similar to: Instrumental variables and quantile regression in R

Greetings: I am looking for some help (probably really basic) with looping. What I want to do is repeatedly sample observations (about 100 per sample) from a large dataset (100,000 observations). I would like the samples labelled sample.1, sample.2, and so on (or some other suitably simple naming scheme). To do this manually I would >smp.1 <- sample(100000, 100) >sample.1 <-

I have a couple of data sets that I want to combine into one data frame. One set contains a number of records on individual observations and includes a geographic descriptor called dacode. The dacode is not unique in that table. The other table contains a number of socio-economic variables for each of the geographic areas identified in the other table. This second table also includes a variable

Dear all, I would like to implement a spatial quantile regression using instrumental variable estimation (according to Su and Yang (2007), Instrumental variable quantile estimation of spatial autoregressive models, SMU economics & statistis working paper series, 2007, 05-2007, p.35 ). I am applying the hedonic pricing method on land transactions in Luxembourg. My original data set contains

Full_Name: Neil Hepburn Version: 2.81 and 2.90 OS: OS-X 10.5.6 Submission from: (NULL) (142.244.28.93) When I create graphs using Rcmdr and then close the quartz display, R blows up and tells me of a segmentation fault. It then gives me *** caught segfault *** address 0xc0000023, cause 'memory not mapped' Possible actions: 1: abort (with core dump, if enabled) 2: normal R exit 3: exit

Dear R user, I have been setting up the models for predicting the volume based on the price information of own product and competitive products. one option is to use instrumental variable to break price into two parts: one part that might be correlated with error term, and the another part that is not. But now I met the problem of choosing instumental variables. I have searched many papers. it

Hi there, I hope to build a model Y ~ X1 + X2 + X3 + X4 with X1 has two instrumental variable A and B, and X2 has one instrumental variable A. I have searched the R site and mailling list, and known that the tsls() from sem package and ivreg() from AER package can deal with instrumental variable regression, however, I don't know how to formula the model. Any suggestion will be really

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)

This is the named article: http://ije.oxfordjournals.org/content/37/5/1161.long maybe it can help you to help me... :-( -- View this message in context: http://r.789695.n4.nabble.com/INSTRUMENTAL-VARIABLES-WITH-BINARY-OUTCOMES-tp4642361p4642363.html Sent from the R help mailing list archive at Nabble.com.

Dear all, I am using the Instrumental Variable approach to estimate the causal effects of TWO endogenous variables in a Mendelian Randomization study. As long as point estimation is concerned, I have no problem: both "ivreg" in library "AER" and "tsls" in library "sem" do the job perfectly. The problems begin when I try to obtain confidence intervals for

Hi, I have a good grasp of grep() and gsub() for finding and extracting character strings. However, I cannot figure out how to use a search term that is stored in a variable when the search string is more complex. #Say I have a string, and want to know whether the last name "Jannings" is in the string. This is done by names=c("Emil Jannings") grep("Emil",names)

I am having problems sending and receiving calls over the T1. They never seem to connect - outbound keeps ringing, inbound gets busy. The T1 looks ok - no errors on the line. Any ideas on what is wrong? I have tried a variety of fxsks and fxoks configurations without avail. This is a single asterisk@home system with a single T1 card. Robbed Bit T1 ami, d4. ------------------inbound call

Hi list, Thanks in advanced. I have written a winbindd client software to do NTLM authentication by using WINBINDD_PAM_AUTH_CRAP message. that is to say, my winbindd client is similar to ntlm_auth, but with high performance. when I test it under large traffic, most of the time they all work well. and the transaction per second can reach more than 900. but sometimes, winbindd will return

I figured it out. The old system (Televantage 3 and 4 I think) has limited specifications on the T1. After setting up the system, I was able to send and recieve calls. I still have some work to do like figuring out faxing and a floating receptionist, but this is a nice start. ----------------------------------------------------Televantage T1 Requirements: Framing: D4 Superframe or Extended

I have a data set that has student test scores along with several categorical variables. I would like to generate a set of summary stats (mean, variance, n) for the data grouped by school authority and by exam topic. I have tried the by() function but that seems to only be able to handle one level of grouping. In particular what I would like is something like the following Board Subject Mean

Hi, I am using the quantile function currently and I have just bumped into a little problem. I have a very small data frame something like this: small_df <- c(7,3,4,7,1,10,12,1,12,4,4,8,6,11,9,10,4,13,3,9,6,5,2,10,7,14,2,7,10,10,7,8,2,11,3,10,11,3,11,14,12,7,6,11) small_df Now in the next step I would like to calculate the quantile value of the previous data frame at the probability of 95%

I am calling quantiles as follows. I don't understand why sometimes the columns (data values) above 95% are returned as "NULL"!! When I drop the percentile down to 92%, I see colums appearing. Why would any quantile be empty? I see sometimes that 95% percentile is being chosen as "1" for my data between 0 and 1, where obviously there's no column value equal to 1. But

Hi, I'm working on a data set that contains a couple of factors and a number of dependent variables. From all of these dependent variables I would like to calculate mean, standard deviation and quantiles. With the function FUN I get all the means and stdev that I want but quantiles are only calculated for the first of the dependent variables (column 8 in the summarize command). What do I

Dear all, I am searching for a way to compute a test comparable to Chuang et al. ("Causality in Quantiles and Dynamic Stock Return-Volume Relations"). The aim of this test is to check wheter the coefficient of a quantile regression granger-causes Y in a quantile range. I have nearly computed everything but I am searching for an estimator of the density of the distribution at several

Dear list I am using the qq.plot command to create quantile-quantile plots. The plot should display a 45 degree reference line upon which the points of the graph should fall if the two distributions being examined are roughly equal. If I try: x<-rchisq(100, df=6) qq.plot(x, dist="chisq", df=6) Then I get a quantile plot which has an intercept of roughly 1 when the line should be

Guys: 1).When I using the 'quantile' function, I get really confused. Here is what I met: > x<-zoo(rnorm(500,0,1)) > quantile(x,0.8) 400 1.060258 > c=rnorm(500,0,1) > quantile(c,0.8) 80% 0.9986075 why do the results display different? Is that because of the different type of the class? 2).And I want to use the 'rollapply' function to compute a