Psych::Mediate 中的错误：找不到对象
Error in Psych::Mediate: Object Not Found
我正在 运行 对 r 中的数据集进行中介分析，无法弄清楚如何让 psych::mediate 工作——我之前和之前对另一个数据集做过同样的事情没有改变任何东西，但由于某种原因它不能使用这个新数据。
我试过：
1. 将 'condition' 变成 condition.f 因子
2.明确命名DATA a "data.frame"
3.在函数中指定"z"或"mod"等不同的参数
4. 检查所有可变列名称的大写。
None 以上似乎有效。
library(psych)
DATA = STEX_S1_FINALCLEAN
Mediation_RA = psych::mediate( y = "DV_See", x = "Share_T", m = "Seff", data = DATA)
print(Mediation_RA,short=F)
我希望得到具有中介值的完整输出，但得到了：
Error in psych::mediate(y = "DV_See", x = "Share_T", m = "Seff", data = DATA) :
object 'ex' not found
我在任何地方都没有看到和反对 'ex'，这不是 DATA 数据框中任何列的名称。
根据@r2evans的建议，您可以使用以下修改函数：
mymediate <- function (y, x, m = NULL, data, mod = NULL, z = NULL, n.obs = NULL,
use = "pairwise", n.iter = 5000, alpha = 0.05, std = FALSE,
plot = TRUE, zero = TRUE, main = "Mediation")
{
cl <- match.call()
if (class(y) == "formula") {
ps <- fparse(y)
y <- ps$y
x <- ps$x
m <- ps$m
mod <- ps$prod
ex <- ps$ex
x <- x[!ps$x %in% ps$m]
z <- ps$z
print(str(ps))
} else {
ex = NULL
}
all.ab <- NULL
if (is.numeric(y))
y <- colnames(data)[y]
if (is.numeric(x))
x <- colnames(data)[x]
if (!is.null(m))
if (is.numeric(m))
m <- colnames(data)[m]
if (!is.null(mod)) {
if (is.numeric(mod)) {
nmod <- length(mod)
mod <- colnames(data)[mod]
}
}
if (is.null(mod)) {
nmod <- 0
}
else {
nmod <- length(mod)
}
var.names <- list(IV = x, DV = y, med = m, mod = mod, z = z,
ex = ex)
if (any(!(unlist(var.names) %in% colnames(data)))) {
stop("Variable names not specified correctly")
}
if (ncol(data) == nrow(data)) {
raw <- FALSE
if (nmod > 0) {
stop("Moderation Analysis requires the raw data")
}
else {
data <- data[c(y, x, m, z), c(y, x, m, z)]
}
}
else {
data <- data[, c(y, x, m, z, ex)]
}
if (nmod == 1) {
mod <- c(x, mod)
nmod <- length(mod)
}
if (!is.matrix(data))
data <- as.matrix(data)
if ((dim(data)[1] != dim(data)[2])) {
n.obs = dim(data)[1]
if (!is.null(mod))
if (zero)
data <- scale(data, scale = FALSE)
C <- cov(data, use = use)
raw <- TRUE
if (std) {
C <- cov2cor(C)
}
}
else {
raw <- FALSE
C <- data
nvar <- ncol(C)
if (is.null(n.obs)) {
n.obs <- 1000
message("The data matrix was a correlation matrix and the number of subjects was not specified. \n n.obs arbitrarily set to 1000")
}
if (!is.null(m)) {
message("The replication data matrices were simulated based upon the specified number of subjects and the observed correlation matrix.")
