R Markdown
FF <- read_csv("FuncFixData.csv")
## Rows: 56 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Group_SW, time, Session_1_2
## dbl (2): Participants, insight
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(FF)
## # A tibble: 6 × 5
## Participants Group_SW time insight Session_1_2
## <dbl> <chr> <chr> <dbl> <chr>
## 1 1 sleep Session1 3 evening
## 2 2 sleep Session1 1 evening
## 3 3 sleep Session1 3 evening
## 4 4 sleep Session1 3 evening
## 5 5 sleep Session1 2 evening
## 6 6 sleep Session1 1 evening
FF$Participants = as.factor(FF$Participants)
FF$Group_SW = as.factor(FF$Group_SW)
FF$time = as.factor(FF$time)
FF$Session_1_2 = as.factor(FF$Session_1_2)
str(FF)
## spc_tbl_ [56 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Participants: Factor w/ 28 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Group_SW : Factor w/ 2 levels "sleep","wake": 1 1 1 1 1 1 1 1 1 1 ...
## $ time : Factor w/ 2 levels "Session1","Session2": 1 1 1 1 1 1 1 1 1 1 ...
## $ insight : num [1:56] 3 1 3 3 2 1 3 2 3 2 ...
## $ Session_1_2 : Factor w/ 2 levels "evening","morning": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "spec")=
## .. cols(
## .. Participants = col_double(),
## .. Group_SW = col_character(),
## .. time = col_character(),
## .. insight = col_double(),
## .. Session_1_2 = col_character()
## .. )
## - attr(*, "problems")=<externalptr>
FF %>%
group_by(Session_1_2, Group_SW) %>%
get_summary_stats(insight, type = "mean_sd")
## # A tibble: 4 × 6
## Group_SW Session_1_2 variable n mean sd
## <fct> <fct> <fct> <dbl> <dbl> <dbl>
## 1 sleep evening insight 14 1.93 0.997
## 2 wake evening insight 14 1.86 0.864
## 3 sleep morning insight 14 2.71 0.994
## 4 wake morning insight 14 2.64 0.929
##outliers?
library(rstatix)
FF %>%
group_by(Session_1_2, Group_SW) %>%
identify_outliers(insight)
## [1] Group_SW Session_1_2 Participants time insight
## [6] is.outlier is.extreme
## <0 rows> (or 0-length row.names)
Two-way mixed ANOVA test
res.aov <- anova_test(
data = FF, dv = insight, wid = Participants,
between = Group_SW, within = time, effect.size = "pes")
get_anova_table(res.aov)
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 pes
## 1 Group_SW 1 26 0.080 0.780 0.003
## 2 time 1 26 0.000 1.000 0.000
## 3 Group_SW:time 1 26 9.621 0.005 * 0.270
## post-hoc test
#Simple main effect of group variable.
#we’ll investigate the effect of the between-subject factor group on insight score at every time point.
# Effect of group at each time point
one.way <- FF %>%
group_by(time) %>%
anova_test(dv = insight, wid = Participants, between = Group_SW, effect.size = "pes") %>%
get_anova_table() %>%
adjust_pvalue(method = "bonferroni")
one.way
## # A tibble: 2 × 9
## time Effect DFn DFd F p `p<.05` pes p.adj
## <fct> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 Session1 Group_SW 1 26 3.85 0.061 "" 0.129 0.122
## 2 Session2 Group_SW 1 26 5.92 0.022 "*" 0.186 0.044
# Pairwise comparisons between group levels
pwc <- FF %>%
group_by(time) %>%
pairwise_t_test(insight ~ Group_SW, p.adjust.method = "bonferroni")
pwc
## # A tibble: 2 × 10
## time .y. group1 group2 n1 n2 p p.signif p.adj p.adj.signif
## * <fct> <chr> <chr> <chr> <int> <int> <dbl> <chr> <dbl> <chr>
## 1 Session1 insight sleep wake 14 14 0.0607 ns 0.0607 ns
## 2 Session2 insight sleep wake 14 14 0.0221 * 0.0221 *
Time of day analysis
FF$Session_1_2 <- as.factor(FF$Session_1_2)
FF$time <- as.factor(FF$time)
result <- FF %>%
group_by(Session_1_2) %>%
summarize(
Mean = mean(insight),
SEM = sd(insight) / sqrt(n()))
ToD<- summary(aov(insight ~ time * Session_1_2, data = FF))
ToD
## Df Sum Sq Mean Sq F value Pr(>F)
## time 1 0.00 0.000 0.000 1.00000
## Session_1_2 1 8.64 8.643 9.621 0.00311 **
## time:Session_1_2 1 0.07 0.071 0.080 0.77908
## Residuals 52 46.71 0.898
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1