search for: supsmu

I used the supersmoother function in the modreg package as follows: super <- supsmu(ilogemp,award) Then I decided that I might want additional explanatory variables (other than ilogemp) in my model. The ppr function in modreg seemed a logical extension of supsmu from univariate to multidimensional explanatory variables. As a "check" I ran the following: pprest <-...

When handed an argument with all NAs, supsmu() causes a sigfault causing termination of R. The following example reproduces the problem (on a linux gentoo system), but I have verified the same behavior on a solaris builds as well as several versions of R. x <- (1:100)/10; y <- sin(pi*x) + rnorm(length(x)); tmp <- supsmu(...

supsmu(periodic=TRUE) can crash R by reading before start of array. To reproduce: set.seed(1) xx <- runif(29000) yy <- rnorm(29000) span <- 0.49 i <- 1 while(i < 200){ cat(i,"\n") int <- supsmu(xx,yy,periodic=T,span=span) i <-i+1 } re...

I am having problems when calling "supsmu" in R 1.6.2 installed on RedHat 8.0. I used the rpm provided on the R site. "supsmu" seems to have not been installed by default - (i.e., as part of the modreg package). It also does not occur as part of any of the contibuted packages - so there apparently is no question about insta...

...feira ",58 "1762",62973,4829,"sexta-feira ",58 2. Running the next code: data <- read.table('errorexample.txt', header=TRUE,sep=",",dec=".") learner <- 'ppr' learner.pars <- list(Duracao ~ ., nterms=1, data=data, kernel='supsmu', na.action=na.omit, max.terms=5, optlevel=1, sm.method='supsmu', bass=0, span=0) model <- do.call(learner, learner.pars) It works fine. 3. But running: data <- read.table('errorexample.txt', header=TRUE,sep=",",dec=".") learner <- 'ppr'...

...uot;, ... ) { par(mar=c(5,4,4,2),oma=c(0,0,0,3)) plot(x, yright, ylim=yrightlim, axes=F,ylab="", xlab=xlab, pch=pch[1],col=col[1], ...) axis(4,pretty(range(yright,na.rm=T),10),col=col[1]) if (linky) lines(x,yright,col=col[1], ...) if (smooth!=0) lines(supsmu(x,yright,span=smooth),col=col[1], lwd=lwds, ...) if(yylab[1]=="") mtext(deparse(substitute(yright)),side=4,outer=T,line=1, col=col[1], ...) else mtext(yylab[1],side=4,outer=T,line=1, col=col[1], ...) par(new=T) plot(x,yleft, ylim=yleftlim, ylab="", axes=F ,xlab=...

Hi lily, You can get fairly good starting values just by eyeballing the curves: plot(y) lines(supsmu(1:20,y)) lines(0.6*cos((1:20)/3+0.6*pi)+17.2) Jim On Wed, Jun 21, 2017 at 9:17 AM, lily li <chocold12 at gmail.com> wrote: > Hi R users, > > I have a question about fitting a cosine curve. I don't know how to set the > approximate starting values. Besides, does the method w...

Dear R users, I am looking for the source code of the implementation of ppr (Projection Pursuit Regression) in R. It will be great if citations of the source papers on which the implementation is based, are also provided. Thank you, Arvind Iyer, Grad student, Deptt. of Biomedical Engineering Viterbi School of Engineering University of Southern California, Los Angeles [[alternative HTML

...], col = "grey") points(x, yright, ylim = yrightlim, ylab = "", xlab = xlab, pch = pch[1], col = col[1], ...) axis(4, pretty(range(yright, na.rm = T), 10), col = col[1]) if (linky) lines(x, yright, col = col[1], ...) if (smooth != 0) lines(supsmu(x, yright, span = smooth), col = col[1], lwd = lwds, ...) if (is.na(yylab[[1]])) mtext(deparse(substitute(yright)), side = 4, outer = T, line = 1, col = col[1], ...) else mtext(yylab[[1]], side = 4, outer = T, line = 1, col = col[1], ...) par(new...

