search for: polspline

Friends, I changed one line of a package at the source level and then rebuilt it. When I run R CMD check, I get an error: installing R.css in C:/polsplineRS.Rcheck ---------- Making package polsplineRS ------------ adding build stamp to DESCRIPTION making DLL ... making hareall.d from hareall.c making heftall.d from heftall.c making lsdall.d from lsdall.c making lspecall.d from lspecall.c making nlsd.d from nlsd.c making polyall.d from polyall....

Dear all, I have a question on how to set up the starting model in POLYCLASS and make sure the terms in the starting model retained in the final POLYCLASS model. In the function POLYMARS, this can be done using the STARTMODEL option. See below for example, I started with model y= b0 + b1*X1 + b2*X2 + b3*X4 + b4*X5 + b5*X2*X5 + e > m00 <- matrix(c( 1, NA, 0, NA, 1, 2,

I was able to hack the code in the polspline package in order to change the plot.polymars function so that I can set the limits of my y-axis. However, I was only able to do this by entering plot.polymars in R, copying the code to Word, changing what I needed, and then pasting the new code in R to redefine the polt.polymars function. I was ab...

Hi.., is there a function in R to fit bivariate splines ? I came across 'polymars' (POLSPLINE) and 'mars' (mda) packages. Are these the one to use or are there other specific commands? Thanks. Harsh

The rms package, a replacement for the Design package, has been updated on CRAN. The most major change is the addition of smooth calibration curves for externally (val.surv function) or internally (calibrate.cph, calibrate.psm) validating a survival model with right-censored data. The polspline package is used to estimate the survival probability at a fixed time point as a function of the predicted survival probability. Some user-requested plotting features have been added to bplot and plot.Predict. Changes in version 2.1-0 * Made Predict not return invisibly if predictors not spe...

...on: flver=function(a1,a2,b1,b2) { lver=-(sum(st*log(exp(a1*x1+a2*x2)))+sum(st*log(hheft(exp(b1*x1+b2*x2)*t,f.heft))) -(exp(a1*x1+a2*x2)/exp(b1*x1-b2*x2))*sum(-log(1-pheft(exp(b1*x1+b2*x2)*t,f.heft)))) } emv=mle(flver,start=list(a1=0,a2=0,b1=0,b2=0)) where hheft and pheft are functions defined in polspline package. My variables are: t: time st: censor x1 and x2: Covariates I don't find any estimation because an error occurs: Non finite/finite difference value [0]. Can I treat this error? I try using "SANN" method and this error occurs too. There is another alternative? Thanks Oly...

The rms package, a replacement for the Design package, has been updated on CRAN. The most major change is the addition of smooth calibration curves for externally (val.surv function) or internally (calibrate.cph, calibrate.psm) validating a survival model with right-censored data. The polspline package is used to estimate the survival probability at a fixed time point as a function of the predicted survival probability. Some user-requested plotting features have been added to bplot and plot.Predict. Changes in version 2.1-0 * Made Predict not return invisibly if predictors not spe...

Hi everyone: Polyclass is a polytomous logistic regression model using linear splines and their tensor products. It provides estimates for conditional class probabilities which can then be used to predict class labels. I know there is Polyclass package in S-plus. So I'm wondering if there is a corresponding package in R? I have been searching for it for quite a while, but still

Hello, Apologies if this question has already arised, hope you can help me to the find the solution to this or point the place to look at. I have a multidimensional piece-wise regression linear problem, i.e. to find not only the regression coefficients for each "interval" but also the beginning and ends of the intervals. To simplify it to the one dimensional case and two intervals,

Hi all, is there any package in R capable of smooth approximation of CDF basing on given sample? (Thus, I am not speaking about ecdf) In particular, I expect very much that the approximation should subject to the property: f(x0)<=f(x1) for x0<x1, where x0 and x1 belong to range of the sample given. Polynomial approximation could be OK for me as well. P.S.

Another 1000 packages were added to CRAN, which took less than 7 months. Today (February 29, 2017), the Comprehensive R Archive Network (CRAN) [1] reports: ?Currently, the CRAN package repository features 8002 available packages.? The rate with which new packages are added to CRAN is increasing. In 2014-2015 we had 1000 packages added to CRAN in 355 days (2.8 per day), the following 1000

...gt; Almost any method of fitting a density estimate would work on > integrating (numerically) the result. it is a nice idea concerning the monotony property, which will be obtained automatically, but I am going to use results of approximation analytically > In particular, look at package polspline, where > p(old)logspline does the integration for you. thank you, I am going to install it. > > is there any package in R capable of smooth approximation of CDF > > basing on given sample? (Thus, I am not speaking about ecdf) > > I think it _is_ an ECDF you want to app...

