How to make a linear model with a constant value in R? I'm working on an unassessed homework problem from unpublished course notes of a statistics module from a second year university mathematics course.
I'm trying to plot a 2-parameter full linear model and a 1-parameter reduced linear model for the same data set. I can't figure out how to plot the 1-parameter model; all attempts so far have either given errors or a non-constant slope.
xs &lt;- c(0,1,2)
ys &lt;- c(0,1,1)
Data &lt;- data.frame(x = xs, y = ys)
mf &lt;- lm(y ~ x, data = Data) # model_full
mr &lt;- lm(y = mean(y), data = Data) # model_reduced; this is the bit I can't get to work
attach(Data)
plot(x, y, xlab=&quot;x&quot;, ylab=&quot;y&quot;)
abline(mf$coefficients[1], mf$coefficients[2])
abline(mr$coefficients[1], mr$coefficients[2])
To use only a single parameter, which will be a constant, you have to specify the lm formula as follows: y ~ 1. R will then fit the model, where the coefficient for this constant will be equal to the mean of y, see also this stats stackexchange answer.