Looping through function arguments (series of contrasts with multcomp::glht) - r

Question

I wish to write a function that runs contrasts over a regression model and bootstraps those results to get confidence intervals, looping that function over a list of contrasts.
I have tried for loops nested within functions, lapply, map ... none seem to get me what I want (returns results for either only the first contrast in the list or the last).
For a single contrast from the list of contrasts, the code looks like this:
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")
But let's assume that instead of just c1, I want to run my function (and boostrap the results) over the following list of contrasts:
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)
Any suggested code for how I would go about this?
(P.S. I know that the linfct argument can take a matrix of contrasts, but I'm specifically looking for a loop/lapply solution).

Answer 1

(In the following I'll reference the objects you create in the example code)
The plan has 2 steps:
preparing a function fun_boot() that takes a contrast object (like c1), and returns a boot object based on it and the df data;
applying that function to the list c_vector of contrasts.
Consequently, the implementation has 2 elements:
# [!] 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
)