Ggpairs(TV) # `stat_bin()` using `bins = 30`. Tv.data<-read.table(system.file("doc","extdata","tv.dat",package="vcdExtra"))ĭimnames(TV) <- list(Day=c("Monday","Tuesday","Wednesday","Thursday","Friday"), Ggpairs(dplyr::select(Arthritis, -ID)) # `stat_bin()` using `bins = 30`. Mosaic(structable(Sex+Treatment~Improved, Arthritis)) # Cramer's V : 0.394 #mosaic(structable(Treatment+Improved ~ Sex, Arthritis)) # X-squared = 10, df = 2, p-value = 0.001 #association statisticsĪssocstats(xtabs(~Treatment + Improved, Arthritis)) # X^2 df P(> X^2) # data: xtabs(~Treatment + Improved, Arthritis) # Marked 6 16 1 5 xtable(xtabs(~ Sex + Treatment, Arthritis))Ĭhisq.test(xtabs(~Treatment + Improved, Arthritis)) # # Treatment Placebo Treated Placebo Treated Structable(Sex + Treatment ~ Improved, Arthritis) # Sex Female Male # Treated 16 5 ftable(tb3) # Improved None Some Marked Tb3 <- xtabs(~Treatment + Sex + Improved, Arthritis) #tb3 <- xtabs(~Treatment + Improved + Sex, Arthritis) # Sum 59 25 84 #3-way table with formula syntax
# Treated 0.659 0.341 #column proportions Tb # 32 with(Arthritis, table(Treatment)) # Treatment # Treated 27 14 tb <- with(Arthritis, table(Treatment, Sex))
With(Arthritis, table(Treatment, Sex)) # Sex #table(Arthritis$Treatment, Arthritis$Sex) The vcd and `vcdExtra packages are particulary handy for working with categorical data.
#Anova in r studio free
# (Adjusted p values reported - free method)
Pretty print out of ANOVA results knitr::kable(nice(a1)) Effect # instruction:inference:plausibility 0.816 0.015478 # instruction:inference:plausibility 0.764 0.0177 * # $ type : Factor w/ 2 levels "original","reversed": 2 2 2 2 1 1 1 1 2 2. # $ what : Factor w/ 2 levels "affirmation".: 2 2 1 1 2 2 1 1 2 2. # $ validity : Factor w/ 2 levels "valid","invalid": 2 1 1 2 2 1 1 2 2 1. # $ plausibility: Factor w/ 2 levels "plausible","implausible": 1 2 2 1 2 1 1 2 1 2. # $ instruction : Factor w/ 2 levels "deductive","probabilistic": 2 2 2 2 2 2 2 2 2 2. In short, this is a 2 x 2 x 2 mixed ANOVA design that can be tested without much difficulty. Let’s consider the experiment of Singmann and Klauer (2011), where they examined the conditional reasoning of individuals based on instruction type (b/w subs: deductive versus probailistic), inference validity (w/i subs: valid versus invalid problems) and plausibility (plausible versus implausible). In general, the aov_ez function from the afex package is an ideal tool for ANOVA analysis because it computes the expected ANOVA table, as well as effect size (generalized eta squared). 2.2 User-friendly coverage of all ANOVA-type designs
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