similar to: extractModelParameters HELP!!!

Hi, I'm generating the name of the variable with paste function and then using that variable name further to get the specific position value from the data.frame, here is the snippet from my code: modelResults <- extractModelParameters("C:/PilotStudy/Mplus_Input/Test", recursive=TRUE) #extractModelParameters reads all the output files from the Test folder and create the

I would like to plot 3 best-fit models in a single panel of a lattice plot, superimposed on 3 corresponding datasets in the same panel. My goal is to show the models as lines of 3 different colors, and the data as points whose colors correspond to the model colors. In essence, I have two levels of grouping: 1) model vs. data, and 2) model number. Since there is only one ?groups? variable, I

I'm trying to use a contributed package (rmetasim) to generate simulated genetic datasets. My scripts work fine when I run them on a Sun workstation running Solaris 7 and when I run them on a ~4 year old laptop PC that I have. However, when I run them on my new laptop (Dell Latitude D410 purchased in August 2005), the simulation will run for a short (variable) period of time, then R

I realized that the script I included in my original post (see below) was written for an older version of rmetasim and will not work with the current version. The only change that need be made to get my script to run with the latest release of rmetasim is to change the command 'simulate.landscape()' to 'sim.landscape()'. My apologies to anyone who tried to use my script and

I am using a multiple additive model (in the quantreg package) and I would like to 'extract' the fitted model formulae ie- for a straight line the formula would be y= 'a+b*c' for my multiple model I would expect somthing more complex because the model is not linear (its a bit like a GAM) but given I can plot the model using # f<-fitted(model) #lines(f) there must be a formula

Hello, Thank you very much for looking at this. I have a "seasonal" user for R. I teach my undergrads and graduates students statistics using R and often find myself trying to solve problems to process student collected data in an efficient way. In this case, I have a data.frame with multiple observations. These are gas concentrations in a chamber and are used to measure into rates,

Hello, When I run the following glm model: modelresult=glm(CID~WS+SS+DV+DS, data=kimu, family=binomial) I get the following warning messages: 1: glm.fit: algorithm did not converge 2: glm.fit: fitted probabilities numerically 0 or 1 occurred What I am trying to do is model my response variable (CID: correct bird identification) as a function of the predictor variables weather state (WS), sea