similar to: contour plot for non-linear models

Hi, I know that it is possible to use C++ functions under the R environment. In any case my intention is the opposite way: use R functions like "glm" or "lm" in my C++ code. How should I deal with this problem? Josep. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html

Dear all, I am doing 200 times simulation. For each time, I generate a matrix and define some function on this matrix to get a 6 dimension vector as my results. As the loop should be slow, I generate 200 matrice first, and save them into a list named ma, then I define zz<-sapply(ma, myfunction) To my surprise, It almost costs me the same time to get my results if I directly use a loop

Dear all, I am doing 200 times simulation. For each time, I generate a matrix and define some function on this matrix to get a 6 dimension vector as my results. As the loop should be slow, I generate 200 matrice first, and save them into a list named ma, then I define zz<-sapply(ma, myfunction) To my surprise, It almost costs me the same time to get my results if I directly use a loop

Hello, I would like to insert S code to my latex document. I was looking for a listings package that supports S. In www.r-project.or in the software/other section there is an item with a link to latex package listings, but it seems that the link is broken. Does anybody know an alternative to get the package? Thanks in advance,

Hello, Do you know if it's possible to create a plot as in matlab with the options hold on and hold off? For example I want to plot in the same figure the theoric cdf of the normal distribution and the empiric cdf from the raw data. Thank you, Josep.

Dear all, Many thanks for your help in the contour plot for non-liner models. I've have found in the ellipse package an easy way to obtain the confidance region for a pair of parameters, for almost any model. But my question is the following. Exist some function to obtain the confidance regions for the regression parameters ( the same plot that appears in page 255 of Venables and Ripley

I have been having the same problem as poster Hodgess, below. It appears that her question was never answered, so I would like to share a solution with the community. The problem is the (apparent?) inability to produce moving range process behavior (a.k.a. "control") charts with individuals data in the package "qcc" (v. 2.0). I have also struggled with the same limitation in

Windows 2000 (v.5.00.2195), R 1.5.1 I have an lme object, fm0, which I examine with anova() and intervals(). The anova output indicates one of the interaction terms is significant, but the intervals output shows that the single parameter for that term includes 0.0 in its 95% CI. I believe that the anova is a conditional (sequential) test; is intervals marginal or approximate? Which should I

I'm having trouble using binomial(link="cloglog") with GLMM in lme4, Version: 0.5-2, Date: 2004/03/11. The example in the Help file works fine, even simplified as follows: fm0 <- GLMM(immun~1, data=guImmun, family=binomial, random=~1|comm) However, for another application, I need binomial(link="cloglog"), and this generates an error for me: >

Hello! I was reading R-intro and I have some suggestions: R-intro.html#A-sample-session rm(fm, fm1, lrf, x, dummy) suggestion rm(fm, fm1, lrf, x, y, w, dummy) The next section will look at data from the classical experiment of Michaelson and Morley to measure the speed of light. file.show("morley.tab") mm <- read.table("morley.tab") suggestion mm <- data(morley)

Hi I have some density estimates obtained from density(). I would like to calculate the sum of squares of these. As the x values of the estimates are not the same, and I would prefer not to restrict the estiomate to a certain range of x values, how can I do the calculation? Lets say: d1 <- density(Data1) d2 <- density(Data2) If the x values would be the same, I would: ssq <- sum(

Hi I am using ggplot2 at the moment and I must say it is definitely better then ggplot - good work. My problem is that I am using facet_grid() in the following way: > p <- ggplot(ssq, aes(x=year, y=-log(ssq))) > p + geom_point() + facet_grid(me*gi~cs*rz) and it works nicely, except that I would like to have, in naddition to the values of me, gi, cs and rz the name of the variable.

Hi, Within my R code, I am using a C++ function as below: overlaps <- matrix(0, nrow=B, ncol=length(N)) overlaps.P <- matrix(0, nrow=B, ncol=length(N)) .C("speedUp", D, S, pD, pS, nrow(D), as.integer(N), length(N), ssq[i], i, as.integer(B), overlaps, overlaps.P, DUP=FALSE) the function "speedUp", is supposed to update matrices overlaps

Hello! I can't compile rtmpdump source on FreeBSD: http://sourceforge.net/projects/rtmpdump/ I obtain the following output: # gmake g++ -Wall -c -o bytes.o bytes.cpp In file included from bytes.cpp:25: bytes.h:37:20: endian.h: No such file or directory bytes.h:38:22: byteswap.h: No such file or directory bytes.h:45:2: #error "Undefined byte and float word order!" bytes.cpp:

Dear All, I'm trying to use waldtest to test poolability (parameter stability) between two logistic regressions. Because I need to use robust standard errors (using sandwich), I cannot use anova. anova has no problems running the test, but waldtest does, indipendently of specifying vcov or not. waldtest does not appear to see that my models are nested. H0 in my case is the the vector of

Dear all, I am estimating a mixed-model in Ubuntu Raring (13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive definite

Dear All, I am trying to use stepAIC for an lmer object but it doesn't work. Here is an example: x1 <- gl(4,100) x2 <- gl(2,200) time <- rep(1:4,100) ID <- rep(1:100, each=4) Y <- runif(400) <=.5 levels(Y) <- c(1,0) dfr <- as.data.frame(cbind(ID,Y,time,x1,x2)) fm0.lmer <- lmer(Y ~ time+x1+x2 + (1|ID), data = dfr, family = binomial)

R experts, I have three columns. c1 and c2 are numeric variables whereas c3 are the clusters classes (nominal variable, 10 different: cluster1, cluster2, cluster3, cluster4, cluster5 ....). I'd like to plot c1 against c2 (easy!) in a 2D plot and put different color depending to the cluster class automatically regardless the number of clusters. Could anyone give a hand? Josep Maria, matrix

Dear all, I wonder which R algorithm could perform a mountain clustering in an spatial grid, in other words, having the coordinates of a map (x,y ... or even more) and then the altitude/height at each point I would like to localise the peaks of that topological surface. Which R algorithm can perform this? I see it as a clustering problem where the peak should be at the center of each cluster.

Yes. It should have something to do with read/write permissions, but it is not clear how it happens. I can write file to C drive using R. I usually write my results matrix to a txt file in C drive. For Rprof(), the boot.out file can be created, but only with one line sample.interval=20000 The situation is the same even if I specify the directory to the D drive,where I have the full