eX <- eigen(C)
data <- matrix(rnorm(nvar * n.obs), n.obs)
data <- t(eX$vectors %*% diag(sqrt(pmax(eX$values,
0)), nvar) %*% t(data))
colnames(data) <- c(y, x, m)
}
}
if ((nmod > 0) | (!is.null(ex))) {
if (!raw) {
stop("Moderation analysis requires the raw data")
}
else {
if (zero) {
data <- scale(data, scale = FALSE)
}
}
}
if (nmod > 0) {
prods <- matrix(NA, ncol = length(ps$prod), nrow = nrow(data))
colnames(prods) <- paste0("V", 1:length(ps$prod))
for (i in 1:length(ps$prod)) {
prods[, i] <- apply(data[, ps$prod[[i]]], 1, prod)
colnames(prods)[i] <- paste0(ps$prod[[i]], collapse = "*")
}
data <- cbind(data, prods)
x <- c(x, colnames(prods))
}
if (!is.null(ex)) {
quads <- matrix(NA, ncol = length(ex), nrow = nrow(data))
colnames(quads) <- ex
for (i in 1:length(ex)) {
quads[, i] <- data[, ex[i]] * data[, ex[i]]
colnames(quads)[i] <- paste0(ex[i], "^2")
}
data <- cbind(data, quads)
x <- c(x, colnames(quads))
}
if (raw) {
C <- cov(data, use = use)
}
if (std) {
C <- cov2cor(C)
}
xy <- c(x, y)
numx <- length(x)
numy <- length(y)
if (!is.null(m)) {
numm <- length(m)
nxy <- numx + numy
m.matrix <- C[c(x, m), c(x, m), drop = FALSE]
}
else {
numm <- 0
nxy <- numx
}
df <- n.obs - nxy - 1
xy.matrix <- C[c(x, m), y, drop = FALSE]
total.reg <- matReg(x, y, m = m, z = z, C = C, n.obs = n.obs)
direct <- total.reg$beta
if (!is.null(z)) {
colnames(direct) <- paste0(colnames(direct), "*")
rownames(direct) <- paste0(rownames(direct), "*")
}
if (numm > 0) {
a.reg <- matReg(x = x, y = m, C = C, z = z, n.obs = n.obs)
b.reg <- matReg(c(x, m), y, C = C, z = z, n.obs = n.obs)
cprime.reg <- matReg(c(x, m), y, C = C, n.obs = n.obs,
z = z)
a <- a.reg$beta
b <- b.reg$beta[-(1:numx), , drop = FALSE]
c <- total.reg$beta
cprime <- cprime.reg$beta
all.ab <- matrix(NA, ncol = numm, nrow = numx)
for (i in 1:numx) {
all.ab[i, ] <- a[i, ] * t(b[, 1])
}
colnames(all.ab) <- m
rownames(all.ab) <- x
ab <- a %*% b
indirect <- c - ab
if (is.null(n.obs)) {
message("Bootstrap is not meaningful unless raw data are provided or the number of subjects is specified.")
mean.boot <- sd.boot <- ci.quant <- boot <- se <- tvalue <- prob <- NA
}
else {
boot <- psych:::boot.mediate(data, x, y, m, z, n.iter = n.iter,
std = std, use = use)
mean.boot <- colMeans(boot)
sd.boot <- apply(boot, 2, sd)
ci.quant <- apply(boot, 2, function(x) quantile(x,
c(alpha/2, 1 - alpha/2), na.rm = TRUE))
mean.boot <- matrix(mean.boot, nrow = numx)
sd.boot <- matrix(sd.boot, nrow = numx)
ci.ab <- matrix(ci.quant, nrow = 2 * numx * numy)
boots <- list(mean = mean.boot, sd = sd.boot, ci = ci.quant,
ci.ab = ci.ab)
}
}
else {
a.reg <- b.reg <- reg <- NA
a <- b <- c <- ab <- cprime <- boot <- boots <- indirect <- cprime.reg <- NA
}
if (!is.null(z)) {
var.names$IV <- paste0(var.names$IV, "*")
var.names$DV <- paste0(var.names$DV, "*")
var.names$med <- paste0(var.names$med, "*")
colnames(C) <- rownames(C) <- paste0(colnames(C), "*")
}
result <- list(var.names = var.names, a = a, b = b, ab = ab,
