similar to: nls() fails on a simple exponential fit, when lm() gets it right?

Hi, I'm making a plot in which the title takes up two lines. The title contains a subscript but when I look at the plot it does'nt seem tocome out properly. The code I'm using is: xtxt = expression(paste('Observed -log( ', IC[50], ' )')) ytxt = expression(paste('Predicted -log( ', IC[50], ' )')) mtxt = expression(paste('Plot of Observed

Dear R-experts, Here below a toy example with some error messages, especially at the end of the code (Tuning the parameters). Your help to correct my R code would be highly appreciated. ####################################### #libraries library(lattice) library(ggplot2) library(caret) library(randomForest) ?? #Data

Hi Sacha, On second thought, perhaps this is more the direction that you want ... X2 = cbind(X_train,y_train) colnames(X2)[3] = "y" regr2<-randomForest(y~x1+x2, data=X2,maxnodes=10, ntree=10) regr regr2 #Make prediction predictions= predict(regr, X_test) predictions2= predict(regr2, X_test) HTH, Eric On Tue, May 9, 2023 at 6:40?AM Eric Berger <ericjberger at gmail.com>

Hi, I have a system of ODE's I can solve with lsoda. Model=function(t,x,parms) { #parameter definitions lambda=parms[1]; beta=parms[2]; d = parms[3]; delta = parms[4]; p=parms[5]; c=parms[6] xdot[1] = lambda - (d*x[1])- (beta*x[3]*x[1]) xdot[2] = (beta*x[3]*x[1]) - (delta*x[2]) xdot[3] = (p*x[2]) - (c*x[3]) return(list(xdot)) } I want

I am trying to specify a mixed model for my research, but I can't quite get it to work. I've spent several weeks looking thru various online sources to no avail. I can't find an example of someone trying to do precisely what I'm trying to do. I'm hoping some smart member of this mailing list may be able to help. First off, full disclosure: (1) I'm an engineer by trade, so

I have never been able to get class probabilities to work and I am relatively new to using these tools, and I am looking for some insight as to what may be wrong. I am using caret with kernlab/ksvm. I will simplify my problem to a basic data set which produces the same problem. I have read the caret vignettes as well as documentation for ?train. I appreciate any direction you can give. I

Hi, I want to format my axis in my persp3d plot. With my data, which I attached I created a persp3d plot with the following code, which I summarized from different code snippets I found: library(rugarch)library(rgl)library(fGarch)fd <-as.data.frame(modelfit,which ='density')color <-rgb(85,141,85,maxColorValue=255)x <-seq(-0.2,0.2,length=100)y <-c(1:2318)f

Hi I am using ggplot to overlay two regression lines on a scatter plot each corresponding to a treatment group. The default plot gives a different slope for each treatment group. However, in some cases i want the lines to be parallel -ie no significant interaction. My code: ggplot(data=df,X,Y,colour=treatment) + geom_point() + geom_smooth(method="lm") I think i use the

I have a small model running under R. This is basically running various power-law relations on a variable (in this case water level in a river) changing spatially and through time. I'd like to include some kind of error propagation to this. My first intention was to use a kind of monte carlo routine and run the model many times by changing the power law parameters. These power laws were

Dear R users, I?d like to pose aquestion about pMCMC and HDP. I have performed a mixed logistic regression by MCMCglmm (a very good package) obtaining the following results: Iterations = 250001:799901 Thinning interval = 100 Sample size = 5500 DIC: 10.17416 G-structure: ~ID_an post.mean l-95% CI u-95% CIeff.samp ID_an 0.7023 0.0001367 3.678 2126 R-structure: ~units post.mean l-95%

Dear all, my colleagues and I have to write some R code for some of our projects. I would like to ask you help to organize us a little. a. Do you know if there is any system that can convert our R scripts to html pages with some nice dependency graphs (which functions calls which). b. Could you please suggest me an easy way to exchange the R code with my colleagues. I know about these version

I'm using caret to assess classifier performance (and it's great!). However, I've found that my results differ between R2.* and R3.* - reported accuracies are reduced dramatically. I suspect that a code change to kernlab ksvm may be responsible (see version 5.16-24 here: http://cran.r-project.org/web/packages/caret/news.html). I get very different results between caret_5.15-61 +