# Repeated measures ANOVA across the last four preshift sessions for SNCCon tunnel mice to assess # whether they had plateaued. Using data file TunnelSNCConPreshift.csv. install.packages('rstatix') install.packages('ggpubr') install.packages('tidyverse') install.packages('psych') TunnelSNCConPreshiftRM$Treatment <-as.factor(TunnelSNCConPreshiftRM$Treatment) TunnelSNCConPreshiftRM$Trial <-as.factor(TunnelSNCConPreshiftRM$Trial) # Quick visualisation of the data bxp <- ggboxplot(TunnelSNCConPreshiftRM, x = "Trial", y = "LCS500", add = "point") bxp # descriptive stats library(psych) describeBy(TunnelSNCConPreshiftRM$LCS500, group = TunnelSNCConPreshiftRM$Trial, digits= 4) # Descriptive statistics by group # group: 1 # vars n mean sd median trimmed mad min max range skew kurtosis se # X1 1 8 22.86 7.95 20.88 22.86 5.1 11.9 36.8 24.9 0.45 -1.16 2.81 # --------------------------------------------------------------------------- # group: 2 # vars n mean sd median trimmed mad min max range skew kurtosis se # X1 1 8 19 4.18 20.14 19 2.25 11.33 23.5 12.17 -0.74 -1.1 1.48 # --------------------------------------------------------------------------- # group: 3 # vars n mean sd median trimmed mad min max range skew kurtosis se # X1 1 8 21.84 3.87 21.02 21.84 2.42 16.38 27.5 11.12 0.32 -1.41 1.37 # --------------------------------------------------------------------------- # group: 4 # vars n mean sd median trimmed mad min max range skew kurtosis se # X1 1 8 26.56 15 21.23 26.56 5.76 16.49 62.42 45.93 1.62 1.13 5.3 # # # normality assumption library(rstatix) TunnelSNCConPreshiftRM %>% group_by(Trial) %>% shapiro_test(LCS500) # # A tibble: 4 x 4 # Trial variable statistic p # # 1 1 LCS500 0.948 0.688 # 2 2 LCS500 0.862 0.127 # 3 3 LCS500 0.916 0.400 # 4 4 LCS500 0.659 0.000758 # q q plot library(ggpubr) ggqqplot(TunnelSNCConPreshiftRM, "LCS500", facet.by = "Trial") # looks good and well distributed. # now to look at the stats anova res.aov <- anova_test(data = TunnelSNCConPreshiftRM, dv = LCS500, wid = Mouse, within = Trial) get_anova_table(res.aov) # ANOVA Table (type III tests) # # Effect DFn DFd F p p<.05 ges # 1 Trial 1.27 8.91 1.224 0.314 0.095