用R绘制预测值并比较随机效应plm模型中的相互作用项的最佳方法是什么？

Question

I have a plm random effects model that includes an interaction between a continuous and a categorical variable (for illustrative purposes, let's say the categorical variable is strong religiosity and the continuous is contribution to charity).

Here is sample data (IRL, data much larger)

ratio     contrib     relig    np_score     ID    year 
.4          3          1          11        1      1990  
0           7          0          8         2      1990
.9          7          1          6         1      1992
.7          6          1          10        1      1994
.1          2          0          4         2      1992
.3          9          0          8         2      1994

I want to plot the predicted outcomes as contribution increases depending on whether the respondent does or does not identify as strongly religious.

m1 <- plm(ratio ~ contrib + relig + np_score + contrib*relig, 
            data = panel_dat, 
            index = c("ID", "year"),
            model = "random", random.method = "amemiya")

I have tried interaction.plot as follows:

interaction.plot(panel_dat$contrib, panel_dat$relig, predict(m1), col = 2:3, lty = 1)

But I get the error term:

Error in tapply(response, list(x.factor, trace.factor), fun) : 
  arguments must have same length

I would ultimately rather do this with ggplot2. Any suggestions? Seems like there should be a simple answer...

Answer 1

This sample code produces the multiline plot below (in ggplot). Not sure if the axes are correct ... you can adjust as needed.

# Create sample dataframe
df <- data.frame(
  id = c(1, 1, 1, 2, 2, 2),
  year = c(2000, 2001, 2002, 2000, 2001, 2002),  
  ratio = c(0.3, 0.6, 0.1, 0.4, 0.5, 0.6),
  contrib = c(310, 220, 230, 140, 0, 10),
  relig = c(1, 1, 1, 3, 3, 3)
)

# Build model and predictions
library(plm)
m1 <- plm(ratio ~ contrib + relig + contrib*relig, data = df, index = c('id','year'))
df$p_ratio <- predict(m1)

# Create plot
library(ggplot2)
ggplot(data=df, aes(x=contrib, y=p_ratio, group=relig))+geom_line(size=2, aes(color=relig))