1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
|
#'
#'
#' Function to performed exploratory mediation with continuous and categorical variables
#'
#' @param data Name of the dataset
#' @param iv Name of independent variable
#' @param mediators Name of mediators
#' @param dv Name of dependent variable
#' @param covariates Name of covariates to be included in model.
#' @param type What type of penalty. Options include lasso, ridge, and enet.
#' @param nfolds Number of cross-validation folds.
#' @param epsilon Threshold for determining whether effect is 0 or not.
#' @param seed Set seed to control CV results
#' @export
#' @examples
#' \dontrun{
#'# example
#'library(ISLR)
#'College1 = College[which(College$Private=="Yes"),]
#'Data = data.frame(scale(College1[c("Grad.Rate","Accept","Outstate","Room.Board","Books","Expend")]))
#'Data$Grad.Rate <- ifelse(Data$Grad.Rate > 0,1,0)
#'Data$Grad.Rate <- as.factor(Data$Grad.Rate)
#'#lavaan model with all mediators
#'model1 <-
#' ' # direct effect (c_prime)
#'Grad.Rate ~ c_prime*Accept
#'# mediators
#'Outstate ~ a1*Accept
#'Room.Board ~ a2*Accept
#'Books ~ a3*Accept
#'Expend ~ a6*Accept
#'Grad.Rate ~ b1*Outstate + b2*Room.Board + b3*Books + b6*Expend
#'# indirect effects (a*b)
#'a1b1 := a1*b1
#'a2b2 := a2*b2
#'a3b3 := a3*b3
#'a6b6 := a6*b6
#'# total effect (c)
#'c := c_prime + (a1*b1) + (a2*b2) + (a3*b3) + (a6*b6)
#''
#'#p-value approach using delta method standard errors
#'fit.delta = sem(model1,data=Data,fixed.x=TRUE,ordered="Grad.Rate")
#'summary(fit.delta)
#'
#'#xmed()
#'
#'iv <- "Accept"
#'dv <- "Grad.Rate"
#'mediators <- c("Outstate","Room.Board","Books","Expend")
#'
#'out <- xmed(Data,iv,mediators,dv)
#'out
#'}
xmed = function (data, iv, mediators, dv, covariates=NULL, type = "lasso", nfolds = 10,
epsilon = 0.001, seed = NULL)
{
res <- list()
Data <- data
if (type == "lasso") {
alpha = 1
}
else if (type == "ridge") {
alpha = 0
}
else if (type == "enet") {
alpha = 0.5
}
var.check = function(data) {
data = as.data.frame(data)
num.response.options = flag = integer(ncol(data))
for (i in 1:ncol(data)) {
num.response.options[i] = flag[i] = NA
num.response.options[i] = length(unique(data[, i]))
if (is.factor(data[, i]) & num.response.options[i] >
2) {
flag[i] = 2
}
else if (num.response.options[i] == 2) {
flag[i] = 1
}
else if (num.response.options[i] != 2) {
flag[i] = 0
}
}
return(flag)
}
check.out <- var.check(data[, c(iv, mediators, dv)])
if (any(check.out == 2)) {
stop("Factor variables with > 2 response options need to be recoded as integer or numeric variables")
}
data.proc <- caret::preProcess(Data[, c(iv, mediators, dv)])
data2 <- predict(data.proc, Data[, c(iv, mediators, dv)])
iv.mat <- as.matrix(data2[, iv])
mediators.mat <- as.matrix(data2[, mediators])
dv.mat <- as.matrix(data2[, dv])
if (sum(is.na(iv.mat)) > 0 | sum(is.na(mediators.mat)) > 0 | sum(is.na(dv.mat)) > 0) {
stop("Missing values are not allowed")
}
if (var.check(dv.mat) == 0) {
dv.class = "gaussian"
}
else if (var.check(dv.mat) == 1) {
dv.class = "binomial"
}
if(!is.null(seed)){
set.seed(seed)
}
b.cv.lasso = glmnet::cv.glmnet(mediators.mat, dv.mat, alpha = alpha, family = dv.class, standardize = FALSE,
lambda = exp(seq(log(0.001), log(5), length.out = 100)), nfolds=nfolds,
penalty.factor = c(rep(1, ncol(data) - 2), 0))
b.coefs = coef(b.cv.lasso, s = b.cv.lasso$lambda.min)[-1,1]
a.cv.lasso = a.fit.lasso = vector("list", ncol(mediators.mat))
a.lambda = numeric(ncol(mediators.mat))
for (i in 1:ncol(mediators.mat)) {
if (var.check(mediators.mat[, i]) == 0) {
med.class = "gaussian"
}
else if (var.check(mediators.mat[, i]) == 1) {
med.class = "binomial"
}
if(!is.null(seed)){
set.seed(seed)
}
a.cv.lasso[[i]] = glmnet::cv.glmnet(as.matrix(cbind(rnorm(nrow(data), 1, 1e-04), iv.mat)), mediators.mat[, i],
alpha = alpha, family = med.class, standardize = FALSE, nfolds=nfolds,
lambda = exp(seq(log(0.001), log(5), length.out = 100)),
intercept = F, penalty.factor = c(0, 1))
a.lambda[i] = a.cv.lasso[[i]]$lambda.min
}
a.coefs = numeric(length(b.coefs))
for (i in 1:length(a.coefs)) {
if (!is.null(a.cv.lasso[[i]])) {
a.coefs[i] = coef(a.cv.lasso[[i]], s = a.cv.lasso[[i]]$lambda.min)[-1,1][2]
}
}
names(a.coefs) = mediators
res$a.coefs <- a.coefs
res$b.coefs <- b.coefs
indirect = a.coefs * b.coefs
selected <- names(indirect[abs(indirect) > epsilon])
indirect <- as.data.frame(indirect)
indirect = round(indirect, 4)
indirect[abs(indirect) < epsilon] = 0
indirect <- t(indirect)
indirect[abs(indirect) >= epsilon] = as.numeric(indirect[abs(indirect) >= epsilon])
res$a.lambda = a.lambda
res$b.lambda = b.cv.lasso$lambda.min
res$selected = selected
res$indirect <- indirect
res$call <- match.call()
class(res) <- "xmed"
return(res)
}
|
