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gls function, very old results

2 messages · Raphael Gottardo, Bill Oliver

Original
Raphael Gottardo
# 
Hello R-users,

I am currently trying to learn how to use the function gls of the nlme
library. I fitted the following model:
Generalized least squares fit by REML
Model: response ~ array + dye + genes + variety + variety * genes +
array * genes + dye * genes
Data: data

I have 11 arrays, 2 dyes, 2 varieties, 3200 genes, and 2 replications
for each.
Therefore I should have the corresponding degrees of freedom and number
of coefficients, but instead I have the following:
Coefficients:
  (Intercept)         array           dye         genes       variety
 5.955503e+00  2.695750e-02  4.120987e-01 -2.499571e-04  2.686421e-01
  array:genes     dye:genes genes:variety
 1.319176e-06 -7.112527e-05  2.660801e-05

Degrees of freedom: 110386 total; 110378 residual
Residual standard error: 1.030704
Denom. DF: 110378
              numDF F-value p-value
(Intercept)       1 7590769  <.0001
array             1   21263  <.0001
dye               1    3069  <.0001
genes             1    4277  <.0001
variety           1    2493  <.0001
array:genes       1      38  <.0001
dye:genes         1      99  <.0001
genes:variety     1      15   1e-04

So I would like to know what I am doing wrong?
I use the following command:
 fit_gls(response~array+dye+genes+variety+variety*genes+array*genes+dye*genes,data=data)

and my dataset looks like this:
   array variety dye genes response flag
1     79       1   1     1 8.395252    0
2     79       1   1     1 8.583917    0
3     79       1   1     2 8.544225    0
4     79       1   1     2 8.423542    0
5     79       1   1     3 7.502186    0
6     79       1   1     3 7.524021    0
7     79       1   1     4 8.188411    0
8     79       1   1     4 8.072779    0
9     79       1   1     5 7.629976    0
10    79       1   1     5 7.524021    0
11    79       1   1     6 7.684784    0
12    79       1   1     6 7.610358    0
13    79       1   1     7 8.366138    0
14    79       1   1     7 8.369621    0
15    79       1   1     8 7.166266    0
16    79       1   1     8 7.038784    0
17    79       1   1     9 7.474205    0
18    79       1   1     9 7.805067    0
19    79       1   1    10 8.339501    0
20    79       1   1    10 8.407155    0

Any suggestion would be greatly appreciated.
Thank you,
raphael

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Bill Oliver
# 
----- Original Message -----
From: "Raphael Gottardo" <raph at alvie-mail.lanl.gov>
To: <r-help at stat.math.ethz.ch>
Sent: Tuesday, July 10, 2001 11:50 AM
Subject: [R] gls function, very old results
fit_gls(response~array+dye+genes+variety+variety*genes+array*genes+dye*genes
,data=data)
It appears that the variables "array", "dye", etc., need to be treated as
"factors". Probably the most convenient approach would be to convert them in
your data frame before carrying out the analysis. For example, the values of
dye could be converted with the following code (mutatis mutandis).
Finding an appropriate model for these data is likely to be a challenging
exercise. I highly recommend the book by Pinheiro and Bates entitled "Mixed
Effects Models in S and S-Splus". These authors explain very clearly how to
carry out mixed-effects modeling.

-Bill

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