[R] Problem with Slope.test function

[Антон Морковин]

Good evening! Thanks for your answer, but I don't really understand how to use vcov matrix for comarision of multiple regression line slopes... 
I've read this theme about using t-test in slope comarision:
http://www.mail-archive.com/r-help@r-project.org/msg01713.html 

but how to use it in case of multiple regression? Is it correct to use intersept and its se of observed/predicted regression line for this comarision?

Thanks,
Anton



18.03.2011, 13:55, "Daniel Kaschek" <daniel.kaschek at physik.uni-freiburg.de>:
> Hi Антон,
>
> my approach would be to use the estimated variance of the slope
> estimator. For your regression models m1 and m2 you can enter
>
> vcov(m1)
> vcov(m2)
>
> to get the variance-covariance matrix of m1 and m2. Then, assuming that
> the slope estimator is normal, you compute the p-value for slope_theo
> with respect to this normal distribution.
>
> On Fri, 2011-03-18 at 10:04 +0300, Антон Морковин wrote:
>
>>  Hi all,
>>  I need to test the significnce of difference between slopes of two regression lines and regression line with theoretical line. I try to use Slope.test function from emu package,
>>  but an error occured...
>>
>>  library(emu)
>>  d1<-data.frame(P1=c(1,2,3,5,7,8,9,13,14,15),
>>   P2=c(1,2,5,8,11,13,15,15,18,24),
>>   R=c(2,7,8,9,16,21,27,31,33,36)) # First data set
>>  m1<-lm(R~P1+P2+P1*P2,data=d1) # Regr. model
>>
>>  d2<-data.frame(P1=c(1,5,4,7,9,1,12,4,4,5),
>>   P2=c(1,2,0,7,4,1,2,0,7,0),
>>   R=c(3,12,15,15,9,7,4,5,6,1)) # Second data set
>>  m2<-lm(R~P1+P2+P1*P2,data=d2) # Regr. model
>>
>>  Slope.test(data.frame(fitted(m1),d1$R),data.frame(fitted(m2),d2$R)) #Doesn't work...
>>
>>  Is it correct to use t-test in this situation and how to compute  df  for it? My solution is:
>>
>>  s1<-coefficients(summary(lm(fitted(m1)~d1$R)))[2,1] #Slopes of regr.line
>>  s2<-coefficients(summary(lm(fitted(m2)~d2$R)))[2,1]
>>  se1<-coefficients(summary(lm(fitted(m1)~d1$R)))[2,2] #SE of slopes
>>  se2<-coefficients(summary(lm(fitted(m2)~d1$R)))[2,2]
>>  df1<-df.residual(lm(fitted(m1)~d1$R)) #D. of f.
>>  df2<-df.residual(lm(fitted(m2)~d1$R))
>>
>>  kk<-function(se1,se2,df1,df2){(se1^2+se2^2)^2/(se1^4/(df1-1)+se2^4/(df2-1))} #D. of f. for Welsch test
>>  tt<-function(s1,s2,se1,se2){(s1-s2)/sqrt(se1^2+se2^2)}
>>  pp<-function(s1,s2,se1,se2,df1,df2){2*pt(-abs(tt(s1,s2,se1,se2)),df=kk(se1,se2,df1,df2))}
>>
>>  pp(s1,s2,se1,se2,df1,df2) # p-value
>>
>>  ## Theoretical line
>>
>>  s3<-0.75
>>  se3<-0
>>  df3<-0
>>
>>  pp(s1,s3,se1,se3,df1,df3) # p-value
>>  pp(s2,s3,se2,se3,df2,df3) # p-value
>>
>>  Thanks,
>>  A.M.
>>
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> --
> Daniel Kaschek
> Physikalisches Institut, Freiburg
> Hermann-Herder-Str. 3
> 79104 Freiburg
>
> Office:  Westbau, 02020
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