all.ab = all.ab, c = c, direct = direct, indirect = indirect,
cprime = cprime, total.reg = total.reg, a.reg = a.reg,
b.reg = b.reg, cprime.reg = cprime.reg, boot = boots,
boot.values = boot, sdnames = colnames(data), data = data,
C = C, Call = cl)
class(result) <- c("psych", "mediate")
if (plot) {
if (is.null(m)) {
moderate.diagram(result)
}
else {
mediate.diagram(result, main = main)
}
}
return(result)
}
您可以使用以下示例测试 mymediate 函数：
library(psych)
mod.k2 <- mymediate(y="OccupAsp", x=c("Intelligence","Siblings","FatherEd","FatherOcc"),
m= c(5:6), data=R.kerch, n.obs=767, n.iter=50)
print(mod.k2)
我正在 运行 对 r 中的数据集进行中介分析，无法弄清楚如何让 psych::mediate 工作——我之前和之前对另一个数据集做过同样的事情没有改变任何东西，但由于某种原因它不能使用这个新数据。
我试过： 1. 将 'condition' 变成 condition.f 因子 2.明确命名DATA a "data.frame" 3.在函数中指定"z"或"mod"等不同的参数 4. 检查所有可变列名称的大写。
None 以上似乎有效。
library(psych)
DATA = STEX_S1_FINALCLEAN
Mediation_RA = psych::mediate( y = "DV_See", x = "Share_T", m = "Seff", data = DATA)
print(Mediation_RA,short=F)
我希望得到具有中介值的完整输出，但得到了：
Error in psych::mediate(y = "DV_See", x = "Share_T", m = "Seff", data = DATA) :
object 'ex' not found
我在任何地方都没有看到和反对 'ex'，这不是 DATA 数据框中任何列的名称。
根据@r2evans的建议，您可以使用以下修改函数：
mymediate <- function (y, x, m = NULL, data, mod = NULL, z = NULL, n.obs = NULL,
use = "pairwise", n.iter = 5000, alpha = 0.05, std = FALSE,
plot = TRUE, zero = TRUE, main = "Mediation")
{
cl <- match.call()
if (class(y) == "formula") {
ps <- fparse(y)
y <- ps$y
x <- ps$x
m <- ps$m
mod <- ps$prod
ex <- ps$ex
x <- x[!ps$x %in% ps$m]
z <- ps$z
print(str(ps))
} else {
ex = NULL
}
all.ab <- NULL
if (is.numeric(y))
y <- colnames(data)[y]
if (is.numeric(x))
x <- colnames(data)[x]
if (!is.null(m))
if (is.numeric(m))
m <- colnames(data)[m]
if (!is.null(mod)) {
if (is.numeric(mod)) {
nmod <- length(mod)
mod <- colnames(data)[mod]
}
}
if (is.null(mod)) {
nmod <- 0
}
else {
nmod <- length(mod)
}
var.names <- list(IV = x, DV = y, med = m, mod = mod, z = z,
ex = ex)
if (any(!(unlist(var.names) %in% colnames(data)))) {
stop("Variable names not specified correctly")
}
if (ncol(data) == nrow(data)) {
raw <- FALSE
if (nmod > 0) {
stop("Moderation Analysis requires the raw data")
}
else {
data <- data[c(y, x, m, z), c(y, x, m, z)]
}
}
else {
data <- data[, c(y, x, m, z, ex)]
}
if (nmod == 1) {
mod <- c(x, mod)
nmod <- length(mod)
}
if (!is.matrix(data))
data <- as.matrix(data)
if ((dim(data)[1] != dim(data)[2])) {
n.obs = dim(data)[1]
if (!is.null(mod))
if (zero)
data <- scale(data, scale = FALSE)
C <- cov(data, use = use)
raw <- TRUE
if (std) {
C <- cov2cor(C)
}
}
else {
raw <- FALSE
C <- data
nvar <- ncol(C)
if (is.null(n.obs)) {
n.obs <- 1000
message("The data matrix was a correlation matrix and the number of subjects was not specified. \n n.obs arbitrarily set to 1000")
}
if (!is.null(m)) {
message("The replication data matrices were simulated based upon the specified number of subjects and the observed correlation matrix.")
