Parameters from Hypothesis Testing.

## Usage

```
# S3 method for 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.**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()`

.

## 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 | 0.024
#> Infant Mortality == 0 | 1.08 | 0.38 | [ 0.07, 2.09] | 2.82 | 0.032
#>
#> 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
# }
```