Dear R users,

 

I have a data structure as follows:

 

id          two       res1      res2      c1         c2         inter

1          -0.786093166     1          0          1          2          6

3          -0.308495749     1          0          0          1          2

5          -0.738033048     1          0          0          0          1

7          -0.52176252      1          0          1          0          4

9          -2.023641189     1          0          1          1          5

10         0.463469887      1          0          1          0          4

14         0.379364681      1          0          1          0          4

17         -1.422889721     1          0          0          0          1

19         -2.582340053     1          0          0          2          3

20         0.721793018      1          0          1          2          6

1          -0.867823661     0          1          1          2          6

3          -0.727418536     0          1          0          1          2

5          -0.331731368     0          1          0          0          1

7          -0.835913405     0          1          1          0          4

9          -0.612541616     0          1          1          1          5

10         -0.063685221     0          1          1          0          4

14         0.3511481         0          1          1          0          4

17         -1.3847698        0          1          0          0          1

19         -2.232306187     0          1          0          2          3

20         0.0014117         0          1          1          2          6

 

I would like to apply multivariate analysis and test interaction effect in
the same model by using lme function. The 'two' variable is a bi-variate
outcome by combining two continuous outcomes. The 'res1' and 'res2' are two
dummy variables to flag these two outcome variables, separately. The
interaction term for c1 and c2 is "inter" with 6 levels. What I have done
are as follows:

 

# capture clustering structure

 

tmp <- groupedData(two~res1+res2+inter|id,data=a); tmp <- na.exclude(tmp);

 

# treat 'inter' as a multi-level variable

 

options(contrasts=c(factor="contr.treatment",ordered="contr.poly"));
tmp$inter <- factor(tmp$inter); contrasts(tmp$inter)

 

# perform multivariate analysis and test interaction effect

 

m1 <- summary(lme(two~res1+res2+inter, random=~1|id, data=tmp))

 

## Error in MEEM(object, conLin, control$niterEM) : 

##        Singularity in backsolve at level 0, block 1

 

Unfortunately, I have been unable to get it work. If I removed these two
dummy variables like this:

 

m2 <- summary(lme(two~inter, random=~1|id, data=tmp))

 

I did get some outputs, but these outputs in the model m2 were wrong. Is
there any way to perform multivariate analysis and test interaction effect
(a multi-level factor) in the same model by lme? In addition, can lme be
used for multivariate analyses of combining several binary outcome
variables? Or it may be doable in some R function?

 

Many thanks for your suggestions.

 

Hui-Ju

 

 

 



The contents of this e-mail message and any attachments are private and confidential communications intended solely for the addressee(s) named in this message. If you are not the intended recipient of this message, please 1) immediately notify the sender by reply e-mail and then delete this message and its attachments and 2) do not read, use, distribute disclose or copy this message and/or any attachments. 

	[[alternative HTML version deleted]]

