| hierarchical_lm {lmhelprs} | R Documentation |
Hierarchical Regression Analysis
Description
Do hierarchical regression analysis on two or more models fitted by 'lm()'.
Usage
hierarchical_lm(...)
Arguments
... |
The outputs of |
Details
It conducted hierarchical
regression analysis on two or more
models fitted by stats::lm().
The models must be able to be ordered
from the simplest to the most complex,
with each more complex model formed
by adding one or more terms to the
simpler model.
ANOVA will be conducted to compare each model with the next more complex model in the order, with R-squared change computed.
Value
If the models can be ordered in a
hierarchical way, the output is an
ANOVA table with the R-squared
estimate of each model, and the
R-squared change of each model
compared to the simpler model
preceding this model in the order.
The class of the output is
hierarchical_lm, with a print
method. If the models cannot be
ordered this way, NA is returned.
How it works
It call hierarchical() firsts to
order the outputs for stats::lm(),
If they can be ordered in a
hierarchical way, they will be passed
to stats::anova(). R-squared and
R-squared change will be computed
if they are available in the
summary() method applied to each
model.
Therefore, in principle, this
function can also be used for the
outputs of other model fitting
functions if their outputs have
stats::anova() and summary()
methods.
Check Datasets Used
The comparison is meaningful only
if all models are fitted to the
same datasets. There is not way
to guarantee this is the case, given
only the output of lm(). However,
there are necessary conditions to
claim that the same datasets are used:
the number of cases are the same,
the means, variances, and covariances
of numerical variables, and the
frequency distributions of variables
common to two models are identical.
If at least one of these conditions
is not met, then two models must have
been fitted to two different datasets.
The function will check these conditions and raise an error if any of these necessary conditions are not met.
Author(s)
Shu Fai Cheung https://orcid.org/0000-0002-9871-9448
See Also
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)
hierarchical_lm(lm1, lm3, lm2)
hierarchical_lm(lm1, lm2, lm4)
# The following models will yield an error message:
tryCatch(hierarchical_lm(lm1, lm3, lm2, lm4), error = function(e) e)