The models are NOT equivalent. Why would you’ll think they were?

— 
David

Sent from my iPhone

> On Feb 9, 2022, at 11:10 PM, Bromaghin, Jeffrey F via R-help 
> <r-help@r-project.org> wrote:
> 
> Hello,
> 
> I was constructing a simple linear model with one categorical (3-levels) and 
> one quantitative predictor variable for a colleague. I estimated model 
> parameters with and without an intercept, sometimes called reference cell 
> coding and cell means coding.
> 
> Model 1: yResp ~ -1 + xCat + xCont
> Model 2: yResp ~ xCat + xCont
> 
> These models are equivalent and the estimated coefficients come out fine, but 
> the R-squared and F statistics returned by summary() differ markedly. I spent 
> some time looking at the code for both lm() and summary.lm() but did not find 
> the source of the difference. aov() and anova() results also differ, so I 
> suspect the issue involves how the sums of squares are being computed. I've 
> also spent some time trying to search online for information on this, without 
> success. I haven't used lm() for quite a while, but my memory is that these 
> differences didn't occur in the distant past when I was teaching.
> 
> Thanks in advance for any insights you might have,
> Jeff
> 
> Jeffrey F. Bromaghin
> Research Statistician
> USGS Alaska Science Center
> 907-786-7086
> Jeffrey Bromaghin, Ph.D. | U.S. Geological Survey 
> (usgs.gov)<https://www.usgs.gov/staff-profiles/jeffrey-bromaghin>
> Ecosystems Analytics | U.S. Geological Survey 
> (usgs.gov)<https://www.usgs.gov/centers/alaska-science-center/science/ecosystems-analytics>
> 
> 
>    [[alternative HTML version deleted]]
> 
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