How to resolve "number of items to replace is not a multiple of replacement length" error in a bootstrapped regression? - r

Question

I am trying to conduct a bootstrapped regression model using code from Andy Field's textbook Discovering Statistics Using R.
I am struggling to interpret an error message that I receive when running the boot() function. From reading other forum posts I understand that it is telling me that there is an imbalance in the number of items between two objects, but I don't understand what this means in my context and how I can resolve it.
You can download my data here (a publicly available Dataset on Airbnb listings) and find my code and the full error message below. I am using a mixture of factored dummy variables and continuous variables as predictors. Thanks in advance for any help!
Code:
bootReg <- function (formula, data, i)
{
d <- data [i,]
fit <- lm(formula, data = d)
return(coef(fit))
}
bootResults <- boot(statistic = bootReg, formula = review_scores_rating ~ instant_bookable + cancellation_policy +
host_since_cat + host_location_cat + host_response_time +
host_is_superhost + host_listings_cat + property_type + room_type +
accommodates + bedrooms + beds + price + security_deposit +
cleaning_fee + extra_people + minimum_nights + amenityBreakfast +
amenityAC + amenityElevator + amenityKitchen + amenityHostGreeting +
amenitySmoking + amenityPets + amenityWifi + amenityTV,
data = listingsRating, R = 2000)
Error:
Error in t.star[r, ] <- res[[r]] :
number of items to replace is not a multiple of replacement length
In addition: Warning message:
In doTryCatch(return(expr), name, parentenv, handler) :
restarting interrupted promise evaluation

Answer 1

The Problem
The problem is your factor variables. When you do an lm() on a subset of your data (which is done over and over again in boot::boot()), you only get coefficients for the factor levels that are present. Then each coefficient draw could be of different lengths. This can be reproduced if you do
debug(boot)
set.seed(123)
bootResults <- boot(statistic = bootReg, formula = review_scores_rating ~ instant_bookable + cancellation_policy +
host_since_cat + host_location_cat + host_response_time +
host_is_superhost + host_listings_cat + property_type + room_type +
accommodates + bedrooms + beds + price + security_deposit +
cleaning_fee + extra_people + minimum_nights + amenityBreakfast +
amenityAC + amenityElevator + amenityKitchen + amenityHostGreeting +
amenitySmoking + amenityPets + amenityWifi + amenityTV,
data = listingsRating, R = 2)
which will allow you to move through the function call one line at a time. After you run the line
res <- if (ncpus > 1L && (have_mc || have_snow)) {
if (have_mc) {
parallel::mclapply(seq_len(RR), fn, mc.cores = ncpus)
}
else if (have_snow) {
list(...)
if (is.null(cl)) {
cl <- parallel::makePSOCKcluster(rep("localhost",
ncpus))
if (RNGkind()[1L] == "L'Ecuyer-CMRG")
parallel::clusterSetRNGStream(cl)
res <- parallel::parLapply(cl, seq_len(RR), fn)
parallel::stopCluster(cl)
res
}
else parallel::parLapply(cl, seq_len(RR), fn)
}
} else lapply(seq_len(RR), fn)
Then try
setdiff(names(res[[1]]), names(res[[2]]))
# [1] "property_typeBarn" "property_typeNature lodge"
There are two factor levels present in the first subset not present in the second. This is causing your problem.
The Solution
Use model.matrix() to expand your factors before hand (following this Stack Overflow post):
df2 <- model.matrix( ~ review_scores_rating + instant_bookable + cancellation_policy +
host_since_cat + host_location_cat + host_response_time +
host_is_superhost + host_listings_cat + property_type + room_type +
accommodates + bedrooms + beds + price + security_deposit +
cleaning_fee + extra_people + minimum_nights + amenityBreakfast +
amenityAC + amenityElevator + amenityKitchen + amenityHostGreeting +
amenitySmoking + amenityPets + amenityWifi + amenityTV - 1, data = listingsRating)
undebug(boot)
set.seed(123)
bootResults <- boot(statistic = bootReg, formula = review_scores_rating ~ .,
data = as.data.frame(df2), R = 2)
(Note that throughout I reduce R to 2 just for faster runtime during debugging).

Answer 2

The way you are defining bootReg and calling it are wrong.
First, you must keep to the order of arguments of the function statistic, in this case bootReg. The first argument is the dataset and the second argument is the indices. Then come other, optional arguments.
bootReg <- function (data, i, formula){
d <- data[i, ]
fit <- lm(formula, data = d)
return(coef(fit))
}
Second, in the call, the other optional arguments will be passed in the dots ... argument. So once again, keep to the order of arguments as defined in help("boot"), section Usage.
bootResults <- boot(data = iris, statistic = bootReg, R = 2000,
formula = Sepal.Length ~ Sepal.Width)
colMeans(bootResults$t)
#[1] 6.5417719 -0.2276868