RenewGLM.inloop: Renewable GLM function processing data batches internally

In luolsph/RenewGLM_pkg: Renewable Estimation and Incremental Inference in Generalized Linear Models with Streaming Datasets

Renewable GLM function processing data batches internally

Description

Looping over data batches inside the function and output the final regression coefficients and their standard errors

Usage

RenewGLM_in(B, tempdatadir, type, init = NA, p, intercept = TRUE)

Arguments

B	index for the terminal data batch
tempdatadir	the directory of the streaming datasets, each data batch includes a covariate matrix X and a response vector y
type	the GLM family you want to fit your data to c("gaussian","binomial","poisson")
init	the initial value for regression coefficients (default is a vector of 0)
p	number of coefficients to be estimated, including intercept
intercept	if intercept is included in the model, default is TRUE

Value

coefficient estimates, standard errors and p-values at data batch B

Examples

N=1000
B=10
p=5
n=N/B
beta<-c(0.2,-0.2,0.2,-0.2,0.2)

tempdatadir<-"~/Desktop/tempdata"
datagenerator_in(beta=beta, n=n, p=p, B=B, family="binomial", construct="cs",
    rho=0.5, tempdatadir=tempdatadir)
RenewGLM_in(B, tempdatadir=tempdatadir, "binomial", p=p, intercept=TRUE)
unlink(tempdatadir)

luolsph/RenewGLM_pkg documentation built on April 17, 2023, 12:36 a.m.