# ============================================================
# TUGAS BIOSTATISTIKA INTERMEDIETE
# PENGARUH TEH DAUN KELOR TERHADAP PENINGKATAN KADAR HEMOGBLOBIN PENEDERITA ANEMIA
# Nama : Fadillah Hana Hafifah
# NIM : 2611018008
# Rpubs : Dilla01
# Mata Kuliah : Biostatistika Intermediete
# Magister Kesehatan Masyarakat Universitas Mulawarman
#
# Data: https://jurnal.stikesmus.ac.id/index.php/avicenna/article/view/590/395
# Analisis sekunder menggunakan RM ANOVA, Mixed Design ANOVA, dan LMM.
# ============================================================
# ======================= PARAMETER ANALISIS =========================
FILE_DATA <- "dtdaunkelor.xlsx"
SHEET_DATA <- "Data"
KELOMPOK_FOKUS <- "Teh Daun Kelor"
FOLDER_HASIL <- "hasil analisis teh daun kelor"
INSTALL_PAKET_OTOMATIS <- TRUE
BUKA_HTML_OTOMATIS <- TRUE
TAMPILKAN_GRAFIK_RSTUDIO <- TRUE
# ======================== PROGRAM UTAMA ==============================
jalankan_analisis <- function() {
waktu_mulai <- Sys.time()
# ---------- Paket ----------
paket <- c(
"readxl", "dplyr", "tidyr", "ggplot2",
"afex", "emmeans", "rstatix", "car", "effectsize",
"lme4", "lmerTest", "pbkrtest", "performance",
"ggpubr", "knitr", "htmltools", "base64enc", "tibble"
)
belum <- paket[!vapply(paket, requireNamespace, logical(1), quietly = TRUE)]
if (length(belum)) {
if (!INSTALL_PAKET_OTOMATIS)
stop("Paket belum tersedia: ", paste(belum, collapse = ", "))
message("Menginstal paket: ", paste(belum, collapse = ", "))
install.packages(belum, repos = "https://cloud.r-project.org")
}
gagal_paket <- paket[!vapply(paket, requireNamespace, logical(1), quietly = TRUE)]
if (length(gagal_paket))
stop("Instalasi gagal: ", paste(gagal_paket, collapse = ", "))
suppressPackageStartupMessages({
library(dplyr)
library(tidyr)
library(ggplot2)
library(afex)
library(emmeans)
library(rstatix)
library(car)
library(effectsize)
library(lme4)
library(lmerTest)
library(pbkrtest)
library(performance)
library(ggpubr)
library(knitr)
library(htmltools)
library(base64enc)
library(tibble)
})
opsi_lama <- options(contrasts = c("contr.sum", "contr.poly"))
on.exit(options(opsi_lama), add = TRUE)
# ---------- Lokasi file ----------
sumber <- unlist(lapply(sys.frames(), function(fr) {
if (!is.null(fr$ofile)) as.character(fr$ofile) else character(0)
}))
if (requireNamespace("rstudioapi", quietly = TRUE) &&
rstudioapi::isAvailable()) {
aktif <- tryCatch(
rstudioapi::getSourceEditorContext()$path,
error = function(e) ""
)
if (nzchar(aktif)) sumber <- c(sumber, aktif)
}
kandidat <- unique(c(
FILE_DATA,
file.path(dirname(sumber), FILE_DATA),
file.path(getwd(), FILE_DATA)
))
kandidat <- kandidat[file.exists(kandidat)]
# Fallback: cari dtdaunkelor dengan ekstensi Excel apa pun
if (!length(kandidat)) {
folder_script <- if (length(sumber)) dirname(tail(sumber, 1)) else getwd()
kandidat_excel <- list.files(
folder_script,
pattern = "^dtdaunkelor\\\\.(xlsx|xls|xlsm)$",
full.names = TRUE,
ignore.case = TRUE
)
if (length(kandidat_excel)) {
kandidat <- kandidat_excel
}
}
if (!length(kandidat)) {
stop(
"Berkas data tidak ditemukan.\\n",
"R sedang mencari: ", FILE_DATA, "\\n",
"Pastikan file Excel dan script R berada pada folder yang sama."
)
}
path_data <- normalizePath(
kandidat[1], winslash = "/", mustWork = TRUE
)
folder <- file.path(dirname(path_data), FOLDER_HASIL)
dir.create(folder, recursive = TRUE, showWarnings = FALSE)
folder <- normalizePath(folder, winslash = "/", mustWork = TRUE)
message("Data: ", path_data)
message("Hasil: ", folder)
# ---------- Fungsi laporan ----------
isi <- character(0)
status <- data.frame()
tambah <- function(html) isi <<- c(isi, html)
escape <- function(x) {
as.character(htmltools::htmlEscape(paste(x, collapse = "\n")))
}
narasi <- function(x) {
tambah(paste0("<p>", escape(x), "</p>"))
}
output <- function(x) {
teks <- capture.output(print(x))
cat(paste(teks, collapse = "\n"), "\n")
tambah(paste0("<pre>", escape(teks), "</pre>"))
invisible(x)
}
tabel <- function(x, nama) {
x <- as.data.frame(x)
write.csv(
x,
file.path(folder, paste0(nama, ".csv")),
row.names = FALSE,
na = "",
fileEncoding = "UTF-8"
)
if (nrow(x)) {
tambah(
paste0(
"<div class='table'>",
as.character(knitr::kable(
x, format = "html", digits = 4
)),
"</div>"
)
)
} else {
narasi("Tidak ada baris untuk tabel ini.")
}
print(x)
invisible(x)
}
gambar <- function(p, nama, lebar = 10, tinggi = 6) {
path <- file.path(folder, paste0(nama, ".png"))
ggplot2::ggsave(
path, plot = p,
width = lebar, height = tinggi, dpi = 180
)
if (TAMPILKAN_GRAFIK_RSTUDIO) {
tryCatch(print(p), error = function(e) {
message(
"Tampilan Plots gagal, tetapi PNG tetap tersimpan: ",
conditionMessage(e)
)
})
}
tambah(
paste0(
"<figure><img src='",
base64enc::dataURI(file = path, mime = "image/png"),
"' alt='", escape(nama),
"'><figcaption>", escape(nama),
"</figcaption></figure>"
)
)
message("Grafik tersimpan: ", path)
}
bagian <- function(judul, f) {
message("\n>>> ", judul)
tambah(paste0("<h2>", escape(judul), "</h2>"))
warnings <- character(0)
ok <- tryCatch(
withCallingHandlers(
{
f()
TRUE
},
warning = function(w) {
warnings <<- c(warnings, conditionMessage(w))
invokeRestart("muffleWarning")
}
),
error = function(e) {
narasi(paste("BAGIAN GAGAL:", conditionMessage(e)))
message("Bagian gagal: ", conditionMessage(e))
FALSE
}
)
if (length(warnings)) {
narasi(
paste(
"Peringatan estimasi:",
paste(unique(warnings), collapse = "; ")
)
)
}
status <<- rbind(
status,
data.frame(
bagian = judul,
status = if (ok) "Selesai" else "Gagal",
peringatan = paste(unique(warnings), collapse = "; ")
)
)
invisible(ok)
}
fp <- function(p) {
if (length(p) == 0 || is.na(p)) return("tidak tersedia")
if (p < .001) "< 0,001"
else paste0(
"= ",
formatC(
p, digits = 3,
format = "f",
decimal.mark = ","
)
)
}
ringkas_interaksi <- function(tab, pcol, model) {
idx <- which(grepl(
"kelompok:waktu|waktu:kelompok",
rownames(tab)
))
if (!length(idx)) {
narasi(
"Efek interaksi tidak teridentifikasi secara otomatis pada tabel model."
)
return(invisible(NULL))
}
p <- as.numeric(tab[idx[1], pcol])
narasi(
paste0(
model,
": interaksi kelompok × waktu memiliki p ",
fp(p),
". ",
if (is.na(p)) {
"Pengujian tidak dapat disimpulkan."
} else if (p < .05) {
"Terdapat bukti bahwa pola perubahan Hb berbeda antarkelompok."
} else {
"Belum ditemukan bukti bahwa pola perubahan Hb berbeda antarkelompok. "
}
)
)
}
# ========================= 1. MEMBACA DATA ==========================
bagian("1. Sumber, desain, dan kelengkapan data", function() {
raw <- as.data.frame(
readxl::read_excel(
path_data,
sheet = SHEET_DATA
)
)
names(raw) <- trimws(names(raw))
kolom_hb <- c(
"Hb_Minggu0",
"Hb_Minggu1",
"Hb_Minggu2",
"Hb_Minggu3"
)
wajib <- c(
"ID",
"Kelompok",
"Umur",
"Pendidikan",
kolom_hb
)
if (!all(wajib %in% names(raw))) {
stop(
"Kolom tidak ditemukan: ",
paste(setdiff(wajib, names(raw)), collapse = ", ")
)
}
level_kelompok <<- c(
"Kapsul Gelatin",
"Teh Daun Kelor",
"Tablet Fe"
)
label_waktu <<- c(
"M0", "M1", "M2", "M3"
)
minggu_nilai <<- c(0, 1, 2, 3)
kel <- trimws(as.character(raw[["Kelompok"]]))
if (anyNA(kel) || any(!kel %in% level_kelompok)) {
stop("Label kelompok tidak dikenali.")
