Subsetting in multiple Imputation ANOVA in R - r

Question

I'd like to ask a question considering subsetting in R. I tried to calculate Multiple Imputation ANOVA using the function mi.anova (miceadds) in R. Actually there is no possibility to only use subsets of the input.
Afterwards I tried to restructure my mids structure into a datlist, subsetting it there and I wanted to return it to a mids structure which was not possible because of the unequal length of the data.frames.
As well I tried to use the with and pool function (mice) to handle the problem, which doesn't give me the expected output, I'd Need.
Actually my last solution would be to rewrite the mi.anova function from the miceadds package which allows me to use subsets. Honestly I don't feel very comfortable when trying to rewrite function, and I don't have any idea how to rewrite it.
Is there maybe anyone who could help me out? Or does anyone suggest another solution?
Thanks a lot & best regards,
Pascal
function (mi.res, formula, type = 2)
{
if (type == 3) {
TAM::require_namespace_msg("car")
}
mi.list <- mi.res
if (class(mi.list) == "mids.1chain") {
mi.list <- mi.list$midsobj
}
if (class(mi.list) == "mids") {
m <- mi.list$m
h1 <- list(rep("", m))
for (ii in 1:m) {
h1[[ii]] <- as.data.frame(mice::complete(mi.list,
ii))
}
mi.list <- h1
}
if (class(mi.res) == "mi.norm") {
mi.list <- mi.list$imp.data
}
if (type == 2) {
anova.imp0 <- lapply(mi.list, FUN = function(dat) {
stats::lm(formula, data = dat)
})
anova.imp <- lapply(anova.imp0, FUN = function(obj) {
summary(stats::aov(obj))
})
}
if (type == 3) {
Nimp <- length(mi.list)
vars <- all.vars(stats::as.formula(formula))[-1]
VV <- length(vars)
ma_contrasts <- as.list(1:VV)
names(ma_contrasts) <- vars
dat <- mi.list[[1]]
for (vv in 1:VV) {
ma_contrasts[[vars[vv]]] <- "contr.sum"
if (!is.factor(dat[, vars[vv]])) {
ma_contrasts[[vars[vv]]] <- NULL
}
}
anova.imp0 <- lapply(as.list(1:Nimp), FUN = function(ii) {
dat <- mi.list[[ii]]
mod1 <- stats::lm(formula, data = dat, contrasts = ma_contrasts)
return(mod1)
})
anova.imp <- lapply(as.list(1:Nimp), FUN = function(ii) {
obj <- anova.imp0[[ii]]
car::Anova(obj, type = 3)
})
}
if (type == 2) {
FF <- nrow(anova.imp[[1]][[1]]) - 1
}
if (type == 3) {
FF <- nrow(anova.imp[[1]]["F value"]) - 2
}
anova.imp.inf <- t(sapply(1:FF, FUN = function(ff) {
micombine.F(sapply(1:(length(anova.imp)), FUN = function(ii) {
if (type == 2) {
r1 <- anova.imp[[ii]][[1]]$"F value"[ff]
}
if (type == 3) {
r1 <- anova.imp[[ii]]$"F value"[ff + 1]
}
return(r1)
}), df1 = ifelse(type == 2, anova.imp[[1]][[1]]$Df[ff],
anova.imp[[1]]["Df"][ff + 1, 1]), display = FALSE)
}))
res <- anova.imp.inf[, c(3, 4, 1, 2)]
res <- matrix(res, ncol = 4)
res[, 3] <- round(res[, 3], 4)
res[, 4] <- round(res[, 4], 6)
g1 <- rownames(anova.imp[[1]][[1]])[1:FF]
if (type == 3) {
g1 <- rownames(anova.imp[[1]])[1 + 1:FF]
}
rownames(res) <- g1
res <- data.frame(res)
if (type == 2) {
SS <- rowMeans(matrix(unlist(lapply(anova.imp, FUN = function(ll) {
ll[[1]][, 2]
})), ncol = length(mi.list)))
}
if (type == 3) {
SS <- rowMeans(matrix(unlist(lapply(anova.imp, FUN = function(ll) {
l2 <- ll["Sum Sq"][-1, 1]
return(l2)
})), ncol = length(mi.list)))
}
r.squared <- sum(SS[-(FF + 1)])/sum(SS)
res$eta2 <- round(SS[-(FF + 1)]/sum(SS), 6)
res$partial.eta2 <- round(SS[-(FF + 1)]/(SS[-(FF + 1)] +
SS[FF + 1]), 6)
g1 <- c("F value", "Pr(>F)")
colnames(res)[3:4] <- g1
colnames(res)[1:2] <- c("df1", "df2")
c1 <- colnames(res)
res <- rbind(res, res[1, ])
rownames(res)[nrow(res)] <- "Residual"
res[nrow(res), ] <- NA
res <- data.frame(SSQ = SS, res)
colnames(res)[-1] <- c1
cat("Univariate ANOVA for Multiply Imputed Data", paste0("(Type ",
type, ")"), " \n\n")
cat("lm Formula: ", formula)
cat(paste("\nR^2=", round(r.squared, 4), sep = ""), "\n")
cat("..........................................................................\n")
cat("ANOVA Table \n")
print(round(res, 5))
invisible(list(r.squared = r.squared, anova.table = res,
type = type))
}