similar to: mva Hclust, heatmap and plotting functions

Hi All, I am using R 2.11.0 on a Ubuntu machine. I have a time series data set and want to run rolling regressions with it. Any suggestions would be useful. Here are the details: (1) I convert relevant variables into time series objects and compute first differences: vad <- ts(data$ALLGVA/data$GDPDEF, start=1948, frequency=1) emp <- ts(data$ALLEMP, start=1948, frequency=1) vad.dif1 <-

Dear all R users, Suppose I have a VECTOR of time series y[t] consists of 2000 data point. For example suppose I have data frame which has two columns. First column represents a time series of exchange rate for 2000 days. And the second column represents the price of a commodity for the same period. Now I want to fit a OLS regression like that, y[t] = a + b*delta[y[t-1]] + c*delta[y[t-2]] +

Picking up on a couple really old threads (e.g. http://osdir.com/ml/ietf.secsh/2001-09/msg00003.html ) I've finally gotten around to this. The EXTPROC support on Linux is missing, but you can find kernel patches for that here http://lkml.org/lkml/2010/6/11/403 I've also fixed up the netkit telnet / telnetd code to work with EXTPROC / LINEMODE on Linux, those patches are here

Hi all, I try to represent a multiple curve graphic where the x-axis is the temperature and the different y-axes are the different X (X22,X43,X44...) some X corresponds to the same molecule (22 and 44 are for CO2 for instance) so I use the same colour for them. I wanna mix the linetype with the colour to be able to visually see the difference between X43 and X45 The best I have done up to now

Dear R-list users, I'm new to principal components and factor analysis. I thought this method can be very useful for me to find relationships between several variables (which I know there is, only don't know which variables exactly and what kind of relation), so as a structure detection method. Now, I'm experimenting with the function prcomp from the mva package. In my source code

Dear R Team, Some minor typos in help pages for package:mva Thank you. Rashid Nassar 1. help(kmeans) Details: [k-means? not sure about this] v The data given by `x' is clustered by the k-Means algorithm. When this terminates, all cluster centres are at to the mean of their

I am using R mainly for multiple linear regression and principal components analyses and I am quite happy with it. I think it is a worderful work you have done. But I am having a problem when using Varimax for rotating loadings obtaines from a princomp (I use the cor = TRUE option): the variance across the components in the variables does not mantain. Let me explain it with an example in which

Hello, I am using the following code to plot an MVA plot. library(affy) library(Biobase) library(limma) library(gcrma) pd<-read.phenoData("Clk.targets.2.txt",header=TRUE, row.names=1,as.is=TRUE,sep="\t") Data <- ReadAffy(filenames=pData(pd)$FileName,phenoData=pd) Print(Data) eset <- gcrma(Data) write.exprs(eset,

Per the discussion about the problems with prcomp() when n << p, which boils down to a problem with svd() when n << p, here is a patch to prcomp() which substitutes La.svd() instead of svd(). -Greg (This is really a feature enhancement, but submitted to R-bugs to make sure it doesn't get lost. ) *** R-1.6.0/src/library/mva/R/prcomp.R Mon Aug 13 17:41:50 2001 ---

Dear List, although the library() command tells me that the pcakage "mva" is installed on my machine, I cannot use its functions or get help() about them. And, strange enough, I never installed the package manually. Has it become a part of R-base? I can't find the package among the package sources on CRAN, so I can't (re)install it manually. I'm using R 1.4.0. Can anyone

Full_Name: Laurent Gautier Version: 1.3.0-patched OS: IRIX 6.5 Submission from: (NULL) (130.225.67.199) Hello, The function La.eigen, called by cmdscale in the package mva behaves an unexplicable way (for me). The following lines show what happened. I tried the very same on linux, and it worked fine. >a <- matrix(c(1,2,3,2),3,3) >a [,1] [,2] [,3] [1,] 1 2 3 [2,]

Hello! I have a question about the factanal function. This function returns at the end test statistics like this: Test of the hypothesis that 4 factors are sufficient. The chi square statistic is 4.63 on 2 degrees of freedom. The p-value is 0.0988 Is it possible to get the chi square statistic and the p-value as variables, not the text on the screen? An object of class "factanal"

Vince Carey found this (thank you!). Since the fix to the problem is not entirely obvious, I post this to R-devel as RFC: help(dist) says: >> Missing values are allowed, and are excluded from all computations >> involving the rows within which they occur. If some columns are >> excluded in calculating a Euclidean, Manhattan or Canberra >> distance, the sum is

Hi All: Package TSERIES is stated to depend on MVA. However, there is no MVA package to be found under the list of package sources. Best wishes, ANDREW tseries: Package for time series analysis Package for time series analysis with emphasis on non-linear and non-stationary modelling Version: 0.7-6 Depends: ts, mva, quadprog Date: 2001-08-27 Author: Compiled by Adrian

Hallo, I have this dataset with repeated measures. There are two within-subject factors, "formant" (2 levels: 1 and 2) and "f2 Ref" (25 levels: 670, 729, 788, 846, 905, 1080, 1100, 1120, 1140, 1170, 1480, 1470, 1450, 1440, 1430, 1890, 1840, 1790, 1740, 1690, 2290, 2210, 2120, 2040, 1950), and one between-subject factor, lang (2 levels:1 and 2). The response variable

Can anyone help explain to me why the two codes below have different result? I thought I can use log(time)~. to replace log(time)~dist+climb+timef.I am using CVlm from DAAG package. I think nihills is preloaded with the package. Thanks in advance. > CVlm(df=nihills, form.lm=formula(log(time)~.),plotit="Observed",m=2)Analysis of Variance Table Response: log(time) Df Sum Sq

Hi all, I'm using quantreg rq() to perform quantile regression on a large data set. Each record has 4 fields and there are about 18 million records in total. I wonder if anyone has tried rq() on a large dataset and how long I should expect it to finish. Or it is simply too large and I should subsample the data. I would like to have an idea before I start to run and wait forever. In addition,

Hi there, as a student sociology, I'm starting to learn about SEM. The course I follow is based on LISREL, but I want to use the SEM-package on R parallel to it. Using LISREL, I found it to be very usable to be able to see the total direct and total indirect effects (standardized and unstandardized) in the output. Can I create these effects using R? I know how to calculate them

Hi I have a follow up question, relating to subsetting to list items. After using the list and min(sapply()) method to adjust the length of the variables, I specify a dynamic regression equation using the variables in the list. My list looks like this: Dcr<- list(Dcre1=DCred1,Dcre2=DCred2,Dcre3=DCred3,Dbobc1=DBoBC1,Dbobc2=DBoBC2,Dbobc3=DBoBC3,...) By specifying the list items with names, I