}
if (anyNA(raw[["ID"]]) || anyDuplicated(raw[["ID"]])) {
stop("ID hilang atau duplikat.")
}
if (!KELOMPOK_FOKUS %in% level_kelompok) {
stop("KELOMPOK_FOKUS tidak dikenali.")
}
if (!all(vapply(raw[kolom_hb], is.numeric, logical(1)))) {
stop("Kolom Hb harus numerik.")
}
if (
any(
!is.finite(as.matrix(raw[kolom_hb])) &
!is.na(as.matrix(raw[kolom_hb]))
)
) {
stop("Terdapat nilai Hb tak hingga.")
}
dat_wide <<- data.frame(
id = factor(raw[["ID"]]),
kelompok = factor(
kel,
levels = level_kelompok
),
usia = raw[["Umur"]],
pendidikan = factor(raw[["Pendidikan"]]),
Hb_M0 = raw[[kolom_hb[1]]],
Hb_M1 = raw[[kolom_hb[2]]],
Hb_M2 = raw[[kolom_hb[3]]],
Hb_M3 = raw[[kolom_hb[4]]]
)
hb_names <<- c(
"Hb_M0", "Hb_M1", "Hb_M2", "Hb_M3"
)
dat_wide$lengkap <- complete.cases(
dat_wide[hb_names]
)
dat_long <<- dat_wide |>
pivot_longer(
all_of(hb_names),
names_to = "waktu",
values_to = "hb"
) |>
mutate(
waktu = factor(
waktu,
levels = hb_names,
labels = label_waktu
),
minggu = minggu_nilai[
as.integer(waktu)
]
) |>
arrange(id, waktu)
wide_cc <<- droplevels(
dat_wide[dat_wide$lengkap, ]
)
long_cc <<- droplevels(
filter(dat_long, lengkap)
)
long_obs <<- droplevels(
filter(dat_long, !is.na(hb))
)
d1 <<- droplevels(
filter(
long_cc,
kelompok == KELOMPOK_FOKUS
)
)
if (
nrow(wide_cc) < 10 ||
n_distinct(d1$id) < 4
) {
stop("Peserta lengkap terlalu sedikit.")
}
write.csv(
dat_wide,
file.path(folder, "data_wide.csv"),
row.names = FALSE,
na = ""
)
write.csv(
dat_long,
file.path(folder, "data_long.csv"),
row.names = FALSE,
na = ""
)
write.csv(
wide_cc,
file.path(folder, "data_complete_case.csv"),
row.names = FALSE,
na = ""
)
message(
"Data berhasil dibaca: ", nrow(dat_wide),
" responden; ", n_distinct(dat_wide$kelompok),
" kelompok; ", nrow(long_obs),
" pengukuran tersedia."
)
narasi(
paste(
"Dataset simulasi terdiri dari",
nrow(dat_wide),
"responden dan",
n_distinct(dat_wide$kelompok),
"kelompok.",
"Setiap responden memiliki pengukuran Hb pada minggu 0, 1, 2, dan 3."
)
)
narasi(
paste(
nrow(wide_cc),
"responden memiliki data lengkap untuk keempat waktu."
)
)
tabel(
dat_long |>
group_by(kelompok, waktu) |>
summarise(
n_total = n(),
n_tersedia = sum(!is.na(hb)),
n_hilang = sum(is.na(hb)),
.groups = "drop"
),
"01_kelengkapan"
)
tabel(
dat_wide |>
group_by(kelompok) |>
summarise(
n_total = n(),
n_lengkap = sum(lengkap),
n_tidak_lengkap = sum(!lengkap),
.groups = "drop"
),
"01_peserta"
)
})
# ==================== 2. DESKRIPTIF & VISUALISASI ==================
bagian("2. Statistik deskriptif dan visualisasi", function() {
desk <- function(d) {
d |>
group_by(kelompok, waktu) |>
summarise(
n = n(),
mean = mean(hb, na.rm = TRUE),
sd = sd(hb, na.rm = TRUE),
median = median(hb, na.rm = TRUE),
min = min(hb, na.rm = TRUE),
max = max(hb, na.rm = TRUE),
se = sd / sqrt(n),
lower_ci = mean - qt(.975, n - 1) * se,
upper_ci = mean + qt(.975, n - 1) * se,
.groups = "drop"
)
}
tabel(
desk(long_obs),
"02_deskriptif_semua_tersedia"
)
tabel(
desk(long_cc),
"02_deskriptif_complete_case"
)
# Perubahan baseline ke minggu 3
perubahan <- wide_cc |>
mutate(
perubahan_M3_M0 = Hb_M3 - Hb_M0
) |>
group_by(kelompok) |>
summarise(
n = n(),
mean_perubahan = mean(
perubahan_M3_M0
),
sd_perubahan = sd(
perubahan_M3_M0
),
median_perubahan = median(
perubahan_M3_M0
),
min_perubahan = min(
perubahan_M3_M0
),
max_perubahan = max(
perubahan_M3_M0
),
.groups = "drop"
)
tabel(
perubahan,
"02_perubahan_baseline_minggu3"
)
p1 <- ggplot(
long_obs,
aes(
x = minggu,
y = hb,
colour = kelompok,
group = kelompok
)
) +
stat_summary(
fun = mean,
geom = "line",
linewidth = 1
) +
stat_summary(
fun = mean,
geom = "point",
size = 2
) +
stat_summary(
fun.data = mean_cl_normal,
geom = "errorbar",
width = .15
) +
scale_x_continuous(
breaks = minggu_nilai
) +
theme_bw(base_size = 12) +
theme(
legend.position = "bottom"
) +
labs(
title = "Profil Rerata Hb Menurut Kelompok dan Waktu",
x = "Minggu",
y = "Hb (g/dL)",
colour = "Kelompok"
)
gambar(
p1,
"02_profil_hb",
10,
6
)
p2 <- ggplot(
long_cc,
aes(
x = minggu,
y = hb,
group = id
)
) +
geom_line(alpha = .15) +
stat_summary(
aes(group = kelompok),
fun = mean,
geom = "line",
colour = "firebrick",
linewidth = 1.1
) +
facet_wrap(~kelompok) +
scale_x_continuous(
breaks = minggu_nilai
) +
theme_bw() +
labs(
title = "Lintasan Individu Hb: Peserta Lengkap",
x = "Minggu",
y = "Hb (g/dL)"
)
gambar(
p2,
"02_lintasan_individu",
11,
7
)
})
# ============================ 3. ASUMSI =============================
bagian("3. Pemeriksaan asumsi pada peserta lengkap", function() {
tabel(
long_cc |>
group_by(kelompok, waktu) |>
identify_outliers(hb),
"03_outlier"
)
tabel(
long_cc |>
group_by(kelompok, waktu) |>
shapiro_test(hb),
"03_shapiro"
)
gambar(
ggpubr::ggqqplot(
long_cc,
"hb",
facet.by = c("kelompok", "waktu")
),
"03_qq_per_sel",
12,
9
)
tabel(
long_cc |>
group_by(waktu) |>
levene_test(hb ~ kelompok),
"03_levene"
)
# Box's M untuk kovarians antar waktu
tabel(
rstatix::box_m(
wide_cc[c("Hb_M0", "Hb_M1", "Hb_M2", "Hb_M3")],
wide_cc$kelompok
),
"03_box_m"
)
narasi(
paste(
"Interpretasi uji asumsi harus mempertimbangkan ukuran sampel,",
"Q-Q plot, outlier, dan konteks desain.",
"Nilai p > 0,05 bukan bukti mutlak bahwa asumsi terpenuhi."
)
)
})
# ======================== 4. RM ANOVA FOKUS =========================
aov1 <- NULL
aov2 <- NULL
bagian("4. RM ANOVA satu arah: Kelompok Teh Daun Kelor", function() {
narasi(
paste(
"Kelompok fokus:",
KELOMPOK_FOKUS,
"—",
n_distinct(d1$id),
"peserta lengkap."
