Nothing
# Author:
# Organisation:
# Date:
library(biometryassist)
library(tidyverse)
######################################################################
# Analysis of <variable or project>
######################################################################
# Design:
# Response variable:
# Structural Component:
# Explanatory component:
# Experimental Unit:
# Observational Unit:
# Residual:
dat <- read.csv("<data file>", stringsAsFactors = TRUE)
str(dat)
# Change necessary columns to factors
dat <- dat %>% mutate(across(c(3), factor)) # Use column numbers
#dat <- dat %>% mutate(across(c(trt), factor)) # Or use column names
ggplot(data = dat, mapping = aes(x = trt, y = RL)) +
geom_boxplot() +
theme_bw()
dat.aov <- aov(RL ~ trt, data = dat) # fitting the model
resplot(dat.aov)
#summary(dat.aov)
anova(dat.aov)
# Predict the means from the model
pred.out <- multiple_comparisons(model.obj = dat.aov, classify = "trt")
pred.out
autoplot(pred.out) +
labs(y = "Predicted Root Length (cm)",
x = "Calcium Concentration")
# If you would like the graph in Calcium Concentration order
str(pred.out)
pred.out$trt <- factor(pred.out$trt,
levels = sort(as.numeric(as.character(pred.out$trt))))
autoplot(pred.out) +
labs(y = "Predicted Root Length (cm)",
x = "Calcium Concentration")
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.