Mediation: Causal Mediation Analysis

In stp4/stp25stat: Functions for statistical computations

Description

Einfache Mediation und Moderationsanalyse mit Sobel-Test Mediation ist gegeben wenn alle Modelle signifikant (1) und (4)

Usage

Mediation(
  y_x,
  y_xm,
  m_x,
  y_inter_xm = NULL,
  data = NULL,
  caption = "",
  note = "",
  output = which_output(),
  include.parameter = TRUE,
  include.p = TRUE,
  include.sobl = TRUE,
  z.transform = TRUE,
  treat = model_info(m_x)$x[1],
  mediator = model_info(m_x)$y,
  type = "long2",
  ...
)

Sobel_Test(y_x, y_xm, m_x, treat, mediator, digits = 2)

Arguments

y_x	y~x formula oder lm-Objekt
y_xm	y~x+m formula oder lm-Objekt
m_x	m~x formula oder lm-Objekt
y_inter_xm	m~x*m formula oder lm-Objekt
data	Daten bei der Verwendendung von Formeln
caption, note, output	an Output
include.parameter, include.sobl	was soll Ausgegeben werden
z.transform	Transformation bei der Verwendendung von Formeln
treat, mediator	Einfluss und Mediatorals string
type, include.p, ...	fuer Regression APA_Tabelle()
digits	Nachkommastellen
y_x, y_xm, m_x	in Sobel-Test lm-Objekte

Details

(1) Y~X (2) Y~X+M (wenn hier M signifikant ist => partielle Mediation) (4) M~X

Moderation ist gegeben wenn die Interaktion (X:M) signifikant ist

(3) Y~ X + M + X:M

Ein Beispiel mit Laavan findet sich unter https://paolotoffanin.wordpress.com/2017/05/06/multiple-mediator-analysis-with-lavaan/

Value

list(param, sobel, methode)

data.frame

Examples

#' 
## https://paolotoffanin.wordpress.com/2017/05/06/multiple-mediator-analysis-with-lavaan/
## simpleMediation
#Projekt("html", "Test")
set.seed(1234)
Motivation <- rnorm(100)
Lerndauer <- 0.5 * Motivation + rnorm(100)
Note <- 0.7 * Lerndauer + rnorm(100)
dat <- data.frame(Note = Note,
                  Lerndauer = Lerndauer,
                  Motivation = Motivation)

dat <- stp25aggregate::Label(dat,
                             Note = "Note (Ziel-Variable)" ,
                             Lerndauer = "Lerndauer (Mediator)",
                             Motivation = "Motivation (Einfluss-Variable)")
Tabelle2(dat, Note, Lerndauer, Motivation)
Mediation(
  Note ~ Motivation,
  Note ~ Motivation + Lerndauer,
  Lerndauer ~ Motivation,
  Note ~ Motivation * Lerndauer,
  dat,
  digits = 2,
  caption = "Einfache Mediation und Moderationsanalyse mit Sobel-Test"
)

model <- ' # direct effect
Note ~ c*Motivation
# mediator
Lerndauer ~ a*Motivation
Note ~ b*Lerndauer
# indirect effect (a*b)
indir := a*b
# total effect
total := c + (a*b)
'

#xyplot(Note ~ value |
#         variable,
#       melt2(dat, Motivation, Lerndauer, by =  ~ Note))


fit <-  lavaan::sem(model, data = dat)
APA2(fit)
#End()
#' 

# Daten -------------------------------------------------------------------


set.seed(1234)
n <- 2 * 10

Motivation <- rnorm(n)
Lerndauer <- 0.5 * Motivation + rnorm(n)
Note <- 0.7 * Lerndauer + rnorm(n)

t0 <- data.frame(
  id = 1:n,
  sex = gl(2, n / 2, labels = c("m", "f")),
  time = 0,
  Note = Note,
  Lerndauer = Lerndauer,
  Motivation = Motivation
)

t1 <- rnorm(n, 0.1, 0.1)
t1 <- data.frame(
  id = 1:n,
  sex = gl(2, n / 2, labels = c("m", "f")),
  time = 1,
  Note = Note * .1 + t1,
  Lerndauer = Lerndauer * .1 + t1,
  Motivation = Motivation * .1 + t1
)

t2 <- rnorm(n, 0.2, 0.5)
t2 <- data.frame(
  id = 1:n,
  sex = gl(2, n / 2, labels = c("m", "f")),
  time = 2,
  Note = Note * .15 + t2  ,
  Lerndauer = Lerndauer * .15 + t2,
  Motivation = Motivation * .15 + t2
)
dat <- rbind(t0, t1, t2)





#model1 <- lm(Note ~ Motivation +time, dat)
#model2 <- lm(Note ~ Motivation + Lerndauer+time, dat)
#model3 <- lm(Lerndauer ~ Motivation+time, dat)


model1 <- lmerTest::lmer(Note ~ Motivation +time+ (1 | id), dat)
model2 <- lmerTest::lmer(Note ~ Motivation + Lerndauer +time+ (1 | id), dat)
model3 <- lmerTest::lmer(Lerndauer ~ Motivation +time+ (1 | id), dat)


Sobel_Test(model1, model2, model3,
           treat="Motivation",
           mediator="Lerndauer" )

stp4/stp25stat documentation built on Sept. 17, 2021, 2:03 p.m.