Hi, I am working on a modeling project for PMI-Australia and interested in using R, especially POLYMARS or PLYCLASS. I use SAS /PC on WINDOWS for the statistical analyses including Modeling. I got R downloaded to the system but can't figure out how to interface with SAS so that I could use SAS datasets already in place. Also I could not find POLYMARS on the library packages. I need to know

Hello all, I'm trying to find out how to perform a 'segmented regression'. I have some data and the 'classical' model used is: y(t) = a + bx(t) + cx(t)^2 + u(t) for x(t) < x(0) y(t) = a + bx(0) + cx(0)^2 + d(x(t) - x(0)) + u(t) for x(t) > x(0) and u(t) = rho.u(t-1) + eps (It's a model using an ARIMA-model, in this case AR(1)-process) The parameters to estimate here

...3,1),rep(1,10),rep(1,12))x) plot(nom.sur) #plot(nom.sur, fun.side=list(rep(1,8),c(1,1,1,3,1,3,1,3,1,3,1,3,1,3,1))) #plot(nom.sur, fun.side=list(rep(1,10), c(1,1,1,1,3,1,3,1,3,1,3,1,3,1,3,1), rep(1,10), rep(1,10))) #table(status) ########################### #or f <- psm(S ~ ...) pa <- 'polspline'%in% row.names(installed.packages()) if(pa) { cal <- calibrate(mod, u=2.5, m=20, B=20) # cmethod= ' hare ' plot(cal) } #################################### # validate v<-validate(mod, method='boot', B=5) v #################################### ############...

...2014-10-22 7000 glmvsd ## 4: 2014-02-05 6000 bilan ## 5: 2013-03-20 5000 Rgnuplot ## 6: 2012-06-21 4000 HIBAG ## 7: 2011-04-24 3000 SPECIES ## 8: 2009-09-10 2000 maticce ## 9: 2007-03-11 1000 cairoDevice ## 10: 2005-02-21 500 micEcon ## 11: 2003-03-19 250 polspline So including the archived packages in this report, 8K was actually reached at the time of useR! 2015 :) Best, Gergely On Mon, Feb 29, 2016 at 12:54 PM, Henrik Bengtsson <henrik.bengtsson at gmail.com> wrote: > Another 1000 packages were added to CRAN, which took less than 7 > months....

Hi there, Does R has built-in codes for nonpara. bivariate regression so that I can estimate the joint distribution of two variables as a function of some covariates? Thanks a lot. --------------------------------------------------- Ximing Wu Department of Economics University of Guelph Guelph, Ontario, Canada, N1G 2W1 Tel: (519) 842-4120, ext 53014 Fax: (519) 763-8497 email: xiwu at

...ata gnu-r r-cran-maps gnu-r r-cran-matchit gnu-r r-cran-matrix gnu-r r-cran-mcmcpack gnu-r r-cran-mgcv gnu-r r-cran-misc3d gnu-r r-cran-mnormt gnu-r r-cran-mnp gnu-r r-cran-multcomp gnu-r r-cran-mvtnorm gnu-r r-cran-nlme gnu-r r-cran-nws gnu-r r-cran-plotrix gnu-r r-cran-polspline gnu-r r-cran-pscl gnu-r r-cran-psy gnu-r r-cran-qtl gnu-r r-cran-quadprog gnu-r r-cran-rcmdr gnu-r r-cran-rcolorbrewer gnu-r r-cran-rcpp gnu-r r-cran-relimp gnu-r r-cran-rggobi gnu-r r-cran-rgl gnu-r r-cran-rglpk gnu-r r-cran-rgtk2 gnu-r r-cran-rjava gnu-r r-cran-rmetrics...

...t;permtest","perturb","pgam","pgirmess","pheno","phpSerialize","PHYLOGR","pinktoe","pixmap","PK","PKfit","plotrix","pls","plsgenomics","plugdensity","polspline","polyapost","polycor","polynom","popgen","portfolio","POT","powerpkg","ppc","pps","prabclus","princurve","ProbForecastGOP","proto","pscl","pspline&q...

Dear All; I have used fbmSim to simulate a fbm sequence, however, when I tried to estimate the Hurst effect, none of the nine procedures gave me an answer close enough to the real value, which is 0.5 (n=1000). So, would you please advice, 1. which is the best method to estimate the H among the 9 mehods, R/S, higuchi or Whittle? 2. how to choose the levels (default=50), minnpts, cutoff values or