The offset(x) term means to include x (or whatever is in the parentheses) into the model as is, without computing a slope for that term. You could also include offset( 1 * x ) instead which might make this a bit more explicit (but would not actually make any difference). Since x by itself is also in the model with a computed slope that slope will measure the difference from 1. So the model is fitting y = b0 + b1 * x + 1 * x + e with b0 and b1 being computed, if we factor that then it is y = b0 + (b1 + 1) * x + e, so the estimate of b1 is how much the overall slope of x differs from 1 and the standard test on b1 now does what you want.
On Sat, May 4, 2013 at 5:35 AM, 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. > -- Gregory (Greg) L. Snow Ph.D. 538...@gmail.com [[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.
