Results of lm() function with a dependent ordered categorical variable? I am trying to understand Ben Bolker's answer to this question.
First, we create a data frame:
set.seed(101)
d <- data.frame(x=sample(1:4,size=30,replace=TRUE))
d$y <- rnorm(30,1+2*d$x,sd=0.01)

Then Mr. Bolker says:
x as ordered factor
coef(lm(y~ordered(x),d))
##  (Intercept) ordered(x).L ordered(x).Q ordered(x).C 
##  5.998121421  4.472505514  0.006109021 -0.003125958 

Now the intercept specifies the value of y at the mean factor level (halfway between 2 and 3); the L (linear) parameter gives a measure of the linear trend (not quite sure I can explain the particular value ...), Q and C specify quadratic and cubic terms (which are close to zero in this case because the pattern is linear); if there were more levels the higher-order contrasts would be numbered 5, 6, ...

My question is, what does the regression formula look like explicitly? 
I thought lm() makes a model like this:

y = 5.9981 + 4.4725 (x_1) + 0.0061 (x_2) - 0.00312 (x_3)

where, since the x_i are categories, they can only be either 0 or 1. 
I do not understand what quadratic and cubic terms have to do with a linear model. Even so, squaring/cubing any of the variables would not make a difference, since 0 ^ 3= 0 and 1^3 = 1. 
 A: In this case, what lm() is doing is converting your "categorical" variable into a numeric sequence in order.
To make this clearer, I'll adapt Bolker's code a bit to make the X variable more obviously categorical:
set.seed(101)
d <- data.frame(x=sample(1:4,size=30,replace=TRUE))
d$y <- rnorm(30,1+2*d$x,sd=0.01)
d$x = factor(d$x, labels=c("none", "some", "more", "a lot"))
coef(lm(y~x, d))
#  (Intercept)       xsome       xmore      xa lot 
#     3.001627    1.991260    3.995619    5.999098

So here, the mean of x=None is in the intercept, and the deviation from that is indicated for each category.
coef(lm(y~ordered(x), d))
#  (Intercept) ordered(x).L ordered(x).Q ordered(x).C 
#  5.998121421  4.472505514  0.006109021 -0.003125958

Conceptually, what's happened here is that the ordered() function converted x into newx using (something similar to):
if (x=="None") newx=-.67
if (x=="some") newx=-.22
if (x=="more") newx=.22
if (x=="a lot") newx=.67

and then it fitted (something like) the model:
$$y = a + b_0 \times newx + b_1 \times newx^2 + b_2 \times newx^3$$
where you have linear $newx$, quadratic $newx^2$, and cubic $newx^3$ components.
Note, I said that it's something like that, because the problem with the model described there is that $newx$, $newx^2$, and $newx^3$ are not at all independent. What lm() does instead is uses a set of contrasts generated by contr.poly(4). These contrasts ensure orthogonality, so that the linear, quadratic and cubic components are independent. But the principle is similar - when fitting ordered factors, lm() fits a linear, quadratic, cubic, etc... component.
You can see this by comparing:
coef(lm(y~ordered(x), d))
#  (Intercept) ordered(x).L ordered(x).Q ordered(x).C 
#  5.998121421  4.472505514  0.006109021 -0.003125958

with
contrasts(d$x) <- contr.poly(4)
coef(lm(y~x, d))
#  (Intercept) ordered(x).L ordered(x).Q ordered(x).C 
#  5.998121421  4.472505514  0.006109021 -0.003125958

Exactly identical. So if you want a fuller understanding of what happened, take a closer look at contr.poly() and orthogonal polynomial contrasts in general.
One thing to note is that there is an implicit assumption hidden in here: the difference between each two levels is assumed to be equal. So "None" is as far from "some" as "some" is from "more", and "more" is from "a lot".
