I am running the new EdgeR package 1.8 and have updated R to 1.12.0 and 
updated all the packages I have installed.

I have an RNAseq experiment with 24 samples and am wanting to run a glm 
analysis using the Cox-Reid common dispersion (and tagwise) paramaters.  I 
have created a DGEList object using a targets file and this successfully 
ran in the calcNormFactors() and in the plotMDS.dge() functions. I have 
also created what appears to be a sensible and ok design matrix using 
model.matrix().  These then are being input to estimateCRDisp() to 
estimate the common dispersion.  The code I have run is as follows (in 
red) with output (blue) which is pretty much exactly the same as the 
worked example in the manual on pp 51-- and does not seem abnormal at 
all......

targets <- read.delim("Lp_NUE_DGEbymSEQ_TARGETS_EdgeRIN.txt", 
stringsAsFactors = FALSE, row.names = "label")
targets$Nitrogen        <- factor(targets$Nitrogen)
targets$Endophyte       <- factor(targets$Endophyte)
targets$Tissue <- factor(targets$Tissue)
targets

## CREATE THE DGEList Object

DGEList.object <- readDGE(targets, skip = 1, comment.char = '#')
colnames(DGEList.object) <- row.names(targets)



## CALCULATE THE NORMALISATION (f) FACTORS 

DGEList.object <- calcNormFactors(DGEList.object)

## Exclude data where rowsums are < 30 YOU CAN CHANGE THIS (there being 24 
samples in the experiment)

DGEList.object <- DGEList.object[rowSums(DGEList.object$counts) > 30, ]
DGEList.object

An object of class "DGEList"
$samples
                                       files Endophyte Tissue Nitrogen 
lib.size norm.factors
Lp_NUE_LFFULL  Lp_NUE_LFFULL_ER_RAWREADS.txt      Free   Leaf     Full 
3786614    0.9244799
Lp_NUE_LFNO3    Lp_NUE_LFNO3_ER_RAWREADS.txt      Free   Leaf  Partial 
5311856    0.8120410
Lp_NUE_LFK        Lp_NUE_LFK_ER_RAWREADS.txt      Free   Leaf     Full 
3482772    0.7224960
Lp_NUE_LFPO4    Lp_NUE_LFPO4_ER_RAWREADS.txt      Free   Leaf     Full 
6208397    0.7367309
Lp_NUE_LFCa      Lp_NUE_LFCa_ER_RAWREADS.txt      Free   Leaf     Full 
3597004    0.8147619
19 more rows ...

$counts
             Lp_NUE_LFFULL  Lp_NUE_LFNO3  Lp_NUE_LFK  Lp_NUE_LFPO4 
Lp_NUE_LFCa Lp_NUE_LFNH4  Lp_NUE_RFFULL   Lp_NUE_RFNO3  Lp_NUE_RFK 
Lp_NUE_RFPO4
>prg_cds_44             345           382         120           261  141   
     215             888           717         698         1112
>prg_cds_141            290           451         126           422  235   
     459             676           559         474          879
>prg_cds_2              233           277         131           333  239   
     376             246           185         124          282
>prg_cds_16             433           608         184           614  346   
     423             924           756         595          913
>prg_cds_84             287           353          90           241  248   
     394             823           612         989         1061
             Lp_NUE_RFCa  Lp_NUE_RFNH4  Lp_NUE_LSFULL  Lp_NUE_LSNO3 
Lp_NUE_LSK Lp_NUE_LSPO4 Lp_NUE_LSCa  Lp_NUE_LSNH4  Lp_NUE_RSFULL 
Lp_NUE_RSNO3
>prg_cds_44          1107           439            954           257  205  
       107          868           106             316         1147
>prg_cds_141          583          1052            773           325  230  
       193          600           264             414         1046
>prg_cds_2            235           454            177           232  177  
       203          138           239             334          228
>prg_cds_16           817          1040           2373           464  220  
       276          895           291             714         2655
>prg_cds_84           890          1653            894           268  169  
       123          714           209             311         1078
             Lp_NUE_RSK  Lp_NUE_RSPO4  Lp_NUE_RSCa  Lp_NUE_RSNH4
>prg_cds_44          622           903          114          474
>prg_cds_141         505           711          205         1166
>prg_cds_2            93           136          157          340
>prg_cds_16         1398          1660          311         1258
>prg_cds_84          893           771          154         1269
63905 more rows ...


