我正在尝试估计一个有序logit模型，包括。通过遵循代码from this tutorial来实现R中的边际效应。我正在使用polr包中的MASS来估计模型，并使用ocME包中的erer来尝试计算边际效应。
估计模型没问题。
logitModelSentiment90 <- polr(availability_90_ord ~ mean_sentiment, data = data, Hess = T,
method = "logistic")
但是，我遇到ocME的问题，该问题会生成以下错误消息：
ocME(logitModelSentiment90)
Error in eval(predvars, data, env) :
numeric 'envir' arg not of length one
下面ocME的文档指出应该使用的对象必须来自polr函数，这似乎正是我正在做的事情。
ocME(w, rev.dum = TRUE, digits = 3)
w = an ordered probit or logit model object estimated by polr from the MASS library.
那么有人可以帮助我了解我在做什么错吗？我已经发布了数据的子集，其中包含模型here的两个变量。在R中，我将DV设置为因子变量，IV是连续的。
旁注：
我可以使用RStata将计算从R传递到Stata来计算边际效应，而不会出现任何问题。但我不想定期执行此操作，因此我想了解是什么原因导致R和ocME出现问题。
stata("ologit availability_90_ord mean_sentiment
mfx", data.in = data)
. ologit availability_90_ord mean_sentiment
Iteration 0: log likelihood = -15379.121
Iteration 1: log likelihood = -15378.742
Iteration 2: log likelihood = -15378.742
Ordered logistic regression Number of obs = 11,901
LR chi2(1) = 0.76
Prob > chi2 = 0.3835
Log likelihood = -15378.742 Pseudo R2 = 0.0000
------------------------------------------------------------------------------
avail~90_ord | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mean_senti~t | .0044728 .0051353 0.87 0.384 -.0055922 .0145379
-------------+----------------------------------------------------------------
/cut1 | -1.14947 .0441059 -1.235916 -1.063024
/cut2 | -.5286239 .042808 -.6125261 -.4447217
/cut3 | .3127556 .0426782 .2291079 .3964034
------------------------------------------------------------------------------
. mfx
Marginal effects after ologit
y = Pr(availability_90_ord==1) (predict)
= .23446398
------------------------------------------------------------------------------
variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X
---------+--------------------------------------------------------------------
mean_s~t | -.0008028 .00092 -0.87 0.384 -.002609 .001004 7.55768
------------------------------------------------------------------------------
答案 0 :(得分：1)
您的模型只有一个解释变量（mean_sentiment），这似乎是ocME的问题。例如，尝试向模型添加第二个变量：
logitModelSentiment90 <- polr(availability_90_ord ~ mean_sentiment + I(mean_sentiment^2),
data = data, Hess = T, method = "logistic")
ocME(logitModelSentiment90)
# effect.0 effect.1 effect.2 effect.3
# mean_sentiment -0.004 -0.001 0 0.006
# I(mean_sentiment^2) 0.000 0.000 0 0.000
只需稍作修改，ocME也可以使用一个自变量正确运行。
尝试以下myocME函数
myocME <- function (w, rev.dum = TRUE, digits = 3)
{
if (!inherits(w, "polr")) {
stop("Need an ordered choice model from 'polr()'.\n")
}
if (w$method != "probit" & w$method != "logistic") {
stop("Need a probit or logit model.\n")
}
lev <- w$lev
J <- length(lev)
x.name <- attr(x = w$terms, which = "term.labels")
x2 <- w$model[, x.name, drop=FALSE]
ww <- paste("~ 1", paste("+", x.name, collapse = " "), collapse = " ")
x <- model.matrix(as.formula(ww), data = x2)[, -1, drop=FALSE]
x.bar <- as.matrix(colMeans(x))
b.est <- as.matrix(coef(w))
K <- nrow(b.est)
xb <- t(x.bar) %*% b.est
z <- c(-10^6, w$zeta, 10^6)
pfun <- switch(w$method, probit = pnorm, logistic = plogis)
dfun <- switch(w$method, probit = dnorm, logistic = dlogis)
V2 <- vcov(w)
V3 <- rbind(cbind(V2, 0, 0), 0, 0)
ind <- c(1:K, nrow(V3) - 1, (K + 1):(K + J - 1), nrow(V3))
V4 <- V3[ind, ]
V5 <- V4[, ind]
f.xb <- dfun(z[1:J] - c(xb)) - dfun(z[2:(J + 1)] - c(xb))
me <- b.est %*% matrix(data = f.xb, nrow = 1)
colnames(me) <- paste("effect", lev, sep = ".")
se <- matrix(0, nrow = K, ncol = J)
for (j in 1:J) {
u1 <- c(z[j] - xb)
u2 <- c(z[j + 1] - xb)
if (w$method == "probit") {
s1 <- -u1
s2 <- -u2
}
else {
s1 <- 1 - 2 * pfun(u1)
s2 <- 1 - 2 * pfun(u2)
}
d1 <- dfun(u1) * (diag(1, K, K) - s1 * (b.est %*% t(x.bar)))
d2 <- -1 * dfun(u2) * (diag(1, K, K) - s2 * (b.est %*%
t(x.bar)))
q1 <- dfun(u1) * s1 * b.est
q2 <- -1 * dfun(u2) * s2 * b.est
dr <- cbind(d1 + d2, q1, q2)
V <- V5[c(1:K, K + j, K + j + 1), c(1:K, K + j, K + j +
1)]
cova <- dr %*% V %*% t(dr)
se[, j] <- sqrt(diag(cova))
}
colnames(se) <- paste("SE", lev, sep = ".")
rownames(se) <- colnames(x)
if (rev.dum) {
for (k in 1:K) {
if (identical(sort(unique(x[, k])), c(0, 1))) {
for (j in 1:J) {
x.d1 <- x.bar
x.d1[k, 1] <- 1
x.d0 <- x.bar
x.d0[k, 1] <- 0
ua1 <- z[j] - t(x.d1) %*% b.est
ub1 <- z[j + 1] - t(x.d1) %*% b.est
ua0 <- z[j] - t(x.d0) %*% b.est
ub0 <- z[j + 1] - t(x.d0) %*% b.est
me[k, j] <- pfun(ub1) - pfun(ua1) - (pfun(ub0) -
pfun(ua0))
d1 <- (dfun(ua1) - dfun(ub1)) %*% t(x.d1) -
(dfun(ua0) - dfun(ub0)) %*% t(x.d0)
q1 <- -dfun(ua1) + dfun(ua0)
q2 <- dfun(ub1) - dfun(ub0)
dr <- cbind(d1, q1, q2)
V <- V5[c(1:K, K + j, K + j + 1), c(1:K, K +
j, K + j + 1)]
se[k, j] <- sqrt(c(dr %*% V %*% t(dr)))
}
}
}
}
t.value <- me/se
p.value <- 2 * (1 - pt(abs(t.value), w$df.residual))
out <- list()
for (j in 1:J) {
out[[j]] <- round(cbind(effect = me[, j], error = se[,
j], t.value = t.value[, j], p.value = p.value[, j]),
digits)
}
out[[J + 1]] <- round(me, digits)
names(out) <- paste("ME", c(lev, "all"), sep = ".")
result <- listn(w, out)
class(result) <- "ocME"
return(result)
}
并运行以下代码：
logitModelSentiment90 <- polr(availability_90_ord ~ mean_sentiment,
data = data, Hess = T, method = "logistic")
myocME(logitModelSentiment90)
# effect.0 effect.1 effect.2 effect.3
# mean_sentiment -0.001 0 0 0.001