)
)
gambar(
ggpubr::ggqqplot(
d1,
"hb",
facet.by = "waktu"
) +
labs(
title = "Q-Q Hb per Waktu: Kelompok Teh Daun Kelor"
),
"04_qq_kelompok_fokus",
10,
6
)
output(
rstatix::anova_test(
data = d1,
dv = hb,
wid = id,
within = waktu,
effect.size = "pes"
)
)
aov1 <<- afex::aov_ez(
id = "id",
dv = "hb",
data = d1,
within = "waktu",
anova_table = list(
es = c("ges", "pes"),
correction = "GG"
)
)
output(aov1)
output(summary(aov1))
output(aov1$Anova)
tabel(
as.data.frame(
aov1$anova_table
) |>
tibble::rownames_to_column("efek"),
"04_anova_rm_gg"
)
output(
effectsize::eta_squared(
aov1,
partial = TRUE
)
)
p <- as.numeric(
aov1$anova_table[
1,
"Pr(>F)"
]
)
narasi(
paste0(
"Efek waktu dengan koreksi Greenhouse-Geisser: p ",
fp(p),
". "
)
)
})
# ======================= 5. POST HOC WAKTU ==========================
bagian("5. Post hoc perubahan waktu dalam kelompok Teh Daun Kelor", function() {
if (is.null(aov1))
stop("Model RM ANOVA belum tersedia.")
em <- emmeans::emmeans(
aov1,
~ waktu,
model = "multivariate"
)
tabel(
summary(em),
"05_emmeans_waktu"
)
tabel(
summary(
pairs(
em,
adjust = "bonferroni"
),
infer = c(TRUE, TRUE)
),
"05_pasangan_waktu_bonferroni"
)
tabel(
summary(
contrast(
em,
"trt.vs.ctrl",
ref = 1,
adjust = "holm"
),
infer = c(TRUE, TRUE)
),
"05_vs_baseline_holm"
)
# Dengan 4 waktu (0,1,2,3), tren dapat mencakup linear, kuadratik, dan kubik.
pol <- poly(
minggu_nilai,
degree = 3
)
koef <- list(
linear_minggu = pol[, 1],
kuadratik_minggu = pol[, 2],
kubik_minggu = pol[, 3]
)
tabel(
summary(
contrast(
em,
method = koef,
adjust = "holm"
),
infer = c(TRUE, TRUE)
),
"05_tren_waktu"
)
narasi(
paste(
"Post hoc digunakan untuk mengetahui pasangan waktu yang berbeda",
"setelah efek waktu diuji pada RM ANOVA.",
"Koreksi Bonferroni digunakan untuk pasangan waktu."
)
)
})
# ====================== 6. FRIEDMAN + WILCOXON ======================
bagian("6. Pembanding nonparametrik: Friedman dan Wilcoxon", function() {
tabel(
rstatix::friedman_test(
d1,
hb ~ waktu | id
),
"06_friedman"
)
tabel(
rstatix::friedman_effsize(
d1,
hb ~ waktu | id
),
"06_kendall_w"
)
w <- wide_cc[
wide_cc$kelompok == KELOMPOK_FOKUS,
]
hb_names <<- c(
"Hb_M0",
"Hb_M1",
"Hb_M2",
"Hb_M3"
)
ij <- combn(
seq_along(hb_names),
2
)
hasil <- lapply(
seq_len(ncol(ij)),
function(j) {
a <- ij[1, j]
b <- ij[2, j]
wt <- wilcox.test(
w[[hb_names[a]]],
w[[hb_names[b]]],
paired = TRUE,
exact = FALSE
)
data.frame(
waktu_1 = c(
"M0", "M0", "M0",
"M1", "M1",
"M2"
)[j],
waktu_2 = c(
"M1", "M2", "M3",
"M2", "M3",
"M3"
)[j],
n_pasangan = nrow(w),
V = unname(wt$statistic),
p = wt$p.value
)
}
) |>
bind_rows() |>
mutate(
p_bonferroni = p.adjust(
p,
"bonferroni"
)
)
tabel(
hasil,
"06_wilcoxon"
)
narasi(
paste(
"Friedman menguji perubahan Hb antarwaktu dalam satu kelompok",
"secara nonparametrik. Wilcoxon signed-rank digunakan untuk",
"perbandingan pasangan waktu."
)
)
})
# ======================= 7. MIXED ANOVA =============================
bagian("7. Mixed Design ANOVA: Kelompok × Waktu", function() {
aov2 <<- afex::aov_ez(
id = "id",
dv = "hb",
data = long_cc,
between = "kelompok",
within = "waktu",
anova_table = list(
es = c("ges", "pes"),
correction = "GG"
)
)
output(aov2)
output(summary(aov2))
output(aov2$Anova)
tabel(
as.data.frame(
aov2$anova_table
) |>
tibble::rownames_to_column("efek"),
"07_mixed_anova_gg"
)
output(
effectsize::eta_squared(
aov2,
partial = TRUE
)
)
ringkas_interaksi(
aov2$anova_table,
"Pr(>F)",
"Mixed Design ANOVA"
)
})
# =================== 8. GRAFIK INTERAKSI ===========================
bagian("8. Visualisasi interaksi Kelompok × Waktu", function() {
if (is.null(aov2))
stop("Mixed ANOVA belum tersedia.")
gambar(
afex::afex_plot(
aov2,
x = "waktu",
trace = "kelompok",
error = "within",
mapping = c(
"colour",
"shape",
"linetype"
)
) +
labs(
title = "Mixed Design ANOVA: Kelompok × Waktu",
x = "Waktu",
y = "Hb (g/dL)"
),
"08_interaksi_anova",
10,
6
)
})
# ==================== 9. EFEK SEDERHANA =============================
bagian("9. Efek sederhana dan post hoc antarkelompok", function() {
if (is.null(aov2))
stop("Mixed ANOVA belum tersedia.")
jt1 <- as.data.frame(
joint_tests(
aov2,
by = "kelompok",
model = "multivariate"
)
)
jt1$p_holm <- p.adjust(
jt1$p.value,
"holm"
)
tabel(
jt1,
"09_efek_waktu_per_kelompok"
)
jt2 <- as.data.frame(
joint_tests(
aov2,
by = "waktu",
model = "multivariate"
)
)
jt2$p_holm <- p.adjust(
jt2$p.value,
"holm"
)
tabel(
jt2,
"09_efek_kelompok_per_waktu"
)
ew <- emmeans(
aov2,
~ waktu | kelompok,
model = "multivariate"
)
tabel(
summary(
contrast(
ew,
"trt.vs.ctrl",
ref = 1,
adjust = "holm"
),
infer = c(TRUE, TRUE)
),
"09_perubahan_vs_baseline"
)
ek <- emmeans(
aov2,
~ kelompok | waktu,
model = "multivariate"
)
tabel(
summary(
pairs(
ek,
adjust = "tukey"
),
infer = c(TRUE, TRUE)
),
"09_kelompok_per_waktu"
)
})
# =================== 10. KONTRAS INTERAKSI ==========================
bagian("10. Kontras perubahan baseline–minggu 3 dan tren", function() {
if (is.null(aov2))
stop("Mixed ANOVA belum tersedia.")
e <- emmeans(
aov2,
~ waktu * kelompok,
model = "multivariate"
)
perubahan <- contrast(
e,
interaction = list(
waktu = list(
"M3-M0" = c(-1, 0, 0, 1)
),
kelompok = "pairwise"
),
adjust = "holm"
)
tabel(
summary(
perubahan,
infer = c(TRUE, TRUE)
),
"10_selisih_perubahan_M3_M0"
)
# Kontras tren linear
lin <- poly(
minggu_nilai,
degree = 3
)[, 1]
tren <- contrast(
e,
interaction = list(
waktu = list(
linear_minggu = lin
),
kelompok = "pairwise"
),
adjust = "holm"
)
tabel(
summary(
tren,
infer = c(TRUE, TRUE)
),
"10_tren_linear_interaksi"
)
narasi(
paste(
"Kontras M3-M0 membandingkan besar perubahan Hb dari baseline",
"hingga minggu ke-3 antarkelompok.",
"Kontras tren linear membandingkan pola kenaikan Hb sepanjang waktu."
)
)
})
# ======================== 11. LMM ===================================
lmm <- NULL
lmm_ri <- NULL
lmm_rs <- NULL
bagian("11. Linear Mixed Model (LMM)", function() {
# Random intercept
lmm_ri <<- lmerTest::lmer(
hb ~ kelompok * waktu + (1 | id),
data = long_obs,
REML = TRUE,
control = lmerControl(
optimizer = "bobyqa"
)
)
# Random slope menggunakan waktu numerik
lmm_rs <<- tryCatch(
lmerTest::lmer(
hb ~ kelompok * waktu +
(1 + minggu | id),
data = long_obs,
REML = TRUE,
control = lmerControl(
optimizer = "bobyqa",
optCtrl = list(
maxfun = 200000
)
)
),
error = function(e) {
narasi(
paste(
"Random slope gagal diestimasi;",
"model random intercept digunakan.",
conditionMessage(e)
)
)
NULL
}
)
info_model <- function(m, nama) {
data.frame(
model = nama,
n_observasi = nobs(m),
AIC = AIC(m),
BIC = BIC(m),
singular = lme4::isSingular(m),
konvergensi = paste(
m@optinfo$conv$lme4$messages,
collapse = "; "
)
)
}
info_ri <- info_model(
lmm_ri,
"random_intercept"
)
info_rs <- if (!is.null(lmm_rs)) {
info_model(
lmm_rs,
"random_intercept_slope"
)
} else {
NULL
}
tabel(
bind_rows(
info_ri,
info_rs
),
"11_perbandingan_model"
)
layak <- !is.null(lmm_rs) &&
!isSingular(lmm_rs) &&
is.null(lmm_rs@optinfo$conv$lme4$messages)
pakai_rs <- layak &&
AIC(lmm_rs) < AIC(lmm_ri)
lmm <<- if (pakai_rs) {
lmm_rs
} else {
lmm_ri
}
narasi(
paste(
"Model yang dipakai:",
if (pakai_rs)
"random intercept + random slope minggu"
else
"random intercept"
)
)
output(
summary(lmm)
)
output(
VarCorr(lmm)
)
hasil <- anova(
lmm,
type = 3,
ddf = "Kenward-Roger"
)
tabel(
as.data.frame(hasil) |>
tibble::rownames_to_column("efek"),
"11_lmm_kr"
)
ringkas_interaksi(
hasil,
"Pr(>F)",
"LMM"
)
output(
performance::icc(lmm_ri)
)
e <- emmeans(
lmm,
~ waktu * kelompok,
lmer.df = "kenward-roger"
)
tabel(
summary(
contrast(
e,
interaction = list(
waktu = list(
"M3-M0" = c(-1, 0, 0, 1)
),
kelompok = "pairwise"
),
adjust = "holm"
),
infer = c(TRUE, TRUE)
),
"11_lmm_selisih_perubahan"
)
tabel(
summary(
emmeans(
lmm,
~ kelompok | waktu,
lmer.df = "kenward-roger"
)
),
"11_lmm_rerata_model"
)
})
# ======================== 12. DIAGNOSTIK LMM ========================
bagian("12. Diagnostik LMM", function() {
if (is.null(lmm))
stop("LMM belum tersedia.")
