[R] Change the order of variables in a linear model
Hello,
the following lines
m <- matrix(c(1,1,9,1,2,6,1,3,7,2,1,4,2,2,5,2,3,1,3,1,2,3,2,-1,3,3,-2), 9,
3, byrow = TRUE, dimnames=list(NULL, cbind('A','B','Y')))
md <- as.data.frame(m)
md$A <- as.factor(md$A)
md$B <- as.factor(md$B)
mm <- model.matrix(Y~A+B+A:B, data=md)
produce
> mm
(Intercept) A2 A3 B2 B3 A2:B2 A3:B2 A2:B3 A3:B3
1 1 0 0 0 0 0 0 0 0
2 1 0 0 1 0 0 0 0 0
3 1 0 0 0 1 0 0 0 0
4 1 1 0 0 0 0 0 0 0
5 1 1 0 1 0 1 0 0 0
6 1 1 0 0 1 0 0 1 0
7 1 0 1 0 0 0 0 0 0
8 1 0 1 1 0 0 1 0 0
9 1 0 1 0 1 0 0 0 1
attr(,"assign")
[1] 0 1 1 2 2 3 3 3 3
attr(,"contrasts")
attr(,"contrasts")$A
[1] "contr.treatment"
attr(,"contrasts")$B
[1] "contr.treatment"
However, instead of the order
(Intercept) A2 A3 B2 B3 A2:B2 A3:B2 A2:B3 A3:B3
| |
changed order
i'd like to have | |
(Intercept) A2 A3 B2 B3 A2:B2 A2:B3 A3:B2 A3:B3
that is, the order of the A:B interaction variables is changed.
Is there a way to freely position variables in a model?
Thank you,
Frank
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[R] set specific contrasts using lapply
I have the following data set
> data
A B X1 X2 Y
1 A1 B1 1.1 2.9 1.2
2 A1 B2 1.0 3.2 2.3
3 A2 B1 1.0 3.3 1.6
4 A2 B2 0.5 2.6 3.1
> sapply(data, class)
A BX1X2 Y
"factor" "factor" "numeric" "numeric" "numeric"
I'd like to set a specific type of contrasts to all the categorical factors
(here A and B).
In a loop, this can be done like this:
for (i in 1:length(data))
{
if (class(data[[i]]) == 'factor')
{
contrasts(data[[i]], length(levels(data[[i]]))) <-
contr.treatment(levels(data[[i]]), contrast=FALSE);
}
}
Can i do this without the loop?
I tried to use the function
set.contrasts <- function(x)
{
if (class(x) == 'factor')
{
contrasts(x, length(levels(x))) <- contr.treatment(levels(x),
contrast=FALSE);
}
}
in
lapply(data, set.contrasts)
However, it doesn't work.
Any ideas?
Thank You
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Re: [R] generalized inverse using matinv (Design)
thank you all. i have deliberately chosen matinv (although obviously an outdated version) because it uses the sweep operator. i know of other methods to calculate generalized inverses. however, it is also true that the sweep operator is capable of computing g2 generalized inverses. The ginv returns the Moore-Penrose inverse, which has nicer numerical properties if computed by svd, but it is just not what i want. (besides, svd is very slow and should not be applied to the normal equations anyway) so lets explain my peculiar interest in matinv: i am trying get my head around the type III SS that SAS popularized. i know that those hypotheses are hated in the R community but they are the standard hypothesis given by all other statistics software. i know how to compute type III SS and how to translate them to meaningful hypothesis of the cell means model. but i do not have an intuitive understanding why they are computed as they are. so i came across my matinv problem when i tried to to compute the generating matrix _ H = (X'X) X'X used for the construction of estimable functions. if the g2-inverse is used, then H has canonical form which simpifies the interpretion. i just wanted to check if it is invariant to cell frequencies. -- View this message in context: http://r.789695.n4.nabble.com/generalized-inverse-using-matinv-Design-tp3747337p3749373.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list 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] generalized inverse using matinv (Design)
i am trying to use matinv from the Design package to compute the generalized inverse of the normal equations of a 3x3 design via the sweep operator. That is, for the linear model y = ยต + x1 + x2 + x1*x2 where x1, x2 are 3-level factors and dummy coding is being used the matrix to be inverted is X'X = 9 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 3 3 0 0 1 1 1 1 0 0 1 0 0 1 0 0 3 0 3 0 1 1 1 0 1 0 0 1 0 0 1 0 3 0 0 3 1 1 1 0 0 1 0 0 1 0 0 1 3 1 1 1 3 0 0 1 1 1 0 0 0 0 0 0 3 1 1 1 0 3 0 0 0 0 1 1 1 0 0 0 3 1 1 1 0 0 3 0 0 0 0 0 0 1 1 1 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 this matrix has rank=9 however, matinv(X'X) falsely returns rank=4, no matter what the tolerance threshold eps is set to. also the defining property of the generalized inverse _ X'X %*% (X'X) %*% X'X = X'X is not satisfied. if i use qr (from the base package) the rank is correctly determined as 9. any ideas? thank you -- View this message in context: http://r.789695.n4.nabble.com/generalized-inverse-using-matinv-Design-tp3747337p3747337.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list 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.
