)

weights

observation weights. Can be total counts if responses are proportion matrices. Default is 1 for each observation

offset

A vector of length nobs that is included in the linear predictor (a nobs x nc matrix for the "multinomial" family). Useful for the "poisson" family (e.g. log of exposure time), or for refining a model by starting at a current fit. Default is NULL. If supplied, then values must also be supplied to the predict function.

tau

Write something about tau

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.

lambda

A user supplied lambda sequence. Typical usage is to have the program compute its own lambda sequence based on nlambda and lambda.min.ratio. Supplying a value of lambda overrides this. WARNING: use with care. Do not supply a single value for lambda (for predictions after CV use predict() instead). Supply instead a decreasing sequence of lambda values. glmnet relies on its warms starts for speed, and its often faster to fit a whole path than compute a single fit.

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

Write something about the details.

Value

An object that inherits from glmnet.

call

the call that produced this object

a0

Intercept sequence of length length(lambda)

beta

For "elnet", "lognet", "fishnet" and "coxnet" models, a nvars x length(lambda) matrix of coefficients, stored in sparse column format ("CsparseMatrix"). For "multnet" and "mgaussian", a list of nc such matrices, one for each class.

lambda

The actual sequence of lambda values used.

dev.ratio

The fraction of (null) deviance explained (for "elnet", this is the R-square). The deviance calculations incorporate weights if present in the model. The deviance is defined to be 2*(loglike_sat - loglike), where loglike_sat is the log-likelihood for the saturated model (a model with a free parameter per observation). Hence dev.ratio=1-dev/nulldev.

nulldev

Null deviance (per observation). This is defined to be 2*(loglike_sat -loglike(Null)); The NULL model refers to the intercept model, except for the Cox, where it is the 0 model.

df

The number of nonzero coefficients for each value of lambda.

dim

dimension of coefficient matrix (ices)

nobs

number of observations

npasses

total passes over the data summed over all lambda values

offset

a logical variable indicating whether an offset was included in the model

jerr

error flag, for warnings and errors (largely for internal debugging).

Author(s)

Chong Wu, Wei Pan
Maintainer: Chong Wu <wuxx0845@umn.edu>

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 = glmTLP(x,y)

ChongWu-Biostat/glmtlp documentation built on May 6, 2019, 11:18 a.m.