similar to: replicating SAS's "proc rank" procedure

Hi try this function ive written it should be self explantory but let me know if you have any problems. I've only been using R for a few eeeks so apologies if its not the most efficient! rankit2<-function(rankvar,cuts,data,factor) { ranker<-rankvar ranker<-0 range<-c(1:cuts) range2<-range/cuts range3<-quantile(factor,range2) over<-length(factor) for (i in 1:over){

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

I have some output from a survey shown below and would like to insert total rows for each category to then be able to use it as a data set for further analysis. Is it possible to do this in R. In this example I would like to insert a row between rows 5 and 6, 10 and 11, 15 and 16, 20 and 21 and put in the sum of the rows 1-5, 6-10, 11-15, 16-20 and to delete row 21 which has NA in it. I would

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) + +

Hello, I am working on a variable selection problem and I wonder whether there is some function or package in R works similar to the 'PROC REG' in SAS? Thank you. Some facts about 'PROC REG': PROC REG in SAS first composes a crossproducts matrix. The matrix can be calculated from input data, reformed from an input correlation matrix, or read in from an SSCP data set. For each

SAS proc rank has ties options of high and low that would allow producing ranks of the type found in the sports pages, e.g., rank (c(1,1,2,2,2,2,3)) == 1 1 3 3 3 3 7 Could R support these ties.methods?

I try to reproduce the SAS proc reg stepwise model selection procedure in R, but the only function I found was "step" which select new variables based on AIC. The SAS procedure I use add a new variable to the model based on F statistics and a pre defined significant level. Then before any new variables are added variables in the model that not meet F statistics at the significant level

Hi everyone, Hi ggplot users in particular, ggplot makes it very easy to plot things given their names when you use it interactively (and therefore can provide the names of the columns). qplot(x,foo,data=A) where A has columns (x,y,foo,bar) for example but I would like to use this from inside a function to which the name of the column is given. I cannot find an elegant way to make this

The function wtd.var(x,w) in Hmisc calculates the weighted variance of x where w are the weights. It appears to me that wtd.var(x,w) = var(x) if all of the weights are equal, but this does not appear to be the case. Can someone point out to me where I am going wrong here? Thanks. Tom La Bone [[alternative HTML version deleted]]

Hello everyone, My problem is better explained with an example: > x=data.frame(a=1:4,b=1:4,c=rnorm(4)) > x a b c 1 1 1 -0.8821089 2 2 2 -0.7082583 3 3 3 -0.5948835 4 4 4 -1.8571443 > y=data.frame(a=c(1,3),b=3,c=rnorm(2)) > y a b c 1 1 3 -0.273155973 2 3 3 0.009517862 Now I want to merge x and y by columns a and b, hence creating a data.frame with all

Hello everyone, I often create some local "libraries" of functions (.R files with only functions in them) that I latter call. In scripts that call a function from such library, I would like to be able to test whether the function is already known in the namespace and, only if it is not, source the library file. I.e. what `require` does for packages, I want to do with my local

Hello everyone, I am (happily) using ggplot2 for all my plotting now and I wondered is there is an easy way to specify xlim and ylim somewhere when using the ggplot syntax, as opposed to the qplot syntax. Eg. qplot(data=mtcars,y=wt, x=qsec,xlim=c(0,30)) <-> ggplot(mtcars, aes(y=wt, x=qsec)) + geom_point() + ??? Indeed the ggplot syntax is in general more flexible and powerful and

Hello everyone, A recent (in 2.5 I suspect) change in R is giving me trouble. I want to apply a function (tolower) to all the columns of a data.frame and get a data.frame in return. Currently, on a data.frame, both apply (for arrays) and lapply (for lists) work, but each returns its native class (resp. matrix and list): apply(mydat,2,tolower) # gives a matrix lapply(mydat,tolower) # gives

Dear all, I am trying to extract rows from a data.frame based on the rowSums != 0. I want to preserve rownames in the first column in the subset. Does anyone know how to extract all species that don't have rowSums equal to zero? Here it is: # dataset x <- data.frame( species=c("sp.1","sp.2","sp.3","sp.4"), site1=c(2,3,0,0), site2=c(0,0,0,0),

Dear List, I am trying to get R's terminal output to a file and to the terminal at the same time, so that I can walk through some tests and keep a log concurrently. The function 'sink' with the option split=TRUE seems to do just that. It works fine for most output but for objects of class htest, the terminal output is incomplete (the lines are there but empty). Here is an