similar to: Set axis limits in mixtools plot

I'm trying to make a transition from S-plus under Windows to R under Linux. My immediate aim is to produce a scatter plot with two y-axes with different scales. That can be done in S-plus with a command of the following form: guiPlot( PlotType="Scatter", DataSet="execon", Columns="years,hstart,ship", AxisType="Multiple Y" ), where years, hstart, and

Dear all, I'm using R 2.8.1 under Windows Vista on a dual core 2,4 GhZ with 4 GB of RAM. I'm trying to reproduce a result out of "Analysis of Financial Time Series" by Ruey Tsay. In R I'm using the fGarch library. After fitting a ar(3)-garch(1,1)-model > model<-garchFit(~arma(3,0)+garch(1,1), analyse) I'm saving the results via > result<-model

Dear R-tisans, I am trying to calculate the 12th root of a transition (square) matrix, but can't seem to obtain an accurate result. I realize that this post is laced with intimations of quantitative finance, but the question is both R-related and broadly mathematical. That said, I'm happy to post this to R-SIG-Finance if I've erred in posting this to the general list. I've

I have 2 questions concerning the EM algorithm. Is it true that the EM algorithm gives unique answers for the means and variances of a mixture of 2 normals? I am using mixtools and I am surprised that it works better than a Bayesian program I wrote. If so can someone say why the mixing probabilities are so good? -- Thanks, Jim. [[alternative HTML version deleted]]

I am trying to install mixtools on Debian Etch and get the following error dell-2 /usr/lib # R CMD INSTALL mixtools_0.1.0.tar.gz * Installing *source* package 'mixtools' ... ** libs gcc -I/usr/share/R/include -I/usr/share/R/include -fpic -g -O2 -std=gnu99 -c new_svalues.c -o new_svalues.o gfortran -fpic -g -O2 -c sphericaldepth.f -o sphericaldepth.o make: gfortran: Command not

Hello all, May be silly question, but what exactly is beta parameter in functions like regmixEM from mixtools package? I mean, how to determine this beta, if i have a set of metrics for each case? Is there a function for that? I have try to put NULL at this parameter, but function just do not work in this case. Cheers, Dima [[alternative HTML version deleted]]

I have two variables which show a typical quantile relation I would like to fit quantile regression models based on both logarithm and polynomial of second order functions within quantreg. Any help appreciated... Cheers Duccio Herewith the values: ------ var1 var2 0.96429 0.00138 1 0.02316 1.03145 0.09323 1.24088 0.77128 1.39869 0.86732 1.33728 0.63674 1.48299 0.96194

Full_Name: nobody Version: 2.2.0 OS: any Submission from: (NULL) (219.66.34.183) 2 x 2 table, such as > x [,1] [,2] [1,] 10 12 [2,] 11 13 > chisq.test(x) Pearson's Chi-squared test with Yates' continuity correction data: x X-squared = 0.0732, df = 1, p-value = 0.7868 but, X-squared = 0.0732 is over corrected. when abs(a*d-b*c) <= sum(a,b,c,d), chisq.value

Hello, I have used the arima.sim function to generate a lot of time series, but to day I got som results that I didn't quite understand. Generating two time series z0 and z1 as eps <- rnorm(n, sd=0.03) z0 <- arima.sim(list(ar=c(0.9)), n=n, innov=eps) and z1 <- arima.sim(list(ar=c(0.9)), n=n, sd=0.03), I would expect z0 and z1 to be qualitatively similar. However, with n=10 the

Dear friends, After running the lm() model, we can get summary resluts like the following: Coefficients: Estimate Std. Error t value Pr(>|t|) x1 0.11562 0.10994 1.052 0.2957 x2 -0.13879 0.09674 -1.435 0.1548 x3 0.01051 0.09862 0.107 0.9153 x4 0.14183 0.08471 1.674 0.0975 . x5 0.18995 0.10482 1.812 0.0732 . x6 0.24832 0.10059 2.469 0.0154 * x7

Dear all, I am currently analyzing count data from a hierarchical design, and I?ve tried to follow the suggestions for a correct estimation of p-values as discusssed at R-Wiki (http://wiki.r-project.org/rwiki/doku.php?id=guides:lmer-tests&s=lme%20and%20aov). However, I have the problem that my model only consists of parameters with just 1 d.f. (intercepts, slopes), so that the

Greetings R-helpers, I am attempting to fit an lme() while specifying a correlation structure, but I'm getting into trouble long before I get to that point. I am receiving the error: Error in y[revOrder] - Fitted : non-conformable arrays It doesn't seem to matter how simple or complex the model I specify is, it always gives this same error message. This makes me suspect something is

The previously posted benchnarks for gcc vs llvm-gfortran had one mistake. I was actually had the gfortran for 4.3.1 installed instead of that from gcc 4.2.4. Below are the polyhedron benchmark results for all three compilers... gfortran 4.2.4 Benchmark Compile Executable Ave Run Number Estim Name (secs) (bytes) (secs) Repeats Err % --------- -------

On Fri, Sep 14, 2007 at 11:42:18PM -0700, Tanya Lattner wrote: > The 2.1 pre-release (version 1) is available for testing: > http://llvm.org/prereleases/2.1/version1/ > > [...] > > 2) Download llvm-2.1, llvm-test-2.1, and the llvm-gcc4.0 source. > Compile everything. Run "make check" and the full llvm-test suite > (make TEST=nightly report). > > Send

Hi all there I am enjoying R since 2 weeks and I come to my first deadlock, il am trying to use predict.Arima in the ts package. I get a "Error in cbind(...) : cannot create a matrix from these types" -- Start R session ----------------------------------------------------- > fitdiv <- arima(data, c(2, 0, 3), xreg = y ) ; print(fitdiv) Call: arima(x = data, order = c(2, 0, 3),

Hi all there I am enjoying R since 2 weeks and I come to my first deadlock, il am trying to use predict.Arima in the ts package. I get a "Error in cbind(...) : cannot create a matrix from these types" -- Start R session ----------------------------------------------------- > fitdiv <- arima(data, c(2, 0, 3), xreg = y ) ; print(fitdiv) Call: arima(x = data, order = c(2, 0, 3),

Dear all, I have tried to calculate a GLMM fit with lmer (lme4) and glmmPQL (MASS), I also used glm for comparison. I am getting very different results from different functions, and I suspect that the problem is with our dataset rather than the functions, but I would appreciate help in deciding whether my suspicions are right. If indeed we are attempting the wrong type of analysis, some

Dear R users, I have now tried out several options of obtaining p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and single-term deletions with subsequent chi-square tests (although I am aware that the latter may be problematic). However, I encountered several problems that can be classified as (1) the quasipoisson lmer model does not give p-values when

I have a data frame with one column "x": > str(data) `data.frame': 20 obs. of 1 variable: $ x: num 0.0495 0.0986 0.9662 0.7501 0.8621 ... Normally, I know that the notation dataframe[indexes,] gives you a new data frame which is the specified set of rows. But I find: > str(data[1:10,]) num [1:10] 0.0495 0.0986 0.9662 0.7501 0.8621 ... Here, it looks like the operation

Dear All, I am working on a large matrix of dimension 20000x700 say 'mat'. I have calculated pearson correlation for the rows of the matrix and their p-values using rcorr function in library Hmisc. Now I wish to filter out those pairs who's PCC value is above 0.8 cut off and p-value is less than 0.05. >library(Hmisc) >mat_cor=rcorr(t(mat),type="pearson")