speedglm: add1&drop1 – R documentation

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add1&drop1

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", "Rao", "LRT", 
                         "Chisq", "F"), x = NULL, k = 2, weights=rep(1,object$n), ...) 
## S3 method for class 'speedglm'
drop1(object, scope, scale = 0, test = c("none", "Rao", "LRT", 
                         "Chisq", "F"), k = 2, weights=rep(1,object$n), ...)
## 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`.
`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.
`weights`	the model weights, if provided in the speedglm object
`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()

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

## Not run: 
set.seed(10)
n <- 1000
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)^2, data = da)
m1 <- step(m0,scope=~(s1+s2+s3)^3)
m1

m2 <- speedglm(y ~ 1, data = da, model=TRUE, y=TRUE)
m2.1 <- add1(m2,scope=~(s1+s2+s3)^2, data = da)
m3 <- step(m2,scope=~(s1+s2+s3)^3)
m3

## End(Not run)

speedglm

Fitting Linear and Generalized Linear Models to Large Data Sets

v0.3-3

GPL

Authors

Marco Enea [aut, cre], Ronen Meiri [ctb] (on behalf of DMWay Analytics LTD), Tomer Kalimi [ctb] (on behalf of DMWay Analytics LTD)

Initial release

2020-12-09