search for: waged

I'm trying to read two data sets in json format from a single .js file. I've tried fromJSON() in both RJSONIOIO and RJSON packages, but they require that the lines be pre-parsed somehow in ways I don't understand. Can someone help? > wheat <- readLines("http://mbostock.github.com/protovis/ex/wheat.js") > str(wheat) chr [1:70] "var wheat = [" "

Hi, I am new to R. My problem is with the ordered logistic model. Here is my question: Generate an order discrete variable using the variable wrwage1 = wages in first full calendar quarter after benefit application in the following way: * wage*1*Ordered *= 1 *if*0 *· wrwage*1 *< *1000 2 *if*1000 *· wrwage*1 *< *2000 3 *if*2000 *· wrwage*1 *< *3000 4 *if*3000 *· wrwage*1 *<

Suppose we observe N individuals, for each of which we have a time-series. How do we correctly create a lagged value of the time-series variable? As an example, suppose I create: A <- data.frame(year=rep(c(1980:1984),3), person= factor(sort(rep(1:3,5))), wage=c(rnorm(15))) > A year person wage 1 1980 1 0.17923212 2 1981

Dear all, I have 1980~1990 eleven datas, every year have three variables, wage gender(1=female, 2=male) race(1=black, 2=white) My original commands is: fig2b<-reldist(y=mu1990$wage,yo=mu1980$wage,.......) I have three questions: 1. If I want to appoint y=women's wage in 1990 yo=women's wage in 1980 2. If I want to appoint y=women's wage in

I am trying to simulate the trajectory of the pension assets of one person. In C-like syntax, it looks like this: daily.wage.growth = 1.001 # deterministic contribution.rate = 0.08 # deterministic 8% Wage = 10 # initial Asset = 0 # initial for (10,000 days) { Asset += contribution.rate * Wage

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 +

Hello, I am fairly new with R and am having trouble finding an optimal group. I checked the help functions for the various optimize commands and it was a little over my head. I have a dataset with 4 columns, name, type, value, and cost. The set consists of a list of people, which have 3 types. I want to choose 6 people, two of each type, and maximize the sum of their values. However, I'm

Dear John Fox, Christian Kleiber, and Achim Zeileis, I am attempting to run various independent variable parameters to assess their suitability. Unfortunately, I hit a snag and couldn't get the tests to run properly. When I used ivreg, I got an error message saying: "Error in eval(predvars, data, env) : object 'WageInequality' not found." Can you please help? Model:

Como usas la función image puedes consultar la ayuda ?image o help(image) y encontrarás el siguiente ejemplo donde se usa un diferente color Palette (mencionada por pepeceb en su respuesta). x <- 10*(1:nrow(volcano)) y <- 10*(1:ncol(volcano)) image(x, y, volcano, col = terrain.colors(100), axes = FALSE) # O puedes usar directamente el número para indicar el color image(x, y, volcano, col =

Hello, I have a set of data including age, wage and education level each called age76, wage76 and grade76 I want to know how i can calculate the average wage of people age 15 to 65 (each year separetly) , only for those who have an education level of 10 12 and 16... -- View this message in context: http://r.789695.n4.nabble.com/Conditional-average-tp4585313p4585313.html Sent from the R help

Hi, I'm trying to do a regression like this: wage.r = lm( log(WAGE) ~ log(EXPER) where EXPER is an integer that goes from 0 to about 50. EXPER contains some zeros, so you can't take its log, and the above regression therefore fails. I would like to make R accept log(0) as 0, is that possible? Or do I have first have to turn the 0's into 1's to be able to do the above

Hello, Hierarchical factors are a very common data structure. For instance, one might have municipalities within states within countries within continents. Other examples include occupational codes, biological species, software types (R within statistical software within analytical software), etc. Such data structures commonly use hierarchical coding systems. For example, the 2007 North

Dear all, I have set up a Labour Demand Error Correction Model for some German federal states. As I expect the labour markets to be correlated I used a Seemingly Unrelated Regression using systemfit in R. My Model is: d(emp)_it = c + alpha*ln(emp)_i,t-1 + beta_1*ln(gdp)_i,t-1 + + beta_2*ln(wage)_i,t-1 + + beta_1*ln(i)_i,t-1 + gamma_1*d(gdp)_it + gamma_2*d(wage)_it with emp_it being the

Dear R-List, My questions concerns missing values. Specifically, is is possible to use different "types" of missingness in a dataset and not a one-size-fits-all NA? For example, data may be missing because of an outright refusal by a respondent to answer a question, or because she didn't know an answer, or because the item simply did not apply. In later analysis it is sometimes

Dear all users, I read all R programs and texts in "Relative Distribution Methods" website, but I encounter two problems when I used it. then I used "Google" to try to find any solutions about the two problems,but I got no useful hints. 1、 In package "reldist" y mean sample from comparison distribution yo mean sample from reference distribution but I

Por favor ver comentario con las las letras mayúsculas data("CPS1985", package = "AER") cps <- CPS1985 cps [1:10,] dim( cps) names( cps) library("quantreg") #regresion usual cps_lm <- lm(log(wage) ~ experience + I(experience^2) + + education, data = cps) #regresion por quantiles, tau son los cuantiles

Hello, I am switching to R from Stata and I am having particular trouble with the transition from Stata's 'xtabond' and 'ivreg' commands to the "plm" package. I am trying to replicate some of the dynamic panel data work using the UK Employment data in Arellano and Bond (1991) and available as 'EmplUK' under the 'plm' package. I have been

Kelis, thanks for your interest. It's hard to say what exactly goes wrong based on the information you provide. However, I would recommend that you first process the data: - Store all variables as the appropriate types (numeric, factor, etc.) in the data frame. Then you don't have to put these things into the model formula. - Employ variable names without spaces, then you don't

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)

[Mod: forwarded from BUGTRAQ -- alex] ---------- Forwarded message ---------- Date: Sun, 6 Jun 1999 19:15:05 +0000 From: noc-wage <wage@IDIRECT.CA> To: BUGTRAQ@NETSPACE.ORG Subject: RedHat 6.0, /dev/pts permissions bug when using xterm Once again I''ve come up with another trivial Denial of Service flaw, (wow, I seem to be good at this Conseal Firewall, +++ath0, ppp byte-stuffing)