当自变量满足条件时，通过GLM模型中的因变量进行循环

Question

我有代码在多个选定因变量（称为result1-4）上进行逻辑回归。我只想在满足自变量条件的情况下运行模型。假设每个结果和类型组合至少需要两位女性。

虚拟数据：

set.seed(5)
df <- data.frame(
  id = c(1:100),
  age = sample(20:80, 100, replace = TRUE),
  sex = sample(c("M", "F"), 100, replace = TRUE, prob = c(0.7, 0.3)),
  type = sample(letters[1:4], 100, replace = TRUE),
  outcome1 = sample(c(0L, 1L), 100, replace = TRUE, prob = c(0.68, 0.32)),
  outcome2 = sample(c(0L, 1L), 100, replace = TRUE, prob = c(0.65, 0.35)),
  outcome3 = sample(c(0L, 1L), 100, replace = TRUE, prob = c(0.60, 0.40)),
  outcome4 = sample(c(0L, 1L), 100, replace = TRUE, prob = c(0.45, 0.55)))

循环GLM的代码（信https://stats.idre.ucla.edu/r/codefragments/looping_strings/）：

outcomelist <- names(df)[5:8]
modelall <- lapply(outcomelist, function(x) {
  glm(substitute(i ~ type + sex, list(i = as.name(x))), family = "binomial", data = df)})

我发现了很多关于循环的问题，但没有其他条件。我在考虑子集，但不是专业人士，但我不知道该放在哪里。

如果这不是一个额外的问题，我希望将每个模型都命名为列表中结果变量的名称（而不是1到4），因为否则添加条件时将很难跟踪模型。

感谢任何帮助！

Answer 1

一种可能是在运行lapply()之前清除数据：

df.new <- df

for(ii in 1:length(outcomelist)){

temp <- outcomelist[ii]

# check the condition for outcome variable ii
condition <- any(aggregate(df$sex=="F", by=list(df$type, df[,temp]), FUN="sum")$x < 2)

if(condition){

# if the condition is met, remove the variable from df and outcomelist  

df.new[,temp]   <- NULL
outcomelist[ii] <- NA

}

}

# lose irrelevant outcomes
outcomelist <- na.omit(outcomelist)

modelall <- lapply(outcomelist, function(x) {
  glm(substitute(i ~ type + sex, list(i = as.name(x))), family = "binomial", data = df.new)})


# name the list
names(modelall) <- outcomelist