Ini adalah bingkai data saya:
Group <- c("G1","G1","G1","G1","G1","G1","G1","G1","G1","G1","G1","G1","G1","G1","G1","G2","G2","G2","G2","G2","G2","G2","G2","G2","G2","G2","G2","G2","G2","G2","G3","G3","G3","G3","G3","G3","G3","G3","G3","G3","G3","G3","G3","G3","G3")
Subject <- c("S1","S2","S3","S4","S5","S6","S7","S8","S9","S10","S11","S12","S13","S14","S15","S1","S2","S3","S4","S5","S6","S7","S8","S9","S10","S11","S12","S13","S14","S15","S1","S2","S3","S4","S5","S6","S7","S8","S9","S10","S11","S12","S13","S14","S15")
Value <- c(9.832217741,13.62390117,13.19671612,14.68552076,9.26683366,11.67886655,14.65083473,12.20969772,11.58494621,13.58474896,12.49053635,10.28208078,12.21945867,12.58276212,15.42648969,9.466436017,11.46582655,10.78725485,10.66159358,10.86701127,12.97863424,12.85276916,8.672953949,10.44587257,13.62135205,13.64038394,12.45778874,8.655142642,10.65925259,13.18336949,11.96595556,13.5552118,11.8337142,14.01763101,11.37502161,14.14801305,13.21640866,9.141392359,11.65848845,14.20350364,14.1829714,11.26202565,11.98431285,13.77216009,11.57303893)
data <- data.frame(Group, Subject, Value)
Lalu saya menjalankan model efek linear-campuran untuk membandingkan perbedaan 3 Grup pada "Nilai", di mana "Subjek" adalah faktor acak:
library(lme4)
library(lmerTest)
model <- lmer (Value~Group + (1|Subject), data = data)
summary(model)
Hasilnya adalah:
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 12.48771 0.42892 31.54000 29.114 <2e-16 ***
GroupG2 -1.12666 0.46702 28.00000 -2.412 0.0226 *
GroupG3 0.03828 0.46702 28.00000 0.082 0.9353
Namun, bagaimana cara membandingkan Group2 dengan Group3? Apa konvensi dalam artikel akademik?
Setelah Anda cocok
lmer
model Anda, Anda dapat melakukan ANOVA, MANOVA, dan beberapa prosedur perbandingan pada objek model, seperti ini:Adapun konvensi dalam makalah akademik, itu akan sangat bervariasi berdasarkan bidang, jurnal, dan materi pelajaran tertentu. Jadi untuk kasus itu, tinjau saja artikel terkait dan lihat apa yang mereka lakukan.
sumber
summary(glht(model, linfct = mcp(Group = "Tukey")))
. Jika Anda ingin melihat deskripsi akademik / statistik lengkap dari berbagai tes yang dapat dilakukan, periksa referensi di?glht
danmulticomp
lebih umum. Saya pikir Hsu 1996 akan menjadi yang utama.mcp
fungsi,Group = Tukey
hanya berarti untuk membandingkan semua kelompok berpasangan dalam variabel "Grup". Itu tidak berarti penyesuaian Tukey.