eX <- eigen(C)
data <- matrix(rnorm(nvar * n.obs), n.obs)
data <- t(eX$vectors %*% diag(sqrt(pmax(eX$values,
0)), nvar) %*% t(data))
colnames(data) <- c(y, x, m)
}
}
if ((nmod > 0) | (!is.null(ex))) {
if (!raw) {
stop("Moderation analysis requires the raw data")
}
else {
if (zero) {
data <- scale(data, scale = FALSE)
}
}
}
if (nmod > 0) {
prods <- matrix(NA, ncol = length(ps$prod), nrow = nrow(data))
colnames(prods) <- paste0("V", 1:length(ps$prod))
for (i in 1:length(ps$prod)) {
prods[, i] <- apply(data[, ps$prod[[i]]], 1, prod)
colnames(prods)[i] <- paste0(ps$prod[[i]], collapse = "*")
}
data <- cbind(data, prods)
x <- c(x, colnames(prods))
}
if (!is.null(ex)) {
quads <- matrix(NA, ncol = length(ex), nrow = nrow(data))
colnames(quads) <- ex
for (i in 1:length(ex)) {
quads[, i] <- data[, ex[i]] * data[, ex[i]]
colnames(quads)[i] <- paste0(ex[i], "^2")
}
data <- cbind(data, quads)
x <- c(x, colnames(quads))
}
if (raw) {
C <- cov(data, use = use)
}
if (std) {
C <- cov2cor(C)
}
xy <- c(x, y)
numx <- length(x)
numy <- length(y)
if (!is.null(m)) {
numm <- length(m)
nxy <- numx + numy
m.matrix <- C[c(x, m), c(x, m), drop = FALSE]
}
else {
numm <- 0
nxy <- numx
}
df <- n.obs - nxy - 1
xy.matrix <- C[c(x, m), y, drop = FALSE]
total.reg <- matReg(x, y, m = m, z = z, C = C, n.obs = n.obs)
direct <- total.reg$beta
if (!is.null(z)) {
colnames(direct) <- paste0(colnames(direct), "*")
rownames(direct) <- paste0(rownames(direct), "*")
}
if (numm > 0) {
a.reg <- matReg(x = x, y = m, C = C, z = z, n.obs = n.obs)
b.reg <- matReg(c(x, m), y, C = C, z = z, n.obs = n.obs)
cprime.reg <- matReg(c(x, m), y, C = C, n.obs = n.obs,
z = z)
a <- a.reg$beta
b <- b.reg$beta[-(1:numx), , drop = FALSE]
c <- total.reg$beta
cprime <- cprime.reg$beta
all.ab <- matrix(NA, ncol = numm, nrow = numx)
for (i in 1:numx) {
all.ab[i, ] <- a[i, ] * t(b[, 1])
}
colnames(all.ab) <- m
rownames(all.ab) <- x
ab <- a %*% b
indirect <- c - ab
if (is.null(n.obs)) {
message("Bootstrap is not meaningful unless raw data are provided or the number of subjects is specified.")
mean.boot <- sd.boot <- ci.quant <- boot <- se <- tvalue <- prob <- NA
}
else {
boot <- psych:::boot.mediate(data, x, y, m, z, n.iter = n.iter,
std = std, use = use)
mean.boot <- colMeans(boot)
sd.boot <- apply(boot, 2, sd)
ci.quant <- apply(boot, 2, function(x) quantile(x,
c(alpha/2, 1 - alpha/2), na.rm = TRUE))
mean.boot <- matrix(mean.boot, nrow = numx)
sd.boot <- matrix(sd.boot, nrow = numx)
ci.ab <- matrix(ci.quant, nrow = 2 * numx * numy)
boots <- list(mean = mean.boot, sd = sd.boot, ci = ci.quant,
ci.ab = ci.ab)
}
}
else {
a.reg <- b.reg <- reg <- NA
a <- b <- c <- ab <- cprime <- boot <- boots <- indirect <- cprime.reg <- NA
}
if (!is.null(z)) {
var.names$IV <- paste0(var.names$IV, "*")
var.names$DV <- paste0(var.names$DV, "*")
var.names$med <- paste0(var.names$med, "*")
colnames(C) <- rownames(C) <- paste0(colnames(C), "*")
}
result <- list(var.names = var.names, a = a, b = b, ab = ab,
all.ab = all.ab, c = c, direct = direct, indirect = indirect,
cprime = cprime, total.reg = total.reg, a.reg = a.reg,
b.reg = b.reg, cprime.reg = cprime.reg, boot = boots,
boot.values = boot, sdnames = colnames(data), data = data,
C = C, Call = cl)
class(result) <- c("psych", "mediate")
if (plot) {
if (is.null(m)) {
moderate.diagram(result)
}
else {
mediate.diagram(result, main = main)
}
}
return(result)
}
您可以使用以下示例测试 mymediate 函数：
library(psych)
mod.k2 <- mymediate(y="OccupAsp", x=c("Intelligence","Siblings","FatherEd","FatherOcc"),
m= c(5:6), data=R.kerch, n.obs=767, n.iter=50)
print(mod.k2)