Displaying 20 results from an estimated 10000 matches similar to: "multinomRob: Error in eigen [..] infinite or missing values in 'x'"
2012 Mar 14
1
Questing on fitting Baseline category Logit model
Dear all,
I am facing some problem with how to fit a "Baseline category Logit
model" with R. Basically I am considering famous "Alligator" data as
discussed by Agresti. This data can also be found here:
https://onlinecourses.science.psu.edu/stat504/node/174
(there is also an accompanying R file, however the underlying R code
could not load the data properly!!!)
Below are
2012 Sep 02
3
Loading Chess Data
All,
What would be the most efficient way to load the data at the following
address into a dataframe?
http://ratings.fide.com/top.phtml?list=men
Thanks,
David
--
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2004 Feb 20
0
New Package: multinomRob
We would like to announce the availability on CRAN of a new package
multinomRob. It does robust estimation of overdispersed multinomial
regression models. The package is also able to estimate overdispersed
grouped multinomial logistic and multivariate-t logistic models. The
code is relatively general; for example, it allows for equality
constraints across parameters and it can handle datasets in
2004 Feb 20
0
New Package: multinomRob
We would like to announce the availability on CRAN of a new package
multinomRob. It does robust estimation of overdispersed multinomial
regression models. The package is also able to estimate overdispersed
grouped multinomial logistic and multivariate-t logistic models. The
code is relatively general; for example, it allows for equality
constraints across parameters and it can handle datasets in
2018 May 29
0
How to generate a conditional dummy in R?
Hi Faradj,
What a problem! I think I have worked it out, but only because the
result is the one you said you wanted.
# the sample data frame is named fkdf
Y2Xby3<-function(x) {
nrows<-dim(x)[1]
X<-rep(0,nrows)
for(i in 1:(nrows-2)) {
if(!is.na(x$Y[i])) {
if(x$Y[i] == 1 && any(is.na(x$Y[(i+1):(i+2)]))) X[i]<-1
if(i > 1) {
if(X[i-1] == 1) X[i]<-0
}
}
2011 Aug 17
1
multinomRob - error message
Hi,
I would like to use the multinomRob function to test election results.
However, depending on which independent variables I include and how
many categories I have in the dependent variable, the model cannot be
estimated.
My data look like this (there are 68 observations):
> head(database)
RESTE09 GAUCHE09 PDC09 PLR09 UDC09 MCG09 RESTE05 GAUCHE05 PDC05
D1 1455
2018 May 29
1
How to generate a conditional dummy in R?
Dear Jim,
wow! It worked! Thanks a lot.
I did as you suggested and it worked well with the real data. Although it gave me this error: Error in if (!is.na(x$Y[i])) { : argument is of length zero. For some reason the X1 produced less observations than it is in the data. But it's not a big deal - I identified those cases and simply deleted from the data (it was countries that only appeared
2018 May 28
3
How to generate a conditional dummy in R?
Hi everyone,
I am trying to generate a conditional dummy variable ?X" with the following rules
set X=1 if Y is =1, two years prior to the NA. [0,0,NA].
For example, if the pattern for Y is 0,0,NA then the X variable is =0 for all the two years prior to the NA. If the pattern for Y is 0,1,NA or 1,0,NA then the X =1 . To be clear, if 1,1,NA then the X=1 that first specific year, it
2017 Oct 17
0
ggridges help
Does the following work for you?
ggplot2::ggplot(plotFrame, aes(x = time, y = depth, height = cycle,
group = depth)) + ggridges::geom_ridgeline(fill="red", min_height=-0.25)
Bill Dunlap
TIBCO Software
wdunlap tibco.com
On Tue, Oct 17, 2017 at 12:43 PM, Roy Mendelssohn - NOAA Federal <
roy.mendelssohn at noaa.gov> wrote:
> I have tried:
>
> ggplot(plotFrame, aes(x =
2017 Oct 17
0
ggridges help
The min_height = -0.25 is there to make it show cycle values down to -1/4.
You may want to change it to -1 so it shows more of the cycle values.
