Yes, you can see that in the new model the slope is now 1- the old slope, so it is measuring difference from 1. Since it is significant that means the slope is significantly different from 1. To test the null that slope = 0.5 just change the offset to "offset(0.5*log(data$SIZE,10))"
To save yourself future grief it is best to not use df$var syntax in formulas but better to do something like: log(y+1)~x, data=mydata Also, it is best to name your data frame something besides data (it can be confused with the data function). On Thu, Apr 26, 2012 at 3:54 PM, Mark Na <mtb...@gmail.com> wrote: > Hi Greg and others, > > Thanks for your replies. Okay, I'm convinced that the offset is the best > approach and wonder if you might have a quick look at what I did. > > Here's the original model containing the slope (0.56) that I'd like to test > if it's different from 1.0 > >>model1 <- glm(log(data$AB.obs+1,10) ~ log(data$SIZE,10) + data$YEAR) > > and its coefficients: > > Estimate Std. Error t value Pr(>|t|) > (Intercept) -1.18253 0.09119 -12.967 < 2e-16 *** > log(data$SIZE, 10) 0.56001 0.02564 21.843 < 2e-16 *** > data$YEAR2008 0.16823 0.04366 3.853 0.000152 *** > data$YEAR2009 0.20299 0.04707 4.313 0.000024 *** > > And here's the model with an offset term: > >>model2 <- glm(log(data$AB.obs+1,10) ~ log(data$SIZE,10) + >> offset(log(data$SIZE,10)) + data$YEAR) > > and its coefficients: > > Estimate Std. Error t value Pr(>|t|) > (Intercept) -1.18253 0.09119 -12.967 < 2e-16 *** > log(data$SIZE, 10) -0.43999 0.02564 -17.162 < 2e-16 *** > data$YEAR2008 0.16823 0.04366 3.853 0.000152 *** > data$YEAR2009 0.20299 0.04707 4.313 0.000024 *** > > So, if I understand correctly, the small P-value corresponding to the SIZE > coefficient in model2 indicates that the slope of 0.56 in model1 is > significantly different from 1.0, right? > > If I may ask one more question: could I use the offset to test if the slope > of 0.56 is different from yet another value, e.g., 0.5? > > Much appreciated. > > Many thanks, Mark Na > > > > > > On Wed, Apr 25, 2012 at 3:27 PM, Greg Snow <538...@gmail.com> wrote: >> >> Doesn't the p-value from using offset work for you? if you really >> need a p-value. The confint method is a quick and easy way to see if >> it is significantly different from 1 (see Rolf's response), but does >> not provide an exact p-value. I guess you could do confidence >> intervals at different confidence levels until you find the level such >> that one of the limits is close enough to 1, but that seems like way >> to much work. You could also compute the p-value by taking the slope >> minus 1 divided by the standard error and plug that into the pt >> function with the correct degrees of freedom. You could even write a >> function to do that for you, but it still seems more work than adding >> the offset to the formula. >> >> On Tue, Apr 24, 2012 at 8:17 AM, Mark Na <mtb...@gmail.com> wrote: >> > Hi Greg. Thanks for your reply. Do you know if there is a way to use the >> > confint function to get a p-value on this test? >> > >> > Thanks, Mark >> > >> > >> > >> > On Mon, Apr 23, 2012 at 3:10 PM, Greg Snow <538...@gmail.com> wrote: >> >> >> >> One option is to subtract the continuous variable from y before doing >> >> the regression (this works with any regression package/function). The >> >> probably better way in R is to use the 'offset' function: >> >> >> >> formula = I(log(data$AB.obs + 1, 10)-log(data$SIZE,10)) ~ >> >> log(data$SIZE, 10) + data$Y >> >> formula = log(data$AB.obs + 1) ~ offset( log(data$SIZE,10) ) + >> >> log(data$SIZE,10) + data$Y >> >> >> >> Or you can use a function like 'confint' to find the confidence >> >> interval for the slope and see if 1 is in the interval. >> >> >> >> On Mon, Apr 23, 2012 at 12:11 PM, Mark Na <mtb...@gmail.com> wrote: >> >> > Dear R-helpers, >> >> > >> >> > I would like to test if the slope corresponding to a continuous >> >> > variable >> >> > in >> >> > my model (summary below) is different than one. >> >> > >> >> > I would appreciate any ideas for how I could do this in R, after >> >> > having >> >> > specified and run this model? >> >> > >> >> > Many thanks, >> >> > >> >> > Mark Na >> >> > >> >> > >> >> > >> >> > Call: >> >> > lm(formula = log(data$AB.obs + 1, 10) ~ log(data$SIZE, 10) + >> >> > data$Y) >> >> > >> >> > Residuals: >> >> > Min 1Q Median 3Q Max >> >> > -0.94368 -0.13870 0.04398 0.17825 0.63365 >> >> > >> >> > Coefficients: >> >> > Estimate Std. Error t value Pr(>|t|) >> >> > (Intercept) -1.18282 0.09120 -12.970 < 2e-16 *** >> >> > log(data$SIZE, 10) 0.56009 0.02564 21.846 < 2e-16 *** >> >> > data$Y2008 0.16825 0.04366 3.854 0.000151 *** >> >> > data$Y2009 0.20310 0.04707 4.315 0.0000238 *** >> >> > --- >> >> > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 >> >> > >> >> > Residual standard error: 0.2793 on 228 degrees of freedom >> >> > Multiple R-squared: 0.6768, Adjusted R-squared: 0.6726 >> >> > F-statistic: 159.2 on 3 and 228 DF, p-value: < 2.2e-16 >> >> > >> >> > [[alternative HTML version deleted]] >> >> > >> >> > >> >> > ______________________________________________ >> >> > 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. >> >> > >> >> >> >> >> >> >> >> -- >> >> Gregory (Greg) L. Snow Ph.D. >> >> 538...@gmail.com >> > >> > >> >> >> >> -- >> Gregory (Greg) L. Snow Ph.D. >> 538...@gmail.com > > -- Gregory (Greg) L. Snow Ph.D. 538...@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.
