similar to: 1D & 2D response plots from RSM models

Dear r users, I wanted to perform contour figure from response surface model result. I typed this: > model = rsm (Index ~ SO(NV, SKLON, pH), bonita) > summary(model) > par(mfrow = c(2, 3)) > contour (model, ~NV+SKLON+pH) However, I got the error answer as follows: Error in lmobj$data[, nm] : object of type 'environment' is not subsettable Has someone any suggestions to

Hola compañeros de la lista. Estoy aprendiendo a usar el paquete "rsm" para superficies de respuesta. Siguiendo este ejemplo todo va bien, hasta que trato de obtener el valor máximo predicho para la variable de respuesta en los valores de x1 y x2 estimados. ------------------------------------------------------------- library("rsm") ChemReact CR <- coded.data(ChemReact,

Hi All I have been fitting regression models and would now like to produce some contour & image plots from the predictors. Is there an easy way to do this? My current (newbie) experience with R would suggest there is but that it's not always easy to find it! f3 <- lm( fc ~ poly( speed, 2 ) + poly( torque, 2 ) + poly( sonl, 2 ) + poly( p_rail, 2 ) + poly( pil_sep, 2 ) + poly( maf, 2

Hola compañeros de la lista. Los molesto con la siguiente duda. En un diseño central compuesto (CCD) con dos factores (V1 y V2) y una variable de respuesta (R), utilizando valores codificados (-1.4142, -1, 0, 1, 1.4182), al aplicar la orden: rsm.segundo.orden <- rsm(R ~ Bloque + SO(V1, V2), data = DATOS.Codificados) Obtengo el siguiente modelo: R = 103.92 -2.16

Hello,everybody! I am a beginner of R. And I want to ask a question. If anybody would help me, thank you very much ahead. I want to plot something like a response surface, and I find the "rsm" package. Some commands are like this: #code head library(rsm) CR = coded.data(ChemReact, x1 ~ Time, x2 ~ Temp) CR.rsm = rsm(Yield ~ SO(x1,x2), data = CR) summary(CR.rsm) contour(CR.rsm,x1~x2)

DDear developeRs, about a year ago, Alex Stolpovsky posted an issue with predict.lm on a fit generated using poly with the raw=TRUE option and too few new data (slightly modified reproducible example below). Alex did not get any reply. I have just stumbled on the same problem, and I think that this is a bug of function poly, which arises from the check whether the polynomial degree is

Hi all I have just started using R (1.8.1) on Mac OS X & am very excited about the potential of such a flexible system with such good graphics potential & so many useful packages available. Congratulations to all the developers & I hope to be contributing some packages myself in the near future, once I have got my head around what is required. It will take me some time to convert

Hi all (and especially Frank), I'm trying to use x=T, y=T in order to run a validated stepwise cox regression in rsm, having multiply imputed using mice. I'm coding model.max<-fit.mult.impute(baseform,cph,miced2,dated.sexrisk2,x=T,y=T) baseform is baseform<-Surv(si.age,si=="Yes")~ peer.press + copy.press + excited + worried + intimate.friend + am.pill.times +

estimados, ajusté una superficie de respuesta con el paquete "rsm" de R y hago un grafico 2D de la superficie de respuesta con contour(), pero como el argumento de esta función es la superficie de respuesta ajustada y ésta se ha ajustado codificando las variables, de acuerdo a la recomendación de la documentación del paquete rsm, obtengo un gráfico con los ejes en unidades codificadas

Dear all, I am attempting to use RSM in order to optimise the analysis of volatile compounds. After changing two variables and performing a series of measurements, I get the following: X1 X2 resp 1 -0.8 -1.0 1101039 2 -0.8 0.0 809947 3 -0.8 1.0 814664 4 0.0 -1.4 759484 5 0.0 0.0 665916 6 0.0 0.0 654054 7 0.0 0.0 641134 8 0.0 0.0 673143 9 0.0 0.0

Hi all We are pretty new to R here and trying to achieve something that we believe is possible but it?s not easy to work out how to do it. We are producing scatterplot matrices for e.g. 10 variables. What we would like to do is superimpose 2D kernel density estimators on top of each plot so that we end up with a scatterplot matrix of 2D kernel density (contour) plots. The kernel density plots

Hi everyone, I hope this question is beyond "read the manual". My task is simple, just to plot the following, but the plot in the middle should be a filled.contour plot: http://gallery.r-enthusiasts.com/graph/Scatterplot_with_marginal_histograms_78 Background: I prefer filled.contour rather than hist2d. Because, I could use kernel smooth, so the plot for discrete data won't be too

Hi All, I am a newcomer to R. Could anyone explain me how to define link functions for either mu/sigma to allow for quadratic trends in the same, when fitting non-stationary GEV distributions? Thanks -- View this message in context: http://www.nabble.com/QUADRATIC-TREND-FOR-LINK-FUNCTIONS-ON-NON-STATIONARY-GEV-tp23360751p23360751.html Sent from the R help mailing list archive at Nabble.com.

Hi, I have just seen Stefan Steinhaus' web page : http://www.uni-franfurt.de/~stst/ncrunch.html I think it would be nice to include "R" as well. I have taken Forrest Young's email on stat-lisp list and changed the stuff for "R" :) Here it is: (someone please check this so we can also send it to Stefan Steinhaus.

Hi All, I am a newcomer to S/R. Could you please let me know how to model quadratic trends for the mul/sigl link functions when fitting non-stationary GEV distributions using the ismev package? Thanks Best Regards, Mohammad Ashrafuz Zaman PhD Candidate School of Engineering Building XC, Room 1.02 (Kingswood Campus) University of Western Sydney Locked Bag 1797, Penrith South DC NSW 1797

Hi, Please see the attached contour plot (I am sorry about the big file). This was created using the vis.gam() function in "mgcv" package. However, my question is somewhat broader. In generating this figure, I first created the contours using vis.gam() and then I plotted the points. These point are plotted on top of the contours so that some of the contour lines are only partially

Sorry everybody, temporary dyslexia. Sent from my BlackBerry wireless smartphone

Hi, I am plotting values of log(hazards ratio) as a function of two predictors, using the plotting function filled.contour(). Here is a simple simulated example of this: x <- seq(0,1, length=20) y <- seq(0,1, length=20) z <- outer(x,y, function(x,y) x^2 + y^2 ) zmat <- matrix( rexp(n=400, rate = z+0.001), 20, 20) filled.contour(x, y, log(zmat),

As part of a project I am doing, I had to present a 2D table as a surface. This was fairly straightforward (using persp), but I needed to interpolate points to make it sort of pretty. This was done by using quadratic interpolation between the existing points to smooth out the surface a bit. As I couldn't find an R function that did this, I wrote an admittedly nasty pair of functions that

Hi all, I would like to plot one data set as a 2d histogram and another one as a contour. I can do it separately with the "hist2d" and "contour" functions, but I wonder if there is a way to combine these two plots into a single one (the ranges of the two plots are the same). Any suggestions? Thanks, Anna [[alternative HTML version deleted]]