diag <- data.frame(
fitted = fitted(lmm),
residual = resid(lmm),
kelompok = long_obs$kelompok,
waktu = long_obs$waktu
)
gambar(
ggplot(
diag,
aes(sample = residual)
) +
stat_qq() +
stat_qq_line() +
theme_bw() +
labs(
title = "Q-Q Residual LMM"
),
"12_qq_residual"
)
gambar(
ggplot(
diag,
aes(
fitted,
residual,
colour = kelompok
)
) +
geom_point(alpha = .35) +
geom_hline(
yintercept = 0,
linetype = 2
) +
theme_bw() +
labs(
x = "Nilai prediksi",
y = "Residual",
title = "Residual terhadap Prediksi"
),
"12_residual_prediksi"
)
gambar(
ggplot(
diag,
aes(
waktu,
residual,
fill = kelompok
)
) +
geom_boxplot() +
theme_bw() +
labs(
title = "Residual menurut Kelompok dan Waktu"
),
"12_residual_per_sel"
)
output(
performance::check_singularity(lmm)
)
output(
performance::check_convergence(lmm)
)
narasi(
paste(
"Diagnostik digunakan untuk menilai residual, outlier,",
"singularitas, dan konvergensi model."
)
)
})
# ================= 13. GRAFIK PREDIKSI LMM ==========================
bagian("13. Grafik rerata prediksi LMM", function() {
if (is.null(lmm))
stop("LMM belum tersedia.")
pred <- as.data.frame(
summary(
emmeans(
lmm,
~ kelompok | waktu,
lmer.df = "kenward-roger"
)
)
)
pred$minggu <- minggu_nilai[
as.integer(pred$waktu)
]
gambar(
ggplot(
pred,
aes(
minggu,
emmean,
colour = kelompok,
group = kelompok
)
) +
geom_line(linewidth = 1) +
geom_point(size = 2.5) +
geom_errorbar(
aes(
ymin = lower.CL,
ymax = upper.CL
),
width = .12
) +
scale_x_continuous(
breaks = minggu_nilai
) +
theme_bw(base_size = 12) +
theme(
legend.position = "bottom"
) +
labs(
title = "Rerata Prediksi LMM dan 95% CI",
x = "Minggu",
y = "Hb (g/dL)",
colour = "Kelompok"
),
"13_profil_prediksi_lmm",
10,
6
)
})
# =============== 14. LMM COMPLETE CASE PEMBANDING ==================
bagian("14. LMM pada peserta lengkap: pembanding ANOVA", function() {
if (is.null(lmm))
stop("LMM belum tersedia.")
mcc <- update(
lmm,
data = long_cc
)
output(
summary(mcc)
)
tab <- anova(
mcc,
type = 3,
ddf = "Kenward-Roger"
)
tabel(
as.data.frame(tab) |>
tibble::rownames_to_column("efek"),
"14_lmm_complete_case"
)
ringkas_interaksi(
tab,
"Pr(>F)",
"LMM peserta lengkap"
)
narasi(
paste(
"Karena data simulasi tidak memiliki missing value,",
"hasil LMM seluruh pengukuran dan complete case diharapkan",
"sangat mirip."
)
)
})
# ===================== 15. INFORMASI & BATAS ========================
bagian("15. Informasi sesi dan batas interpretasi", function() {
narasi(
paste(
"Taraf signifikansi ditetapkan pada α = 0,05.",
"Interpretasi mempertimbangkan nilai p, ukuran efek,",
"interval kepercayaan, dan arah perubahan."
)
)
narasi(
paste(
"DATA INI ADALAH DATA SIMULASI.",
"Kesamaan dengan angka jurnal hanya bersifat agregat.",
"Hasil p-value dari data simulasi tidak boleh diklaim sebagai",
"hasil analisis ulang data asli penelitian."
)
)
output(
sessionInfo()
)
})
# ======================= 16. SIMPAN LAPORAN =========================
tambah("<h2>Status setiap bagian</h2>")
tabel(
status,
"status_analisis"
)
tambah(
"<details><summary>Catatan penggunaan</summary>"
)
narasi(
paste(
"Letakkan file Excel dan script R dalam folder yang sama.",
"Kemudian jalankan seluruh script.",
"Folder hasil akan dibuat otomatis di samping file Excel."
)
)
tambah("</details>")
css <- paste0(
"body{font:16px/1.6 Arial,sans-serif;",
"max-width:1100px;margin:40px auto;padding:0 24px;",
"color:#18332e}",
"h1,h2{color:#0b6b4f}",
"h2{border-top:1px solid #d9e4e0;padding-top:24px}",
"pre{background:#f0f5f3;padding:16px;overflow:auto;",
"font-size:13px;color:#182822}",
".table{overflow:auto}",
"table{border-collapse:collapse;font-size:13px;width:100%}",
"td,th{border:1px solid #d9e4e0;padding:8px;text-align:left}",
"th{background:#e7f2ee}",
"img{max-width:100%}",
"summary{cursor:pointer;font-weight:bold}"
)
selesai <- sum(
status$status == "Selesai"
)
header <- paste0(
"<!doctype html><html lang='id'><head>",
"<meta charset='UTF-8'>",
"<meta name='viewport' content='width=device-width,initial-scale=1'>",
"<title>Analisis Hb Teh Daun Kelor — Pengukuran Berulang</title>",
"<style>", css, "</style></head><body>",
"<h1>Analisis Pengukuran Berulang: Hemoglobin</h1>",
"<table class='identitas'><tbody>",
"<tr><th>Nama</th><td>Fadillah Hana Hafifah</td></tr>",
"<tr><th>NIM</th><td>2611018008</td></tr>",
"<tr><th>Rpubs</th><td>Dilla01</td></tr>",
"<tr><th>Mata Kuliah</th><td>Biostatistika Intermediete</td></tr>",
"<tr><th>Program Studi / Institusi</th>",
"<td>Magister Kesehatan Masyarakat Universitas Mulawarman</td></tr>",
"</tbody></table>",
"<p>Data simulasi · 3 kelompok × 4 waktu (minggu 0–3)</p>",
"<p>Dibuat: ",
escape(format(Sys.time())),
" · Bagian selesai: ",
selesai,
"/",
nrow(status),
"</p>"
)
path_html <- file.path(
folder,
"laporan_analisis_hb_teh_daun_kelor.html"
)
writeLines(
c(
header,
isi,
"</body></html>"
),
path_html,
useBytes = TRUE
)
saveRDS(
list(
data_wide = dat_wide,
data_long = dat_long,
aov_satu = aov1,
aov_campuran = aov2,
lmm = lmm,
status = status
),
file.path(
folder,
"objek_analisis_hb.rds"
)
)
writeLines(
capture.output(sessionInfo()),
file.path(
folder,
"sessionInfo.txt"
)
)
message(
"\nLaporan tersedia: ",
path_html,
"\nDurasi: ",
round(
as.numeric(
difftime(
Sys.time(),
waktu_mulai,
units = "secs"
)
),
1
),
" detik."
)
if (
any(status$status == "Gagal")
) {
message(
"Terdapat bagian analisis yang gagal."
