绘制具有随机和固定模型的面板数据混合效应模型

Question

我正在研究面板数据模型，我现在正在使用 lme4 包中的混合模型，我还使用了基于随机、固定、LSDV、Fisrt_diff 等的模型...

我有一个绘制所有模型系数的函数。在 ggplot 中，但是从 lme4 绘制系数是一个问题，我可以让它工作：

有没有办法让下面的代码适用于所有型号，包括型号 mixed？

library(plm)
library(lme4)
library(ggplot2)

mixed <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
fixed = plm(Reaction ~ Days, data = sleepstudy, index = c("Subject", "Days"), model = "within")
random = plm(Reaction ~ Days, data = sleepstudy, index = c("Subject", "Days"), model = "random")
pool = plm(Reaction ~ Days, data = sleepstudy, index = c("Subject", "Days"), model = "pooling")
first_diff = plm(Reaction ~ Days, data = sleepstudy, index = c("Subject", "Days"), model = "fd")
# Function to extract point estimates
ce <- function(model.obj) {
  extract <- summary(get(model.obj))$coefficients[2:nrow(summary(get(model.obj))$coefficients), 1:2]
  return(data.frame(extract, vars = row.names(extract), model = model.obj))
}

# Run function on the three models and bind into single data frame
coefs <- do.call(rbind, sapply(paste0(list(
  "fixed", "random", "pool", "first_diff"
)), ce, simplify = FALSE))

names(coefs)[2] <- "se"

gg_coef <- ggplot(coefs, aes(vars, Estimate)) +
  geom_hline(yintercept = 0, lty = 1, lwd = 0.5, colour = "red") +
  geom_errorbar(aes(ymin = Estimate - se, ymax = Estimate + se, colour = vars),
                lwd = 1, width = 0
  ) +
  geom_point(size = 3, aes(colour = vars)) +
  facet_grid(model ~ ., scales="free") +
  coord_flip() +
  guides(colour = FALSE) +
  labs(x = "Coefficient", y = "Value") +
  ggtitle("Raw models coefficients")

gg_coef

Answer 1

您当前代码的错误是

data(sleepstudy)
mixed <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
coefficients(summary(mixed))
             Estimate Std. Error   t value
(Intercept) 251.40510   6.823773 36.842535
Days         10.46729   1.545958  6.770744

Days 在 sleepstudy 数据集中是数字，并使用了连续预测变量。使用你的 ce 函数，这 returns 一个错误，因为行名被删除， 2:nrow(..).

要获得与您的其他模型相似的估计值，请将天数设置为因数并将随机效应设置为（1|天）。我认为（天数 | 主题）没有意义。

sleepstudy$Days = factor(sleepstudy$Days)
mixed <- lmer(Reaction ~ Days + (1 | Subject), sleepstudy)

并且我们使用 drop=FALSE 稍微更改了您的 ce 代码，以防止空 row.names

ce <- function(model.obj) {
  summ.model <- summary(get(model.obj))$coefficients
  extract <- summ.model[2:nrow(summ.model),drop=FALSE, 1:2]
  return(data.frame(extract, vars = row.names(extract), model = model.obj))
}

coefs <- do.call(rbind, sapply(paste0(list(
  "fixed", "random", "pool", "first_diff","mixed"
)), ce, simplify = FALSE))

names(coefs)[2] <- "se"

运行你剩下的：

gg_coef <- ggplot(coefs, aes(vars, Estimate)) +
  geom_hline(yintercept = 0, lty = 1, lwd = 0.5, colour = "red") +
  geom_errorbar(aes(ymin = Estimate - se, ymax = Estimate + se, colour = vars),
                lwd = 1, width = 0
  ) +
  geom_point(size = 3, aes(colour = vars)) +
  facet_grid(model ~ ., scales="free") +
  coord_flip() +
  guides(colour = FALSE) +
  labs(x = "Coefficient", y = "Value") +
  ggtitle("Raw models coefficients")

gg_coef

绘制具有随机和固定模型的面板数据混合效应模型

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