add1&drop1: Wrappers to the 'add1' and 'drop1' methods for speedlm and...

In speedglm: Fitting Linear and Generalized Linear Models to Large Data Sets

Wrappers to the add1 and drop1 methods for speedlm and speedglm objects

Description

These are adviced to be used for speedlm and speedglm models fitted on moderately large data sets. It is also possible to use stepAIC function from package MASS.

Usage

## S3 method for class 'speedlm'
## S3 method for class 'speedlm'
add1(object, scope, scale = 0, test = c("none", "Chisq","F"), 
                       x = NULL, k = 2, data, ...)
## S3 method for class 'speedlm'
drop1(object, scope, scale = 0, all.cols = TRUE,
                        test = c("none","Chisq", "F"), k = 2, data, ...)
## S3 method for class 'speedlm'
extractAIC(fit, scale = 0, k=2,...)
## S3 method for class 'speedlm'
nobs(object, use.fallback = FALSE, ...)

## S3 method for class 'speedglm'
## S3 method for class 'speedglm'
add1(object, scope, scale = 0, test = c("none", "LRT", 
                         "Chisq", "F"), x = NULL, k = 2, ...) 
## S3 method for class 'speedglm'
drop1(object, scope, scale = 0, test = c("none", "LRT", 
                         "Chisq", "F"), k = 2, ...)
## S3 method for class 'speedglm'
extractAIC(fit, scale = 0, k=2,...)
## S3 method for class 'speedglm'
nobs(object, use.fallback = FALSE, ...) 

Arguments

object	a speedlm or speedglm object for which model=TRUE was previously set to.
fit	a speedlm or speedglm object
scope	see add1 from package stats.
scale	see add1 from package stats.
all.cols	see drop1 from package stats.
test	see add1 from package stats. Currently, test "Rao" is not implemented.
x	see add1 from package stats.
k	see add1 from package stats.
data	the data that the model was previously fitted to. If not provided, these will be searched in the parent environment.
use.fallback	logical. Should fallback methods be used to try to guess the value?
...	further optional arguments.

Details

It is possible to use functions step() and stepAIC() for both speedlm and speedglm objects but objects fitted using updateWithMoreData().

Value

An object of classes "anova" and "data.frame" summarizing the differences in fit between the models.

Warnings

Note that these functions have been poorly tested and need to be checked out more carefully.

Author(s)

Ronen Meiri and Marco Enea

Examples

set.seed(10)
n <- 50
k <- 3
x <- round(matrix(rnorm(n * k), n, k), digits = 3)
beta <- c(0.05,0.5,0.8,1.3,1.8)
y <- drop(tcrossprod(cbind(1,x,x[,2]*x[,3]),t(beta))) + rnorm(n,,0.2)
colnames(x) <- c("s1", "s2", "s3") 
da <- data.frame(y, x)

m0 <- speedlm(y ~ 1, data = da,model=TRUE, y=TRUE)
m0.1 <- add1(m0,scope=~s1+s2+s3, data = da)
m1 <- step(m0,scope=~s1+s2+s3)
m1

speedglm documentation built on May 31, 2023, 7:58 p.m.