...], col = "grey") points(x, yright, ylim = yrightlim, ylab = "", xlab = xlab, pch = pch[1], col = col[1], ...) axis(4, pretty(range(yright, na.rm = T), 10), col = col[1]) if (linky) lines(x, yright, col = col[1], ...) if (smooth != 0) lines(supsmu(x, yright, span = smooth), col = col[1], lwd = lwds, ...) if (is.na(yylab[[1]])) mtext(deparse(substitute(yright)), side = 4, outer = T, line = 1, col = col[1], ...) else mtext(yylab[[1]], side = 4, outer = T, line = 1, col = col[1], ...) par(new...

If I have, say, five scatter points and want to connect them together into a smooth curve. I did plot(x,y,type="l"), but the graph is five segments connecting with each other, but not a smooth curve. I wonder if there is a line type that is a curve. Thanks! -- View this message in context: http://r.789695.n4.nabble.com/connecting-points-into-a-smooth-curve-tp3021796p3021796.html Sent

How do I plot using multiple(2) y-axes? I have two series that use the same x-data, but have very different scales. Appreciate any feedback, Per Bak

Dear useRs, I have this dataset (D) with three columns. > dput(D) structure(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 2.990484802, 3.005018792, 3.019552781, 3.03408677, 3.048620759, 3.063154749, 3.077688738, 3.092222727, 3.106756717, 3.121290706, 3.135824695, 3.150358684, 3.164892674, 3.179426663, 3.193960652, 3.208494642, 3.223028631, 3.23756262,

Hi R users, I have a question about fitting a cosine curve. I don't know how to set the approximate starting values. Besides, does the method work for sine curve as well? Thanks. Part of the dataset is in the following: y=c(16.82, 16.72, 16.63, 16.47, 16.84, 16.25, 16.15, 16.83, 17.41, 17.67, 17.62, 17.81, 17.91, 17.85, 17.70, 17.67, 17.45, 17.58, 16.99, 17.10) t=c(7, 37, 58, 79, 96,

hi all i have another probably simple question. I have three variables say x, y and z. x and y are quite large and z is relative small. how can one plot the three variables on the same graph with two separate axis? (one for x and y and the other for z) e.g. x<-c(101,110,150,167,120) y<-c(120,135,175,95,200) z<-c(0.001, 0.15, 0.6, 0.8, 1) regards Allan

Hi to all, First of all, I prefer to tell that I am a R-newbie, so I apologize if this is a silly question (I have tried looking in the manuals, but without luck). I have two variables, y and z, that I want to plot against x in the same plot. I have done this before, using points() after plot(). But now the problem is that y and z are in different units of measurement, and their ranges are very

...ssion Fit ppr Projection Pursuit Regression predict.loess Predict Loess Curve or Surface predict.smooth.spline Predict from Smoothing Spline Fit scatter.smooth Scatter Plot with Smooth Curve Fitted by Loess smooth.spline Fit a Smoothing Spline supsmu Friedmans's SuperSmoother Check on any Comprehensive R Archive Network (CRAN) site for information on obtaining R libraries and subscribing to the lists. The master CRAN site is http://www.ci.tuwien.ac.at/R/ -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-....

I have a mortality data over many years and I wish to plot the data and also add some smoother to clearly highlight the trend. How could I do that in R with base graphics or ggplot? I have the following sample data: require(lubridate) mdate<-seq(ymd('2000-01-01'),ymd('2010-12-31'), by = '1 day') death<- rnorm(4018, 80, 45) df<-cbind(mdate,death)

...ssion Fit ppr Projection Pursuit Regression predict.loess Predict Loess Curve or Surface predict.smooth.spline Predict from Smoothing Spline Fit scatter.smooth Scatter Plot with Smooth Curve Fitted by Loess smooth.spline Fit a Smoothing Spline supsmu Friedmans's SuperSmoother Probably only bug-fixing left, but I would welcome comments about the extent of the loess functionality. lqs.tar.gz: ========== cov.rob Robust Estimation of Multivariate Location and Scatter lqs...

Hi all, I am doing a projection pursuit regression using the ppr() function from modreg. I would also like to use predict.ppr(). However, I cannot find any information about it in the help files. There is a link to predict.ppr in the index for modreg, but that link is to the help for ppr(). Has predict.ppr() not been implemented? If not, does anyone have a suggestion as to how to implement