)
}
if (
BUKA_HTML_OTOMATIS &&
interactive()
) {
try(
utils::browseURL(path_html),
silent = TRUE
)
}
invisible(
list(
folder = folder,
status = status
)
)
}
# ======================= EKSEKUSI ANALISIS =============================
hasil_analisis <- jalankan_analisis()
## Registered S3 method overwritten by 'lme4':
## method from
## na.action.merMod car
## Data: D:/S2 MKM/KULIAH/BIOSTATISTIK/tugas pak fathur/revisi/Analisis teh daun kelor/ke-2/dtdaunkelor.xlsx
## Hasil: D:/S2 MKM/KULIAH/BIOSTATISTIK/tugas pak fathur/revisi/Analisis teh daun kelor/ke-2/hasil analisis teh daun kelor
##
## >>> 1. Sumber, desain, dan kelengkapan data
## Data berhasil dibaca: 141 responden; 3 kelompok; 564 pengukuran tersedia.
## kelompok waktu n_total n_tersedia n_hilang
## 1 Kapsul Gelatin M0 47 47 0
## 2 Kapsul Gelatin M1 47 47 0
## 3 Kapsul Gelatin M2 47 47 0
## 4 Kapsul Gelatin M3 47 47 0
## 5 Teh Daun Kelor M0 47 47 0
## 6 Teh Daun Kelor M1 47 47 0
## 7 Teh Daun Kelor M2 47 47 0
## 8 Teh Daun Kelor M3 47 47 0
## 9 Tablet Fe M0 47 47 0
## 10 Tablet Fe M1 47 47 0
## 11 Tablet Fe M2 47 47 0
## 12 Tablet Fe M3 47 47 0
## kelompok n_total n_lengkap n_tidak_lengkap
## 1 Kapsul Gelatin 47 47 0
## 2 Teh Daun Kelor 47 47 0
## 3 Tablet Fe 47 47 0
##
## >>> 2. Statistik deskriptif dan visualisasi
## kelompok waktu n mean sd median min max se lower_ci
## 1 Kapsul Gelatin M0 47 11.05 0.379 11.06951 10.3 11.8 0.05528283 10.93872
## 2 Kapsul Gelatin M1 47 11.24 0.295 11.26123 10.5 11.7 0.04303017 11.15338
## 3 Kapsul Gelatin M2 47 11.27 0.278 11.28898 10.5 11.8 0.04055047 11.18838
## 4 Kapsul Gelatin M3 47 11.46 0.316 11.49054 10.3 11.9 0.04609334 11.36722
## 5 Teh Daun Kelor M0 47 10.93 0.363 11.00338 10.1 11.5 0.05294899 10.82342
## 6 Teh Daun Kelor M1 47 11.27 0.307 11.32588 10.7 11.9 0.04478055 11.17986
## 7 Teh Daun Kelor M2 47 11.78 0.404 11.85093 11.0 12.7 0.05892946 11.66138
## 8 Teh Daun Kelor M3 47 12.20 0.500 12.28486 11.3 13.4 0.07293250 12.05319
## 9 Tablet Fe M0 47 10.90 0.316 10.90760 10.3 11.5 0.04609334 10.80722
## 10 Tablet Fe M1 47 11.20 0.227 11.20937 10.5 11.7 0.03311135 11.13335
## 11 Tablet Fe M2 47 12.04 0.465 12.05734 11.0 12.8 0.06782722 11.90347
## 12 Tablet Fe M3 47 12.51 0.543 12.52776 11.3 13.5 0.07920469 12.35057
## upper_ci
## 1 11.16128
## 2 11.32662
## 3 11.35162
## 4 11.55278
## 5 11.03658
## 6 11.36014
## 7 11.89862
## 8 12.34681
## 9 10.99278
## 10 11.26665
## 11 12.17653
## 12 12.66943
## kelompok waktu n mean sd median min max se lower_ci
## 1 Kapsul Gelatin M0 47 11.05 0.379 11.06951 10.3 11.8 0.05528283 10.93872
## 2 Kapsul Gelatin M1 47 11.24 0.295 11.26123 10.5 11.7 0.04303017 11.15338
## 3 Kapsul Gelatin M2 47 11.27 0.278 11.28898 10.5 11.8 0.04055047 11.18838
## 4 Kapsul Gelatin M3 47 11.46 0.316 11.49054 10.3 11.9 0.04609334 11.36722
## 5 Teh Daun Kelor M0 47 10.93 0.363 11.00338 10.1 11.5 0.05294899 10.82342
## 6 Teh Daun Kelor M1 47 11.27 0.307 11.32588 10.7 11.9 0.04478055 11.17986
## 7 Teh Daun Kelor M2 47 11.78 0.404 11.85093 11.0 12.7 0.05892946 11.66138
## 8 Teh Daun Kelor M3 47 12.20 0.500 12.28486 11.3 13.4 0.07293250 12.05319
## 9 Tablet Fe M0 47 10.90 0.316 10.90760 10.3 11.5 0.04609334 10.80722
## 10 Tablet Fe M1 47 11.20 0.227 11.20937 10.5 11.7 0.03311135 11.13335
## 11 Tablet Fe M2 47 12.04 0.465 12.05734 11.0 12.8 0.06782722 11.90347
## 12 Tablet Fe M3 47 12.51 0.543 12.52776 11.3 13.5 0.07920469 12.35057
## upper_ci
## 1 11.16128
## 2 11.32662
## 3 11.35162
## 4 11.55278
## 5 11.03658
## 6 11.36014
## 7 11.89862
## 8 12.34681
## 9 10.99278
## 10 11.26665
## 11 12.17653
## 12 12.66943
## kelompok n mean_perubahan sd_perubahan median_perubahan min_perubahan
## 1 Kapsul Gelatin 47 0.41 0.1172426 0.415633 0.000000
## 2 Teh Daun Kelor 47 1.27 0.1503855 1.281473 1.036458
## 3 Tablet Fe 47 1.61 0.2280302 1.620164 1.000000
## max_perubahan
## 1 0.6154056
## 2 1.9000000
## 3 2.0000000
## Grafik tersimpan: D:/S2 MKM/KULIAH/BIOSTATISTIK/tugas pak fathur/revisi/Analisis teh daun kelor/ke-2/hasil analisis teh daun kelor/02_profil_hb.png
## Grafik tersimpan: D:/S2 MKM/KULIAH/BIOSTATISTIK/tugas pak fathur/revisi/Analisis teh daun kelor/ke-2/hasil analisis teh daun kelor/02_lintasan_individu.png
##
## >>> 3. Pemeriksaan asumsi pada peserta lengkap
## kelompok waktu id usia pendidikan lengkap hb minggu is.outlier
## 1 Kapsul Gelatin M3 R106 33 SMA/SMK TRUE 10.3 3 TRUE
## 2 Tablet Fe M1 R089 32 D4/S1 TRUE 10.5 1 TRUE
## is.extreme
## 1 FALSE
## 2 FALSE
## kelompok waktu variable statistic p
## 1 Kapsul Gelatin M0 hb 0.9773596 0.487982942
## 2 Kapsul Gelatin M1 hb 0.9703362 0.272842746
## 3 Kapsul Gelatin M2 hb 0.9802160 0.601192396
## 4 Kapsul Gelatin M3 hb 0.9292456 0.007116373
## 5 Teh Daun Kelor M0 hb 0.9601496 0.109067227
## 6 Teh Daun Kelor M1 hb 0.9664581 0.193433092
## 7 Teh Daun Kelor M2 hb 0.9722968 0.323138287
## 8 Teh Daun Kelor M3 hb 0.9696043 0.255901827
## 9 Tablet Fe M0 hb 0.9739478 0.371312517
## 10 Tablet Fe M1 hb 0.9784597 0.530174897
## 11 Tablet Fe M2 hb 0.9721565 0.319291533
## 12 Tablet Fe M3 hb 0.9786255 0.536704387
## Grafik tersimpan: D:/S2 MKM/KULIAH/BIOSTATISTIK/tugas pak fathur/revisi/Analisis teh daun kelor/ke-2/hasil analisis teh daun kelor/03_qq_per_sel.png
## waktu df1 df2 statistic p
## 1 M0 2 138 0.8943447 0.4112317939
## 2 M1 2 138 3.3835326 0.0367656964
## 3 M2 2 138 7.1936276 0.0010670883
## 4 M3 2 138 8.7860576 0.0002560742
## statistic p.value parameter
## 1 NaN NaN 20
## method
## 1 Box's M-test for Homogeneity of Covariance Matrices
##
## >>> 4. RM ANOVA satu arah: Kelompok Teh Daun Kelor
## Grafik tersimpan: D:/S2 MKM/KULIAH/BIOSTATISTIK/tugas pak fathur/revisi/Analisis teh daun kelor/ke-2/hasil analisis teh daun kelor/04_qq_kelompok_fokus.png
## ANOVA Table (type III tests)
##
## $ANOVA
## Effect DFn DFd F p p<.05 pes
## 1 waktu 3 138 2020.991 8.74e-114 * 0.978
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 1 waktu 0.001 1.48e-60 *
##
## $`Sphericity Corrections`
## Effect GGe DF[GG] p[GG] p[GG]<.05 HFe DF[HF] p[HF]
## 1 waktu 0.37 1.11, 51.11 8.75e-44 * 0.373 1.12, 51.46 4.53e-44
## p[HF]<.05
## 1 *
##
## Anova Table (Type 3 tests)
##
## Response: hb
## Effect df MSE F ges pes p.value
## 1 waktu 1.11, 51.11 0.02 2020.99 *** .600 .978 <.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 25058.0 1 28.4012 46 40585 < 2.2e-16 ***
## waktu 44.1 3 1.0036 138 2021 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Mauchly Tests for Sphericity
##
## Test statistic p-value
## waktu 0.0014991 1.4824e-60
##
##
## Greenhouse-Geisser and Huynh-Feldt Corrections
## for Departure from Sphericity
##
## GG eps Pr(>F[GG])
## waktu 0.37035 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## HF eps Pr(>F[HF])
## waktu 0.3729157 4.531419e-44
##
## Type III Repeated Measures MANOVA Tests: Pillai test statistic
## Df test stat approx F num Df den Df Pr(>F)
## (Intercept) 1 0.99887 40585 1 46 < 2.2e-16 ***
## waktu 1 0.99782 6709 3 44 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## efek num Df den Df MSE F pes ges Pr(>F)
## 1 waktu 1.111051 51.10833 0.01963581 2020.991 0.9777454 0.5999105 8.746663e-44
## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------
