On 03 Dec 2013, at 01:08 , David Gwenzi <dgwe...@gmail.com> wrote: > Dear all > > I have observations done in 4 different classes and the between classes > *variance* is too high that I decided to run a model without pooling the > *variance*. I used the following code first : > model<-lm(y~x+factor(class)) > and got the following output: > Coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 52.41405 17.38161 3.015 0.00658 ** > x 0.27679 0.07387 3.747 0.00119 ** > factor(class)2 92.68083 32.26645 2.872 0.00912 ** > factor(class)3 197.82029 33.24916 5.950 6.63e-06 *** > factor(class)4 105.61266 55.18373 1.914 0.06937 . > --- > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > Residual standard error: 43.07 on 21 degrees of freedom > Multiple R-squared: 0.9206, Adjusted R-squared: 0.9055 > F-statistic: 60.91 on 4 and 21 DF, p-value: 2.976e-11 > > My understanding of this output is that class 1 is used as a baseline > (constant) and each other class's p values means for example the dependent > value in class 2 is significantly different from that of class 1. > Now I ran the model again, but without using a constant i.e > model<-lm(y~x+factor(class)-1) > and got the following output: > Coefficients: > Estimate Std. Error t value Pr(>|t|) > x 0.27679 0.07387 3.747 0.00119 ** > factor(class)1 52.41405 17.38161 3.015 0.00658 ** > factor(class)2 145.09488 39.42651 3.680 0.00139 ** > factor(class)3 250.23434 40.61189 6.162 4.11e-06 *** > factor(class)4 158.02672 64.09549 2.465 0.02238 * > --- > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > Residual standard error: 43.07 on 21 degrees of freedom > Multiple R-squared: 0.9801, Adjusted R-squared: 0.9754 > F-statistic: 207.1 on 5 and 21 DF, p-value: < 2.2e-16 > > Can somebody please tell me how to interpret this one now? what do the > classes' P values mean ? Do they merely show if they significantly > contribute to the model or whether they are significantly different from > the overall mean or not? Does it mean if one class had a p value > 0.05 it > would mean the observations from that class are not significantly > contributing to the model?
The estimates are of the per-class intercept and the P-value corresponds to a test that said intercept is zero (which is very rarely a relevant hypothesis). -- Peter Dalgaard, Professor Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.