Created
January 21, 2020 21:38
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R Script to simulate missing not at random data and look at performance of different imputation strategies.
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| library(tidyverse) | |
| n <- 150 | |
| sensitivity_threshold <- 5 | |
| data <- tibble( | |
| a = rgamma(n = n, shape = 5, rate = 0.5), | |
| b = rgamma(n = n, shape = a/2, rate = 0.5) | |
| ) | |
| generate_missing_data <- function(i){ | |
| data_w_missing <- data %>% | |
| mutate(obs = 1:n()) %>% | |
| pivot_longer(c(a, b), names_to = "variable") %>% | |
| mutate( | |
| distance_from_threshold = sensitivity_threshold - value, | |
| prob_of_missing = ifelse(distance_from_threshold > 0, | |
| distance_from_threshold/sensitivity_threshold, | |
| 0), | |
| missing = as.logical(rbinom(n = n(), size = 1, prob = prob_of_missing)), | |
| value = ifelse(missing, NA, value) | |
| ) %>% | |
| pivot_wider( | |
| id_cols = obs, | |
| names_from = variable, | |
| values_from = value | |
| ) %>% | |
| mutate(type = "no impute") | |
| bind_rows( | |
| data_w_missing, | |
| mutate(data_w_missing, | |
| a = ifelse(is.na(a), 0, a), | |
| b = ifelse(is.na(b), 0, b), | |
| type = "impute zero"), | |
| mutate(data_w_missing, | |
| a = ifelse(is.na(a), mean(a, na.rm = TRUE), a), | |
| b = ifelse(is.na(b), mean(b, na.rm = TRUE), b), | |
| type = "impute mean"), | |
| mutate(data_w_missing, | |
| a = ifelse(is.na(a), median(a, na.rm = TRUE), a), | |
| b = ifelse(is.na(b), median(b, na.rm = TRUE), b), | |
| type = "impute median"), | |
| mutate(data_w_missing, | |
| a = ifelse(is.na(a), min(a, na.rm = TRUE), a), | |
| b = ifelse(is.na(b), min(b, na.rm = TRUE), b), | |
| type = "impute min") | |
| ) %>% | |
| mutate(i = i) | |
| } | |
| num_sims <- 100 | |
| correlations <- 1:num_sims %>% | |
| map_dfr(generate_missing_data) %>% | |
| group_by(type, i) %>% | |
| summarise( | |
| pearson = cor(a,b, use = "complete.obs"), | |
| spearman = cor(a,b, method = "spearman", use = "complete.obs") | |
| ) %>% | |
| pivot_longer(c(pearson, spearman), names_to = "correlation_type") | |
| true_correlations <- tibble( | |
| correlation_type = c("pearson", "spearman"), | |
| truth = c(cor(data$a, data$b), cor(data$a, data$b, method = "spearman"))) | |
| ggplot(correlations, aes(x = value)) + | |
| geom_density(fill = "steelblue", color = "steelblue") + | |
| facet_grid(type~correlation_type) + | |
| geom_vline( | |
| data = true_correlations, | |
| aes(xintercept = truth), | |
| color = "black" | |
| ) + | |
| labs(title = "Distribution of correlations by imputation type", | |
| subtitle = "Missingness structure is related to distance from minimum threashold\nVertical line is true correlation value", | |
| x = "correlation") | |
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