similar to: dataframes of unequal size

Hi dears, I expect that Mr Thomas Lumley will read this message. I have data from a complexe stratified survey. The population is divide in 12 regions and a region consist to and urban area and rural one. there to region just with urbain area. stratification variable is a combinaison of region and area type (urban/rural) In rural area, subdivision are sample with probabilties proporionnal to

Hello R people, I am looking to pay someone to help write some R code. Inputs: Study identifier: ID Number for the study, each ID number is for one study only each block set should only be used for that study. This will require that you store the results from the blocks someplace on the file system. Trait #1: dichotomous rural / urban Trait #2: dichotomous sick / healthy Assignment Ratio:

Dear all, I'm trying to create a legend for my graph. I hope to have the title as "Land Use Type" and the two elements being "Urban" and "Rural" with a red point and green point respectively. So far I have the following command, but obviously it isn't correct: > legend("topright", title="Land Use Type", cex=0.75, pch=16,

Hello; Our survey is structured as : To be investigated area is divided into 6 regions, within each region, one urban community and one rural community are randomly selected, then samples are randomly drawn from each selected uran and rural community. The problems is that in urban/rural stratum, we only have one sample. In this case, how to do bootstrap? Any comments or hints are greatly

Dear all r-users, Suppose I have two data frame: A 1 3 4 5 2 and B 5 6 3 5 Now I want combine this two data frames without losing any value from either data frame. More precisely I want to see A B 1 5 3 6 4 3 5 5 2 NA I tried with cbind function but failed, as it only works when two data frames have equal number of rows. Can anyone suggest me any code that can be used for any

Hi, Last week my class conducted an experiment by putting out clay caterpillars to look at the effects of urbanization, color, and location on caterpillar predation. There were two sites (urban, rural), three colors (green, yellow, red) and two locations at each site (edge, interior). The entire data set is below. I've checked out the MASS book, Dalgaard's book, and the R-help archives

I have two variables. A <- rep(c(1:9), 2) B <- rep(c(2:10),2) -- Renuka Sane http://www.nyx.net/~rsane [[alternative HTML version deleted]]

Question on graphs: The default case for drawing a graph in R, is where a little space is left on the x and y axis before the first tick i.e. even if I say xlim=c(0,1) -- there will be some space between the edge of the x-axis and where 0 is placed. If I want 0 on the edge, how do I do it in R? Area under the curve: I have a 45 degree line and a curve above or below it. Is there a way in R

I have a function (see below) that does some bootstrapping (I am happy to expand offline why I could use existing functions.) I put my results into and empty matrix and add a row of results with each iteration. My problem is i am a new user to R and I don't understand data frames, matrices, elements, and vectors well. What I would like is to have a data frame I can manipulate outside of the

Sorry for the off-topic, but perhaps this will be of interest to other asterisk based ITSPs. We are starting service in a rural area where the ILEC has the rural "monopoly". From what we have read in the FCC docs this does NOT exempt them from number portability, but what does it take for us to qualify to receive their numbers? To date we simply have a few voice trunks to them,

It appears I broke ggplot in my script, but that maybe it is because the caffeine has worn off or maybe it is late in the day.   I thought I was beginning to understand ggplot, but I have encountered a silly little issue.   For some reason the following does not produce a histogram with fill due to the Person's characteristics: (Note that VADeaths_flat_df$Data works fine...)    

Hi, I wonder if anyone can answer the following or point me in the direction of how to obtain answers to the questions. Below is Output from R and further down are the questions raised and explanation of the study. Output from R: glm(formula = CB95TO00 ~ URB + INC, family = poisson) Deviance Residuals: Min 1Q Median 3Q Max -1.2272 -1.1290 0.2709 0.4272 2.1376

I am trying to do a forecasting exercise for a series, x. My forecast model consists of the following I first regress log(x) on time and dummy variables for each month. lm(log(x) ~ time + monthly dummies) I then use predict() to obtain a prediction for the next year. I then fit an AR(6)/AR(12) model on the residuals of the regression. I use predict() here also to obtain the prediction for the

Buenas noches, Soy nueva en R y a veces me cuesta pensar los cálculos de manera más práctica, por los que les agradecería la ayuda. Tengo una matriz de datos con una dim = 35745 19, correspondientes a ubicaciones de 39 perros, cada perro tiene poco más o poco menos de 1000 datos. Necesito saber el % de uso de hábitat natural. Es un sencillo cálculo de porcentaje, pero me gustaría hacerlo mas

We have working in a rural project and I have selected Asterisk. Because * will work with a prepay system we need a precise answer supervision for outgoing calls. The pilot system will have 8 trunks to PSTN. The Telephone Company provides EURO BRI and analog CO trunks. On CO trunks the answer supervision is reversal polarity. The village to be served will be connected through a WLAN. Thus,

I'm using R for PCA and? factor analysis. I want to create biplots of varimax rotated factors that color-code points by their classification. My research is on streams that are urban and rural. So, I want to color code them by this classification. If you just do a biplot from prcomp or princomp, you cannot add this color. So, I have used some code developed by a graduate student in our

Hi all, I am not sure how to handle interactions with categorical predictors in the GAM models. For example what is the different between these bellow two models. Tests are indicating that they are different but their predictions are essentially the same. Thanks a bunch, > gam.1 <- gam(mortality.under.2~ maternal_age_c+ I(maternal_age_c^2)+ + s(birth_year,by=wealth) + +

Buenas, tengo la siguiente inquietud. Tengo un data frame con los microdatos de la ECH de mi pais. He realizado un cuadro sin problemas en excel pero me gustaria una rutina de R para poder generar la tabla. El data.frame se llama ECH2009. Cuenta con las siguientes variables para este caso: idhogar region estrato integranteshogar expansor ingpercapsvl Computador TvAbonado Internet 1 capital 1 2

I'm looking into costs and feasibility of moving a live feed from a FM radio station from the station to a point that's past the usable range of their radio signal. It's a rural location and Internet service is not available at the station. If the destination was closer or their transmitter was more powerful, I could avoid this step and just plug in a radio, but.... My best idea so

Dear list, I am fitting a logistic multi-level regression model and need to test the difference between the ordinary logistic regression from a glm() fit and the mixed effects fit from glmer(), basically I want to do a likelihood ratio test between the two fits. The data are like this: My outcome is a (1,0) for health status, I have several (1,0) dummy variables RURAL, SMOKE, DRINK, EMPLOYED,