[R] simple test on slope of lm()
Douglas Bates
bates at stat.wisc.edu
Mon Nov 25 15:40:38 CET 2002
Yves Brostaux <brostaux.y at fsagx.ac.be> writes:
> To test the slope of your linear model against an arbitrary value, you
> can use lm() directly with a little trick. Let b1 be the value of the
> slope you want to test your model against, then use lm(y-b1*x~x). The
> significance test of the slope for this lm model gives the answer you
> need.
>
>
> > x <- rnorm(10)
> > y <- 2*x + rnorm(10)
> > lm(y~x)
> > lm(y-2*x~x)
Or you could use an offset term in the model.
lm(y ~ x + offset(2*x))
> x <- 1:10
> y <- 2*x + rnorm(10, sd = 0.2)
> summary(lm(y ~ x))
Call:
lm(formula = y ~ x)
Residuals:
Min 1Q Median 3Q Max
-0.324065 -0.187921 0.001240 0.183084 0.363249
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.008868 0.163598 0.054 0.958
x 1.994596 0.026366 75.650 1.04e-12 ***
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 0.2395 on 8 degrees of freedom
Multiple R-Squared: 0.9986, Adjusted R-squared: 0.9984
F-statistic: 5723 on 1 and 8 DF, p-value: 1.039e-12
> summary(lm(y ~ x + offset(2 * x)))
Call:
lm(formula = y ~ x + offset(2 * x))
Residuals:
Min 1Q Median 3Q Max
-0.324065 -0.187921 0.001240 0.183084 0.363249
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.008868 0.163598 0.054 0.958
x -0.005404 0.026366 -0.205 0.843
Residual standard error: 0.2395 on 8 degrees of freedom
Multiple R-Squared: 0.9986, Adjusted R-squared: 0.9984
F-statistic: 5723 on 1 and 8 DF, p-value: 1.039e-12
-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch
_._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
More information about the R-help
mailing list