View source: R/bruceR-stats_5_advance.R
med_summary | R Documentation |
Tidy report of mediation analysis,
which is performed using the mediation
package.
med_summary(model, digits = 3, nsmall = digits, file = NULL)
model |
Mediation model built using |
digits, nsmall |
Number of decimal places of output. Default is |
file |
File name of MS Word ( |
Invisibly return a data frame containing the results.
PROCESS
## Not run: library(mediation) # ?mediation::mediate ## Example 1: OLS Regression ## Bias-corrected and accelerated (BCa) bootstrap confidence intervals ## Hypothesis: Solar radiation -> Ozone -> Daily temperature lm.m = lm(Ozone ~ Solar.R + Month + Wind, data=airquality) lm.y = lm(Temp ~ Ozone + Solar.R + Month + Wind, data=airquality) set.seed(123) # set a random seed for reproduction med = mediate(lm.m, lm.y, treat="Solar.R", mediator="Ozone", sims=1000, boot=TRUE, boot.ci.type="bca") med_summary(med) ## Example 2: Multilevel Linear Model (Linear Mixed Model) ## (models must be fit using "lme4::lmer" rather than "lmerTest::lmer") ## Monte Carlo simulation (quasi-Bayesian approximation) ## (bootstrap method is not applicable to "lmer" models) ## Hypothesis: Crips -> Sweetness -> Preference (for carrots) data = lmerTest::carrots # long-format data data = na.omit(data) # omit missing values lmm.m = lme4::lmer(Sweetness ~ Crisp + Gender + Age + (1 | Consumer), data=data) lmm.y = lme4::lmer(Preference ~ Sweetness + Crisp + Gender + Age + (1 | Consumer), data=data) set.seed(123) # set a random seed for reproduction med.lmm = mediate(lmm.m, lmm.y, treat="Crisp", mediator="Sweetness", sims=1000) med_summary(med.lmm) ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.