尝试使用 tidy 进行功率分析并使用 clmm2 [英] Trying to use tidy for a power analysis and using clmm2

问题描述

我正在尝试对我正在进行的 clmm2 分析进行功效分析.这是特定统计模型的代码:

I'm trying to do a power analysis on a clmm2 analysis that I'm doing. This is the code for the particular statistical model:

test <- clmm2(risk_sensitivity ~ treat + sex + dispersal + 
sex*dispersal + treat*dispersal + treat*sex,random = id, data = datasocial, Hess=TRUE)

现在，我有以下功能:

sim_experiment_power <- function(rep) {
  s <- sim_experiment(n_sample = 1000,
                      prop_disp = 0.10,
                      prop_fem = 0.35,
                      disp_probability = 0.75,
                      nondisp_probability = 0.90,
                      fem_probability = 0.75,
                      mal_probability = 0.90)
  broom.mixed::tidy(s) %>%
    mutate(rep = rep)
}
my_power <- map_df(1:10, sim_experiment_power)

sim_experiment 函数的细节不相关，因为它们按预期工作.要知道的重要一点是它会产生统计 clmm2 结果.我对上述功能的目标是进行功效分析.但是，我收到以下错误:

The details of the function sim_experiment are not relevant because they are working as expected. The important thing to know is that it spits up a statistical clmm2 result. My objective with the function above is to do a power analysis. However, I get the following error:

错误:类 clmm2 的对象没有整洁的方法

Error: No tidy method for objects of class clmm2

我对 R 有点陌生，但我想这意味着 tidy 不适用于 clmm2.有人知道解决此问题的方法吗?

I'm a bit new to R, but I guess it means that tidy doesn't work with clmm2. Does anyone know a work-around for this issue?

这是我在上面发布的代码之后的内容，这最终是我想要得到的.

This is what follows the code that I posted above, which is ultimately what I'm trying to get.

然后您可以绘制模拟中的估计分布.

You can then plot the distribution of estimates across your simulations.

ggplot(my_power, aes(estimate, color = term)) +
  geom_density() +
  facet_wrap(~term, scales = "free")

您也可以将功效计算为小于您的 alpha 的 p 值的比例.

You can also just calculate power as the proportion of p-values less than your alpha.

my_power %>%
  group_by(term) %>%
  summarise(power <- mean(p.value < 0.05))

推荐答案

对于你需要的，你可以写一个函数来返回具有相同列名的系数:

For what you need, you can write a function to return the coefficients with the same column name:

library(ordinal)
library(dplyr)
library(purrr)

tidy_output_clmm = function(fit){
  results = as.data.frame(coefficients(summary(fit)))
  colnames(results) = c("estimate","std.error","statistic","p.value")
  results %>% tibble::rownames_to_column("term")
}

然后我们使用一个例子来应用它，我以序数对葡萄酒数据集进行采样:

Then we apply it using an example where I sample the wine dataset in ordinal:

sim_experiment_power <- function(rep) {
  idx = sample(nrow(wine),replace=TRUE)
  s <- clmm2(rating ~ temp, random=judge, data=wine[idx,], nAGQ=10,Hess=TRUE)
  tidy_output_clmm(s) %>% mutate(rep=rep)
}

my_power <- map_df(1:10, sim_experiment_power)

绘图工作:

ggplot(my_power, aes(estimate, color = term)) +
  geom_density() +
  facet_wrap(~term, scales = "free")

权力也是如此:

my_power %>% group_by(term) %>% summarise(power = mean(p.value < 0.05))
    # A tibble: 5 x 2
  term     power
  <chr>    <dbl>
1 1|2        0.9
2 2|3        0.1
3 3|4        1  
4 4|5        1  
5 tempwarm   1  

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