search for: aexp

Hi, I'm a bit puzzled by the gamlss fitting of exponential and lognormal data. Gamlss seems to think that exponentially distributed data fits better with a lognormal distribution, and vice versa. For example, X <- rexp(1000) Gexp <- gamlss(X~1,family=EXP) # X~1 is X tilde 1 GAMLSS-RS iteration 1: Global Deviance = 2037.825 GAMLSS-RS iteration 2: Global Deviance = 2037.825 Glno

Dear all, I am trying to fit a mixed-effects non linear regression, but I have some trouble with it. My data are a balanced panel of 904 subjects with 8 observations (at regular periods) per subject. The functional form of my model is Y=Aexp(-BX1)X2 +e. I want to allow parameters A and B to vary among subjects and also include an autocorrelation term. I have already fitted a standard nonlinear regression to the data, but I keep having problems with NLME. I have defined my data as a groupedData object, and when I try to fit the model I...

...3 Platform: Alpha URL: http://www.snailbook.com/faq/scp-ossh-to-ssh2.auto.html OS/Version: OSF/1 Status: NEW Severity: normal Priority: P3 Component: core AssignedTo: wayned@samba.org ReportedBy: russ.r.ramsey@aexp.com QAContact: rsync-qa@samba.org Using rsync from OSF with commercial ssh.com to Solaris with Sun ssh works fine. Using rsync from Solaris with Sun (OpenSSH derived) to OSF fails with: select: Bad file number rsync: connection unexpectedly closed (0 bytes received so far) [sender] rsync...

Hi, I am stuck with creating a conditional categorical variable in R If my dataframe data_2 has 3 variables A,B,C I want to create variable D which would be something like : data_2$D <- ifelse(data_2$A < data_2$B & promotion_ind =="N",1,0), this throws up an error "In Ops.factor(data_2$A,data_2$B) : < not meaningful for factors I have tried converting the

I''ve run across a slightly unusual - at least to me - situation where my load average bumps up to 200-300, as shown by prstat or uptime, but %cpu utilized on the system ranges from 30 to 50%. When I check the run occupancy, via dtrace, I see a very wide range of queued processes on the various cpu''s. Anyone got an idea on how to check on two things: 1) how to determine why

Hi, I have a logit model built in R which I am trying to validate . Could you please help me with creating lift charts and area under ROC for the same. I am trying to use package ROCR The indicative code for this is : newlogit3<- glm(Attrition_ind ~ Time.in.AXP+ Age +as.factor(Increase.in.Rating.in.Current.year) +as.factor(Below.Market.Merit.Budget)+as.factor(Drop.in.AIA)

Hi, I'am using the sentient analysis available in "R2.15". Now the challenge that I'am facing is that the Naïve Baye's has an inbuilt list of 6500 words in which it has been trained. So my question that can I increase the number of words on which this algorithm is trained? In case i can increase the list can someone tell me the procedure? Regards Nalin American Express

Hi, Is there any package in R which can help me do rule based text mining. -Nalin American Express made the following annotations on Mon Jun 18 2012 00:27:17 ****************************************************************************** "This message and any attachments are solely for the intended recipient and may contain confidential or privileged information. If you are not the

Hi everyone, I am trying to validate a logistic model built in R. Not my version of R is 2.12 and I cannot install ROCR. I have gone to a point where I have the predicted values using the code: pred1 = predict(trainlogit1,testdata_1, type = "response") How do I proceed from here? Is there another way in which I can plot lift charts? My model output is: Call: glm(formula =

Could someone help me on this please? -----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Dwaipayan Dasgupta Sent: Thursday, May 03, 2012 1:43 PM To: r-help at r-project.org Subject: [R] Validation of logistic models in R 2.12 Hi everyone, I am trying to validate a logistic model built in R. Not my version of R is 2.12 and I

Hi, I am trying to do some predictive modeling around attrition and want to split the dataset into test and train (80:20) and keep the ratio of attritees:non attrites same. In my dataset the attrition indicator is coded as 0(for non-attritees) and 1 (for attritees) and I want to keep the ratio of 0's to 1 similar. I apologize for this trivial question but this is my second week with R.