## waktu | 0.98 | [0.97, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
##
## >>> 5. Post hoc perubahan waktu dalam kelompok Teh Daun Kelor
## waktu emmean SE df lower.CL upper.CL
## M0 10.93 0.05294899 46 10.82342 11.03658
## M1 11.27 0.04478055 46 11.17986 11.36014
## M2 11.78 0.05892946 46 11.66138 11.89862
## M3 12.20 0.07293250 46 12.05320 12.34680
##
## Confidence level used: 0.95
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## M0 - M1 -0.34 0.009442873 46 -0.3660356 -0.3139644 -36.006 <0.0001
## M0 - M2 -0.85 0.008978829 46 -0.8747562 -0.8252438 -94.667 <0.0001
## M0 - M3 -1.27 0.021935980 46 -1.3304813 -1.2095187 -57.896 <0.0001
## M1 - M2 -0.51 0.014284348 46 -0.5493844 -0.4706156 -35.703 <0.0001
## M1 - M3 -0.93 0.028327865 46 -1.0081049 -0.8518951 -32.830 <0.0001
## M2 - M3 -0.42 0.014114816 46 -0.4589170 -0.3810830 -29.756 <0.0001
##
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 6 estimates
## P value adjustment: bonferroni method for 6 tests
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## M1 - M0 0.34 0.009442873 46 0.3165374 0.3634626 36.006 <0.0001
## M2 - M0 0.85 0.008978829 46 0.8276904 0.8723096 94.667 <0.0001
## M3 - M0 1.27 0.021935980 46 1.2154959 1.3245041 57.896 <0.0001
##
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 3 estimates
## P value adjustment: holm method for 3 tests
## contrast estimate SE df lower.CL upper.CL t.ratio
## linear_minggu 0.9659814 0.017774704 46 0.9218167 1.0101460 54.346
## kuadratik_minggu 0.0400000 0.011025577 46 0.0126048 0.0673952 3.628
## kubik_minggu -0.0581378 0.005164766 46 -0.0709706 -0.0453049 -11.257
## p.value
## <0.0001
## 0.0007
## <0.0001
##
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 3 estimates
## P value adjustment: holm method for 3 tests
##
## >>> 6. Pembanding nonparametrik: Friedman dan Wilcoxon
## .y. n statistic df p method
## 1 hb 47 141 3 2.300576e-30 Friedman test
## .y. n effsize method magnitude
## 1 hb 47 1 Kendall W large
## waktu_1 waktu_2 n_pasangan V p p_bonferroni
## 1 M0 M1 47 0 2.47578e-09 1.485468e-08
## 2 M0 M2 47 0 2.47578e-09 1.485468e-08
## 3 M0 M3 47 0 2.47578e-09 1.485468e-08
## 4 M1 M2 47 0 2.47578e-09 1.485468e-08
## 5 M1 M3 47 0 2.47578e-09 1.485468e-08
## 6 M2 M3 47 0 2.47578e-09 1.485468e-08
##
## >>> 7. Mixed Design ANOVA: Kelompok × Waktu
## Anova Table (Type 3 tests)
##
## Response: hb
## Effect df MSE F ges pes p.value
## 1 kelompok 2, 138 0.54 15.38 *** .174 .182 <.001
## 2 waktu 1.26, 173.64 0.03 3120.68 *** .560 .958 <.001
## 3 kelompok:waktu 2.52, 173.64 0.03 405.05 *** .248 .854 <.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 74427 1 74.217 138 138390.389 < 2.2e-16 ***
## kelompok 17 2 74.217 138 15.379 9.342e-07 ***
## waktu 100 3 4.420 414 3120.676 < 2.2e-16 ***
## kelompok:waktu 26 6 4.420 414 405.048 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Mauchly Tests for Sphericity
##
## Test statistic p-value
## waktu 0.056703 1.4451e-82
## kelompok:waktu 0.056703 1.4451e-82
##
##
## Greenhouse-Geisser and Huynh-Feldt Corrections
## for Departure from Sphericity
##
## GG eps Pr(>F[GG])
## waktu 0.41943 < 2.2e-16 ***
## kelompok:waktu 0.41943 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## HF eps Pr(>F[HF])
## waktu 0.4214785 2.174341e-121
## kelompok:waktu 0.4214785 3.293506e-73
##
## Type III Repeated Measures MANOVA Tests: Pillai test statistic
## Df test stat approx F num Df den Df Pr(>F)
## (Intercept) 1 0.99900 138390 1 138 < 2.2e-16 ***
## kelompok 2 0.18226 15 2 138 9.342e-07 ***
## waktu 1 0.98773 3649 3 136 < 2.2e-16 ***
## kelompok:waktu 2 1.28058 81 6 274 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## efek num Df den Df MSE F pes ges
## 1 kelompok 2.000000 138.0000 0.53780424 15.37888 0.1822598 0.1737959
## 2 waktu 1.258281 173.6428 0.02545438 3120.67571 0.9576515 0.5596747
## 3 kelompok:waktu 2.516562 173.6428 0.02545438 405.04768 0.8544450 0.2480927
## Pr(>F)
## 1 9.342418e-07
## 2 8.191974e-121
## 3 7.251227e-73
## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## ----------------------------------------------
## kelompok | 0.18 | [0.09, 1.00]
## waktu | 0.96 | [0.95, 1.00]
## kelompok:waktu | 0.85 | [0.84, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
##
## >>> 8. Visualisasi interaksi Kelompok × Waktu
## Grafik tersimpan: D:/S2 MKM/KULIAH/BIOSTATISTIK/tugas pak fathur/revisi/Analisis teh daun kelor/ke-2/hasil analisis teh daun kelor/08_interaksi_anova.png
##
## >>> 9. Efek sederhana dan post hoc antarkelompok
## model term kelompok df1 df2 F.ratio p.value p_holm
## 1 waktu Kapsul Gelatin 3 138 382.701 1.152313e-66 1.152313e-66
## 4 waktu Teh Daun Kelor 3 138 1723.755 3.916247e-109 7.832494e-109
## 7 waktu Tablet Fe 3 138 2221.287 1.479735e-116 4.439205e-116
## model term waktu df1 df2 F.ratio p.value p_holm
## 1 kelompok M0 2 138 2.367 9.754681e-02 1.950936e-01
## 3 kelompok M1 2 138 0.747 4.756994e-01 4.756994e-01
## 5 kelompok M2 2 138 47.368 2.178206e-16 6.534619e-16
## 7 kelompok M3 2 138 63.650 2.592431e-20 1.036973e-19
## kelompok = Kapsul Gelatin:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## M1 - M0 0.19 0.01284673 138 0.1588658 0.2211342 14.790 <0.0001
## M2 - M0 0.22 0.01652068 138 0.1799619 0.2600381 13.317 <0.0001
## M3 - M0 0.41 0.02503323 138 0.3493316 0.4706684 16.378 <0.0001
##
## kelompok = Teh Daun Kelor:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## M1 - M0 0.34 0.01284673 138 0.3088658 0.3711342 26.466 <0.0001
## M2 - M0 0.85 0.01652068 138 0.8099619 0.8900381 51.451 <0.0001
## M3 - M0 1.27 0.02503323 138 1.2093316 1.3306684 50.733 <0.0001
##
## kelompok = Tablet Fe:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## M1 - M0 0.30 0.01284673 138 0.2688658 0.3311342 23.352 <0.0001
## M2 - M0 1.14 0.01652068 138 1.0999619 1.1800381 69.004 <0.0001
## M3 - M0 1.61 0.02503323 138 1.5493316 1.6706684 64.315 <0.0001
##
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 3 estimates
