Hi r-help,
I have been using your RHmm package for some time and have recently
had to try using the package for a new dataset.
Basically I have a dataset with a number of discrete observation
variables that change over time, and I would love to try modeling them
using a HMM.
Basically I was wond
z
>> zz <- factor(ifelse( z %in% c("a", "b"),"d" ,z))
>> zz
>>
>> Cheers,
>> Bert
>>
>> On Fri, Aug 9, 2013 at 7:10 AM, Claus O'Rourke
>> wrote:
>>> Hello R-Help,
>>> I have a variable with >
Hello R-Help,
I have a variable with > 32 levels and I'd like to split this into two
variables such that both new variables have >= 32 variables. This is
to handle the limit of 32 level predictor variables in R's Random
Forest implementation. Might someone be able to suggest an elegant way
to do th
Thanks for clarifying!
On Thu, Jan 10, 2013 at 12:47 PM, Uwe Ligges
wrote:
>
>
> On 08.01.2013 21:14, Claus O'Rourke wrote:
>>
>> Hi all,
>> I've encountered an issue using svm (e1071) in the specific case of
>> supplying new data which may not have th
Hi all,
I've encountered an issue using svm (e1071) in the specific case of
supplying new data which may not have the full range of levels that
were present in the training data.
I've constructed this really primitive example to illustrate the point:
> library(e1071)
> training.data <- data.frame
Hi,
I'm a newbie to the world of HMMs and HMMs in R. I've had a look at
the hmm package and the RHmm package but I couldn't see anything
straightforward on how a labelled sequential dataset with observed
values and underlying states might be used to construct and train a
HMM based on that data and
Brilliant - that was really useful!
On Tue, Mar 15, 2011 at 3:46 PM, Ista Zahn wrote:
> Hi Claus,
>
> On Tue, Mar 15, 2011 at 9:33 AM, Claus O'Rourke
> wrote:
>> Hi,
>> I am trying to recursively apply a function to a selection of columns
>> in a dataframe. I
Hi,
I am trying to recursively apply a function to a selection of columns
in a dataframe. I've had a look around and from what I have read, I
should be using some version of the apply function, but I'm really
having some headaches with it.
Let me be more specific with an example.
Say I have a dat
Dear r-help list,
I would like to run a mixed design anova to compare the results from
one population sample to another. Here my within subject variable
(stiulusID) has 45 levels and my between subject variable (group) has
two levels. In addition to my number of levels in the within subject
variabl
Dear list,
Following up on an earlier post, I would like to reorder a dataset and
compute pairwise correlations. But I'm having some real problems
getting this done.
My data looks something like:
Participant Stimulus Measurement
p1 s`15
p1 s`2
Dear R Help,
I have a general question - I know this is the R list, but I hope
someone can help me out a little as I've always found the help here to
be absolutely fantastic.
I have run a psychological study where participants are given multiple
stimuli and their responses to those stimuli are me
Thanks. It works perfectly.
On Thu, Apr 29, 2010 at 6:24 PM, Henrique Dallazuanna wrote:
> Try with grepl:
>
> data$ContainsThe <- ifelse(grepl("the",data$Utt),"y","n")
>
> On Thu, Apr 29, 2010 at 2:17 PM, Claus O'Rourke
> wrote:
>&g
Hi all,
I'm writing a script to do some basic text analysis in R. Let's assume
I have a data frame named data which contains a column named 'utt'
which contains strings. Is there a straightforward way to achieve
something like this:
data$ContainsThe <- ifelse(startsWith(data$Utt,"the"),"y","n")
Thanks everyone for the replies, that sure cleared up some things for me.
On Fri, Apr 23, 2010 at 9:11 AM, Jan van der Laan
wrote:
> When you just want to calculate the probability of belong to class A
> or B of a new observation xi and do not have to do any new model
> estimations or other analy
Dear all,
I have a couple of short noob questions for whoever can take them. I'm
from a very non-stats background so sorry for offending anybody with
stupid questions ! :-)
I have been using logistic regression care of glm to analyse a binary
dependent variable against a couple of independent var
Thank you both for your advice. I'll follow up on it, but it is good
to know that this is a known effect.
Claus
On Wed, Mar 31, 2010 at 3:02 PM, Stephan Kolassa wrote:
> Hi Claus,
>
> welcome to the wonderful world of collinearity (or multicollinearity, as
> some call it)! You have a near linear
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