sprinter (version 1.1.0)

screen.main.glm: Adaptive function for screening main effects and fitting a generalized linear model

Description

The adaptive function selects the most important main effects and fits a generalized linear model for the usage in the argument screen.main in the function sprinter. Two methods are available for this screening step: Either a generalized linear model with variable selection by adjusted univariate p-values (fit.uniGlm) can be performed or a model is build via GAMBoost (fit.GAMBoost).

Usage

fit.GAMBoost(time, status, x, unpen.index = NULL, 
             seed = 123, 
             stepno = NULL,
             penalty = 100,
             maxstepno = 200,
             standardize = T, 
             criterion = 'deviance',
             family = gaussian(),
             trace = T, ...)
fit.uniGlm(time, status, x, unpen.index = NULL, 
              method = 'bonferroni', 
              family = gaussian(), 
              sig = 0.05, ...)

Arguments

time

vector of length n specifying the response.

status

censoring indicator. These functions are not constructed for time-to-event data. Therefore, all entities of this vector are zero.

x

n * p matrix of covariates.

unpen.index

vector with indices of mandatory covariates, where parameter estimation should be performed unpenalized.

seed

Seed for random number generator.

stepno

number of boosting steps.

criterion

indicates the criterion to be used for selection in each boosting step. "pscore" corresponds to the penalized score statistics, "score" to the un-penalized score statistics. Different results will only be seen for un-standardized covariates ("pscore" will

maxstepno

maximum number of boosting steps to evaluate, i.e, the returned "optimal" number of boosting steps will be in the range [0,maxstepno].

standardize

logical value indicating whether covariates should be standardized for estimation. This does not apply for mandatory covariates, i.e., these are not standardized.

penalty

penalty value for the update of an individual smooth function in each boosting step.

family

a description of the error distribution. This can be a character string naming a family function, a family function or the result of a call to a family function.

trace

logical value indicating whether progress in estimation should be indicated by printing the name of the covariate updated.

method

method for adjusting p-values. A variable is selected if its adjusted p-value is less than sig.

sig

selection level. A variable is selected if its adjusted p-value is less than sig

...

further arguments passed to methods.

Value

• The adaptive function returns the following values:
• model
  Generalized linear model. fit.uniGlm returns an object of class glm and fit.GAMBoost returns an object of class GAMBoost.
• xnames
  vector of length p containing the names of the covariates.
• indmain
  vector of length p containing the indices of the selected covariates.
• beta
  vector of length p containing the coefficients of the selected covariates.

Details

fit.uniGlm and fit.GAMBoost are adapted as functions for the usage of screening main effects in the function sprinter. Both approaches fit a generalized linear model. fit.uniGlm{ fit.uniCox fits a generalized linear regression model after a variable selection step. Therefore univariate regression models are performed for each variable. All variables with univariate adjusted p-values less than sig are selected for the multivariate model. The variables with indices unpen.index are mandatory for the multivariate regression model. } fit.GAMBoost{ fit.GAMBoost fits a generalized regression model by using GAMBoost. If the number of boosting steps is not specified in stepno the step number is evaluated by cross validation. See more information about fitting a generalized regression model by GAMBoost in GAMBoost. } Implementing new functions for the argument screen.main{ New functions for screening potential main effects can be implemented by the user. Therefore, the function must be constructed in a way that a generalized linear model is generated. This model should be returned together with the names of the variables used in the model, called xnames, and their corresponding indices, called indmain. The following arguments must be enclosed in this function: ll{ time vector of length n specifying the outcome. status censoring indicator, which is always NULL in the case fitting a generalized linear models x n * p matrix of covariates. unpen.index vector with indices of mandatory covariates. } With this instructions the user can create new functions for screening main effects. To set further arguments for this function create a list of arguments, which can be quoted in args.screen.main. In the next step the screened main effects will be used for orthogonalizing the data by computing residuals corresponding to the selected main effects and the mandatory covariates. }

See Also

p.adjust, glm, GAMBoost