R语言在不同样本量下的Littles MCAR检验

library(BaylorEdPsych)library(simglm)library(ggplot2)library(dplyr)library(mice)fixed <- ~1 + age + incomefixed_param <- c(2, 0.3, 1.3)cov_param <- list(dist_fun = c('rnorm', 'rnorm'),                  var_type = c("single", "single"),                  opts = list(list(mean = 0, sd = 4),                              list(mean = 0, sd = 3)))

ggplot(little.mcar.p, aes(x = n, y = p)) + geom_boxplot() +  geom_crossbar(aes(ymin = q025, y = q05, ymax = q075), data = summarise(    group_by(little.mcar.p, n), q025 = quantile(p, .025, na.rm = TRUE),    q05 = quantile(p, .05, na.rm = TRUE), q075 = quantile(p, .075, na.rm = TRUE)  )) +  geom_hline(yintercept = .05) +  scale_y_continuous(breaks = seq(0, 1, .05), limits = c(0, 1)) +  labs(x = "Sample size", y = "p-value",       title = "Little's MCAR test for data that are MCAR",       subtitle = "2000 replications",       caption = paste(paste("For the narrow boxes, going from top to bottom, lines",                             "represent 7.5th, 5th and 2.5th percentiles of p-values."),                       "Test maintains nominal error rate across wide range of sample sizes.",                       sep = "n"))

ggplot(little.mcar.p.mar, aes(x = n, y = p)) + geom_boxplot() +  geom_crossbar(aes(ymin = q925, y = q95, ymax = q975), data = summarise(    group_by(little.mcar.p.mar, n), q925 = quantile(p, .925, na.rm = TRUE),    q95 = quantile(p, .95, na.rm = TRUE), q975 = quantile(p, .975, na.rm = TRUE)  ), linetype = 2) +  geom_hline(yintercept = .05) +  scale_y_continuous(breaks = seq(0, 1, .05), limits = c(0, 1)) +  labs(x = "Sample size", y = "p-value",       title = "Little's MCAR test for data that are MAR",       subtitle = "2000 replications",       caption = paste(paste("For the dashed boxes, going from top to bottom, lines",                             "represent 97.5th, 95th and 92.5th percentiles of p-values."),                       "Test only maintains nominal error rate around sample size of 120.",                       sep = "n"))