similar to: How might one write this better?

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

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 *<

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

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 +

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:

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 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

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, 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

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 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 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

Hi all, I need a little help for construct an state's map on R. The first problem is to get the data. I have a datafile of longitude and latitude in the follow format: trajectory latitude longtude T -22.045618 -51.287056 T -22.067078 -51.265888 T -22.067039 -51.207249 T -22.059690 -48.089695 T -22.075529 -48.074608 T -22.072460 -48.044472 T -22.062767 -48.298473 T -22.077349

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Hello. I have 5 measurement points, my dependent variable is ordinal (0 - 3), and I want to visualize my data. I'm pretty new to R. What I want is to find out whether people with different baseline covariates have different trajectories, so I want a plot with the means trajectory of my dependent variable (the individual points do not make a lot of sense in ordinal data) on each measurement

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

I search an plugin or gem, but don''t find nothing satisfactory. I believe to be stranger a technology that nails the DRY, have that create you vary equal tables, instead of using inheritance. Exists an soluction for this? I want a solution similar to this: create_table :people |t| do t.column :name t.column :address end create_table :customer |t| do t.column :person_id

Hi all, I am trying to plot the fitted trajectories for each individual from an individual growth model (fit with a linear mixed effects model in lme). How can I plot each person's trajectory in the *same* panel, along with the mean-level trajectory? Below is an image of a plot similar to what I'm trying to create (from: http://jpepsy.oxfordjournals.org/content/31/10/1002/F6.large.jpg):

Hi, I'm trying to read some trajectory files (text files) which have the form: # trial n # t X Y 0 1 2 0.2 1 3 0.4 1.2 4 ... # trial n+1 # t X Y 0 1 2 0.2 1.3 3.3 0.4 1.5 5 ... ... where the symbol # means that the line is not a numeric value, but I still need to read the number of the trial (n=1, 2, etc.). I

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