similar to: Non-parametric predictive modelling consultant required

Hi All, Just thought you might be interested in a recently released R package, spm: Spatial Predictive Modelling. It aims to introduce some novel, accurate, hybrid geostatistical and machine learning methods for spatial predictive modelling. Of 22 functions available in spm, two functions are for accuracy assessment. Perhaps they are not only useful tools for spatial predictive modelling

I'm a newcomer to llvm, but what you've done so far is very impressive. Llvm is a godsend to anybody who is attempting to implement their own their own language. :-) My company is considering using llvm as the backend for a small matlab-like language for scientific computation; our other option is MSIL. After reading through the documentation, I noticed that llvm seems to have one major

Dear R users, A new version (1.1.0) of the ?spm? package for spatial predictive modelling is now available on CRAN. The introductory vignette is available here: https://cran.rstudio.com/web/packages/spm/vignettes/spm.html There are several new enhancements to the package including a fast version of random forest in using ranger (rg) library(ranger) and the ability to convert relevant

Version 1.2.0 of pmml has been released and is available from CRAN. The pmml package (http://rattle.togaware.com/pmml.html) is part of the Rattle data mining suite http://rattle.togaware.com. It generates representations of analytic models built in R using the open standard predictive modelling markup language (http://www.dmg.org/). PMML represents analytic models in an application independent

Version 1.2.0 of pmml has been released and is available from CRAN. The pmml package (http://rattle.togaware.com/pmml.html) is part of the Rattle data mining suite http://rattle.togaware.com. It generates representations of analytic models built in R using the open standard predictive modelling markup language (http://www.dmg.org/). PMML represents analytic models in an application independent

Dear list, I?m wondering if there is something analogous to the TukeyHSD function that could be used for parametric terms in a GAM. I?m using the mgcv package to fit models that have some continuous predictors (modeled as smooth terms) and a single categorical predictor. I would like to do post hoc test on the categorical predictor in the models where it is significant. Any suggestions?

R-Listers. The following is a rant originally sent privately to Frank Harrell in response to remarks he made on this list. The ideas are not new or original, but he suggested I share it with the list, as he felt that it might be of wider interest, nonetheless. I have real doubts about this, and I apologize in advance to those who agree that I should have kept my remarks private. In view of this,

Would anybody be able to advise on which package would offer the best approach for producing a model able to predict the probability of species occupation based upon a range of variables, some of them catagorical (eg. ten soil types where the numbers assigned are not related to any qualitative/quantitative continuum or vegetation type) and others continuous such as field size or vegetation height.

I am new to R. I'm using the function GAM and wanted to get standard errors and p-values for the parametric terms (I fitted a semi-parametric models). Using the function anova() on the object from GAM, I only get p-values for the nonparametric terms. Does anyone know if and how to get standard errors for the parametric terms? Thanks. Jean G. Orelien

Dear R experts, I have a problem that is a related to the question raised in this earlier post ??? https://stat.ethz.ch/pipermail/r-help/2007-January/124064.html My situation is different in that I have only 2 predictors (coordinates x,y) for local regression but a number of global ("parametric") offsets that I need to consider. Essentially, I have a spatial distortion overlaid over a

Hi I have data from an experiment that used a repeated-measures factorial 2x2 design (i.e. each participant contributed data to both levels of both factors). I need a non-parametric version of the repeated-measures factorial ANOVA to analyse the data. SPSS only has non-parametric tests for one-way ANOVAs but I have been told that the test I need can be implemented using the R software.

Dear R developers! Considering I got no response or comments in the general r-help forum so far, perhaps my question is actually better suited for this list? I have added some more hopefully relevant technical details to my original post (edited below). Any comments gratefully received! Best regards, David Kreil. ---------- Dear R experts, I have a problem that is a related to the question

Hi, I am trying to estimate the sample size needed for the comparison of two groups on a certain measurement, given some previous data at hand. I find that the data collected does not follow a normal distribution, so I would like to use a non-parametric option for sample size calculation. I found the pwr package but I don't think it has this option and on the internet found that

Hello, I am interested in parametric multi comparison tests such as Dunnett, Duncan, Tukey, Newman-Keuls, Bonferonni, Scheffe, and non-parametric tests such as Kruskal-Wallis, and Mann-Whitney U. Are there packages that include most of these tests in each category? Many packages exist for an individual test but their outputs vary in great detail (test statistics, p-values, etc.)

Dear R experts, I have a problem that is a related to the question raised in this earlier post https://stat.ethz.ch/pipermail/r-help/2007-January/124064.html My situation is different in that I have only 2 predictors (coordinates x,y) for local regression but a number of global ("parametric") offsets that I need to consider. Essentially, I have a spatial distortion overlaid over a

Hi * i'm trying to do my statistical analysis with R (on a debianGNULinux) i've installed R and now i'll try to import my data from .xls Where to search for non parametric test in R ? I'm studing neurons velocity and i've to check if after the perfusion with a factor x the velocity changes but as my variable is not normally ditributed i've to try with non parametric

Dear all, I fitted a non-parametric model using GAM function in R. i.e., gam(y~s(x1)+s(x2)) #where s() is the smooth function Then I obtained the coefficients(a and b) for the non-parametric terms. i.e., y=a*s(x1)+b*s(x2) Now if I want to use this estimated model to do optimization or sensitivity analysis, I am not sure how to incorporate the smooth function since s() may not

Hi, I was trying to fit a parametric survival model with Weibull distribution on counting process type of data (NOT interval censor data), but the survreg(Surv(T1,T2,event)~x,data,dist="weibull") did not seem to work. Anyone can help me with that? Thanks, Rachel Memorial Sloan-Kettering Cancer Center -- View this message in context:

Dear All, I would like to fit some parametric survival models using left truncated, right censored data in R. However I am having problems finding a function to fit parametric survival models which can handle left truncated data. I have tested both the survreg function in package survival: fit1 <- survreg(Surv(start, stop, status) ~ X + Y + Z, data=data1) and the psm function in package

Hello, I'm quite new to R and want to make a Weibull-regression with the survival package. I know how to build my "Surv"-object and how to make a standard-weibull regression with "survreg". However, I want to fit a translated or 3-parametric weibull dist to account for a failure-free time. I think I would need a new object in survreg.distributions, but I don't know how