This function is a minor modification of the lm() function
to allow the use of a pre-specified design matrix. It is not intended for
public use but only to support modelTest.lm.
Usage
lm2(
formula,
data,
subset,
weights,
na.action,
model = TRUE,
x = FALSE,
y = FALSE,
qr = TRUE,
singular.ok = TRUE,
contrasts = NULL,
offset,
designMatrix,
yObserved,
...
)Arguments
- formula
An object of class "formula" although it is only minimally used
- data
the dataset
- subset
subset
- weights
any weights
- na.action
Defaults to
na.omit- model
defaults to
TRUE- x
defaults to
FALSE- y
defaults to
FALSE- qr
defaults to
TRUE- singular.ok
defaults to
TRUE- contrasts
defaults to
NULL- offset
missing by default
- designMatrix
a model matrix / design matrix (all numeric, pre coded if applicable for discrete variables)
- yObserved
the observed y values
- ...
additional arguments
Examples
mtcars$cyl <- factor(mtcars$cyl)
m <- lm(mpg ~ hp * cyl, data = mtcars)
x <- model.matrix(m)
y <- mtcars$mpg
m2 <- JWileymisc:::lm2(mpg ~ 1 + cyl + hp:cyl, data = mtcars,
designMatrix = x[, -2, drop = FALSE],
yObserved = y)
anova(m, m2)
#> Analysis of Variance Table
#>
#> Model 1: mpg ~ hp * cyl
#> Model 2: mpg ~ 1 + cyl + hp:cyl
#> Res.Df RSS Df Sum of Sq F Pr(>F)
#> 1 26 238.47
#> 2 27 294.20 -1 -55.739 6.0773 0.02061 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
rm(m, m2, x, y)