similar to: panel data unit root tests

Hi, The question is as follows: has anyone coded panel data unit root tests with R? Even the "first generation" tests (see e.g. Levin & Lin 1993; Pesaran, & Smith & Im 1996; Maddala & Wu 1999) would be sufficient for my needs. To my understanding, these are rather easy to code, but as I have taken just my first steps in coding with R, existing code would save me

I thought I would share the following. System: Mac Pro 2.26GHz, OSX, 8GB of memory (not a constraint), R 2.11.0, 64bit version. Task: I have a long data set: 2.2 million long observations (factor xid, factor yid, variable zcontent), which I want to map into a sparse matrix of 948 columns and 16,350 rows. There are two commonly used functions to accomplish this: library(stats); outcome =

At first sorry for the possibly dumb question of a newbie. I am using the hydrogeo package to visualize approx. 300 data within the Piper-Hill diagrams. The package is using a dataframe with 6 columns, first of them is containing the row.names, next four with the data itself. The last column contains "WaterTypes" dividing data to several categories. The diagram is created by the

Is there an existing function to compute Cook-Weisberg confidence curves for non-linear model parameters in R? Kari Ruohonen -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !)

I am looking for examples of code that demonstrates the fine tuning of the strip panels in lattice graphics and uses plotmath characters. The code for the graphic is as follows: xyplot(lagy ~ n | rho1 * rho2, data= data, layout=c(2,6), span = 1, xlab = "Sample Size", ylab = "Bias in the Coefficient for the Lag of X", type = "o") rho1 is a four level factor

hello im new here i want to swich to Ubuntu 9.04 my pc is: Code: 2 gb dual ram 800 mhz intel e1600 @2.40 ghz 2.40 ghz 9600 GT 512 mb gddr3 256 bit hdd sata drive i play some games on Windows Vista but id like to know how some games would work.. when i had 7300gt 256mb ddr3 i had some lag in "last chaos" but only when i was surounded by a lot of monsters these are the list of games

Hi, I fitted a linear model: fit <- lm(y ~ a * b + c - 1 , na.action='na.omit') Now I want to extract only the a * b effects with confidence intervals. Of course, I can just add the coefficients by hand, but I think there should an easier way. I tried with predict.lm using the 'terms' argument, but I didn't manage to do it. Any hints are appreciated, best, joerg

I am trying to explore nlmer by running some nlme examples from Pinheiro & Bates (2000). I do not seem to find information how to specify fixed effects covariates to nlmer models. Specifically, I tried to run the "Carbon Dioxide Uptake" example from p. 368 onwards in the PB200 book. The model without fixed effects covariates runs well but how to tell nlmer to include Type and

Hi al, I have a dataset (see attached), which basically involves 4 treatments for a chemotherapy drug. Samples were taken from 2 biopsy locations, and biopsy were taken at 2 time points. So each subject has 4 data points (from 2 biopsy locations and 2 time points). The objective is to study treatment difference.? I used lme to fit a mixed model that uses "biopsy.site nested within pid"

> myfit1 <- gls(col1 ~ col2+col3+col4+col5+col6-1, data=data2, corr=corAR1( 0.3202), method='ML') Error: cannot allocate vector of size 199712 Kb if I get rid of the "corr=corAR1(0.3202)" option, it works okay... can anybody help me? thanks a lot! [[alternative HTML version deleted]]

Dear R-helpers, I'd like to understand how to test the statistical significance of a random effect in gamm. I am using gamm because I want to test a model with an AR(1) error structure, and it is my understanding neither gam nor gamm4 will do the latter. The data set includes nine short interrupted time series (single case designs in education, sometimes called N-of-1 trials in medicine)

Hi, I wonder if someone could help. I needed to transfer (copy) a workspace file that had been generated in linux (R 2.11) to windows running the same version of R 2.11 (but of course windows binary). Usually, there is no problem in doing this and all objects work as expected. I am often doing this to be able to produce wmf or emf graphic files that I need. This time I had some spectra that I

Hi, I would appreciate of someone could explain how the residual standard error is computed for rlm models (MASS package). Usually, one would expect to get the residual standard error by > sqrt(sum((y-fitted(fm))^2)/(n-2)) where y is the response, fm a linear model with an intercept and slope for x and n the number of observations. This does not seem to work for rlm models and I am wondering

I was hoping to fit along the lines of g<-gl(20,5) y<-runif(100) fit<-lme(fixed=y~g,correlation=corAR1(0,~1|g)) But I get the error "Incompatible formulas for groups in "random" and "correlation"" Any help would be greatly appreciated. Ben

Hi, I wonder if predict.gam is supposed to work with family=negbin() definition? It seems to me that the values returned by type="response" are far off the observed values. Here is an example output from the negbin examples: > set.seed(3) > n<-400 > dat<-gamSim(1,n=n) > g<-exp(dat$f/5) > dat$y<-rnbinom(g,size=3,mu=g) >

Hi, I'm trying to set up AR(1) as a correlation structure in modeling some data (attached file data.txt in text format) with lme, but have trouble getting it to work. Incent, Correctness, and Oppor are 3 categorical variables, Beta is a response variable, and Time is an equally-spaced variable with 6 time points (treated as a categorical variable as well). Basically I want to model the

Hi, I have faced a problem with the merge() function when trying to merge two data frames that have a common index but the second one does not have cases for all indexes in the first one. With usual variables R fills in the missing cases with NA if all=T is requested. But if the variable is a matrix R seems to insert NA only to the first column of the matrix and fill in the rest of the columns by

Dear All Hopefully someone out there can point out what I am missing! I have a (large, several hundred) dataset of gardens in which over two years the presence/absence of a particular bird species is noted each week. I have good reason to believe there is a difference between the two years in the weekly proportion of gardens and would like to assess this, before going on to look in more detail at

Dear all; I experienced a memory allocation error while running gnls: > wBlplfe.wc <- update(wBlplfe.w, corr=corAR1()) Error: cannot allocate vector of size 37950 Kb In addition: Warning message: Reached total allocation of 256Mb: see help(memory.size) Increased memory limit to 512, 640 M, the allocation error persisted: > memory.limit(512) NULL > wBlplfe.wc <-

Dear all, I am trying to define my own corStruct which is different from the classical one available in nlme. The structure of this correlation is given below. I am wondering to know how to continue with this structure by using specific functions (corMatrix, getCovariate, Initialize,...) in order to get a structure like corAR1, corSymm which will be working for my data. Thanks in advance.