similar to: Exporting summary plm results to latex

Hello, I am trying to run a fixed effects panel regression on data containing 5 columns and 1,494 rows. I read the data in as follows: >drugsXX<-read.csv(file="C:\\Folder\\vX.X\\Drugs\\drugsXX_panel.csv", head=TRUE, sep=",") Verified it read in correctly and had a good data.frame: >dim(drugsXX) [1] 1494 5 >drugs XX produce expected data with correct column

In the rockchalk package, I want to provide functions for regression objects that are "well behaved." If an object responds to the methods that lm or glm objects can handle, like coef(), nobs(), and summary(), I want to be able to handle the same thing. It is more difficult than expected to ask a given fitted model object "do you respond to these functions: coef(), nobs(),

Dear "plm"-Package insiders, [I posted the following observation is April already but unfortunately I am not aware of any answers. With the hope that someone found an answer in the mean time, I ask again:] I realized the following difficulty with the summary.plm function (demonstrated with the example from the ?plm documentation). library(plm) data("Produc",

Hi! I noticed that there is a (minor) bug either the command all.equal() or in the "plm" package. I demonstrate this using an example taken from the documentation of plm(): ====================================== R> data("Produc", package="plm") R> zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, + data=Produc,

Hi all, I have estimated a random panel model using plm function. I have a question about the vector of resduals obtained with the object $residuals. example: data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, model="random", data = Produc, index = c("state","year")) res<-zz$residuals #

Hi all, what indicates the parameter theta in the summary of a random effect panel model estimated with the plm function? example: data("Produc", package = "plm") zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, model="random", data = Produc, index = c("state","year")) summary(zz) Effects: var std.dev

Hello, Apologies in advance if this is a stupid question. I am running R on Ubuntu 9. R version 2.9.2 (2009-08-24) I am trying to work with plm. I think the library is installed, as I can do > library(plm) Loading required package: kinship Loading required package: survival Loading required package: splines Loading required package: nlme Loading required package: lattice [1] "kinship

Hello, I am trying to conduct a Hausman test to choose between FE estimators and RE estimators. When I try to run: library(plm) fixed <- plm(ROS ~ DiffClosenessC +ZZiele + AggSK + nRedundantStrecken + Degree + KantenGew + BetweennessC + SitzKappazitaet, data=Panel,index=c("id","time"),model="within") summary(fixed) fixef(fixed) random <-plm(ROS ~

Dear R-helpers, I have a very simple question and I really hope that someone could help me I would like to estimate a simple fixed effect regression model with clustered standard errors by individuals. For those using Stata, the counterpart would be xtreg with the "fe" option, or areg with the "absorb" option and in both case the clustering is achieved with "vce(cluster

Hi all, I am statistically confused tonight. When the assumptions to a random effects estimator are warranted, random effects should be the more efficient estimator than the fixed effects estimator because it uses fewer degrees of freedom (estimating just the variance parameter of the normal rather than using one df for each included fixed effect, I thought). However, I don't find this to be

Hello list, I'm following the paper (http://www.jstatsoft.org/v27/i02/paper) on how to use "plm" to run panel regressions, and am having trouble with what I believe should be something very basic. When I run the command (p.9 in the paper): R> dynformula(emp~wage+capital,log=list(capital=FALSE,TRUE),lag=list(emp=2,c(2,3)),diff=list(FALSE,capital=TRUE)) I see: emp ~ wage +

> On Mar 31, 2016, at 1:00 PM, Paul Johnson <pauljohn32 at gmail.com> wrote: > > In the rockchalk package, I want to provide functions for regression > objects that are "well behaved." If an object responds to the methods > that lm or glm objects can handle, like coef(), nobs(), and summary(), > I want to be able to handle the same thing. > > It is more

When using the PLM package (version 1.2-8), I encounter the probem that calling the FGLS estimator evokes strange behavior, when choosing the "random" effects model. After calling the PGGLS function to estimate FGLS, PLM gives me a warning, stating that the "random" model has been replaced with the "pooling" model. I would, however, really like to estimate the random

Hey, I have this TukeyHSD output from which I would like to extract only the P-values (p adj, last number). The problem is that the test output is a character list. How can I "break" this sentence to separate the Pv? Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Fe1$Fe ~ Fe1$genotype) $`Fe1$genotype` diff lwr upr

Dear plm Package users, I use the plm package a lot but I have not updated it for some times. Now I realized the following difficulty with the summary.plm function (demonstrated with the example from the ?plm documentation). library(plm) data("Produc", package="Ecdat") estimation_method<-"within" estimation_effect<-"individual" zz

Dear friends, I am trying to understand and implement instrumental variables regression using R. I found a small (simple) example here which purportedly illustrates the mechanics (using 2-stage least-squares): http://www.r-bloggers.com/a-simple-instrumental-variables-problem/ Basically, here are the R commands (reproducible example) from that site: # ------ begin R library(AER)

I have a contingency table with a 0/1 variable (fwr) that flags a particular condition, a weight variable (cnt) and other variables (e.g. zz) characterizing that observation of the table. I am trying to use GLM. By converting the variables to factors and running the following regression: fwr1<-factor(fwr) zz1<-factor(zz1) res1<-glm(fwr1 ~ zz1,weights=cnt) Is that appropriate? Paul M.

Hello, Apologies in advance if this is a stupid question. I am running R on Ubuntu 9. R version 2.9.2 (2009-08-24) I am trying to work with plm. I think the library is installed, as I can do > library(plm) Loading required package: kinship Loading required package: survival Loading required package: splines Loading required package: nlme Loading required package: lattice [1] "kinship

Hi, this is probably quite stupid but I have no clue what's wrong. Let's say I write the function hline <- function() { cat("\\hline \n") } and call hline() from within a Sweave chunk. Why is there no carriage return after the \hline in the resulting tex file? if I call hline() hline() in the chunk, then I get \hline \hline in the tex code without a linebreak in

Dear R Helpers, I did a search for deleting rows based on conditions but wasn't able to find an example that addressed the error that I am getting. I am hoping that this is a simple syntax phenomenon that somebody else knows off the top of their head. My apologies for not providing a reproducible example but I think that the information given will allow someone to give me a hint. I want to