Parameters from Hypothesis Testing — model_parameters.glht • parameters

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

# \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
# }