Bill Dunlap
TIBCO Software
wdunlap tibco.com
On Tue, Oct 17, 2017 at 1:26 PM, Roy Mendelssohn - NOAA Federal <
roy.mendelssohn at noaa.gov> wrote:
> yes, thanks, and I was getting close to that. One thing I found is the
> manual says the
2017 Oct 17
2
ggridges help
I have tried:
ggplot(plotFrame, aes(x = time, y = cycle, height = cycle, group = depth)) + geom_ridgeline()
ggplot(plotFrame, aes(x = time, y = depth, height = cycle, group = depth)) + geom_ridgeline()
ggplot(plotFrame, aes(x = time, y = depth, group = depth)) + geom_density_ridges()
none are producing a plot that was a ridgeline for each depth showing the time series at that depth. The plot
2017 Oct 17
0
ggridges help
...and your question is...?
... and the code you tried that didn't work was?
Bert
On Oct 17, 2017 12:22 PM, "Roy Mendelssohn - NOAA Federal" <
roy.mendelssohn at noaa.gov> wrote:
> Hi All:
>
> I am just not understanding ggridges. The data I have are time series at
> different depths in the ocean. I want to make a joy plot of the time
> series by depth.
2017 Oct 17
2
ggridges help
Hi All:
I am just not understanding ggridges. The data I have are time series at different depths in the ocean. I want to make a joy plot of the time series by depth.
If I was just doing a ggplot2 line plot I would be doing:
ggplot(plotFrame, aes(x = time, y = cycle, group = depth)) + geom_line()
but translating that to ggridges has not worked right. Below is the result from dput() of a
2017 Oct 17
2
ggridges help
yes, thanks, and I was getting close to that. One thing I found is the manual says the height is the distance above the y-line, which should be, but doesn't have to be positive. In fact, the time series are estimates of a cycle, and has negative values, which unfortunately are not included in my sub-sample. And the negative values are not handled properly (the series disappears for
2011 Jan 19
1
Using subset to filter data table
I am having difficulty understanding how I would constrain a data set by
filtering out 'records' based on certain criteria.
Using SQL I could query using 'select * from my.data where LithClass in
('sand', 'clay')' or some such.
Using subset, there seem to be ghosts left behind (that is, all of the
LithClass *.Labels* remain after subset)
> dput(tcc)
2008 Oct 09
1
YALAQ - Yet Another LApply Question
Hello,
Two lapply questions (system info and sample data below):
1) Why does the first form of command1 add the name of y _after_ the
str() output rather than before as does the second (preferred) form?
# command1 version1
invisible(lapply(ls(pattern='bn'), function(y) cat(y, "\n",
str(get(y)), "\n") ))
# command1 version2 (preferred output)
2009 Mar 07
2
piecewise linear regression
Hi - I'd like to construct and plot the percents by year in a small data set
(d) that has values between 1988 and 2007. I'd like to have a breakpoint
(buy no discontinuity) at 1996. Is there a better way to do this than in
the code below?
> d
year percent se
1 1988 30.6 0.32
2 1989 31.5 0.31
3 1990 30.9 0.30
4 1991 30.6 0.28
5 1992 29.3 0.25
6 1994 30.3
2006 Sep 13
1
reshaping a dataset
Hi,
I'm trying to move to R the last few data handling routines I was
performing in SAS.
I'm working on stomach content data. In the simplified example I
provide below, there are variables describing the origin of each prey
item (nbpc is a ship number, each ship may have been used on
different trips, each trip has stations, and individual fish (tagno)
can be caught at each
2008 Nov 15
1
GAMs and GAMMS with correlated acoustic data
Greetings
This is a long email.
I'm struggling with a data set comprising 2,278 hydroacoustic estimates of
fish biomass density made along line transects in two lakes (lakes
Michigan and Huron, three years in each lake). The data represent
lakewide surveys in each year and each data point represents the estimate
for a horizontal interval 1 km in length.
I'm interested in comparing
2008 Sep 02
0
Error in .local(object, ...) : test vector does not match model !
I am getting a really strange error when I am using predict on an ksvm model. The error is "Error in .local(object, ...) : test vector does not match model !". I do understand that this happens when the test vectors do not match the
Model. But in this case it is not so. I am attaching a portion of both the test data used for prediction and the data used to build the model. I could