similar to: Fitting polynomial (power greater than 2)

In Germany the Unemployment Agency uses a sectioning of the german map that is different from the usual Administrative Boundaries. Some demographic data are available in Administrative Boundaries only, some in Unemployment Boundaries only. I would like to generate estimates in one boundary system of data availabe in the other boundary system, and would appreciate advice concerning the following

Dear all, I'm trying to remove some text after the period (a decimal point) in the data frame 'hi', below. This is one step in formatting a table. So I would like e.g. "2.0" to become "2" and "5.3" to be "5.3", where the variable digordered contains the number of digits after the decimal that I would like to display, in the same order in which

Dear addresses, I need perform a batch of 10 000 simulations for each of 4 options considered. (The idea is to obtain the parameter estimates in a heteroskedastic linear regression model - with additive or mixed heteroskedasticity - via the Kenward-Roger small-sample adjusted covariance matrix of disturbances). For this purpose I wrote an R program which would capture all possible options (true

> sessionInfo() R version 2.15.0 (2012-03-30) Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit) locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 attached base packages: [1] grid datasets splines utils stats [6] graphics grDevices methods base other attached packages: [1] effects_2.1-0 colorspace_1.1-1 [3] nnet_7.3-1

Dear All, I would like to ask if it is possible to estimate a hazard function using the muhaz command when all the data is right-censored. My data has information of the number of weeks people has been unemployed but all of them are unemployed at the date of the survey, that is, I cannot observed when the individuals leave the unemployment state. I appreciate your help, Best, Monica L.

Hello, I have the following problem. I am running simulations on possible states of a set of agents (1=employed, 0=unemployed). I store these simulated time series in a matrix like the following, where rows indicates time periods, columns the number of agents (4 agents and 8 periods in this case): Atr=[ 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 0 1 0 1 0

Hi, I am working on fitting a proportional hazard model to predict the probability of default for mortgage loans. I have a question regarding survfit function. My historical data set is a pool of loans with monthly observed default status for the next 24 months. The data is left truncated (delayed entry to observation window after the loan is opened) and right censored. I would like to

We would like to load data from Statistics Canada (http://www.statcan.gc.ca/) using R, for example, Employment and unemployment rates. It seems to me that the tables are displayed in HTML. I was wondering if you know how to load these tables. Thanks, -james

Can someone please help with the error message below? thanks! Start: AIC=-Inf value ~ 1 + Core_CPI__ + GDP_change + Unemployment + housing + interest + S_P + d1 + d2 + d3 Error in if (any(ch)) { : missing value where TRUE/FALSE needed In addition: Warning message: attempting model selection on an essentially perfect fit is nonsense

Dear R List, I have a dataset with the following structure: """personal_id, p_0, p_1, p_2, .... , p_36, p_37 1, NA, 1, 4, .... , 1, NA 2, NA, NA, NA, .... , 4, NA . . . 6020, NA, 3, 3, ...., NA, NA 6021, NA, 2, 2, ...., 4, NA """ I used some made-up data. It is just meant to show the structure of the dataset. The variables of interest are p_0, ... p_37.

Dear R experts,   I'm running the following stationary bootstrap programming to find the parameters estimate of a linear model:     X<-runif(10,0,10) Y<-2+3*X a<-data.frame(X,Y) coef<-function(fit){   fit <- lm(Y~X,data=a)    return(coef(fit)) }  result<- tsboot(a,statistic=coef(fit),R = 10,n.sim = NROW(a),sim = "geom",orig.t = TRUE)   Unfortunately, I got this

I could use some advice regarding xyplot. I've got 2 time series. Both cover approximately the same period of time (ie, 1940 to 2009). But one series has annual data and the other has monthly data. One refers to university enrollment; the other to unemployment rates. Both are currently in the same data frame. I'd like to use the monthly times series as a light grayscale background for a

Ran a bunch of variables in R and the final result of StepAIC is as below: Why are the first 5 variables kept in the stepwise result?? Are the last 4 variables finally chosen after Stepwise? Thanks Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.315e-01 2.687e-01 0.490 0.63611 Core_CPI__ 1.290e-02 7.496e-03 1.721 0.11927 GDP_change -3.482e-03 2.075e-03 -1.678 0.12767

R users, I am a beginner in R. I have a time series data on various macro variables like GDP, unemployment rate, consumer spending, imports, exports etc. I am interested in calculating year over year growth rate for each of those variables. Is there any simple function in R for carrying out such repeated operations. I would like to carry out growth rate between two consecutive yearly row data

Hi everyone, This is not so much of an R question as a statistics question. I currently work for the largest pre employment screening company in Canada. Upper management has noticed that noticed that usually a month or so before any big kind of economic shock happens, that our incoming files (requests for a background check) jump up or down. As the company statistician, they've asked me

I have a database of bookmarks and tags, and want to count the number of bookmarks each tag is assigned to, for example: user "tyler" has the tag "concerts" on 15 out of his 30 bookmarks. This code: hash = Hash.new("0"); @user.tags.each do |t| @user.bookmarks.each do |b| x = 0 if b.tags.include?(t) then hash[b.id] =+ 1 puts "hash is #{hash}

I could use some advice. I've got 2 time series. Both cover approximately the same period of time (ie, 1940 to 2009). But one series has annual data and the other has monthly data. One refers to university enrollment; the other to unemployment rates. Both are currently in the same data frame. I'd like to use the monthly times series as a light grayscale background for a plot of the

Hi All I found survfit function was very slow for a large dataset and I am looking for an alternative way to quickly get the predicted survival probabilities. My historical data set is a pool of loans with monthly observed default status for 24 months. I would like to fit the proportional hazard model with time varying covariate such as unemployment rates and time constant variables at loan

dear R users, I have troubles performing Moran.I test as suggested on http://www.ats.ucla.edu/stat/r/faq/morans_i.htm my spatial data are longitude and lattitide of communities. The calculation of the inverse distance matrix according to the homepage (using my data) datAL <- read.csv2("C:\\Konvergenz AL.csv", header=T) ALdist <- as.matrix(dist(cbind(datAL$L?nge,

Hello R wizards, What is the best way to read a data file containing both fixed-width and tab-delimited files? (More detail follows.) _*Details:*_ The U.S. Bureau of Labor Statistics provides local area unemployment statistics at ftp://ftp.bls.gov/pub/time.series/la/, and the data are documented in the file la.txt <ftp://ftp.bls.gov/pub/time.series/la/la.txt>. Each data file has five