bonferroni correction code from scratch r

#BONFERRONI CORRECTION CODE FROM SCRATCH R HOW TO#
#BONFERRONI CORRECTION CODE FROM SCRATCH R INSTALL#

It makes an assumption that the two factor variables are independent. Not the above fitted model is called additive model. These results would lead us to believe that changing delivery methods (supp) or the dose of vitamin C, will impact significantly the mean tooth length. dose is the most significant factor variable. res.aov2 F)ĭose 2 2426.4 1213.2 82.81 F) corresponding to the p-value of the test.įrom the ANOVA table we can conclude that both supp and dose are statistically significant. The function summary.aov() is used to summarize the analysis of variance model. The R function aov() can be used to answer this question. We want to know if tooth length depends on supp and dose. Ggboxplot(my_data, x = 'dose', y = 'len', color = 'supp', # Color box plot by a second group: 'supp' # Plot tooth length ('len') by groups ('dose') If(!require(devtools)) install.packages('devtools')ĭevtools::install_github('kassambara/ggpubr')

#BONFERRONI CORRECTION CODE FROM SCRATCH R INSTALL#

Install the latest version of ggpubr from GitHub as follow (recommended): Here, we’ll use the ggpubr R package for an easy ggplot2-based data visualization. To use R base graphs read this: R base graphs. Two-way interaction plot, which plots the mean (or other summary) of the response for two-way combinations of factors, thereby illustrating possible interactions. Box plot to plot the data grouped by the combinations of the levels of the two factors.

#BONFERRONI CORRECTION CODE FROM SCRATCH R HOW TO#

In the next sections I’ll describe how to analyse data from balanced designs, since this is the simplest case.īox plots and line plots can be used to visualize group differences: We have 2X3 design cells with the factors being supp and dose and 10 subjects in each cell. Question: We want to know if tooth length depends on supp and dose. # Convert dose as a factor and recode the levels We’ll convert it as a factor variable (i.e., grouping variable) as follow. įrom the output above, R considers “dose” as a numeric variable. First, install dplyr if you don’t have it: install.packages('dplyr') # Show a random sampleĭplyr::sample_n(my_data, 10) len supp dose To get an idea of what the data look like, we display a random sample of the data using the function sample_n().

Bonferroni correction code from scratch r