## Produce an MDS plot of the data

#plotMDS.dge(DGEList.object, main = "MDS plot for Lp_NUE_DGEbymSEQ")


## DEFINE THE DESIGN MATRIX

design <- model.matrix(~ Endophyte + Tissue + Nitrogen , data = targets)
print("This is the design matrix")
design

                (Intercept) EndophyteSTWT TissueRoot NitrogenPartial
Lp_NUE_LFFULL             1             0          0               0
Lp_NUE_LFNO3              1             0          0               1
Lp_NUE_LFK                1             0          0               0
Lp_NUE_LFPO4              1             0          0               0
Lp_NUE_LFCa               1             0          0               0
Lp_NUE_LFNH4              1             0          0               1
Lp_NUE_RFFULL             1             0          1               0
Lp_NUE_RFNO3              1             0          1               1
Lp_NUE_RFK                1             0          1               0
Lp_NUE_RFPO4              1             0          1               0
Lp_NUE_RFCa               1             0          1               0
Lp_NUE_RFNH4              1             0          1               1
Lp_NUE_LSFULL             1             1          0               0
Lp_NUE_LSNO3              1             1          0               1
Lp_NUE_LSK                1             1          0               0
Lp_NUE_LSPO4              1             1          0               0
Lp_NUE_LSCa               1             1          0               0
Lp_NUE_LSNH4              1             1          0               1
Lp_NUE_RSFULL             1             1          1               0
Lp_NUE_RSNO3              1             1          1               1
Lp_NUE_RSK                1             1          1               0
Lp_NUE_RSPO4              1             1          1               0
Lp_NUE_RSCa               1             1          1               0
Lp_NUE_RSNH4              1             1          1               1
attr(,"assign")
[1] 0 1 2 3
attr(,"contrasts")
attr(,"contrasts")$Endophyte
[1] "contr.treatment"

attr(,"contrasts")$Tissue
[1] "contr.treatment"

attr(,"contrasts")$Nitrogen
[1] "contr.treatment"


## ESTIMATE THE Cox-Reid common dispersion

DGEList.object <- estimateCRDisp(DGEList.object, design)
names(DGEList.object)


This step is giving  the following error:::


Loading required package: MASS
Error: NA/NaN/Inf in foreign function call (arg 1)
In addition: Warning messages:
1: glm.fit: algorithm did not converge 
2: step size truncated due to divergence 
> names(DGEList.object)
[1] "samples" "counts" 

Clearly the function is not working. I have tried two different computers 
and a run it in 64 bit but always with the same result. I have also tried 
using a balanced design and a simple design all with the same results. I 
have used the DGEList object to analyse the data using the original 
estimateCommonDisp() and exactTest() which worked fine. 


 I was hoping someone might be able to help resolve this for me???? 


Thanks in advance....................... Josquin

The sessionInfo() and traceback() are below:


> sessionInfo()
R version 2.12.0 (2010-10-15)
Platform: i386-pc-mingw32/i386 (32-bit)

locale:
[1] LC_COLLATE=English_Australia.1252  LC_CTYPE=English_Australia.1252 
LC_MONETARY=English_Australia.1252 LC_NUMERIC=C 
[5] LC_TIME=English_Australia.1252 

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base 

other attached packages:
[1] MASS_7.3-8  edgeR_1.8.1

loaded via a namespace (and not attached):
[1] limma_3.6.0


> traceback()
4: .Fortran("dqrls", qr = x[good, ] * w, n = ngoodobs, p = nvars, 
       y = w * z, ny = 1L, tol = min(1e-07, control$epsilon/1000), 
       coefficients = double(nvars), residuals = double(ngoodobs), 
       effects = double(ngoodobs), rank = integer(1L), pivot = 1L:nvars, 
       qraux = double(nvars), work = double(2 * nvars), PACKAGE = "base")
3: glm.fit(design, y[i, ], offset = offset[i, ], family = f)
2: adjustedProfileLik(spline.disp[i], y.select, design = design, 
       offset = offset.mat.select + lib.size.mat.select)
1: estimateCRDisp(DGEList.object, design)
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