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# Author:
# Organisation:
# Date:
######################################################################
# Analysis of <variable or project>
######################################################################
# Design:
# Response variable:
# Structural Component:
# Explanatory component:
# Experimental Unit:
# Observational Unit:
# Residual:
# Load required packages
library(tidyverse)
library(biometryassist)
# Read in data
dat <- read.csv("your_data_here.csv")
# Check data structure
str(dat)
# Change the required columns to factors for analysis
# Structural columns (row, column, block etc) and treatment columns should be factors
dat <- dat %>% mutate(across(c(1:5, 7:8), factor))
# dat <- dat %>% mutate(across(c(Row, Column, Blocks, Wplots, Subplots, Nitrogen, Variety), factor)) # Equivalently
# Explore the data as necessary
summary_graph(dat, response, exp_var = c(var1, var2), resp_units = "Y axis units")
# fitting the model
dat.aov <- aov(response ~ structural + treatments, data = dat) # fitting the model
# Check residual plots for appropriate model before moving on
resplot(dat.aov)
#summary(dat.aov)
anova(dat.aov)
# Prediction
pred.out <- multiple_comparisons(model.obj = dat.aov, classify = "treatments")
pred.out
# Graph the predicted values
autoplot(pred.out) +
labs(x = "Treatments", y = "Predicted Response")
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