Parameters are different from functions, and offset is a function. Kindly read the help for that function and the references given there. --------------------------------------------------------------------------- Jeff Newmiller The ..... ..... Go Live... DCN:<jdnew...@dcn.davis.ca.us> Basics: ##.#. ##.#. Live Go... Live: OO#.. Dead: OO#.. Playing Research Engineer (Solar/Batteries O.O#. #.O#. with /Software/Embedded Controllers) .OO#. .OO#. rocks...1k --------------------------------------------------------------------------- Sent from my phone. Please excuse my brevity.
Elaine Kuo <elaine.kuo...@gmail.com> wrote: >Hello, > >Thanks. >But the parameter offset is new to me. >Please kindly explain why setting offset to x will give a significant >test >of whether the slope coefficient is different from one. >(I checked the ?lm but still do not understand it well) > >Thanks again > >Elaine > > >On Wed, May 1, 2013 at 11:12 AM, Thomas Lumley <tlum...@uw.edu> wrote: > >> Or use an offset >> >> lm( y ~ x+offset(x), data = dat) >> >> The offset gives x a coefficient of 1, so the coefficient of x in >this >> model is the difference between the coefficient of x in the model >without >> an offset and 1 -- the thing you want. >> >> -thomas >> >> >> On Wed, May 1, 2013 at 2:54 PM, Paul Johnson <pauljoh...@gmail.com> >wrote: >> >>> It is easy to construct your own test. I test against null of 0 >first so I >>> can be sure I match the right result from summary.lm. >>> >>> ## get the standard error >>> seofb <- sqrt(diag(vcov(lm1))) >>> ## calculate t. Replace 0 by your null >>> myt <- (coef(lm1) - 0)/seofb >>> mypval <- 2*pt(abs(myt), lower.tail = FALSE, df = lm1$df.residual) >>> >>> ## Note you can pass a vector of different nulls for the >coefficients >>> myt <- (coef(lm1) - c(0,1))/seofb >>> >>> We could write this into a function if we wanted to get busy. Not a >bad >>> little homework exercise, I think. >>> >>> >>> >>> >>> > dat <- data.frame(x = rnorm(100), y = rnorm(100)) >>> > lm1 <- lm(y ~ x, data = dat) >>> > summary(lm1) >>> >>> Call: >>> lm(formula = y ~ x, data = dat) >>> >>> Residuals: >>> Min 1Q Median 3Q Max >>> -3.0696 -0.5833 0.1351 0.7162 2.3229 >>> >>> Coefficients: >>> Estimate Std. Error t value Pr(>|t|) >>> (Intercept) -0.001499 0.104865 -0.014 0.989 >>> x -0.039324 0.113486 -0.347 0.730 >>> >>> Residual standard error: 1.024 on 98 degrees of freedom >>> Multiple R-squared: 0.001224, Adjusted R-squared: -0.008968 >>> F-statistic: 0.1201 on 1 and 98 DF, p-value: 0.7297 >>> >>> > seofb <- sqrt(diag(vcov(lm1))) >>> > myt <- (coef(lm1) - 0)/seofb >>> > mypval <- 2*pt(abs(myt), lower.tail = FALSE, df = lm1$df.residual) >>> > myt >>> (Intercept) x >>> -0.01429604 -0.34650900 >>> > mypval >>> (Intercept) x >>> 0.9886229 0.7297031 >>> > myt <- (coef(lm1) - 1)/seofb >>> > mypval <- 2*pt(abs(myt), lower.tail = FALSE, df = lm1$df.residual) >>> > myt >>> (Intercept) x >>> -9.550359 -9.158166 >>> > mypval >>> (Intercept) x >>> 1.145542e-15 8.126553e-15 >>> >>> >>> On Tue, Apr 30, 2013 at 9:07 PM, Elaine Kuo ><elaine.kuo...@gmail.com> >>> wrote: >>> >>> > Hello, >>> > >>> > >>> > >>> > I am work with a linear regression model: >>> > >>> > y=ax+b with the function of lm. >>> > >>> > y= observed migration distance of butterflies >>> > >>> > x= predicted migration distance of butterflies >>> > >>> > >>> > >>> > Usually the result will show >>> > >>> > if the linear term a is significantly different from zero based on >the >>> > p-value. >>> > >>> > Now I would like to test if the linear term is significantly >different >>> from >>> > one. >>> > >>> > (because I want to know if the regression line (y=ax+b) is >significantly >>> > from the line with the linear term =1 and the intercept =0) >>> > >>> > >>> > >>> > Please kindly advise if it is possible >>> > >>> > to adjust some default parameters in the function to achieve the >goal. >>> > >>> > Thank you. >>> > >>> > >>> > Elaine >>> > >>> > [[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. >>> > >>> >>> >>> >>> -- >>> Paul E. Johnson >>> Professor, Political Science Assoc. Director >>> 1541 Lilac Lane, Room 504 Center for Research Methods >>> University of Kansas University of Kansas >>> http://pj.freefaculty.org http://quant.ku.edu >>> >>> [[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. >>> >> >> >> >> -- >> Thomas Lumley >> Professor of Biostatistics >> University of Auckland >> > > [[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. ______________________________________________ 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.