Error in bootstrap test

General
1 
L3_null <- lmer(ACER ~ 1 + (1 | hospital/pid), data = newdata, control = lmerControl(optimizer = "bobyqa"))



as.data.frame(VarCorr(L3_null)) %>%
  select(grp, vcov) %>%
  mutate(
    icc = cumsum(vcov)/sum(vcov)*100
  )

library(lmeresampler)
L3_mdlBS <- bootstrap(L3_mdl1, .f=fixef, type = "case", B = 2000, resample = c(FALSE, TRUE, FALSE))

confint(L3_mdlBS, type = "perc")

Hello,
I am trying to runt he following code however the second section isn't running (iv been sat here for 5 minutes and nothing has run) I think there is a problem with the FALSE, TRUE, FALSE part but I have tried to change it just to FALSE, TRUE and it comes up with this error:

Error in case_bootstrap.merMod(model, .f, B, resample) : 'resample' is not the same length as the number of grouping variables. Please specify whether to resample the data at each level of grouping, including at the observation level.

2

Warning: Some predictor variables are on very different scales: consider rescaling

how do you rescale variables ?

3

We need a reproducible example (reprex)

4 
library(tidyverse)
library(car)
library(Matrix)
library(lme4)

# Read in data set
data <- read_csv("https://uoepsy.github.io/data/dapr3_2122_report1.csv")
head(data)

# Remove N/A from data set 
newdata <- na.omit(data)

#lm controlling for age and education
lm <- lm(ACER ~ age + educ, data = newdata)
summary(lm)

# Cooks Distance
cooksd = cooks.distance(lm)
influential <- as.numeric(names(cooksd) [(cooksd > 4/694)])
cleandata <- newdata[-influential,]

cleandata %>% 
  mutate(hospppt = interaction(hospital,pid)) %>%
  summarise(across(everything(), n_distinct))

L3_mdl <- cleandata %>% mutate(across(c(birthweight, hospital, pid, age, educ)))
                          
L3_mdl0 <- lmer(ACER ~ timepoint * birthweight + (1 | hospital/pid),data = cleandata)

plot(L3_mdl0, type=c("p","smooth"))

Compared to the model (black lines bellow), the participents scores (coloured lines) are more

library(broom.mixed)

augment(L3_mdl0) %>%
  ggplot(aes(x=timepoint, col=pid)) + 
  geom_point(aes(y = ACER))+
  geom_path(aes(y = ACER))+
  geom_path(aes(y = .fitted), col="black", alpha=.3)+
  guides(col="none")+
  facet_wrap(~pid)

L3_mdl1 <- lmer(ACER ~ timepoint * birthweight + (1 + timepoint | hospital/pid), data = cleandata)

plot(L3_mdl1, type=c("p","smooth"))

library(HLMdiag)
infl <- hlm_influence(L3_mdl1, level=1)
dotplot_diag(infl$cooksd, cutoff = "internal")

infpid <- hlm_influence(L3_mdl1, level= "pid:hospital")
dotplot_diag(infpid$cooksd, cutoff = "internal", index = infpid$`pid:hospital`) +
  scale_y_continuous(limits = c(0,.05))

`

del <- case_delete(L3_mdl1, level = "pid:hospital", delete = "10:HSP12")
cbind(del$fixef.original, del$fixef.delete)

del1 <- case_delete(L3_mdl1, level = "pid:hospital", delete = "4:HSP10")
cbind(del1$fixef.original, del1$fixef.delete)


del2 <- case_delete(L3_mdl1, level = "pid:hospital", delete = "3:HSP5")
cbind(del2$fixef.original, del2$fixef.delete)

del3 <- case_delete(L3_mdl1, level = "pid:hospital", delete = "4:HSP11")
cbind(del3$fixef.original, del3$fixef.delete)

infhosp <- hlm_influence(L3_mdl1, level = "hospital")
dotplot_diag(infhosp$cooksd, cutoff = "internal", index = infhosp$hospital)

L3_null <- lmer(ACER ~ 1 + (1 | hospital/pid), data = newdata, control = lmerControl(optimizer = "bobyqa"))



as.data.frame(VarCorr(L3_null)) %>%
  select(grp, vcov) %>%
  mutate(
    icc = cumsum(vcov)/sum(vcov)*100
  )

library(lmeresampler)
L3_mdlBS <- bootstrap(L3_mdl1, .f=fixef, type = "case", B = 2000, resample = c(FALSE, FALSE, TRUE))

is.atomic(L3_mdlBS)

summary(L3_mdlBS)
confint(L3_mdlBS, type = "perc")

That is my code above, I think I might need to dress the warnings on rescaling variables first and then the bootstrap model might work but I don't know how to rescale my variables?

5

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