Displaying 20 results from an estimated 8000 matches similar to: "checking if multinom converged"
2005 Apr 13
2
multinom and contrasts
Hi,
I found that using different contrasts (e.g.
contr.helmert vs. contr.treatment) will generate
different fitted probabilities from multinomial
logistic regression using multinom(); while the fitted
probabilities from binary logistic regression seem to
be the same. Why is that? and for multinomial logisitc
regression, what contrast should be used? I guess it's
helmert?
here is an example
2008 Jun 20
1
omnibus LR in multinomial model
If one estimates a model using multinom, is it possible to perform the
omnibus LR test ( the analogue to omnibus F in linear models ) using
the output
from multinom ? The residual deviance is there but I was hoping I could
somehow pull out the deviance based on just using an intercept ?
Sample code is below from the CAR book but I wasn't sure how to do it
based on that example. Thanks
2005 Nov 30
1
multinom crashes (when I do something stupid) (PR#8358)
Full_Name: Rob Foxall
Version: 2.2.0
OS: Windows XP
Submission from: (NULL) (149.155.96.5)
I was using multinom from nnet package, when I did something stupid -- I entered
in an incorrect factor variable as response. This factor had only one level.
Instead of R telling me not to be so dumb, it crashed, clicking on debug coming
up with the message "An exception 'Unhandled Win32
2005 Apr 12
1
factors in multinom function (nnet)
Dear All:
I am interested in multinomial logit models (function multinon, library nnet) but I'm having troubles in choose whether to define the predictors as factors or not.
I had posted earlier this example (thanks for the reply ronggui):
worms<- data.frame(year= rep(2000:2004, c(3,3,3,3,3)),age=rep(1:3,5),
2000 Mar 20
3
: multinom()
Dear R users,
Does anyone know if it is possible to use multinom to do a polychotomous
fit using one categorical and one numeric variable as response. The
doc. for multinom states that for formula , response can be K>2 classes.
Is this 2 and more, or as I have understood it only greater than 2. I
have tried fitting my data, but have only encountered error messages.
On another note, Is it
2005 Jul 08
1
explained deviance in multinom
Hi:
I'm working with multinomial models with library nnet, and I'm trying to get the explained deviance (pseudo R^2) of my models.
I am assuming that:
pseudo R^2= 1 - dev(model) / dev (null)
where dev(model) is the deviance for the fitted model and dev(null) is the deviance for the null model (with the intercept only).
library(nnet)
full.model<- multinom(cbind(factor1,
2009 Feb 16
2
Using eval in multinom argument
Hi,
I am having difficulty entering a 'programmable' argument into the multinom function from the nnet package. Interactively, I can get the function to work fine by calling it this way:
z1=multinom(formula = class.ind(grp[-outgroup])~ (PC1 + PC2 + PC3), data=data.frame(scores))
However I need to be able to change the number of variables I am looking for in 'scores' and so am
2005 May 13
1
multinom(): likelihood of model?
Hi all,
I'm working on a multinomial (or "polytomous") logistic regression
using R and have made great progress using multinom() from the nnet
library. My response variable has three categories, and there are two
different possible predictors. I'd like to use the likelihoods of
certain models (ie, saturated, fitteds, and null) to calculate
Nagelkerke R-squared values for
2009 Jun 13
1
Insignificant variable improves AIC (multinom)?
Hi,
I am trying to specify a multinomial logit model using the multinom function from the nnet package. Now I add another independent variable and it halves the AIC as given by summary(multinom()). But when I call Anova(multinom()) from the car package, it tells me that this added variable is insignificant (Pr(>Chisq)=0.39). Thus, the improved AIC suggests to keep the variable but the Anova
2010 Dec 10
2
Need help on nnet
Hi,
Am working on neural network.
