similar to: Predicted values from a logistic model

Dear R users: I have a question regarding the output for two of the functions in the `sn' package, which deals with the mle fitting of skew normal curves to linear regressions. I'm using the examples and the dataset given as an example in the online documentation for this package, for the functions `msn.fit' and `msn.mle'. I'm following the example code in the

Dear R-list members, I have been a user of SPSS for a few years and quite new to R. I read the documentation and tried samples but I have some problems to obtain results for a logistic regression under R. The following SPSS script LOGISTIC REGRESSION vir /METHOD = FSTEP(LR) d007 d008 d009 d010 d011 d012 d013 d014 d015 d016 d017 d018 d069 d072 d073 /SAVE = PRED COOK SRESID

Dear All, I am trying to reproduce the example that I found online here http://bit.ly/11VG4ha However, when I run my script (pasted at the end of the email), I notice that there is a factor 2 between the values for the coefficients for the categorical variable female calculated by my script and in the online example. Any idea about where this difference comes from? Besides, how can I

With DSC, bpp can be fractional in multiples of 1/16. Change drm_dp_calc_pbn_mode to reflect this, adding a new parameter bool dsc. When this parameter is true, treat the bpp parameter as having units not of bits per pixel, but 1/16 of a bit per pixel v2: Don't add separate function for this Cc: amd-gfx at lists.freedesktop.org Cc: nouveau at lists.freedesktop.org Cc: intel-gfx at

I am trying to simulate a dataset using Parallel Latent CTT model and this is what i have done so far: (START) #Importing psych library for all the simulation related functions library(psych) # Settting the working directory path to C:/NCME path="C:/NCME" setwd(path) #Using the function to generate the data GenData <- congeneric.sim(N=500, loads =

Hi all, I am getting the following error message: > mymodel = glm(response ~ . , family=binomial, data=C); Error: cannot allocate vector of size 734.2 Mb In addition: Warning messages: 1: In array(0, c(n, n), list(levs, levs)) : Reached total allocation of 1535Mb: see help(memory.size) 2: In array(0, c(n, n), list(levs, levs)) : Reached total allocation of 1535Mb: see help(memory.size) 3:

Hello All, I'm trying to print specific row and column for Observed_Scores matrix, however, when I execute the command "Observed_Scores[1,1]", I get the message "Error in Observed_Scores[1, 1] : incorrect number of dimensions". Could you please help and let me know where is the mistake? Here is my program: library(ltm) library(psych) # Settting the working directory

Hi All, I'm generating 10 different data sets with 1 and 0 in a matrix form and writing the output in separate files. Now I need to stack all these data sets in one vector and I know that stack only operates on list or data frame however I got these data sets by converting list to a matrix so can't go backwards now. Is there a way i can still use Stack? Please see the program:

Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model fit<-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) My covariate has three levels ("A","B" and "C") and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to

If the observations are normally distributed and the 2xk design is balanced, theory requires that the tests for interaction and row effects be independent. In my program, appended below, this would translate to cntT (approx)= cntR*cntI/N if all R routines were functioning correctly. They aren't. sim2=function(size,N,p){ cntR=0 cntC=0 cntI=0 cntT=0 cntP=0 for(i in 1:N){

Hi all, Initially I thought this was a bug in the built-in mocking framework(and it still may be), but I better hash it out on the mailing list before I file/reopen the ticket: http://rspec.lighthouseapp.com/projects/5645/tickets/478-mocks-on-constants#ticket-478-6 I thought my example illustrated my problem, but obviously I was passing the wrong arguments to the mock. I revised my example to

Hello! I am using package "VAR". I've fitted my model: mymodel<-VAR(mydata,myp,type="const") I can extract the Log Liklihood for THE WHOLE MODEL: logLik(mymodel) How could I calculate (other than manually) the corresponding Akaike Information Criterion (AIC)? I tried AIC - but it does not take mymodel: AIC(mymodel) # numeric(0) Thank you! -- Dimitri Liakhovitski

Greetings useRs, What is the easiest way to create a design matrix of several factor variables? Function gendata in Design seems to do that for a fitted model, but how to do that only on several factor vectors?? The result should be a df with one row for each distinct combination of levels of factors eg for (M,F) (Y,O) We get M Y M O F Y F O In reality I will have more than 1000 rows so doing

I have dose response data analyzed with the package 'drc'. 'summary(mymodel)' prints my kinetic parameters. I want that text in an ASCII text file. I want to get exactly what I would get if I copied and pasted from the terminal window. I've read the documentation on data export to text files here:

Hello again, I guess this is more of a Ruby question, but here goes anyways... I want to intercept all calls to non existent class methods and then call an existing class method with the non existent name. In code... class MyModel < ActiveRecord::Base def MyModel.method_missing(symbol, *args) MyModel.func(symbol.id2name) end def MyModel.func(func_called) puts "You

Can anybody share some examples of their Money implementation ? I''m trying to setup a Model to use this library, but can''t seem to wrap my head around how it is exactly supposed to work. My Model (Foo) looks like: =============== composed_of :commission, :class_name => "Money", :mapping => [ %w(commission_cents cents), %w(commission_currency currency) ] Yet, in

Hi everyone, Can someone please help me in these questions?: 1)if I use crossvalidation with svm, do I have to use this equation to calculate RMSE?: mymodel <- svm(myformula,data=mydata,cross=10) sqrt(mean(mymodel$MSE)) But if I don’t use crossvalidation, I have to use the following to calculate RMSE: mymodel <- svm(myformula,data=mydata) mytest

Can anyone show me how to refer to an object name that is passed to a function, from within the function? For example: MyModel <- 1 test <- function(x) { if(x == 1) {cat("x is a valid object.\n")} } test(x) What I would like this to do is pass MyModel to function test, and if it passes a test, be able to print "MyModel is a valid object." Thanks. -- View this

In my environment.rb I say the following: MyModel.disable_ferret puts MyModel.ferret_enabled? The console works as expected: script/console false >> MyModel.ferret_enabled? => false I can then edit an instance, ferret remains disabled and the changes are not immediately indexed. However, when I start mongrel Ferret is initially disabled, but then somehow becomes enabled again

Dear users, I'll appreciate your help with this (hopefully) simple problem. I have a model object which was fitted to inputs X1, X2, X3. Now, I'd like to use this object to make predictions on a new data set where only X1 and X2 are available (just use the estimated coefficients for these variables in making predictions and ignoring the coefficient on X3). Here's my attempt but, of