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 classPMCMR
,trendPMCMR
orosrt
(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. 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. Forcompare_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 (fordrop
) 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. Ifkeep
is a character vector, every parameter name in the "Parameter" column that matches the regular expression inkeep
will be selected from the returned data frame (and vice versa, all parameter names matchingdrop
will be excluded). Furthermore, ifkeep
has more than one element, these will be merged with anOR
operator into a regular expression pattern like this:"(one|two|three)"
. Ifkeep
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 wheremodel_parameters()
returns multiple columns with parameter components, like inmodel_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 liketype
orparallel
are passed down tobootstrap_model()
. Further non-documented arguments aredigits
,p_digits
,ci_digits
andfooter_digits
to set the number of digits for the output. Ifs_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 (seebayestestR::p_direction()
for details).groups
can be used to group coefficients. It will be passed to the print-method, or can directly be used inprint()
, see documentation inprint.parameters_model()
. 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 setpretty_names = FALSE
to speed up computation of the summary table.
Examples
# \donttest{
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)
}
#>
#> Attaching package: ‘mvtnorm’
#> The following object is masked from ‘package:mclust’:
#>
#> dmvnorm
#>
#> Attaching package: ‘survival’
#> The following object is masked from ‘package:boot’:
#>
#> aml
#>
#> Attaching package: ‘TH.data’
#> The following object is masked from ‘package:MASS’:
#>
#> geyser
#> # Fixed Effects
#>
#> Parameter | Coefficient | SE | 95% CI | t(41) | p
#> ----------------------------------------------------------------------------
#> Agriculture == 0 | -0.17 | 0.07 | [-0.36, 0.01] | -2.45 | 0.080
#> Examination == 0 | -0.26 | 0.25 | [-0.93, 0.41] | -1.02 | 0.785
#> Education == 0 | -0.87 | 0.18 | [-1.36, -0.39] | -4.76 | < .001
#> Catholic == 0 | 0.10 | 0.04 | [ 0.01, 0.20] | 2.95 | 0.023
#> Infant Mortality == 0 | 1.08 | 0.38 | [ 0.07, 2.09] | 2.82 | 0.033
#>
#> p-value adjustment method: single-step
#>
#>
#> Uncertainty intervals (equal-tailed) and p-values (two-tailed) computed
#> using a Wald t-distribution approximation.
if (require("PMCMRplus", quietly = TRUE)) {
model <- suppressWarnings(
kwAllPairsConoverTest(count ~ spray, data = InsectSprays)
)
model_parameters(model)
}
#> Conover's all-pairs test
#>
#> Group1 | Group2 | Statistic | p | alternative | Distribution | p_adjustment
#> --------------------------------------------------------------------------------
#> B | A | 0.89 | 0.988 | two.sided | q | single-step
#> C | A | -13.58 | < .001 | two.sided | q | single-step
#> C | B | -14.47 | < .001 | two.sided | q | single-step
#> D | A | -8.87 | < .001 | two.sided | q | single-step
#> D | B | -9.76 | < .001 | two.sided | q | single-step
#> D | C | 4.71 | 0.017 | two.sided | q | single-step
#> E | A | -10.95 | < .001 | two.sided | q | single-step
#> E | B | -11.84 | < .001 | two.sided | q | single-step
#> E | C | 2.63 | 0.437 | two.sided | q | single-step
#> E | D | -2.09 | 0.681 | two.sided | q | single-step
#> F | A | 1.15 | 0.964 | two.sided | q | single-step
#> F | B | 0.26 | > .999 | two.sided | q | single-step
#> F | C | 14.74 | < .001 | two.sided | q | single-step
#> F | D | 10.02 | < .001 | two.sided | q | single-step
#> F | E | 12.11 | < .001 | two.sided | q | single-step
# }