How can I use function or loop to simplify my program?

everyone
I am doing this:

# LCA data
library(poLCA)
data("carcinoma")
f <- f <- cbind(A, B, C ,D ,E, F, G) ~ 1 

#1.1 Create LCA model k=1~4
lc <-list()
for(i in 1:4){
    lc[[i]] <- poLCA(f, carcinoma, nclass=i)
}

#1.2 Separate list
lca1 <- lc[[1]]
lca2 <- lc[[2]]
lca3 <- lc[[3]]
lca4 <- lc[[4]]


#1.3 The values needed to create Lo-Mendell-Rubin likelihood ratio test 
lca1_ll <-             lca1$llik  #k=1
lca1_param <-          lca1$npar
lca1_classes <- length(lca1$P)

lca2_ll <-             lca2$llik  #k=2
lca2_param <-          lca2$npar
lca2_classes <- length(lca2$P)

lca3_ll <-             lca3$llik  #k=3
lca3_param <-          lca3$npar
lca3_classes <- length(lca3$P) 

lca4_ll <-             lca4$llik  #k=4
lca4_param <-          lca4$npar
lca4_classes <- length(lca4$P)

n <- lca1$Nobs

library(tidyLPA)

#1.4 Calculate Lo-Mendell-Rubin likelihood ratio test p-value
calc_k12 <- 
  calc_lrt(n,
           lca1_ll, lca1_param, lca1_classes,   # k-1
           lca2_ll, lca2_param, lca2_classes)     # k

calc_k23 <-
  calc_lrt(n,
           lca2_ll, lca2_param, lca2_classes,   # k-1
           lca3_ll, lca3_param, lca3_classes)     # k

calc_k34 <-
  calc_lrt(n,
           lca3_ll, lca3_param, lca3_classes,   # k-1
           lca4_ll, lca4_param, lca4_classes)     # k


clutabletest <- as.data.frame(cbind(calc_k12, calc_k23,   # Create table
                                    calc_k34))

clutabletest <- as.data.frame(t(clutabletest))

How can i use function or loop to simplify the program of 1.3(The values needed to create Lo-Mendell-Rubin likelihood ratio test) and 1.4(Calculate Lo-Mendell-Rubin likelihood ratio test p-value) steps.
Thank you!

First, use lapply.

lc <- lapply(1:4, \(i) poLCA::poLCA(f, carcinoma, nclass=i, verbose=0))

Then loop over lc with sapply,

t(sapply(seq_along(lc)[-length(lc)], \(i) {
lrt <- tidyLPA::calc_lrt(lc[[i]]$Nobs, lc[[i]]$llik, lc[[i]]$npar, length(lc[[i]]$P),
lc[[i + 1]]$llik, lc[[i + 1]]$npar, length(lc[[i + 1]]$P))
}))
#             lr    lmr_lr df        lmr_p
# [1,] 414.41596 387.35127  8 9.496374e-79
# [2,]  47.10372  44.02747  8 5.622002e-07
# [3,]   2.42389   2.26559  8 9.717677e-01

or to get the rownames:

lapply(seq_along(lc)[-length(lc)], \(i) {
lrt <- tidyLPA::calc_lrt(lc[[i]]$Nobs, lc[[i]]$llik, lc[[i]]$npar, length(lc[[i]]$P),
lc[[i + 1]]$llik, lc[[i + 1]]$npar, length(lc[[i + 1]]$P))
`rownames<-`(t(lrt), sprintf('%s:%s', i, i + 1))
}) |> do.call(what=rbind)
#            lr    lmr_lr df        lmr_p
# 1:2 414.41596 387.35127  8 9.496374e-79
# 2:3  47.10372  44.02747  8 5.622002e-07
# 3:4   2.42389   2.26559  8 9.717677e-01

EDIT:

If you do that often, you could wrap it all in a function:

fun <- \(f, data, n) {
lc <- lapply(seq_len(n), \(i) poLCA::poLCA(f, data, nclass=i, verbose=0))
tests <- lapply(seq_along(lc)[-length(lc)], \(i) {
lrt <- tidyLPA::calc_lrt(lc[[i]]$Nobs, lc[[i]]$llik, lc[[i]]$npar, length(lc[[i]]$P),
lc[[i + 1]]$llik, lc[[i + 1]]$npar, length(lc[[i + 1]]$P))
`rownames<-`(t(lrt), sprintf('%s:%s', i, i + 1))
})
do.call('rbind', tests)
}
fun(f, carcinoma, n=4)
#             lr    lmr_lr df        lmr_p
# 1:2 414.415961 387.35127  8 9.496374e-79
# 2:3  47.103717  44.02747  8 5.622002e-07
# 3:4   4.880175   4.56146  8 8.032532e-01

Data:

data("carcinoma")
f <- cbind(A, B, C, D, E, F, G) ~ 1