sjstats (version 0.17.4)

p_value: Get p-values from regression model objects

Description

This function returns the p-values for fitted model objects.

Usage

p_value(fit, ...)
# S3 method for lmerMod
p_value(fit, p.kr = FALSE, ...)

Arguments

fit

A model object.

...

Currently not used.

p.kr

Logical, if TRUE, the computation of p-values is based on conditional F-tests with Kenward-Roger approximation for the df (see 'Details').

Value

A data.frame with the model coefficients' names (term), p-values (p.value) and standard errors (std.error).

Details

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'). A shortcut is the summary()-method, which simply calls print(..., summary = TRUE).

Examples

# NOT RUN {
data(efc)
# linear model fit
fit <- lm(neg_c_7 ~ e42dep + c172code, data = efc)
p_value(fit)

# 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)

# 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

# add information on df and t-statistic
print(pv, summary = TRUE)
# or
summary(pv)

# }