Below is the coding and the output
> library (nnet)
> uplift.nn<-nnet (PVU~ConsumerValue+Duration+PromoVolShare,y,size=3)
# weights: 16
initial value 4068.052704
final value 3434.194253
converged
> summary (uplift.nn)
a 3-3-1 network with 16 weights
options were -
b->h1 i1->h1 i2->h1 i3->h1
16.64 6.62 149.93
2011 Apr 08
1
multinom() residual deviance
Running a binary logit model on the data
df <- data.frame(y=sample(letters[1:3], 100, repl=T),
x=rnorm(100))
reveals some residual deviance:
summary(glm(y ~ ., data=df, family=binomial("logit")))
However, running a multinomial model on that data (multinom, nnet)
reveals a residual deviance:
summary(multinom(y ~ ., data=df))
On page 203, the MASS book says that "here the
2006 Feb 22
2
does multinomial logistic model from multinom (nnet) has logLik?
I want to get the logLik to calculate McFadden.R2 ,ML.R2 and
Cragg.Uhler.R2, but the value from multinom does not have logLik.So my
quetion is : is logLik meaningful to multinomial logistic model from
multinom?If it does, how can I get it?
Thank you!
ps: I konw VGAM has function to get the multinomial logistic model
with logLik, but I prefer use the function from "official" R
2001 Dec 12
2
Output from the multinom-function
Hello folks,
Let me first apologize: I'm not a professional nor a
mathematician, just an ordinary guy, fooling around
with the excellent R-package. I know the basic
principles behind statistics, but haven't read
anything more advanced than the ordinary first
probability and statistics courses.
Enough disclaimers? Good! I was examining the
multinom-function (in the nnet-package) the other
2003 Nov 13
1
what does this multinom error mean?
I have RedHat linux 9 with R 1.8.
I'm estimating models with multinom with a dependent variable that has 3
different values. Sometimes the models run fine and I can understand
the results.
Sometimes when I put in another variable, I see an indication that the
estimation did work, but then I can't get the summary method to work.
It's like this:
> votemn1 <-
2006 Jun 27
1
weights in multinom
Best R Help,
I like to estimate a Multinomial Logit Model with 10 Classes. The
problem is that the number of observations differs a lot over the 10
classes:
Class | num. Observations
A | 373
B | 631
C | 171
D | 700
E | 87
F | 249
G | 138
H | 133
I | 162
J | 407
Total: 3051
Where my data looks like:
x1 x2 x3 x4 Class
1 1,02 2 1 A
2 7,2 1 5 B
3 4,2 1 4 H
1 4,1 1 8 F
2 2,4 3 7 D
1 1,2 0 4 J
2 0,9
2013 Jan 20
0
multinom and stargazer
I am trying to create a LaTex table based on a multinom (nnet) object using
the stargazer command.
I have created a small data frame to demonstration the problem:
data <- data.frame(age=1:21, hight=20:40,
ed=c(1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3))
data$ed <- as.factor(data$ed)
I then make a multinomial model using the command multinom from the nnet
package:
model <- multinom(ed ~
2010 Mar 17
1
question about multinom function (nnet)
Dear All.
I have the following table that I want to analyze using multinom
function
freq segments sample
4271 Seg1 tumour
4311 Seg2 tumour
3515 Seg1 normal
3561 Seg2 normal
I want to compare model with both factors to the one where only sample
is present.
model1=multinom(freq~segments+sample,data=table)
model2=multinom(freq~ sample,data=table)
2003 Sep 30
1
NNet value and convergence
Hi,
I'm using the R nnet package and have a few simple (?) questions.
What is the "value " that is output after every 10 iterations during the training of the network and how is it calculated?
# weights: 177
initial value 506.134586
iter 10 value 128.222774
iter 20 value 95.399782
iter 30 value 87.184564
...
Is the "value" the error, if not, is there any way
2004 Feb 23
3
library nnet
DeaR useRs:
I am looking for a function which fits a multinomial model and in Baron?s
page I find the function "multinom" in package "nnet" but this package is
deprecated.
I suppose that this function is now in other package but I can't find it.
Can you help me?
Thanks.
2009 Nov 02
1
modifying predict.nnet() to function with errorest()
Greetings,
I am having trouble calculating artificial neural network
misclassification errors using errorest() from the ipred package.
I have had no problems estimating the values with randomForest()
or svm(), but can't seem to get it to work with nnet(). I believe
this is due to the output of the predict.nnet() function within
cv.factor(). Below is a quick example of the problem I'm