one factor is different sowing method
Another factor is different fertilizer doses
I sugest that you the function lm()
So after you have read in your data into a datframe you define the variables
Sowing.methods, fertiliser.dose and Block as factors using e.g. the function as.factor
Then you can call the lm()function as follows:
model1
Dear
You can do this using function in R according to the suggestion of Kristian Kristensen.
Also in R you can use package ExpDes for 2 factorial RCBD design.
You can see this attachment given below.
https://cran.r-project.org/web/packages/ExpDes/ExpDes.pdf
you can use aov() function to get the anova table besides the result from linear regression.
https://aravind-j.github.io/augmentedRCBD/articles/Data_Analysis_with_augmentedRCBD.html
You can use augmented RCBD function. Please follow the following link.
I suggest you use 'doebioresearch package'. Is very good in factorial data analysis. Most especially agricultural experiments.
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