interpreting results of regression using ordinal predictors in R

Dear friends,

Being very new to this, I was wondering if I could get some pointers
and guidance to interpreting the results of performing a linear
regression with ordinal predictors in R.

Here is a simple, toy example:

y <- c(-0.11, -0.49, -1.10,  0.08,  0.31, -1.21, -0.05, -0.40, -0.01,
      -0.12, 0.55, 1.34, 1.00, -0.31, -0.73, -1.68,  0.38,  1.22,
      -1.11, -0.20)

x <- ordered(c(2, 3, 3, 3, 5, 1, 2, 2, 1, 6, 0, 3, 4, 2, 2, 4, 1, 1,  
1,
            1))
x
# [1] 2 3 3 3 5 1 2 2 1 6 0 3 4 2 2 4 1 1 1 1
# Levels: 0 < 1 < 2 < 3 < 4 < 5 < 6

lm(formula = y ~ x)

# Call:
# lm(formula = y ~ x)

# Coefficients:
# (Intercept)          x.L          x.Q          x.C      
x^4          x^5
# -0.01679     -0.20788      0.46917        -0.45520 -0.05721      
-0.28696
# x^6
# -0.31417

....

Question: What exactly, does x.L, x.Q, x.C, x^4,

etc stand for? How do
these names, etc get assigned in the coefficients? Where do I find  
more
about this information?

Note that my question is not on lm (I think), but rather on how lm
outputs the results of regressions involving ordered predictors. Some
references would be great.

Please post responses to this mailing list.

Thanks very much again for all your help!


-- 

Below ...

-- Bert

On Wed, Jan 2, 2013 at 9:54 PM, David Winsemius <dwinsemius at comcast.net 

wrote:

...

Search on "orthogonal polynomials".

Yes, but this does not explain what these have to do with ordered  
factors and contrasts in a linear model.
That information is contained in "An Introduction to R," the  
tutorial that ships with R and which the OP should have perused  
before posting. Detailed explanation beyond that is too technical  
for here, I would say.