[R] simple test on slope of lm()

[Warnes, Gregory R]

The gregmisc library contains a function 'glh.test' for testing general
linear hypotheses which can be used to perform this test:

> x <- rnorm(100)
> y <- x + rnorm(100)
> model <- lm(y ~ x)
> model

Call:
lm(formula = y ~ x)

Coefficients:
(Intercept)            x  
    -0.1031       1.0172  

> library(gregmisc)
Loading required package: MASS 

Attaching package `gregmisc':


        The following object(s) are masked from package:base :

         lowess 

> # test:  0*b0 + 1*b1 = 1 
> glh.test( model, cm=c(0,1), d=1)

         Test of General Linear Hypothesis      
Call:
glh.test(reg = model, cm = c(0, 1), d = 1)
F = 0.0285, df1 =  1, df2 = 98, p-value = 0.8663 


-Greg

> -----Original Message-----
> From: Fred JEAN [mailto:frederic.jean at univ-brest.fr]
> Sent: Friday, November 22, 2002 4:09 PM
> To: r-help at stat.math.ethz.ch
> Subject: [R] simple test on slope of lm()
> 
> 
> Hello
> 
> I want to compare the slope (let's say 'b') of a linear model 
> obtained 
> with lm() to a theoretical value (let's say 'th').
> 
> To do so, I think I should compute a 't value' using 
> something like :
> 
> (b - 'th')/standard.deviation(b)
> 
> and then look at the p-value of this computed t.
> 
> I don't understand how to do this in a simple way, just using lm() 
> outputs for example. 
> 
> I'm sorry of such a naive/newbie question, and thank you so much for 
> taking time to answer.
> 
> Fred
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