## P value adjustment: holm method for 3 tests
## waktu = M0:
## contrast estimate SE df lower.CL upper.CL
## Kapsul Gelatin - Teh Daun Kelor 0.12 0.07295836 138 -0.0528582 0.2928582
## Kapsul Gelatin - Tablet Fe 0.15 0.07295836 138 -0.0228582 0.3228582
## Teh Daun Kelor - Tablet Fe 0.03 0.07295836 138 -0.1428582 0.2028582
## t.ratio p.value
## 1.645 0.2305
## 2.056 0.1030
## 0.411 0.9111
##
## waktu = M1:
## contrast estimate SE df lower.CL upper.CL
## Kapsul Gelatin - Teh Daun Kelor -0.03 0.05746451 138 -0.1661491 0.1061491
## Kapsul Gelatin - Tablet Fe 0.04 0.05746451 138 -0.0961491 0.1761491
## Teh Daun Kelor - Tablet Fe 0.07 0.05746451 138 -0.0661491 0.2061491
## t.ratio p.value
## -0.522 0.8607
## 0.696 0.7662
## 1.218 0.4445
##
## waktu = M2:
## contrast estimate SE df lower.CL upper.CL
## Kapsul Gelatin - Teh Daun Kelor -0.51 0.08048831 138 -0.7006987 -0.3193013
## Kapsul Gelatin - Tablet Fe -0.77 0.08048831 138 -0.9606987 -0.5793013
## Teh Daun Kelor - Tablet Fe -0.26 0.08048831 138 -0.4506987 -0.0693013
## t.ratio p.value
## -6.336 <0.0001
## -9.567 <0.0001
## -3.230 0.0044
##
## waktu = M3:
## contrast estimate SE df lower.CL upper.CL
## Kapsul Gelatin - Teh Daun Kelor -0.74 0.09562820 138 -0.9665692 -0.5134308
## Kapsul Gelatin - Tablet Fe -1.05 0.09562820 138 -1.2765692 -0.8234308
## Teh Daun Kelor - Tablet Fe -0.31 0.09562820 138 -0.5365692 -0.0834308
## t.ratio p.value
## -7.738 <0.0001
## -10.980 <0.0001
## -3.242 0.0042
##
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 3 estimates
## P value adjustment: tukey method for comparing a family of 3 estimates
##
## >>> 10. Kontras perubahan baseline–minggu 3 dan tren
## waktu_custom kelompok_pairwise estimate SE df lower.CL
## M3-M0 Kapsul Gelatin - Teh Daun Kelor -0.86 0.03540233 138 -0.945798
## M3-M0 Kapsul Gelatin - Tablet Fe -1.20 0.03540233 138 -1.285798
## M3-M0 Teh Daun Kelor - Tablet Fe -0.34 0.03540233 138 -0.425798
## upper.CL t.ratio p.value
## -0.774202 -24.292 <0.0001
## -1.114202 -33.896 <0.0001
## -0.254202 -9.604 <0.0001
##
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 3 estimates
## P value adjustment: holm method for 3 tests
## waktu_custom kelompok_pairwise estimate SE df
## linear_minggu Kapsul Gelatin - Teh Daun Kelor -0.6842368 0.03020487 138
## linear_minggu Kapsul Gelatin - Tablet Fe -0.9861060 0.03020487 138
## linear_minggu Teh Daun Kelor - Tablet Fe -0.3018692 0.03020487 138
## lower.CL upper.CL t.ratio p.value
## -0.7574387 -0.6110349 -22.653 <0.0001
## -1.0593079 -0.9129041 -32.647 <0.0001
## -0.3750711 -0.2286673 -9.994 <0.0001
##
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 3 estimates
## P value adjustment: holm method for 3 tests
##
## >>> 11. Linear Mixed Model (LMM)
## model n_observasi AIC BIC singular konvergensi
## 1 random_intercept 564 -307.2290 -246.5382 FALSE
## 2 random_intercept_slope 564 -487.0326 -417.6718 FALSE
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: hb ~ kelompok * waktu + (1 + minggu | id)
## Data: long_obs
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: -519
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9300 -0.3602 0.0089 0.3759 3.6133
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 0.091247 0.30207
## minggu 0.003229 0.05682 0.67
## Residual 0.005294 0.07276
## Number of obs: 564, groups: id, 141
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 11.487500 0.030880 137.999983 372.009 < 2e-16 ***
## kelompok1 -0.232500 0.043670 137.999982 -5.324 4.01e-07 ***
## kelompok2 0.057500 0.043670 137.999982 1.317 0.19013
## waktu1 -0.527500 0.008927 184.675436 -59.091 < 2e-16 ***
## waktu2 -0.250833 0.005821 392.996897 -43.089 < 2e-16 ***
## waktu3 0.209167 0.005821 392.996897 35.931 < 2e-16 ***
## kelompok1:waktu1 0.322500 0.012625 184.675435 25.545 < 2e-16 ***
## kelompok2:waktu1 -0.087500 0.012625 184.675436 -6.931 6.77e-11 ***
## kelompok1:waktu2 0.235833 0.008233 392.996897 28.647 < 2e-16 ***
## kelompok2:waktu2 -0.024167 0.008233 392.996897 -2.936 0.00353 **
## kelompok1:waktu3 -0.194167 0.008233 392.996898 -23.585 < 2e-16 ***
## kelompok2:waktu3 0.025833 0.008233 392.996898 3.138 0.00183 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) klmpk1 klmpk2 waktu1 waktu2 waktu3 klm1:1 klm2:1 klm1:2
## kelompok1 0.000
## kelompok2 0.000 -0.500
## waktu1 -0.632 0.000 0.000
## waktu2 -0.323 0.000 0.000 0.150
## waktu3 0.323 0.000 0.000 -0.511 -0.446
## klmpk1:wkt1 0.000 -0.632 0.316 0.000 0.000 0.000
## klmpk2:wkt1 0.000 0.316 -0.632 0.000 0.000 0.000 -0.500
## klmpk1:wkt2 0.000 -0.323 0.162 0.000 0.000 0.000 0.150 -0.075
## klmpk2:wkt2 0.000 0.162 -0.323 0.000 0.000 0.000 -0.075 0.150 -0.500
## klmpk1:wkt3 0.000 0.323 -0.162 0.000 0.000 0.000 -0.511 0.256 -0.446
## klmpk2:wkt3 0.000 -0.162 0.323 0.000 0.000 0.000 0.256 -0.511 0.223
## klm2:2 klm1:3
## kelompok1
## kelompok2
## waktu1
## waktu2
## waktu3
## klmpk1:wkt1
## klmpk2:wkt1
## klmpk1:wkt2
## klmpk2:wkt2
## klmpk1:wkt3 0.223
## klmpk2:wkt3 -0.446 -0.500
## Groups Name Std.Dev. Corr
## id (Intercept) 0.302071
## minggu 0.056825 0.672
## Residual 0.072763
## efek Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## 1 kelompok 0.1628456 0.08142279 2 138.0000 15.37888 9.342426e-07
## 2 waktu 25.2930004 8.43100013 3 294.6541 1587.81207 1.674382e-181
## 3 kelompok:waktu 7.8754406 1.31257343 6 328.7935 247.02195 1.417505e-118
## # Intraclass Correlation Coefficient
##
## Adjusted ICC: 0.925
## Unadjusted ICC: 0.333
## waktu_custom kelompok_pairwise estimate SE df
## M3-M0 Kapsul Gelatin - Teh Daun Kelor -0.86 0.04107627 145.62
## M3-M0 Kapsul Gelatin - Tablet Fe -1.20 0.04107627 145.62
## M3-M0 Teh Daun Kelor - Tablet Fe -0.34 0.04107627 145.62
## lower.CL upper.CL t.ratio p.value
## -0.9594847 -0.7605153 -20.937 <0.0001
## -1.2994847 -1.1005153 -29.214 <0.0001
## -0.4394847 -0.2405153 -8.277 <0.0001
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 3 estimates
## P value adjustment: holm method for 3 tests
## waktu = M0:
## kelompok emmean SE df lower.CL upper.CL
## Kapsul Gelatin 11.05 0.04532188 142.64 10.96041 11.13959
## Teh Daun Kelor 10.93 0.04532188 142.64 10.84041 11.01959
## Tablet Fe 10.90 0.04532188 142.64 10.81041 10.98959
##
## waktu = M1:
## kelompok emmean SE df lower.CL upper.CL
## Kapsul Gelatin 11.24 0.05112505 146.65 11.13896 11.34104
## Teh Daun Kelor 11.27 0.05112505 146.65 11.16896 11.37104
## Tablet Fe 11.20 0.05112505 146.65 11.09896 11.30104
##
## waktu = M2:
## kelompok emmean SE df lower.CL upper.CL
## Kapsul Gelatin 11.27 0.05754021 144.77 11.15627 11.38373
## Teh Daun Kelor 11.78 0.05754021 144.77 11.66627 11.89373
## Tablet Fe 12.04 0.05754021 144.77 11.92627 12.15373
##
## waktu = M3:
## kelompok emmean SE df lower.CL upper.CL
## Kapsul Gelatin 11.46 0.06438469 140.27 11.33271 11.58729
## Teh Daun Kelor 12.20 0.06438469 140.27 12.07271 12.32729
## Tablet Fe 12.51 0.06438469 140.27 12.38271 12.63729
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
