Plotting Panel data Mixed Effect model with Random and Fixed models

Question

I am working on panel data models and I am now using Mixed model from lme4 package, I also Used model basen on random, fixed, LSDV, Fisrt_diff, etc...

I have a function that plot all models coeffs. in ggplot, however plotting coefficients from lme4 is an issue I can make it work:

Is there a way hot to make below code work for all model, including also model 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

The error you have with the current code, is that

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 is numeric in the sleepstudy dataset and used a continuous predictor. Using your ce function, this returns an error because the row names are dropped, with 2:nrow(..).

To get similar estimates to your other models, set Days to factor and random effect to (1|Day). I don't think (Days | Subject) make sense.

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

and we alter your ce code slightly, using drop=FALSE,to prevent the empty 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"

run the rest of what you have:

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