From: Liaw, Andy <andy_liaw_at_merck.com>

Date: Wed 10 May 2006 - 05:39:42 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed May 10 07:08:49 2006

Date: Wed 10 May 2006 - 05:39:42 EST

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 well-served to peruse them instead of bumping your head on the wall over 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
**>
**>
**>
**>
**>
*

>

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