search for: modelresults

...l problem (stable isotopes with ellipses superimposed on data) there will be additional panels but I am creating only one panel here, for simplicity. #generate test "model results" x<-1:9 y<-rep(c(1,3,1),3) model<-c(rep("a",3),rep("b",3),rep("c",3)) modelresults<-data.frame(x,y,model) #generate test "data" x<-c(2,5,8) y<-rep(1.5,3) model<-c("a","b","c") data<-data.frame(x,y,model) #combine them into one data set combined<-make.groups(modelresults,data) #custom panel function panel.dualplot <-...

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 following variables in R for each file read: #C..PilotStudy.Mplus_Input.Test.rep1.out.unstandardized.est #C..PilotStudy....

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

...;est.' column. The following code demonstrates how I'm extracting the parameters and referencing the est. column: CurrentDir = paste("D:/Dissertation/Mplus_Output_2Class_LGMM/",a,"-",b,"-",c,"-",d,"-",e,"-",f, sep="") modelResults <- extractModelParameters(CurrentDir, recursive=FALSE, dropDimensions=TRUE) numReps = length(list.files(CurrentDir, pattern="*.out")) after the above code is executed, 'modelResults' has all the data from the out files as different elements of the returned list. I'm acces...

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