我希望撰寫一個在回歸模型上運行對比并引導這些結果以獲得置信區間的函式，將該函式回圈到對比串列中。
我嘗試了嵌套在函式、lapply、map 中的回圈……似乎沒有一個能得到我想要的東西（只回傳串列中的第一個對比或最后一個對比的結果）。
對于對比串列中的單個對比，代碼如下所示：
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")
但是讓我們假設我想在下面的對比串列中運行我的函式（并提升結果），而不只是 c1：
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)
有什么建議的代碼來說明我將如何去做嗎？（PS 我知道 linfct 引數可以采用對比矩陣，但我專門尋找回圈/lapply 解決方案）。
uj5u.com熱心網友回復：
（在下面我將參考您在示例代碼中創建的物件）
該計劃有兩個步驟：
準備一個函式，該函式
fun_boot()接受一個對比物件（如）并基于它c1回傳一個物件和資料；bootdf將該函式應用于
c_vector對比串列。
因此，實作有兩個元素：
# [!] 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
)
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