similar to: problem lme using corSymm()

Dear all, I have written a function for calculating the volume of a tree (=trad) or snag (=h?gst). The included volume regreesion model includes ten parameter values, which are tree species specific. bj?rk.formh?jd.pars is an object which includes the parameter values (parameter set) for birch (=bj?rk). There is one row per tree in the data object. > relev.kols[1:5,

Trying following example from Pinheiro and Bates in order to fit an ARMA(1,1) model: library(nlme) fm1Ovary.lme<-lme(follicles~sin(2*pi*Time)+cos(*pi*Time),data=Ovary,random=p dDiag(~sin(2*pi*Time))) fm5Ovary.lme<-update(fm1Ovary.lme,corr=corARMA(p=1,q=1)) I get follwing error message: Error in "coef<-.corARMA"(`*tmp*`, value = c(62.3428455941166, 62.3428517930051 :

Inthe code below I was trying to to obtain the GMM estimates for CAPM (REGRESSION) for 36 stocks each have 180 observations,however it only gives me one output rather than 36. In SAS i would just put in a *By statement*. I have a variable TICKER that categorize them into 36 groups. *How can I obtain all 36 output instead of just one.* **

Dear R-helpers, I am using a vrtest on time series data. My commands are as follows; read.table("B.txt",sep="\t",fill=TRUE, na.strings = "NA") require(vrtest) rm(list=ls(all=TRUE)) datamat <- read.table("B.txt",sep="\t",fill=TRUE, na.strings = "NA") column <- 1 nob <- nrow(datamat) y <-

Hello, I am trying to simulate 10 relicates of 100-tables. Each table is a 2 x 3 and 80% pf the tables are true nulls and 20% are non-nulls. The nulls follow the Hardy Weinberg distribution (ratio) 1:2:1. I have the code below but the p-values are not what I am expecting. I want to use the Cochran Armitage trend test to get the p-values. num.reps=10 num.vars=1000 pi0 = 80 num.subjects = 100

Hi, I usually use lme from the nlme library. Now I have read an article about lmer in Rnews and lmer seemed to me more comfortable to use. Unfortunately, I didn't find out how to use covariance structures (e. g. corSymm(), corAR1()). Is there a way to use them similarly as in lme ? Is it implemented ? If somebody knows, please let me know. Thank you very much in advance, Stephan

Dear all, I know there have been various questions posted over the years about loops but I'm afraid that I'm still stuck. I am using Windows XP and R 2.9.2. I am generating some data using the multivariate normal distribution (within the 'mnormt' package). [The numerical values of sanad and covmat are not important.] > datamat <-

I tried following commands: amp~time|subject/trial #this was the grouping structure of the data plot(dip,inner=~condition,layout=c(2,2)) after the plot command I obtained this error message: Error in if(!any(cond.max.level - cond.current.level <0)&&(row-1)* : missing value where TRUE/FALSE needed This error only occured in compination with "layout". It was no

A multinomial logit model can be specified as a conditional logit model after restructuring the data. Doing so gives flexibility in imposing restrictions on the dependent variable. One application is to specify a loglinear model for square tables, e.g. quasi-symmetry or quasi-independence, as a multinomial logit model with covariates. Further details on this technique and examples with several

Folks, I have been using the VAR {vars} program to find a fit for the following bi-variate time series (subset): bivariateTS<-structure(c(0.950415958293559, 0.96077848972081, 0.964348957109053, 0.967852884998915, 0.967773510751625, 0.970342843688257, 0.97613937178359, 0.980118627997436, 0.987059493773907, 0.99536830931504, 1.00622672085718, 1.01198013845981, 1.01866618122606,

Hello, I want to draw a histogram of the mean of sample observations drawn from multivariate t distribution. I am getting the following error corresponding to the code I used. Though I am getting the graph, but I am curious to know the warning message. Warning messages: 1: In if (freq) x$counts else { : the condition has length > 1 and only the first element will be used 2: In if (!freq)

Hello, I'm working with R version 1.5.0 in Windows. I've written a function (SummaryMat, segment below) which uses a loop to repeatedly call another function (PercentsMat, segment below). PercentsMat creates a matrix and adds rows to it each time it is called. I use deparse(substitute(...)) to get the names of the lists sent to PercentsMat to use them as row names in the generated

it occupies the nerazzurri champions league final win over the psychological advantage, and for bayern speaking, lost the champions league blemish also have hope in February of knockouts compensated, however with seven months at the top of the highest in Europe now compared both teams are suffering a flourishing after the lonely time. After the end of the season, inter milan and bayern Munich are

Hi I constructed a mixed-effects model from longitudinal repeated measurements of lab values in 22 patients seperated into two groups with the groups as fixed effect using lme. I thought about using the jackknife procedure, i. e., removing any one subject and calculating the fixed effect, to assess the stability of the fixed effect and thereby validate the model. I suppose this has been done in

Dear all, I have a performance problem in terms of computing time. I estimate mixed models on a fairly large number of subgroups (10000) using lme(.) within the by(.) function and it takes hours to do the calculation on a fast notebook under Windows. I suspect by(.) to be a poor implementation for doing individual analysis on subgroups. Is there an alternative and more efficient way for doing

Dear all, I am trying to make a barplot with clustered pairs of bars, using class=numeric data and the following command: barplot(c(bline_precip[10,9], bline_runoff[10,9], cccma_precip[10,9], cccma_runoff[10,9], csiro_precip[10,9], csiro_runoff[10,9], ipsl_precip[10,9], ipsl_runoff[10,9], mpi_precip[10,9], mpi_runoff[10,9], ncar_precip[10,9], ncar_runoff[10,9], ukmo_precip[10,9],

Dear All, I've successfully import my synteny data to R by using scan command. Below show my results. My major problem with my data is how am i going to combine the column names with the data( splt) where i have tried on cbind but a warning message occur. I have realized that the splt data only have 5 column instead of 6. Please help me with this!! I want my data to be a numerical

I am using read.csv to read a CSV file (produced by saving an Excel file as a CSV file). The columns containing dates are being read as factors. Because of this, I can not compute follow-up time, i.e. Followup<-postDate-preDate. I would appreciate any suggestion that would help me read the dates as dates and thus allow me to calculate follow-up time. Thanks John John Sorkin M.D., Ph.D. Chief,

Greetings list, I am new to programming in R, and am using nnet() function for a project on neural networking. Firstly I wish to ask if there is any pdf explaining the algorithm nnet uses, which could tell me what the objects of the nnet class, like 'conn', 'nconn, 'nsunits', n and 'nunits' mean, and how weights are calculated. The package pdf has little or no