##
## >>> 12. Diagnostik LMM
## Grafik tersimpan: D:/S2 MKM/KULIAH/BIOSTATISTIK/tugas pak fathur/revisi/Analisis teh daun kelor/ke-2/hasil analisis teh daun kelor/12_qq_residual.png
## Grafik tersimpan: D:/S2 MKM/KULIAH/BIOSTATISTIK/tugas pak fathur/revisi/Analisis teh daun kelor/ke-2/hasil analisis teh daun kelor/12_residual_prediksi.png
## Grafik tersimpan: D:/S2 MKM/KULIAH/BIOSTATISTIK/tugas pak fathur/revisi/Analisis teh daun kelor/ke-2/hasil analisis teh daun kelor/12_residual_per_sel.png
## [1] FALSE
## [1] TRUE
## attr(,"gradient")
## [1] 3.823238e-07
##
## >>> 13. Grafik rerata prediksi LMM
## Grafik tersimpan: D:/S2 MKM/KULIAH/BIOSTATISTIK/tugas pak fathur/revisi/Analisis teh daun kelor/ke-2/hasil analisis teh daun kelor/13_profil_prediksi_lmm.png
##
## >>> 14. LMM pada peserta lengkap: pembanding ANOVA
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: hb ~ kelompok * waktu + (1 + minggu | id)
## Data: long_cc
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e+05))
##
## REML criterion at convergence: -519
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9300 -0.3602 0.0089 0.3759 3.6133
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## id (Intercept) 0.091247 0.30207
## minggu 0.003229 0.05682 0.67
## Residual 0.005294 0.07276
## Number of obs: 564, groups: id, 141
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 11.487500 0.030880 137.999983 372.009 < 2e-16 ***
## kelompok1 -0.232500 0.043670 137.999982 -5.324 4.01e-07 ***
## kelompok2 0.057500 0.043670 137.999982 1.317 0.19013
## waktu1 -0.527500 0.008927 184.675436 -59.091 < 2e-16 ***
## waktu2 -0.250833 0.005821 392.996897 -43.089 < 2e-16 ***
## waktu3 0.209167 0.005821 392.996897 35.931 < 2e-16 ***
## kelompok1:waktu1 0.322500 0.012625 184.675435 25.545 < 2e-16 ***
## kelompok2:waktu1 -0.087500 0.012625 184.675436 -6.931 6.77e-11 ***
## kelompok1:waktu2 0.235833 0.008233 392.996897 28.647 < 2e-16 ***
## kelompok2:waktu2 -0.024167 0.008233 392.996897 -2.936 0.00353 **
## kelompok1:waktu3 -0.194167 0.008233 392.996898 -23.585 < 2e-16 ***
## kelompok2:waktu3 0.025833 0.008233 392.996898 3.138 0.00183 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) klmpk1 klmpk2 waktu1 waktu2 waktu3 klm1:1 klm2:1 klm1:2
## kelompok1 0.000
## kelompok2 0.000 -0.500
## waktu1 -0.632 0.000 0.000
## waktu2 -0.323 0.000 0.000 0.150
## waktu3 0.323 0.000 0.000 -0.511 -0.446
## klmpk1:wkt1 0.000 -0.632 0.316 0.000 0.000 0.000
## klmpk2:wkt1 0.000 0.316 -0.632 0.000 0.000 0.000 -0.500
## klmpk1:wkt2 0.000 -0.323 0.162 0.000 0.000 0.000 0.150 -0.075
## klmpk2:wkt2 0.000 0.162 -0.323 0.000 0.000 0.000 -0.075 0.150 -0.500
## klmpk1:wkt3 0.000 0.323 -0.162 0.000 0.000 0.000 -0.511 0.256 -0.446
## klmpk2:wkt3 0.000 -0.162 0.323 0.000 0.000 0.000 0.256 -0.511 0.223
## klm2:2 klm1:3
## kelompok1
## kelompok2
## waktu1
## waktu2
## waktu3
## klmpk1:wkt1
## klmpk2:wkt1
## klmpk1:wkt2
## klmpk2:wkt2
## klmpk1:wkt3 0.223
## klmpk2:wkt3 -0.446 -0.500
## efek Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## 1 kelompok 0.1628456 0.08142279 2 138.0000 15.37888 9.342426e-07
## 2 waktu 25.2930004 8.43100013 3 294.6541 1587.81207 1.674382e-181
## 3 kelompok:waktu 7.8754406 1.31257343 6 328.7935 247.02195 1.417505e-118
##
## >>> 15. Informasi sesi dan batas interpretasi
## R version 4.6.1 (2026-06-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
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## Matrix products: default
## LAPACK version 3.12.1
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## locale:
## [1] LC_COLLATE=English_Indonesia.utf8 LC_CTYPE=English_Indonesia.utf8
## [3] LC_MONETARY=English_Indonesia.utf8 LC_NUMERIC=C
## [5] LC_TIME=English_Indonesia.utf8
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## time zone: Asia/Makassar
## tzcode source: internal
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] tibble_3.3.1 base64enc_0.1-6 htmltools_0.5.9 knitr_1.51
## [5] ggpubr_1.0.0 performance_0.18.2 pbkrtest_0.5.5 lmerTest_3.2-1
## [9] effectsize_1.0.3 car_3.1-5 carData_3.0-6 rstatix_1.1.0
## [13] emmeans_2.0.4 afex_1.5-1 lme4_2.0-6 Matrix_1.7-5
## [17] ggplot2_4.0.3 tidyr_1.3.2 dplyr_1.2.1
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## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 farver_2.1.2 S7_0.2.2
## [4] fastmap_1.2.0 bayestestR_0.19.0 rpart_4.1.27
## [7] digest_0.6.39 estimability_2.0.0 lifecycle_1.0.5
## [10] cluster_2.1.8.2 magrittr_2.0.5 compiler_4.6.1
## [13] rlang_1.3.0 Hmisc_5.3-0 sass_0.4.10
## [16] tools_4.6.1 yaml_2.3.12 data.table_1.18.6.1
## [19] ggsignif_0.6.4 labeling_0.4.3 htmlwidgets_1.6.4
## [22] plyr_1.8.9 RColorBrewer_1.1-3 abind_1.4-8
## [25] foreign_0.8-91 withr_3.0.3 purrr_1.2.2
## [28] numDeriv_2016.8-1.1 stats4_4.6.1 nnet_7.3-20
## [31] grid_4.6.1 datawizard_1.4.0 colorspace_2.1-3
## [34] scales_1.4.0 MASS_7.3-65 insight_1.5.4
## [37] cli_3.6.6 mvtnorm_1.4-2 rmarkdown_2.31
## [40] ragg_1.5.2 reformulas_0.4.4 generics_0.1.4
## [43] otel_0.2.0 rstudioapi_0.19.0 reshape2_1.4.5
## [46] parameters_0.29.3 readxl_1.5.0.1 minqa_1.2.8
## [49] cachem_1.1.0 stringr_1.6.0 splines_4.6.1
## [52] parallel_4.6.1 cellranger_1.1.0 vctrs_0.7.3
## [55] boot_1.3-32 jsonlite_2.0.0 htmlTable_2.5.0
## [58] Formula_1.2-6 systemfonts_1.3.2 jquerylib_0.1.4
## [61] glue_1.8.1 nloptr_2.2.1 stringi_1.8.9
## [64] gtable_0.3.6 pillar_1.11.1 R6_2.6.1
## [67] textshaping_1.0.5 Rdpack_2.6.6 evaluate_1.0.5
## [70] lattice_0.22-9 rbibutils_2.4.1 backports_1.5.1
## [73] broom_1.0.13 bslib_0.12.0 Rcpp_1.1.2
## [76] checkmate_2.3.4 gridExtra_2.3.1 nlme_3.1-169
## [79] xfun_0.60 pkgconfig_2.0.3
## bagian status
## 1 1. Sumber, desain, dan kelengkapan data Selesai
## 2 2. Statistik deskriptif dan visualisasi Selesai
## 3 3. Pemeriksaan asumsi pada peserta lengkap Selesai
## 4 4. RM ANOVA satu arah: Kelompok Teh Daun Kelor Selesai
## 5 5. Post hoc perubahan waktu dalam kelompok Teh Daun Kelor Selesai
## 6 6. Pembanding nonparametrik: Friedman dan Wilcoxon Selesai
## 7 7. Mixed Design ANOVA: Kelompok × Waktu Selesai
## 8 8. Visualisasi interaksi Kelompok × Waktu Selesai
## 9 9. Efek sederhana dan post hoc antarkelompok Selesai
## 10 10. Kontras perubahan baseline–minggu 3 dan tren Selesai
## 11 11. Linear Mixed Model (LMM) Selesai
## 12 12. Diagnostik LMM Selesai
## 13 13. Grafik rerata prediksi LMM Selesai
## 14 14. LMM pada peserta lengkap: pembanding ANOVA Selesai
## 15 15. Informasi sesi dan batas interpretasi Selesai
## peringatan
## 1
## 2
## 3 NaNs produced
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## 8 Panel(s) show a mixed within-between-design.\nError bars do not allow comparisons across all means.\nSuppress error bars with: error = "none"
## 9 NaNs produced
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## Laporan tersedia: D:/S2 MKM/KULIAH/BIOSTATISTIK/tugas pak fathur/revisi/Analisis teh daun kelor/ke-2/hasil analisis teh daun kelor/laporan_analisis_hb_teh_daun_kelor.html
## Durasi: 42.7 detik.