similar to: "Conditional" average

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 have a set of data with 205 988 observation sand 10 variables , three of which are Legal_status, Date_of_incorporation and Last_year. I set my time horizon from 1989 to 2009. Now I want to know when a company is dead. If Last_year is bigger or equal to 2009 then I say that a new "variable" last is 2009. If Last_year is smaller than 2009 then my new variable "last" is equal

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

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

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 =

Hi, I have a polynomial of 2n^2-5n+3 and I have my n values going up in powers of 2 i.e. n=2,4,8,16?.. I wanted to fit this curve to the function A*n*log2(n) +B*n where A and B are to be found. How would i do this? Thank you Jaymin

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

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:

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 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)

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

How do I go about generating a WEIGHTED mean (and standard error) of a variable (e.g., expenditures) for each level of a categorical variable (e.g., geographic region)? I'm looking for something comparable to PROC MEANS in SAS with both a class and weight statement. Thanks. Marc [[alternative HTML version deleted]]

Dear R users, I had a research question which involves a simultaneous equation system, one is the common continuous dependent variable, y1, say wage, or log wage, another one is a latent variable, y*, which I only observe up to a ordinal scale, say attitudes toward a problem, taking values as y2= 1, 2, 3 or 4. Both of them have other exogeneious variables. I have been search on internet for

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

I am concidering taking the Jump to Linux. I have SuSE. 6.3 at the moment.. but don't want to loose some of my M.$. Apps.. So I hear about wine.. as being able to run some of the apps.. this is great.. News.. for the Weary anyway.. So does this Wine work with SuSE.. Please forgive me I haven't even played with Linux. yet.. just trying to get all my questions sorta answered before I wage

Hello, I have a set of 100 variables with 1560 observations. I did an O.L.S regression of three of these variables on a fourth. But there are problems of endogeneity... So I look in my dataset for instruments to do an IV. I can't find a good instrument because their correlation with my endogeneous variables are too low. But I see that when I create a combined variable composed of 12 variables

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