The null hypothesis is different (and the different numerator Df is the givaway).
> lm0 <- lm(y~-1, df1) > anova(lm0,lm1) Analysis of Variance Table Model 1: y ~ -1 Model 2: y ~ x Res.Df RSS Df Sum of Sq F Pr(>F) 1 3 149.0 2 1 1.5 2 147.5 49.167 0.1003 -pd > On 9 Aug 2018, at 10:58 , John <miao...@gmail.com> wrote: > > Hi, > > I try to run the same f-test by lm (with summary) and the function > "linearHypothesis" in car package. Why are the results (p-values for the > f-test) different? > > >> df1<-data.frame(x=c(2,3,4), y=c(7,6,8)) >> lm1<-lm(y~x, df1) >> lm1 > > Call: > lm(formula = y ~ x, data = df1) > > Coefficients: > (Intercept) x > 5.5 0.5 > >> summary(lm1) > > Call: > lm(formula = y ~ x, data = df1) > > Residuals: > 1 2 3 > 0.5 -1.0 0.5 > > Coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 5.500 2.693 2.043 0.290 > x 0.500 0.866 0.577 0.667 > > Residual standard error: 1.225 on 1 degrees of freedom > Multiple R-squared: 0.25, Adjusted R-squared: -0.5 > F-statistic: 0.3333 on 1 and 1 DF, p-value: 0.6667 > >> linearHypothesis(lm1, c("(Intercept)=0", "x=0")) > Linear hypothesis test > > Hypothesis: > (Intercept) = 0 > x = 0 > > Model 1: restricted model > Model 2: y ~ x > > Res.Df RSS Df Sum of Sq F Pr(>F) > 1 3 149.0 > 2 1 1.5 2 147.5 49.167 0.1003 > > 2018-08-03 13:54 GMT+08:00 Annaert Jan <jan.anna...@uantwerpen.be>: > >> You can easily test linear restrictions using the function >> linearHypothesis() from the car package. >> There are several ways to set up the null hypothesis, but a >> straightforward one here is: >> >>> library(car) >>> x <- rnorm(10) >>> y <- x+rnorm(10) >>> linearHypothesis(lm(y~x), c("(Intercept)=0", "x=1")) >> Linear hypothesis test >> >> Hypothesis: >> (Intercept) = 0 >> x = 1 >> >> Model 1: restricted model >> Model 2: y ~ x >> >> Res.Df RSS Df Sum of Sq F Pr(>F) >> 1 10 10.6218 >> 2 8 9.0001 2 1.6217 0.7207 0.5155 >> >> >> Jan >> >> From: R-help <r-help-boun...@r-project.org> on behalf of John < >> miao...@gmail.com> >> Date: Thursday, 2 August 2018 at 10:44 >> To: r-help <r-help@r-project.org> >> Subject: [R] F-test where the coefficients in the H_0 is nonzero >> >> Hi, >> >> I try to run the regression >> y = beta_0 + beta_1 x >> and test H_0: (beta_0, beta_1) =(0,1) against H_1: H_0 is false >> I believe I can run the regression >> (y-x) = beta_0 +beta_1‘ x >> and do the regular F-test (using lm functio) where the hypothesized >> coefficients are all zero. >> >> Is there any function in R that deal with the case where the >> coefficients are nonzero? >> >> John >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> mailto:R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> 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. >> >> >> > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Office: A 4.23 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.