R/methods_PMCMRplus.R
, R/methods_multcomp.R
model_parameters.glht.Rd
Parameters from Hypothesis Testing.
# S3 method for PMCMR
model_parameters(model, ...)
# S3 method for glht
model_parameters(model, ci = 0.95, exponentiate = FALSE, verbose = TRUE, ...)
model  Object of class 

...  Arguments passed to or from other methods. For instance, when

ci  Confidence Interval (CI) level. Default to 0.95 (95%). 
exponentiate  Logical, indicating whether or not to exponentiate the
the coefficients (and related confidence intervals). This is typical for
logistic regression, or more generally speaking, for models with log
or logit links. Note: Deltamethod 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 
verbose  Toggle warnings and messages. 
A data frame of indices related to the model's parameters.
# \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: ‘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  0.079
#> Examination == 0  0.26  0.25  [0.93, 0.41]  1.02  0.785
#> Education == 0  0.87  0.18  [1.35, 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  0.033
#>
#> pvalue adjustment method: singlestep
#>
if (require("PMCMRplus", quietly = TRUE)) {
model < kwAllPairsConoverTest(count ~ spray, data = InsectSprays)
model_parameters(model)
}
#> Warning: Ties are present. Quantiles were corrected for ties.
#> Conover's allpairs test
#>
#> Group1  Group2  Statistic  p  alternative  Distribution  p_adjustment
#> 
#> B  A  0.89  0.988  two.sided  q  singlestep
#> C  A  13.58  < .001  two.sided  q  singlestep
#> C  B  14.47  < .001  two.sided  q  singlestep
#> D  A  8.87  < .001  two.sided  q  singlestep
#> D  B  9.76  < .001  two.sided  q  singlestep
#> D  C  4.71  0.017  two.sided  q  singlestep
#> E  A  10.95  < .001  two.sided  q  singlestep
#> E  B  11.84  < .001  two.sided  q  singlestep
#> E  C  2.63  0.437  two.sided  q  singlestep
#> E  D  2.09  0.681  two.sided  q  singlestep
#> F  A  1.15  0.964  two.sided  q  singlestep
#> F  B  0.26  > .999  two.sided  q  singlestep
#> F  C  14.74  < .001  two.sided  q  singlestep
#> F  D  10.02  < .001  two.sided  q  singlestep
#> F  E  12.11  < .001  two.sided  q  singlestep
# }