r: for loop to simulate predictions when random sampling is applied - r

Question

I am trying to simulate how replacement/reassignment of values on random samples affect predictions conveyed by AUC.
I have a tumor classification in a dataframe denoted df$who which has levels 1, 2, 3 corresponding to the severity of the tumor lesion.
Intro to the question
Lets say the baseline data looks like this:
set.seed(1)
df <- data.frame(
who = as.factor(sample(1:3, size = 6000, replace = TRUE, prob = c(0.8, 0.15, 0.05))),
age = round(runif(n = 6000, min = 18, max = 95), digits = 1),
gender = sample(c("m", "f"), size = 6000, replace = TRUE, prob = c(1/3, 2/3)),
event.time = runif(n = 6000, min = 8, max = 120),
event = as.factor(sample(0:2, size = 6000, replace = TRUE, prob = c(0.25, 0.2, 0.55)))
)
And a standard cause-specific Cox regression looks like:
library(survival)
a_baseline <- coxph(Surv(event.time, event == 1) ~ who + age + gender, data = df, x = TRUE)
From which AUC can be obtained as a measure of predictive performance. Here, leave-one-out bootstrap on 5-year prediction on df$event == 1.
library(riskRegression)
u <- Score(list("baseline" = a_baseline),
Surv(event.time, event == 1) ~ 1,
data = df,
times = 60,
plots = "cal",
B = 50,
split.method = "loob",
metrics = c("auc", "brier")
)
# The AUC is then obtained
u$AUC$score$AUC[2]
Question
I want to simulate how re-classifying a random 5% of df$who == 1 to dfwho == 2 affect the 5-year prediction on df$event == 1
I want to create 10 separate and simulated subsets of the baseline data df, but each containing a random allocation of 5% df$who == 1 to .. == 2. Then, I want to apply each of these 10 separate and simulated subsets to predict the 5-year risk of df$event == 1.
I have applied a for loop to this. The expected output is dataframe that tells me which of the 10 simulated datasets yielded the highest and lowest u$AUC$score$AUC[2] (i.e., the best and worst prediction).
I am new to for loop, but here is my go (that obviously did not work).
all_auc <- data.frame() ## create a dataframe to fill in AUC from all 10 simulated sub-datasets
for(i in 1:10){ #1:10 represent the simulated datasets from 1 to 10
df[i] <- df #allocating baseline data to each of the 10 datasets
df[i]$who[sample(which(df[i]$who==1), round(0.05*length(which(df[i]$who==1))))]=2 #create the random 5% allocation of who==1 to who==2 in the i'th simulated dataset
ith_cox <- coxph(Surv(event.time, event == 1) ~ who + age + gender, data = df[i], x = TRUE) #create the i'th Cox regression based on the i´th dataset
# create the predictions based on the i´th Cox
u[i] <- Score(list("baseline" = ith_cox),
Surv(event.time, event == 1) ~ 1,
data = df[i],
times = 60,
plots = "cal",
B = 50,
split.method = "loob",
metrics = c("auc", "brier")
)
# summarize all AUC from all 10 sub-datasets
all_auc <- u[i]$AUC$score$AUC[2]
}
(1) I could not get this for loop to work as described, and
(2) the final dataframe all_auc should provide only which of the 10 datasets yielded the worst and best predictions (I will then use these two data sets for further analysis).
A final note
This is only a reproducible example. The for loop will be applied to 10.000 simulated datasets in our analysis. I do not know if this could affect the answer - but, it illustrates the importance of the result: a dataframe (or vector?) that simply tells me which simulated dataset yielded the best vs worst predictions, and that I subsequently will be able to use these two dataframes for furter analysis, eg df2930 and df8939.