# [R] (g)lm ordinal or scaled values?

Liaw, Andy andy_liaw at merck.com
Tue May 9 21:39:42 CEST 2006

```You got what you got in R because you didn't tell R that the variable is
ordinal.  You get a bunch of lines when you tell R that var2 is ordinal
because the output you get is for the individual coefficients, not the
variable.  A k-level categorical variable (ordered or otherwise) has
associated with it k-1 coefficients (thus the k-1 degrees of freedom).  You
probably want to do either summary() or anova() on the output of lm() to get
the ANOVA table that give you the F-test for the term.

There are good online materials that discuss these basic linear models in R
(e.g., http://www.stat.lsa.umich.edu/~faraway/book/), and you would be
these confusions.

From: Knut Krueger
>
>
> Liaw, Andy schrieb:
>
> >Ordinal variables should be stored as ordered factors in R.
> See ?ordered.
> >
> thnk`s for your reply. I tried to store as ordered factors
> var <- as.ordered(var2) but if I am calling the
> lm(dependent~var) I get a long list of values.
> If I call
> var <- as.numeric(var2)
> lm(dependent~var)
>
> then I get:
>
> Residuals:
>      Min       1Q   Median       3Q      Max
> -0.93563  0.01378  0.16272  0.25546  0.57862
>
> Coefficients:
>             Estimate Std. Error t value Pr(>|t|)
> (Intercept)  1.79513    0.02787  64.412  < 2e-16 ***
> mean        -0.28102    0.05993  -4.689 4.66e-06 ***
>
> the SPSS call and Result  is:
>
> SPSS (13)
>     Analyse
>         General Linear Model
>                 Univariate
> Dependent Variable -> Dependent
> Fixed Factors       -> var2
>
> OK
>
>
>
>
> Tests of Between-Subjects Effects
> Dependent Variable: dependent
>  | --------------- | ------------------------- | --- | ------------- |
> -------- | ---- |
>  | Source             | Type III Sum of Squares | df  | Mean
> Square | F
>          | Sig. |
>  | --------------- | ------------------------- | --- | ------------- |
> -------- | ---- |
>  | Corrected Model | 11,269(a)                   | 41  | ,275
>
>   | 1,668    | ,012 |
>  | --------------- | ------------------------- | --- | ------------- |
> -------- | ---- |
>  | Intercept       | 361,340                 | 1   | 361,340     |
> 2193,088 | ,000 |
>  | --------------- | ----------------------- | --- | ----------- |
> -------- | ---- |
>  | var2            | 11,269                  | 41  | ,275        |
> 1,668    | ,012 |
>  | --------------- | ----------------------- | --- | ----------- |
> -------- | ---- |
>  | Error           | 31,964                  | 194 | ,165
> |          |      |
>  | --------------- | ----------------------- | --- | ----------- |
> -------- | ---- |
>  | Total           | 773,000                 | 236 |
> |          |      |
>  | --------------- | ----------------------- | --- | ----------- |
> -------- | ---- |
>  | Corrected Total | 43,233                  | 235 |
> |          |      |
>  | --------------- | ----------------------- | --- | ----------- |
> -------- | ---- |
> a R Squared = ,261 (Adjusted R Squared = ,104)
>
>
> Maybe anybody is able to show me the difference between SPSS
> and R calls
> because the P value ic complete different
> The url for the Data (if anybody would like to try)
>
> http://biostatistic.de/temp/testr.csv
>
> Regards Knut
>
>
>
>
>
>

```