similar to: Regressions with fixed-effect in R

Hello Giovanni I made a minor modification to your function, which now allows to compute the within R-sq in Twoways Within models (see below). However I ran into an issue that I have already encountered before: whenever I try to fit Twoways Within models on my unbalanced data, the process is strangely slow and I usually terminate it either after ~15min or when my CPU hits 100C. This is similar to

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

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 ~

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(),

Hi, I am trying to run a panel regression using 88 observations and 9 variables. In-built Hausman Test did not work, then I found a code for auxiliary regression method for the Hausman test. The panel models are: fe=plm(gd ~ l+g+o+c+g1+h+n+r, model = "within", data = new.frame,index = c("id")) re=plm(gd ~ l+g+o+c+g1+h+n+r, model = "random", data = new.frame,index =

Dear R users, I am trying to simulate some multitrait-multimethod models using the packages sem and psych but whatever I do to deal with models which do not converge I always get stuck and get error messages such as these: "Error in summary.sem(M1) : coefficient covariances cannot be computed" "Error in solve.default(res$hessian) : System ist f?r den Rechner singul?r: reziproke

Hi there, I run two regressions: y = a1 + b1 * x + e1 y = a2 + b2 * z + e2 I want to test against the null hypothesis: b1 = b2. How do I design the test? I think I can add two equations together and divide both sides by 2: y = 0.5*(a1+a2) + 0.5*b1 * x + 0.5*b2 * z + e3, where e3 = 0.5*(e1 + e2). or just y = a3 + 0.5*b1 * x + 0.5*b2 * z + e3 If I run this new regression, I can test against

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

Dear R users I get different F-statistic results for a "within" model, when using "time" or "twoways" effects in plm() [1] and when manually specifying the time control dummies [2]. [1] vignette("plm") [2] http://cran.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf Two examples below: library("AER") data("Grunfeld", package =

Hi there, I am looking for a function that takes a vector as input and generates all pair wise combination of the elements of the input vector. For example, the input vector is c(1,2,3,4). The output vector is c(1 and 2, 1 and 3, 1 and 4, 2 and 3, 2 and 4, 3 and 4). The representation here is generic. I can use a for loop to do it, but it gets slow when the number of elements of the input

Hola, quiero compartir con vosotros mi problema y la solución que me han planteado. Mi programa carga Outcomes.csv y Set-A.csv (descargados de http://garrickadenbuie.com/blog/2013/04/11/visualize-physionet-data-with-r/, apartado Getting Started --> the code and the data set) de unos 50MB entre los dos. Mi código era: # Transforma csv a data frame seta <- read.csv('Set-A.csv');

Hi, How would one go about determining if the slope terms from an analysis of covariance model are different from eachother? Based on the example from MASS: library(MASS) # parallel slope model l.para <- lm(Temp ~ Gas + Insul, data=whiteside) # multiple slope model l.mult <- lm(Temp ~ Insul/Gas -1, data=whiteside) # compare nested models: anova(l.para, l.mult) Analysis of Variance

Hola Carlos, bueno la verdad es que mi pregunta era algo general, cuando no has usado data.table no parece muy intuitivo pasar de la forma de programar a la que estás más acostumbrado (bucles, notación matricial...) a esa otra. Aun no tengo un cálculo complejo concreto pero lo tendré que hacer... solo quería saber si se puede, y parece que sí, asi que será cuestión de empaparse un poco de

Hi there, When I extract data from SQLite to R, the data types (or modes) of the extracted data seems to be determined by the value of the first row. Please see the following example. When I put the missing values first, the column extracted is of the mode character. > str(dbGetQuery(sql.industry, + "select pya_var from annual_data3 + order by

sorry for asking again and again my Requirement: i am connecting to teradata database and i am accessing tables and table data also, i need generate graphs using that data and also i need to forecast the results. for example i have a table xyz Store Year Revenue abc 2010 $557889 def 2010 $697356 i want to draw a barplot and i want to

Dear all I used R2WinBUGS package's bugs() function to generate MCMC results. Then I tried to save the simulation draws in R, using read.bugs() function. Here is a simple test: ###################### library(coda) library(R2WinBUGS) #fake some data to test beta0=1 beta1=1.5 beta2=-1 beta3=2 N=200 x1=rnorm(N, mean=0,sd=1) x2=rnorm(N, mean=0,sd=1) x3=rnorm(N, mean=0,sd=1) lambda2= exp(beta0+

Dear Group, I am an economics student starting with PhD work in London. As preparation I would like to get to know R a little bit better. For Stata there are tons of books, however, can you recommend a book for R? I have some substantiated econometrics knowledge, so it should be more a how-to book. Best regards Thiemo --- Thiemo Fetzer, Economist http://freigeist.devmag.net

Léandre BASSOLE PhD Student CNRS-CERDI 65 Bd Francois Mitterrand Boite Postale 320 63009 Clermont-Ferrand CEDEX 1 FRANCE Tel : +33 4 73 17 74 45 Fax : +33 4 73 17 74 28 ----- Forwarded Message ---- From: Leandre Bassole <leandrebassole@yahoo.co.uk> To: r-help@r-project.org Sent: Saturday, 27 October, 2007 8:41:05 PM Subject: Hi all!! I am a new user of R. I am very familar to Stata, but

Quiero comparar varias dosis letales 50% (LD50) usando análisis probit. He seguido un ejemplo que viene en paquete DRC, pero no obtengo el resultado esperado. Lo que quiero es saber si las LD50s, son diferentes y si la diferencias son estadísticamente significativas. Gracias de antemano. José Arturo e-mail. jafarfan@uady.mx <grejon@uady.mx> e-mail alterno. jafarfan@gmail.com

Dear R users, I wish to manually demean a panel over time and entities. I tried to code the Wansbeek and Kapteyn (1989) transformation (from Baltagi's book Ch. 9). As a benchmark I use both the pmodel.response() and model.matrix() functions in package plm and the results from using dummy variables. As far as I understood the transformation (Ch.3), Q%*%y (with y being the dependent variable)