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--- r:repeated_measure_anova [2024/05/08 08:17] – hkimscil
+++ r:repeated_measure_anova [2024/05/08 08:23] (current) – [E.g. 2] hkimscil
@@ Line 1: / Line 1: @@
 ====== E.g. 1 ======
+{{r:rep.meas.anova.csv}}
 <code>
 ###################################################
@@ Line 44: / Line 44: @@
 <code>
-> # Data preparation
-> # Wide format
-> # install.packages("datarium")
-> # library(datarium)
->
-> data("selfesteem", package = "datarium")
-> head(selfesteem, 3)
-# A tibble: 3 × 4
-     id    t1    t2    t3
-  <int> <dbl> <dbl> <dbl>
-     1  4.01  5.18  7.11
-     2  2.56  6.91  6.31
-     3  3.24  4.44  9.78
->
-> # Gather columns t1, t2 and t3 into long format
-> # Convert id and time into factor variables
->
-> # for %>% function, dplyr packages needed
-> # install.packages("dplyr")
-> # library(dplyr)
->
-> # for convert_as_factor function, rstatix needed
-> # install.packages("rstatix")
-> # library(rstatix)
->
-> selfesteem <- selfesteem %>%
-+   gather(key = "time", value = "score", t1, t2, t3) %>%
-+   convert_as_factor(id, time)
-> head(selfesteem, 3)
-# A tibble: 3 × 3
-  id    time  score
-  <fct> <fct> <dbl>
-1     t1     4.01
-2     t1     2.56
-3     t1     3.24
->
-> # get_summary_stats(group_by(selfesteem, time),
-> # score, type = "mean_sd")
-> # the above is the same as the below
-> selfesteem %>%
-+   group_by(time) %>%
-+   get_summary_stats(score, type = "mean_sd")
-# A tibble: 3 × 5
-  time  variable     n  mean    sd
-  <fct> <fct>    <dbl> <dbl> <dbl>
-t1    score       10  3.14 0.552
-t2    score       10  4.93 0.863
-t3    score       10  7.64 1.14
->
->
-> res.aov <- anova_test(
-+   data = selfesteem,
-+   dv = score,
-+   wid = id,
-+   within = time)
-> get_anova_table(res.aov)
-ANOVA Table (type III tests)
-  Effect DFn DFd      F        p p<.05   ges
-   time   2  18 55.469 2.01e-08     * 0.829
->
-> # ges = generalized effect size
-> # F
-> # (2,18)
->
 > ###################################################
 > ###################################################
@@ Line 185: / Line 120: @@
 </code>
 ====== E.g. 2 ======
+{{:r:rep.meas.anova.eg.movie.review.csv}}
 <code>
 # the second
@@ Line 197: / Line 133: @@
 #view data
 movrev
-write.csv(movrev, file="rep.meas.anova.mov.rev.csv")
+write.csv(movrev, file="rep.meas.anova.eg.movie.review.csv")
 movrev$movie <- factor(movrev$movie)
@@ Line 221: / Line 157: @@
 </code>
+<code>
+> # the second
+> movrev <- data.frame(reviewer=rep(1:5, each=3),
++                  movie=rep(1:3, times=5),
++                  score=c(88, 84, 92,
++                             76, 78, 90,
++                             78, 94, 95,
++                             80, 83, 88,
++                             82, 90, 99))
+>
+> #view data
+> movrev
+   reviewer movie score
+         1     1    88
+         1     2    84
+         1     3    92
+         2     1    76
+         2     2    78
+         2     3    90
+         3     1    78
+         3     2    94
+         3     3    95
+        4     1    80
+        4     2    83
+        4     3    88
+        5     1    82
+        5     2    90
+        5     3    99
+> write.csv(movrev, file="rep.meas.anova.eg.movie.review.csv")
+>
+> movrev$movie <- factor(movrev$movie)
+> movrev$reviewer <- factor(movrev$reviewer)
+>
+> # Error(reviewer) = reviewer error should be isolated
+> # The above is the same as Error(reviewer/movie)
+> m.aov <- aov(score ~ movie
++              + Error(reviewer),
++              data = movrev)
+> #view model summary
+> summary(m.aov)
+Error: reviewer
+          Df Sum Sq Mean Sq F value Pr(>F)
+Residuals  4  173.7   43.43
+Error: Within
+          Df Sum Sq Mean Sq F value  Pr(>F)
+movie      2  363.3  181.67   10.19 0.00632 **
+Residuals  8  142.7   17.83
+---
+Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+>
+> # pairwise.t.test(movrev$score, movrev$movie, paired = T, p.adjust.method = "bonf")
+> attach(movrev)
+The following objects are masked from movrev (pos = 5):
+    movie, reviewer, score
+> pairwise.t.test(score, movie, paired = T, p.adjust.method = "bonf")
+	Pairwise comparisons using paired t tests
+data:  score and movie
+     2
+0.628 -
+0.029 0.060
+P value adjustment method: bonferroni
+> # or
+> with(movrev,
++      pairwise.t.test(score, movie,
++                      paired = T,
++                      p.adjust.method = "bonf"))
+	Pairwise comparisons using paired t tests
+data:  score and movie
+     2
+0.628 -
+0.029 0.060
+P value adjustment method: bonferroni
+>
+</code>