similar to: Problem with PVCM: "0 (non-NA) cases"

Dear all I am trying to fit Variable Coefficients Models on Unbalanced Panel Data. I managed to fit such models on balanced panel data (the example from the "plm" vignette), but I failed to do so on my real, unbalanced panel data. I can reproduce the error on a modified example from the vignette: > require(plm) > data("Hedonic") > Hed <- pvcm(mv ~ crim + zn + indus

Hi everybody, Got some doubts here. I'm kinda desperate for help, so please ask me if anything isn't clear. I have a database with this structure (panel data structure): > head(dados_2) Tempo Safra Data Resposta Perc_Resg_Acum Alta_Temporada Flexi Promo 1 1 1 200701 0.04223216 0 1 0 0 2 1 2 200702 0.02801536 0

Dear R-users, I have the following problem: I am running the function 'pvcm' from the 'plm' Panel Data package. Inside this function 'solve' is called and gives for a certain individual data series an exception because of singularity. I would like to know which individual data series causes this error. I tried to debug it, but this is truly painful, as solve is called

Hello, I am using {plm} to estimate panel models. I want to estimate a model that includes fixed effects for time and individual, but has a random individual effect for the coefficient on the independent variable. That is, I would like to estimate the model: Y_it = a_i + a_t + B_i * X_it + e_it Where i denotes individuals, t denotes time, X is my independent variable, and B (beta) is the

Hello. Not an easy question at all, and it has little to do with software, alas! Veeeeeery loosely speaking: if the homogeneity hypothesis is rejected, then, depending on data availability, you may still be able to treat the data like a panel by: a) ignoring the results of the poolability test b) allowing the coefficients to vary. Of course, a) requires some courage while b) requires more

Hi all, I have a hypothesis test of proportion question without using R. I hope you could help to answer my question. Here is the statement. ********************* Preliminary data from a feasibility study showed a potential failure level of 1%. A sample size was calculated to detect a potential shift from this 1% level to a 2.5%, at an alpha level of 0.025 and beta of 0.1. The sample size

Hi everyone, So I am trying to see which ecological parameter of different parks in nyc influence the most the diversity of the medium-sized mammals in those parks. I have a bunch of different parameters for each park I'm done studying and the presence (1) and absence (0) of each mammal. I wanted to do a principal component analysis of those data to know which ones of those parameters are the

Dear experts, I'm new in R and trying to learn by writing a version of the Perceptron Algorithm. How can I tell in the code below to stop the iteration when the condition in the "for loop" is not satisfied for all training examples? Thanks in advance for your help! ## Generate a linearly separable data set in R2 sample <- as.data.frame(cbind(runif(n=100),runif(n=100)))

Dear r-helpers, I am working with plm package. I am trying to fit a fixed effects (or a 'within') model of the form y_it = a_i + b_i*t + e_it, i.e. a model with an individual-specific intercept and an individual- specific slope. Does plm support this directly? Thanks in advance! Otto Kassi

Dear R-Help, My problem/bug came to light,when fitting a linear model using stepwise selection. I'd started with the straightfoward command step(lm(y~., dataset)) This worked fine, but because this starts with all the possible explanatory variables, it results in a model with too many explanatory variables. Hence I wanted to start with just a constant and do forward selection, to get a

Hello, I am working on my thesis and can't really figure out how to produce a reasonable graph from the output from my glm., I could just give the R-output in my results and then discuss them, but it would be more interesting if I could visualise what is going on. My research is how bees react to different fieldmargins, for this I have 4 different types of field margin (A,B,C & D) and

Hi.? There appears to be a bug in R function manova.? My friend and I both ran it the same way as shown below (his run) with the shown data set. His results are shown below. we both got the same results.? I was running with R 2.3.1. I'm not sure what version he used. Thanks very much, David Booth Kent State University -----Original Message----- From: dvdbooth at cs.com To: kberk at

Dear R People: So thanks to your help, I have the following: > dog3.df <- read.delim("c:/Users/erin/Documents/dog1.txt",header=FALSE,sep="\t") > dog3.df V1 V2 1 1/1/2000 dog 2 1/1/2000 cat 3 1/1/2000 tree 4 1/1/2000 dog 5 1/2/2000 cat 6 1/2/2000 cat 7 1/2/2000 cat 8 1/2/2000 tree 9 1/3/2000 dog 10 1/3/2000 tree 11 1/6/2000 dog 12 1/6/2000

I am finding with the patch that all of the Polyhedron 2005 benchmarks pass on i686-apple-darwin9. Could someone clarify the regression rules for releases? Not building a secondary language on a primary target is usually considered a P1 regression for FSF gcc. Not doing so here gives one the impression that llvm.org isn't playing by the same rules. No one is ever going to want to use these

Dear Spencer and Prof. Fox, Thank you for your replies. I'll very appreciate, if you have any ideas concerning the problem described below. First, I'd like to describe the model in brief. In general I consider a model with three equations. First one is for annual GRP growth - in general it looks like: 1) GRP growth per capita = G(investment, migration, initial GRP per

On Mon, Feb 5, 2018 at 8:27 AM, Kay Diederichs <kay.diederichs at uni-konstanz.de> wrote: > grub-install /dev/nvme0n1 Running this on computers with UEFI firmware is not good advice, it's an obsolete command. People should use the prebaked grubx64.efi binary that comes in the grub2-efi package, and is a signed binary so it can support UEFI Secure Boot. If you run grub2-install, a

I am trying to use the survfit() function with the newdata argument to produce predicted survivor curves for a particular covariate profile. The main purpose of the plot will be to visualise the effect of snp1, coded 0 and 1. In my Cox model I have stratified by one variable, edu, and so I know I will automatically get a separate curve for each strata. My problem is how to deal with the

Hello, I'm running a number of arima models using the "arima" function. Often, when lag length gets too high, these model don't converge and an error message appears as this: > reg <- arima(y,order=c(7,0,7),xreg=isr) Warning message: In arima(y, order = c(7, 0, 7), xreg = isr) : possible convergence problem: optim gave code=1 In this case, when you print the results

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

Hi, We did some performance test and found the create/delete files performance of btrfs is very poor. The test is that we create 50000 files and measure the file-create time first, and then delete these 50000 files and measure the file-delete time. (The attached file is the reproduce program) The result is following: (Unit: second) Create file performance BtrFS Ext4 Total times: