similar to: MARS for complex samples / survey data?

Hi R-users!! I have some trouble with the survey pakage and i would be very glad if you can give me an advice. I have a sample from a survey where household were interviewed. The sample has 4 criteria on which the stratification was based: REGION, SIZE OF HOUSEHOLD, SIZE OF LOCALITY, AGE OF HEAD OF HOUSEHOLD. Since i don't have the whole information in each cell of the cross

A new package `survey' for analysing complex survey samples is on CRAN. It handles stratification, clustering, and unequal sampling probabilities in descriptive statistics, glms, and general maximum likelihood fitting. The package is still under development: - it doesn't do the finite population correction to variances - it needs some real life worked examples Most importantly,

A new package `survey' for analysing complex survey samples is on CRAN. It handles stratification, clustering, and unequal sampling probabilities in descriptive statistics, glms, and general maximum likelihood fitting. The package is still under development: - it doesn't do the finite population correction to variances - it needs some real life worked examples Most importantly,

Dear WizaRds, Working through sampling theory, I tried to comprehend the concept of stratification and apply it with Survey to a small example. My question is more of theoretic nature, so I apologize if this does not fully fit this board's intention, but I have come to a complete stop in my efforts and need an expert to help me along. Please help: age<-matrix(c(rep(1,5), rep(2,3),

Good afternoon! I'm trying to use the Survey package for a stratified sample which has 4 criteria on which the stratification is based. I would like to get the corrected weights and for every element i get a weight of 1 E.g: tipping design <- svydesign (id=~1, strata= ~regiune + size_loc + age_rec_hhh + size_hh, data= tabel) and then weights(design) gives

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

Hi, All Thanks to Robert Baskin, Thomas Lumley, and Spencer Graves for the valuable helps. I have learned a lot from this discussion. I put all discussions together without editing, so we can see how things are evolved. Likely, I have a lot of articles to read. As in the discussion, mixed modeling approach is a poosible but may be over-kill in my posted data analyses. I will explore other

Our CEO, Dr. Dan Steinberg, is planning to visit Europe in May. He would like the opportunity to introduce statisticians (and statistically minded people) to data mining, data mining applications and to forefront data mining tools. Our algorithms are probably familiar to many statisticians (CART, MARS, MART, TreeNet and RandomForests), although it isn't necessary to be a statistician to

Hello everybody I don't understand how I'm supposed to use svydesign caracteristics to explain to R that my sampling design is the following one Data base = tab1 here are the five first rows of the database (nrow = 11792) num esp Quarters Totcat Totshp Totgt Tbtpos fpc1 Totanim Id_An 10 2045 G

Hello everyone,   I am a very new user of R and I have a query about the cuminc function in the package cmprsk. In particular I would like to verify that I am interpreting the output correctly when we have a stratification variable.   Hypothetical example:   group : fair hair, dark hair fstatus: 1=Relapse, 2=TRM, 0=censored strata: sex (M or F)   Our data would be split into:   Fair, male,

I'm working across the statistical literature to find methods for calculating prediction intervals for GLM, BRT (boosted regression tree) models and MARS (multivariate adaptive regression spline) models, but unfortunately my statistical background is too weak to understand most of the stuff I read. I would by satisfied by knowing how to code this in R (and accept the methods as black

Hello everyone, I am a very new user of R and I have a query about the cuminc function in the package cmprsk. In particular I would like to verify that I am interpreting the output correctly when we have a stratification variable. Hypothetical example: group : fair hair, dark hair fstatus: 1=Relapse, 2=TRM, 0=censored strata: sex (M or F) Our data would be split into: Fair, male,

Hello, Does R have any capabilities (or are there any add on packages) which can do estimation of standard statistical models (means, regression, logistic regression, etc) which take into account not only weights (e.g. post-stratification weights) but also the sample design, such as stratification and clustering information (to compute a robust taylor linearized variance estimator, for

Terry - your example didn't demonstrate the problem because the variable that interacted with strata (zed) was not a factor variable. But I had stated the problem incorrectly. It's not that there are too many strata terms; there are too many non-strata terms when the variable interacting with the stratification factor is a factor variable. Here is a simple example, where I have

Terry - your example didn't demonstrate the problem because the variable that interacted with strata (zed) was not a factor variable. But I had stated the problem incorrectly. It's not that there are too many strata terms; there are too many non-strata terms when the variable interacting with the stratification factor is a factor variable. Here is a simple example, where I have

-------------------------------------------------- From: "Ahoussou Sylvie" <sylvie.ahoussou at antilles.inra.fr> Sent: Friday, September 12, 2008 9:48 AM To: "Thomas Lumley" <tlumley at u.washington.edu> Subject: Re: [R] Complex sampling survey _ Use of survey package > Thanks for your answer > > I think I made a mistake when I recopied the 5 first rows of

Hello all, So I'm doing some data analysis using MARS. I have a matrix of 65 independent variables, from which I'm trying to predict 71 dependent variables. I have 900+ data points (so a 900x136 matrix of data), which I randomly split into training and validation sets, for ~450 data points in each set. Occasionally, this works well, and I get decent predictions. However, quite often

Hello all useRs, I am tring make a simple surface plot ( 2 by 2 terms of a MARS model (with earth package) but I get the follow error message: > plotmo( mars ) Error: bad index (missing column in x?) I don't no how to workround this... :-( I thanks in advanced by some help! Thanks. Cleber ############### > > ### example code: > library( earth ) > data( gasoline,

Hi, I am using function mars() in package mda to find knots in a whole bunch of predictor variables. I hope to be able to replicate all or some of the basis functions that the MARS software from Salford Systems creates. When I ran mars() on a small dataset, I was able to get the knots. However, when I tried running mars() on a larger dataset (145 predictor variables), for a different

I'm trying to use mars{mda} to model functions that look fairly close to a sequence of straight line segments. Unfortunately, 'mars' seems to totally miss the obvious places for the knots in the apparent first order spline model, and I wonder if someone can suggest a better way to do this. The following example consists of a slight downward trend followed by a jump up after