similar to: Help with manipulation of svytotal output

Hi, I am using the package "demography" from Rob Hyndman for the Lee-Carter-Model. It is an amazing powerful tool but I am struggling with one issue: *I want to compute different quintiles for the cumulative survival probability derived from the Lee-Carter-Forecast (e.g. the 50%-quintile, 75%-quintile and 99%-quintile) for the next 10 years. * I am sure the package possess this

Hi, I have a mysql table with fields userid,track,frequency e.g u1,1,10 u1,2,100 u1,3,110 u1,4,200 u1,5,120 u1,6,130 . u2,1,23 . . where "frequency" is the number of times a music track is played by a "userid" I need to turn my 'frequency' table into a rating table (it's for a recommender system). So, for each user, I need to categorise the frequency of tracks

Dear All, I am economist and working on poverty / income inequality. I need descriptive statitics like the ratio of education expentitures between different income quintiles where each household has a different weight. After a bit of google search I found 'Hmisc' and 'quantreg' libraries for weighted quantiles. The problem is that these packages give me only weighted quintiles;

Hi, I am trying in R to indicate in which quintile a value of a variable is for every month of my data frame in this case based on volatility. For each month I want to know for each stock if it is in the most volatile quintile of if it is in one of the others. So far I have come up with the following function (see below). Unfortunately, the function only works in some cases and often gives the

Hi, I am looking to build even quintiles for a set of data. I managed to get it done, but I would like to know if there is a more direct way to write the data from my loop output x in the bottom of the code into the "empty" matrix p1, which I filled with NA''s. The way I am doing it at the moment is, more or less adding the matrix x after p1 and then deleting in a second step

Hi All: Greetings, and best wishes from the festive times here at Kolkata, India -- the time of Durga Puja celebrations. I seek your advice as I try plotting lines for my data. The problem: I have created a dataframe that looks like this (name: myFrame): lowest second third fourth highest significance INAS 0.107 0.115 0.123 0.115 0.166 0.000 MMA 0.091 0.107 0.115

Hello- I wonder if there's a way to replicate SAS rank procedure where it ranks a variable by a certain number of groups. For example, it's very easy to calculate quintile rank in SAS, but I couldn't find the similar function in R. Does anyone know how to do this? Thanks. -- View this message in context:

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) + +

Hi everyone, I'm experiencing difficulty getting the results I want when I use a nested for loop. I have a data set to which I perform some calculations, and then try to apply a regression over a rolling window. The code runs, but the regression results I am getting (intercept and slope) are simply the same, repeated again and again in the results matrix. The regression does not seem to be

Dear list, I got some surprising results when using the svytotal routine from the survey package with data containing missing values. Some example code demonstrating the behaviour is included below. I have a stratified sampling design where I want to estimate the total income. In some strata some of the incomes are missing. I want to ignore these missing incomes. I would have expected that

Dear WizaRds, dear Thomas, First of all, I want to tell you how grateful I am for all your support. I wish I will be able to help others along one day the same way you do. Thank you so much. I am struggling with a multistage sampling design: library(survey) multi3 <- data.frame(cluster=c(1,1,1,1 ,2,2,2, 3,3), id=c(1,2,3,4, 1,2,3, 1,2), nl=c(4,4,4,4, 3,3,3, 2,2), Nl=c(100,100,100,100,

Hi, I’m using the svyby for total statistics, for example: svyby(~p_igov,~div_a,desenho_nps,svytotal,drop.empty.groups=TRUE,vartype =c("se","var","cvpct")) In the numerical variable p_igov (and others) I have many non responses but if I maintain the NA it doesn’t work. summary(base_nps$p_igov) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's

First, I appoligise for the rooky question, but... I'm trying to obtain standard errors, confidence intervals, etc. from a sample design and have been trouble getting the results for anything other than the basic total or mean for the overall survey from the survey package. For example, using the following dataset, strata,cluster,vol A,1,18.58556192 A,1,12.55175443 A,1,21.65882438

Hi R users, could someone help me to find out which formulas, for standard error calculation, are used in following example: a=data.frame(weights=rep(c(10,1),c(4,1)),fpc=rep(41,5),uk=rep(1,5)) srs<-svydesign(id=~1, weights=~weights, data=a) srs1<-svydesign(id=~1, weights=~weights,fpc=~fpc, data=a) svytotal(~uk,srs) total SE uk 41 9 svytotal(~uk,srs1) total SE uk 41

Dear List: I am working to understand some differences between the results of the svymean() function in the survey package and from code I have written myself. The results from svymean() also agree with results I get from SAS proc surveymeans, so, this suggests I am misunderstanding something. I am never comfortable with "I did what the software" does mentality, so I am working to

This is blanket access for root from any host with valid keys (and password) - I want to limit that access to 4 hosts no matter what keys or passwords are provided/used. I have looked at shosts.equiv but this doesn't stop other hosts being allowed. I have also looked at "AllowUsers root at host1 root at host2 ..." but this also means including every user we have for all other

Dear All I am trying to use svychisq with a two-dimensional table 4 x 5. The command I am using is summary(svytable(~dietperception+dietstatus,dudleyls1rake,na.rm=TRUE),"C hisq") It is throwing up an error message as follows: Error in NCOL(y) : only 0's may be mixed with negative subscripts In addition: Warning messages: 1: is.na() applied to non-(list or vector) in:

Dear Professor Lumley I am relatively new to using R and also to logistic regression. We have analysed our Dudley Health Survey using the survey package. I am now trying to look at associations using svyglm but I am unsure of how to interpret the output and present the resulting model or whether there are any other things I should do to check the validity of the model. Below is an example of

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Hi, I'm trying to compute survey svytable statistic on subsets by using the svyby function. Here is the code: b<-svyby(~V024+V751, by=~V025, design=strat2, svytable, round=TRUE) The vars, V024, V751 and V025 are factors. The by var has 2 levels, and hence there will be two subsets. strat2 is created by the svydesign function. It's giving me the following error: >