Many thanks Ivan ! This is fairly clear to me, now... When I dumped the
data.frame, I found strange to have a "table" declaration for deg, but
was not able to judge if it was a problem or not (I would have expected
something as "numeric")
Your workaround is fine to me (I do not need to carry on
On Fri, 21 Apr 2023 09:02:37 +0200
Patrick Giraudoux wrote:
> I meet an error with glm.nb that I cannot explain the origin (and
> find a fix). The model I want to fit is the following:
>
> library(MASS)
>
> glm.nb(deg~offset(log(durobs))+zone,data=db)
>
> and the data.frame is dumped below.
T
Dear Listers,
I meet an error with glm.nb that I cannot explain the origin (and find a
fix). The model I want to fit is the following:
library(MASS)
glm.nb(deg~offset(log(durobs))+zone,data=db)
and the data.frame is dumped below.
Has anyone an idea about what the trouble comes from ? (except
Thanks for all your help. I will try to fix this.
Very helpful.
Best,
Daofeng
On Fri, Jun 7, 2013 at 11:25 AM, Sarah Goslee wrote:
> As Marc already pointed out, take a close look at this part of your loop:
>
> R> i <- 6
> R>
> R> y <- as.numeric(data[i,-1])
> R> y
> [1] 3 3 3 3 4 4 4 4
> R> g
Thanks Marc...still didn't get it...
Tried this...
> str(group)
num [1:8] 1 1 1 1 0 0 0 0
> fit=glm.nb(y~group)
Error in while ((it <- it + 1) < limit && abs(del) > eps) { :
missing value where TRUE/FALSE needed
> group <- c('1','1','1','1','0','0','0','0')
> str(group)
chr [1:8] "1" "1" "1" "
Thank you Sarah and Marc for your fast and nice response.
Apology for didn't include all information.
I have a input file like following:
gene1 18 15 13 13 16 9 20 24
gene2 15 8 8 7 0 12 18 4
gene3 10 9 8 12 9 11 12 12
gene4 4 0 4 3 0 5 0 0
gene5 0 1 0 0 1 5 1 0
gene6 3 3 3 3 4 4 4 4
gene7 0 4 0
Sorry Sarah.
> dput(dat)
structure(list(gene = structure(c(1L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 2L), .Label = c("gene1", "gene10", "gene2", "gene3",
"gene4", "gene5", "gene6", "gene7", "gene8", "gene9"), class = "factor"),
b1 = c(18L, 15L, 10L, 4L, 0L, 3L, 0L, 4L, 11L, 6L), b2 = c(15L,
8L,
As Marc already pointed out, take a close look at this part of your loop:
R> i <- 6
R>
R> y <- as.numeric(data[i,-1])
R> y
[1] 3 3 3 3 4 4 4 4
R> group
[1] 1 1 1 1 0 0 0 0
R> fit <- glm.nb(y~group)
Error in while ((it <- it + 1) < limit && abs(del) > eps) { :
missing value where TRUE/FALSE neede
Hi,
On Fri, Jun 7, 2013 at 11:36 AM, Daofeng Li wrote:
> Thank you Sarah and Marc for your fast and nice response.
> Apology for didn't include all information.
>
> I have a input file like following:
>
> gene1 18 15 13 13 16 9 20 24
> gene2 15 8 8 7 0 12 18 4
> gene3 10 9 8 12 9 11 12 12
> gene4
On Jun 7, 2013, at 10:36 AM, Daofeng Li wrote:
> Thank you Sarah and Marc for your fast and nice response.
> Apology for didn't include all information.
>
> I have a input file like following:
>
> gene1 18 15 13 13 16 9 20 24
> gene2 15 8 8
On Jun 7, 2013, at 9:44 AM, Daofeng Li wrote:
> Dear R Community,
>
> I have encountered a problem while using the R function glm.nb.
> The code that produce the error was following two lines:
>
> group=c(1,1,1,1,0,0,0,0)
> fit=glm.nb(y~group)
>
> While the y contains 8 sets of number like:
>
Hi,
On Fri, Jun 7, 2013 at 10:44 AM, Daofeng Li wrote:
> Dear R Community,
>
> I have encountered a problem while using the R function glm.nb.
> The code that produce the error was following two lines:
>
> group=c(1,1,1,1,0,0,0,0)
> fit=glm.nb(y~group)
>
> While the y contains 8 sets of number li
Dear R Community,
I have encountered a problem while using the R function glm.nb.
The code that produce the error was following two lines:
group=c(1,1,1,1,0,0,0,0)
fit=glm.nb(y~group)
While the y contains 8 sets of number like:
gene2750 1 0 0 1 5 1
On Sun, 21 Oct 2012, Eiko Fried wrote:
I am running 9 negative binomial regressions with count data.
The nine models use 9 different dependent variables - items of a clinical
screening instrument - and use the same set of 5 predictors. Goal is to
find out whether these predictors have different
I am running 9 negative binomial regressions with count data.
The nine models use 9 different dependent variables - items of a clinical
screening instrument - and use the same set of 5 predictors. Goal is to
find out whether these predictors have differential effects on the items.
Due to various
-project.org] On
> Behalf Of hesicaia [dbo...@dal.ca]
> Sent: 14 August 2009 04:31
> To: r-help@r-project.org
> Subject: [R] glm.nb versus glm estimation of theta.
>
> Hello,
>
> I have a question regarding estimation of the dispersion parameter (theta)
> for generalized linear
ld be close to (though not necessarily equal to)
unity.
From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf Of
hesicaia [dbo...@dal.ca]
Sent: 14 August 2009 04:31
To: r-help@r-project.org
Subject: [R] glm.nb versus glm estimation of
correspond to a specific likelihood).
Thus, dispersion and theta are really different things although both of
them can be used to capture overdispersion.
I understand, there are two main methods to fit glm's using the nb error
structure in R: glm.nb() or glm() with the negative.binomial(theta) f
Hello,
I have a question regarding estimation of the dispersion parameter (theta)
for generalized linear models with the negative binomial error structure. As
I understand, there are two main methods to fit glm's using the nb error
structure in R: glm.nb() or glm() with the negative.bin
ou cannot easily
predict from the resulting fitted model object.
Bill Venables
http://www.cmis.csiro.au/bill.venables/
-Original Message-
From: David Croll [mailto:david.cr...@gmx.ch]
Sent: Wednesday, 25 March 2009 7:28 PM
To: Venables, Bill (CMIS, Cleveland); r-help@r-project.org
Subject
y, 25 March 2009 12:36 PM
To: r-help@r-project.org
Subject: [R] glm.nb() giving strongly different results
Dear colleagues,
I have performed several dozens of glm.nb(response ~ variable) analyses
weeks ago, and when I looked through the results today I saw that many
of the results have quite dif
cmis.csiro.au/bill.venables/
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of David Croll
Sent: Wednesday, 25 March 2009 12:36 PM
To: r-help@r-project.org
Subject: [R] glm.nb() giving strongly different results
Dear colleagues,
I have
Dear colleagues,
I have performed several dozens of glm.nb(response ~ variable) analyses
weeks ago, and when I looked through the results today I saw that many
of the results have quite different intercept values despite the
response part remained the same.
I'm quite sure I did same kind
dear listers,
i tried to use glm.nb to estimate a nega. binomial but have no luck to
get the result. here is the code:
model <- glm.nb(Y ~ ., data = mydata)
I am not sure if I have missed anything.
thanks for your insight!
wensui
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