This function returns the p-values for fitted model objects.
p_value(fit, p.kr = FALSE)
fit | A fitted model object of class |
---|---|
p.kr | Logical, if |
A data.frame
with the model coefficients' names (term
),
p-values (p.value
) and standard errors (std.error
).
For linear mixed models (lmerMod
-objects), the computation of
p-values (if p.kr = TRUE
) is based on conditional F-tests
with Kenward-Roger approximation for the df, using the
pbkrtest-package. If pbkrtest is not available or
p.kr = FALSE
, or if x
is a glmerMod
-object,
computation of p-values is based on normal-distribution assumption,
treating the t-statistics as Wald z-statistics.
If p-values already have been computed (e.g. for merModLmerTest
-objects
from the lmerTest-package), these will be returned.
The print()
-method has a summary
-argument, that - in
case p.kr = TRUE
- also prints information on the approximated
degrees of freedom (see 'Examples').
data(efc) # linear model fit fit <- lm(neg_c_7 ~ e42dep + c172code, data = efc) p_value(fit)#> term p.value std.error #> 1 (Intercept) 0.000 0.566 #> 2 e42dep 0.000 0.133 #> 3 c172code 0.207 0.198# Generalized Least Squares fit library(nlme) fit <- gls(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary, correlation = corAR1(form = ~ 1 | Mare)) p_value(fit)#> term p.value std.error #> 1 (Intercept) 0.000 0.665 #> 2 sin(2 * pi * Time) 0.000 0.645 #> 3 cos(2 * pi * Time) 0.198 0.698# lme4-fit library(lme4) sleepstudy$mygrp <- sample(1:45, size = 180, replace = TRUE) fit <- lmer(Reaction ~ Days + (1 | mygrp) + (1 | Subject), sleepstudy) pv <- p_value(fit, p.kr = TRUE)#># normal output pv#> term p.value std.error #> 1 (Intercept) 0 9.790 #> 2 Days 0 0.815# add information on df and t-statistic print(pv, summary = TRUE)#> term p.value std.error df statistic #> 1 (Intercept) 0 9.790 22.938 25.670 #> 2 Days 0 0.815 160.967 12.868