data.org: Data Organization and Identify Potential Mediators

Description

Do a preliminary data analysis to identify potential mediators and covariates. Each variable listed in jointm is forced in the final estimation model as a mediator. Also organize the data into a format that can be directly used for the mediation analysis functions.

Usage

data.org(x, y, pred, mediator=NULL, contmed = NULL, binmed = NULL,  binref = rep(1,length(binmed)),  catmed = NULL,  catref = rep(1,length(catmed)), jointm = NULL, refy=NULL, family1 = NULL, predref = NULL,  alpha = 0.1, alpha2 = 0.1)

Arguments

a data frame contains the predictor, all potential mediators and covariates

the vector of outcome variable.

pred

the column number that locates the predictor in x or the predictor name.

mediator

the list of mediators (column numbers in x or by variable names). The mediators to be checked can be identified by "contmed", "binmed" and "catmed", or by this argument, "mediator", where binary and categorical mediators in x are identified as factors or characters, the reference group is the first level of the factor or factorized character. If the reference groups need to be changed, the binary or categorical mediators can be listed in binmed or catmed, and the corresponding reference group in binref or catref.

contmed

a vector of variable names or column numbers that locate the potential continuous mediators in x.

binmed

a vector of column numbers that locate the potential binary mediators in x.

binref

the defined reference groups of the binary potential mediators in binmed.

catmed

a vector of variable names or column numbers that locate the potential categorical mediators in x.

catref

the defined reference groups of the categorical potential mediators in catmed.

jointm

a list that identifies the mediators that need to be considered jointly, where the first item indicates the number of groups of mediators to be considered jointly, and each of the following items identifies the variable names or column numbers of the mediators in x for each group of joint mediators.

refy

if y is binary, the reference group of y. The default is the first level of as.factor(y).

family1

define the conditional distribution of y given x, and the linkage function that links the mean of y with the system component in generalized linear model. The default value of family1 is binomial(link = "logit") for binary y, and gaussian(link="identity") for continuous y.

predref

if the predictor is binary, identify the reference group of the binary predictor. The default is the first level of the predictor.

alpha

the significance level at which to test if the potential mediators (identified by contmed, binmed, and catmed) can be used as a covariate or mediator in estimating y when all variables in x are included in the model. The default value is alpha=0.1

alpha2

the significant level at which to test if a potential mediator is related with the predictor. The default value is alpha2=0.1.

Value

data.org returns a list with the organized data and identifiers of the potential mediators in the organized data set.

References

Baron, R.M., and Kenny, D.A. (1986) . The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers Soc Psychol, 51(6), 1173-1182.

Examples

Run this code

data("weight_behavior")
#binary x
 #binary y
 x=weight_behavior[,2:14]
 y=weight_behavior[,15]
 data.b.b.2.1<-data.org(x,y,pred=2,mediator=6:13,jointm=list(n=1,j1=c(6,8,10)),predref="M",
   alpha=0.4,alpha2=0.4)
 summary(data.b.b.2.1)
 #Or you can specify the potential mediators and change the reference 
 #group for binary or categorical mediators. In the following code,
 #potential continuous mediators are columns 8,9,10,12, and 13 of x,
 #binary mediators are columns 7 and 11, and categorical mediator is
 #column 6 of x with 1 to be the reference group for all categorical
 #and binary mediators. 
  data.b.b.2<-data.org(x,y,pred=2,contmed=c(8:10,12:13),binmed=c(7,11),
   binref=c(1,1),catmed=6,catref=1,jointm=list(n=1,j1=c(6,8,10)),predref="M",
   alpha=0.4,alpha2=0.4) 
  summary(data.b.b.2)
 #use the mediator argument instead of contmet, binmed and catmed
 
 #continuous y
 x=weight_behavior[,2:14]
 y=weight_behavior[,1]
 data.b.c.2<-data.org(x,y,pred=2,mediator=6:13,jointm=list(n=1,j1=8:10), 
   predref="M",alpha=0.4,alpha2=0.4)
 summary(data.b.c.2)
 
#continuous x
 #binary y
 x=weight_behavior[,2:14]
 y=weight_behavior[,15]
 data.c.b.2<-data.org(x,y,pred=1,mediator=6:13,catref=1,jointm=list(n=2,j1=8:10,j2=c(6,8)),
   alpha=0.4,alpha2=0.4)
 summary(data.c.b.2)
 
 #continuous y
 x=weight_behavior[,2:14]
 y=weight_behavior[,1]
 data.c.c.2<-data.org(x,y,pred=1,contmed=c(8:10,12:13),binmed=c(7,11),
   binref=c(1,1),catmed=6,catref=1,jointm=list(n=2,j1=8:10,j2=c(6,8)),
   alpha=0.4,alpha2=0.4)
 summary(data.c.c.2)