On Thu, 9 Aug 2018, John 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?
The standard F test in the summary output tests the hypothesis that all
coefficients _except the intercept_ are zero. Thus, all of these are the
same:
summary(lm1)
## ...
## F-statistic: 0.3333 on 1 and 1 DF, p-value: 0.6667
linearHypothesis(lm1, "x = 0")
## ...
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 2 2.0
## 2 1 1.5 1 0.5 0.3333 0.6667
lm0 <- lm(y ~ 1, data = df1)
anova(lm0, lm1)
## ...
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 2 2.0
## 2 1 1.5 1 0.5 0.3333 0.6667
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.
______________________________________________
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.
