| model_parameters.glht | R Documentation |
Parameters from Hypothesis Testing
Description
Parameters from Hypothesis Testing.
Usage
## S3 method for class 'glht'
model_parameters(
model,
ci = 0.95,
exponentiate = FALSE,
keep = NULL,
drop = NULL,
verbose = TRUE,
...
)
Arguments
model |
Object of class multcomp::glht() (multcomp)
or of class PMCMR, trendPMCMR or osrt (PMCMRplus).
|
ci |
Confidence Interval (CI) level. Default to 0.95 (95%).
|
exponentiate |
Logical, indicating whether or not to exponentiate the
coefficients (and related confidence intervals). This is typical for
logistic regression, or more generally speaking, for models with log or
logit links. It is also recommended to use exponentiate = TRUE for models
with log-transformed response values. For models with a log-transformed
response variable, when exponentiate = TRUE, a one-unit increase in the
predictor is associated with multiplying the outcome by that predictor's
coefficient. Note: Delta-method standard errors are also computed (by
multiplying the standard errors by the transformed coefficients). This is
to mimic behaviour of other software packages, such as Stata, but these
standard errors poorly estimate uncertainty for the transformed
coefficient. The transformed confidence interval more clearly captures this
uncertainty. For compare_parameters(), exponentiate = "nongaussian"
will only exponentiate coefficients from non-Gaussian families.
|
keep |
Character containing a regular expression pattern that
describes the parameters that should be included (for keep) or excluded
(for drop) in the returned data frame. keep may also be a
named list of regular expressions. All non-matching parameters will be
removed from the output. If keep is a character vector, every parameter
name in the "Parameter" column that matches the regular expression in
keep will be selected from the returned data frame (and vice versa,
all parameter names matching drop will be excluded). Furthermore, if
keep has more than one element, these will be merged with an OR
operator into a regular expression pattern like this: "(one|two|three)".
If keep is a named list of regular expression patterns, the names of the
list-element should equal the column name where selection should be
applied. This is useful for model objects where model_parameters()
returns multiple columns with parameter components, like in
model_parameters.lavaan(). Note that the regular expression pattern
should match the parameter names as they are stored in the returned data
frame, which can be different from how they are printed. Inspect the
$Parameter column of the parameters table to get the exact parameter
names.
|
drop |
See keep.
|
verbose |
Toggle warnings and messages.
|
... |
Arguments passed to or from other methods. For instance, when
bootstrap = TRUE, arguments like type or parallel are passed down to
bootstrap_model().
Further non-documented arguments are:
-
digits, p_digits, ci_digits and footer_digits to set the number of
digits for the output. groups can be used to group coefficients. These
arguments will be passed to the print-method, or can directly be used in
print(), see documentation in print.parameters_model().
If s_value = TRUE, the p-value will be replaced by the S-value in the
output (cf. Rafi and Greenland 2020).
-
pd adds an additional column with the probability of direction (see
bayestestR::p_direction() for details). Furthermore, see 'Examples' for
this function.
For developers, whose interest mainly is to get a "tidy" data frame of
model summaries, it is recommended to set pretty_names = FALSE to speed
up computation of the summary table.
|
Value
A data frame of indices related to the model's parameters.
Examples
if (require("multcomp", quietly = TRUE)) {
# multiple linear model, swiss data
lmod <- lm(Fertility ~ ., data = swiss)
mod <- glht(
model = lmod,
linfct = c(
"Agriculture = 0",
"Examination = 0",
"Education = 0",
"Catholic = 0",
"Infant.Mortality = 0"
)
)
model_parameters(mod)
}
if (require("PMCMRplus", quietly = TRUE)) {
model <- suppressWarnings(
kwAllPairsConoverTest(count ~ spray, data = InsectSprays)
)
model_parameters(model)
}
