Check a list of 'lm' objects to see whether they are be ordered in a way for doing hierarchical regression analysis.
Arguments
- ...
The outputs of
lm(), that is, one or morelm-class objects. The outputs of other model fitting functions may also be used, but should be used with cautions. Please refer to the "How it works" section in "Details."
Value
If the models can be ordered in a
hierarchical way, the output is a
list of the original lm
outputs, sorted from the model with
the smallest number of terms to the
model with the largest number of
terms. If the models cannot be
ordered this way, NA is returned.
Details
Two models can be compared by hierarchical regression analysis if one model can be formed by adding one or more terms to the other model.
This function checks whether a list
of lm outputs can be ordered from
the simplest model to the most
complex model, with a more complex
model formed by adding one or more
terms to a simpler model.
How it works
It extracts the terms in each model
by stats::terms() and then extracts
the labels of the terms by
labels(). The labels are then used
to determine the hierarchical order.
Therefore, in principle, this
function can be used for the outputs
of other model fitting functions as
long as their outputs support the
stats::terms() and the labels can
be used to determine hierarchical
order of two models.
Author
Shu Fai Cheung https://orcid.org/0000-0002-9871-9448
Examples
dat <- data_test1
lm1 <- lm(y ~ x1 + x2, dat)
lm2 <- lm(y ~ x1 + x2 + x3 + x4, dat)
lm3 <- lm(y ~ x1 + cat1 + cat2 + x2 + x3 + x4, dat)
lm4 <- lm(y ~ x1 + x2*x3 + x4, dat)
# The order of entry does not matter
hierarchical(lm1, lm4, lm2)
#> [[1]]
#>
#> Call:
#> lm(formula = y ~ x1 + x2, data = dat)
#>
#> Coefficients:
#> (Intercept) x1 x2
#> -0.1231 0.6016 0.2797
#>
#>
#> [[2]]
#>
#> Call:
#> lm(formula = y ~ x1 + x2 + x3 + x4, data = dat)
#>
#> Coefficients:
#> (Intercept) x1 x2 x3 x4
#> -0.04715 0.41484 0.27693 0.35712 0.36060
#>
#>
#> [[3]]
#>
#> Call:
#> lm(formula = y ~ x1 + x2 * x3 + x4, data = dat)
#>
#> Coefficients:
#> (Intercept) x1 x2 x3 x4 x2:x3
#> -0.04627 0.41504 0.27698 0.35490 0.35950 -0.01706
#>
#>
# The following three models yield NA
hierarchical(lm3, lm4, lm2)
#> [1] NA