Dear all,
I'm facing a problem in estimation of glm model with weibull distribution. I
run this :
eqn0<-formula(fdh~cup1+cup2+cup3+cup4+fin1+vd1+cm2+cm4+milieu+cpro1+cpro2+cpro3a+cpro3b+schef+log(y))
regWeib0<-vglm(eqn0,family=weibull,subset(br, fdh<1))
I have en estimation but there is a messa
Hello,
I am using multinomial logit regression for the first time, and I am trying to
understand the warnings and errors I get.
My data consists of 200 to 600 samples with ~25 predictors (these are principal
components). The response has three categories.
I use the function "vglm" from the pa
Hi All,
I am using vglm & the fitted values is NA. As in the R documentation that
for Pareto1, if the estimate of k is less than or equal to unity then the
fitted values will be NA.
what is NA means ?
how to solve it ?
how to get the k estimate ? (in the R document the estimate of alpha is
f.
I am glad it works. But you should reply to the list, thus more people
can trace the process of the question.
As to your second question, I have no idea how to solve it. I suggest
you start another thread. There will be someone able to answer you in
the R mailing list. Remember to reply to the
My previous expression is ok. But I agree using argument 'data' will be
a better choice especially when there are many variables from the object
specified by 'data'.
On 2010-9-1 16:23, Gavin Simpson wrote:
On Wed, 2010-09-01 at 11:05 +0800, Dejian Zhao wrote:
try
fit=vglm(mydata[,"Loss"]~
On Wed, 2010-09-01 at 11:05 +0800, Dejian Zhao wrote:
> try
> fit=vglm(mydata[,"Loss"]~1,pareto1(location=alpha),trace=TRUE,crit="c")
No, please don't. That is not a good example of formula use in R.
Several responders have already pointed out that the 'R' way of doing
this would be to use a data
try
fit=vglm(mydata[,"Loss"]~1,pareto1(location=alpha),trace=TRUE,crit="c")
On 2010-9-1 3:20, choonhong ang wrote:
Hi All,
could anybody help me to understand what is this error means ?
mydata=read.table("C:/Documents and
Settings/angieb/Desktop/CommercialGL/cl_ilf_claimdata.csv",header=TRUE,
Hi,
My guess is that vglm() cannot find "Loss" because you have not set
the data argument. Something like:
fit = vglm(Loss ~ 1, pareto1(location = alpha), trace = TRUE, crit =
"c", data = mydata)
out to work if "Loss" is a variable in the object "mydata"
HTH,
Josh
On Tue, Aug 31, 2010 at 12
On Aug 31, 2010, at 3:20 PM, choonhong ang wrote:
Hi All,
could anybody help me to understand what is this error means ?
mydata=read.table("C:/Documents and
Settings/angieb/Desktop/CommercialGL/
cl_ilf_claimdata.csv",header=TRUE,sep=",")
names(mydata)
[1] "ILFTable""liabLimit" "AnnA
Hi All,
could anybody help me to understand what is this error means ?
mydata=read.table("C:/Documents and
Settings/angieb/Desktop/CommercialGL/cl_ilf_claimdata.csv",header=TRUE,sep=",")
> names(mydata)
[1] "ILFTable""liabLimit" "AnnAggLimit" "DedAmt" "Loss"
"TIL"
> fit=vglm(Loss~1,pa
On 4 Nov 2009, at 18:40, Gavin Simpson wrote:
Additionally, while extracting the t value is a piece of cake with
polr(), the p-value I get a nowhere close to a null distribution.
Yes - I see that polr() also doesn't produce p-values in the output
from
summary. You can use it to get "a" p-va
On Wed, 2009-11-04 at 18:19 +, Federico Calboli wrote:
> On 4 Nov 2009, at 18:11, Gavin Simpson wrote:
> > Is there a particular reason for choosing a VGLM here? My reading of
> > your post suggests the response is an univariate, ordered factor and
> > VGLMs are especially for multivariate resp
On 4 Nov 2009, at 18:11, Gavin Simpson wrote:
Is there a particular reason for choosing a VGLM here? My reading of
your post suggests the response is an univariate, ordered factor and
VGLMs are especially for multivariate responses. In which case, can
you
not use polr() in package MASS that co
On Wed, 2009-11-04 at 14:00 +, Federico Calboli wrote:
> Hi All,
I can't answer your questions myself - try Thomas Yee, the author and
maintainer of the VGAM package - but:
> I'm fitting an proportional odds model using vglm() from VGAM.
Is there a particular reason for choosing a VGLM here?
Hi All,
I'm fitting an proportional odds model using vglm() from VGAM.
My response variable is the severity of diseases, going from 0 to 5 (the
severity is actually an ordered factor).
The independent variables are: 1 genetic marker, time of medical observation,
age, sex. What I *need* is a
Hi all,
I am trying to put the summary output of vglm{VGAM} into a Latex table using
mtable(Memisc}. I think I solved the problem regarding to the fact that vglm
produces a "vglm" object which is not accepted by mtable by default defining a
getSummary.vglm function.
However summary.vglm adds
1(X1>X2) 1(X3>X4) on N1, N2 (for the part depending on X1,X2)
and N1,N2,X1,X2 [or N1,N2,1(X1>X2) ] for the responses depending on X3,X4.
X3,X4 could be interpreted like second period variables and X1,X2 like first
period variables.
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