循环遍历函数参数(与multcomp：：glht的一系列对比)

Question

我希望编写一个在回归模型上运行反差的函数，并引导这些结果以获得置信区间，并在一个对比列表上循环该函数。

我尝试过嵌套在函数中的循环，应用，映射.似乎没有人能得到我想要的结果(只返回列表中的第一个对比或最后一个对比的结果)。

对于对比列表中的一个对比，代码如下所示：

df <- data.frame(

  H0013301_new_data = c(0,2,3,6,0,4,2,4,8,1),
  drink_stat94_KEYES_2 = c("Heavy","Abstainer","Occasional","Moderate","Abstainer","Occasional","Heavy","Moderate","Moderate","Abstainer"),
  drink_stat02_KEYES_2 = c("Heavy","Abstainer","Occasional","Abstainer","Abstainer","Heavy","Heavy","Moderate","Moderate","Abstainer"),
  drink_stat06_KEYES_2 = c("Occasional","Abstainer","Occasional","Abstainer","Occasional","Heavy","Heavy","Moderate","Moderate","Heavy"),
FIN_weight_survPS_trimmed=
c(.5,2.4,.6,4.8,1.2,.08,.34,.56,1.6,.27)
)

#reordering factors
df$drink_stat94_KEYES_2<-fct_relevel(df$drink_stat94_KEYES_2, "Abstainer", "Occasional", "Moderate", "Heavy")
contrasts(df$drink_stat94_KEYES_2)<-contr.treatment(4,base=1)
df$drink_stat02_KEYES_2<-fct_relevel(df$drink_stat02_KEYES_2, "Abstainer", "Occasional", "Moderate", "Heavy")
contrasts(df$drink_stat02_KEYES_2)<-contr.treatment(4,base=1)
df$drink_stat06_KEYES_2<-fct_relevel(df$drink_stat06_KEYES_2, "Abstainer", "Occasional", "Moderate", "Heavy")
contrasts(df$drink_stat06_KEYES_2)<-contr.treatment(4,base=1)



#defining contrast
c1 <- rbind("A,A,A"=c(1,0,0,0,0,0,0,0,0,0)
                ) 

#defining function to feed to boostrap
fc_2<-function(d,i){
 TrialOutcomeModel_M<-lm(H0013301_new_data ~ drink_stat94_KEYES_2 + drink_stat02_KEYES_2 + drink_stat06_KEYES_2, weights=FIN_weight_survPS_trimmed, data = d[i,]) 
 test <- multcomp::glht(TrialOutcomeModel_M, linfct=c1) 
 return(coef(test))
}
boot_out<-boot(data=df, fc_2, R=500) 
boot.ci(boot_out, type="perc")

但是，让我们假设，不只是c1，我想运行我的函数(并引导结果)在下面的对比列表：

c1 <- rbind("A,A,A"=c(1,0,0,0,0,0,0,0,0,0)
                ) 
c2 <- rbind("A,A,O"=c(1,0,0,0,0,0,0,1,0,0)
                ) 
c3 <- rbind("A,A,M"=c(1,0,0,0,0,0,0,0,1,0)
                ) 

c_vector<-list(c1,c2,c3)

我该怎么做有什么建议吗？(P.S.我知道linfct参数可以采用一个对比矩阵，但我专门寻找循环/lapply解决方案)。

Answer 1

(在下面的示例代码中，我将引用您创建的对象)

计划有两个步骤：

1. 正在准备一个函数fun_boot()，该函数fun_boot()接受一个对比对象(如c1)，并基于该对象和df数据返回boot对象；

1. 将该函数应用于对比列表c_vector .

因此，implementation有两个元素：

# [!] Assume all required libraries loaded
# [!] Assume all necessary data exists

# Step 1
fun_boot <- function(contrast)
{
    # Make statistic function
    fun_statistic <- function(d, i)
    {
        TrialOutcomeModel_M <- lm(
           formula = H0013301_new_data ~ drink_stat94_KEYES_2 + drink_stat02_KEYES_2 + drink_stat06_KEYES_2,
           data    = d[i,],
           weights = FIN_weight_survPS_trimmed
        ) 
        test <- multcomp::glht(
           TrialOutcomeModel_M,
           linfct = contrast
        )
        return(coef(test))
    }

    # Make boot call (hehe)
    return (boot(
       data      = df,
       statistic = fun_statistic,
       R         = 500
    ))
}

# Step 2
boot_out_vector <- lapply(
   X   = c_vector,
   FUN = fun_boot
)