similar to: mean and sd of each serial position

I am trying to write a function that will run a linear model and plot the regression coeficients with their corresponding means. I am having two problems. I can get the plot with the function below, but I am having trouble labeling the points. function(y,x1,x2,x3,x4){ outlm<-lm(y~x1+x2+x3+x4) imp<-as.data.frame(outlm$coef[-1]) meanvec<-c(mean(x1),mean(x2),mean(x3),mean(x4))

I have a set of data for US t-bill returns and US stock returns frm 1980-2012. I am trying to bootstrap the data and obtain the minimum variance portfolio and repeat this portfolio 1000 times. However I am unable to get the correct code function for the minimum variance portfolio. When I tried to enter Opt(OriData+1, 1, 5, 0), I get "error:subscript out of bounds" Please help!

Dear R user, I have written a function which returns max,min and variation of a power (see below) Power is a given matrix(1,n) I call the function >Variation<-VAR(p,(n-deltat)) Now the problem is when I want plot(Results[1],Results[2]). Not possible! I become the following error (in english it means: Error in as.double.default(x) :Object cannot be transformed in double) >

Hi all! I was wondering if you can help me with a little sampling issue I'm having in R. I have a database with 100 observations. I need to sample n=9 sample size, 200 times (i.e. get 200 samples of size 9). N (pop. size) =100 Each sample can't contain the same observation more than one time (i.e. the program needs to check if the obs. has already been sampled into the sample -

I have a question about some strange results I get when using lmer to build a logistic mixed effects model. I have a data set of about 30k points, and I'm trying to do backwards selection to reduce the number of fixed effects in my model. I've got 3 crossed random effects and about 20 or so fixed effects. At a certain point, I get a model (m17) where the fixed effects are like this

Hi: I have been able to finally crunch my data by importing it by week(thank you all for your help),but here we go again.. Now I'am trying to do it for the the whole year. Since the dataset is huge I'm only making a 3 weeks dataframe. - I want to get the mean of pd by week - I want to count the number of days by week and bind it to the existing dataframe(x) - I want to remove the

Hi All, is there any effiective and dense/compact method to calculate the mean of a list of - of course coincident - matrices on an element by element basis? The resulting matrix' [i, j]-th element is the mean of the list's matrices' [i, j]-th elements respectively... Iterating by for statement is quite straightforward, but I am seeking for a more elegant solution, and my attempt with

Dear R users, I'm trying to write a function which returns minimum,maximum,mean of a vector(power) I've done the following : VAR<-function(power,length){ for(i in tml:length)){ tvar[i]<-i pmean[i]<-mean(power[i:i+deltat]) pmin[i]<-min(power[i:i+deltat]) pmax[i]<-max(power[i:i+deltat]) varmax[i]<-100*(pmax[i]-pmean[i])/pmean[i]

Dear R-helpers, I have a basic question on using loops. I have a panel data set with different variables measured for "n" firms over "t" time periods. A snapshot of the data is given below id    t    X1    X2 1    1     4    3 1    2    9    2 1    3    7    3 1    4    6    6 2    1    6    4 2    2    5    3 2    3    1    1 3    1    9    6 3    2    5    5 thus total

Hi, It would be great if one of the experts could comment on the difference between Hadley's dotlength and ...length? The fact that someone bothered to implement a new primitive for that when there seems to be a very simple and straightforward R-only solution suggests that there might be some gotchas/pitfalls with the R-only solution. Thanks, H. On 05/03/2018 08:34 AM, Hadley Wickham

The one difference I see, is the necessity to pass the dots to the function dotlength : dotlength <- function(...) nargs() myfun <- function(..., someArg = 1){ n1 <- ...length() n2 <- dotlength() n3 <- dotlength(...) return(c(n1, n2, n3)) } myfun(stop("A"), stop("B"), someArg = stop("c")) I don't really see immediately how one can

Hello, JIT compiler interferes with internal R completions: compiler::enableJIT(2) utils:::functionArgs("density", '') gives: utils:::functionArgs("density", '') Note: no visible global function definition for 'bw.nrd0' Note: no visible global function definition for 'bw.nrd' Note: no visible global function definition for 'bw.ucv'

Hello R-dev, Yesterday, while I was testing the newly implemented function pmean in package kit, I noticed a mismatch in the output of the below R expressions. set.seed(123) n=1e3L idx=5 x=rnorm(n) y=rnorm(n) z=rnorm(n) a=(x[idx]+y[idx]+z[idx])/3 b=mean(c(x[idx],y[idx],z[idx])) a==b # [1] FALSE For idx= 1, 2, 3, 4 the last line is equal to TRUE. For 5, 6 and many others the difference is small

(Sorry for the cross-post--- I wasn't sure which list is more appropriate...) Hi everyone, I've run into segfaults when using my randomForest package on large dataset (e.g., 100 x 15200) and large number of trees (e.g., ntree=7000 and mtry=3000). I'm wondering if anyone can give me some hints on where to look for the problem. The randomForest package mainly consists of two things:

(Sorry for the cross-post--- I wasn't sure which list is more appropriate...) Hi everyone, I've run into segfaults when using my randomForest package on large dataset (e.g., 100 x 15200) and large number of trees (e.g., ntree=7000 and mtry=3000). I'm wondering if anyone can give me some hints on where to look for the problem. The randomForest package mainly consists of two things:

I am writing a function to work with class I am defining. I have a question about using nargs() inside of parentheses function. nargs() shows the same for supplying 1 argument, or no arguments at all. Here is a small example: > "[.myclass"<-function(x,...) print(nargs()-1) > x<-c(1,2,3) > class(x)<-"myclass" > x[] [1] 1 > x[1] [1] 1 > x[1,2] [1] 2

I initially sent this to the biocore mailing list - but it was suggested that r-devel would also find it interesting. Many of us use a macro like #define CALL_DEF(fname, nargs) { #fname, (DL_FUNC)&fname, nargs} for use in function registration for use with .Call. For example, using the example from R Extension manual, if we want to register a C function myCall with three arguments, we

I have an S3 class called "tis" (Time Indexed Series) which may or may not have multiple columns. I have a function "[<-.tis" that I've reproduced below. My question is this: inside of "[<-.tis", how can I distinguish between calls of the form x[i] <- someValue and x[i,] <- someValue ? In either case, nargs() is 3, and looking at the values

I have an S3 class called "tis" (Time Indexed Series) which may or may not have multiple columns. I have a function "[<-.tis" that I've reproduced below. My question is this: inside of "[<-.tis", how can I distinguish between calls of the form x[i] <- someValue and x[i,] <- someValue ? In either case, nargs() is 3, and looking at the values

Hello everybody, I've run into some strange behavior with memory sanitizer that I can't explain and hope somebody with more knowledge of the implementation would be able to help me out or at least point me into the right direction. For background, I'm using memory sanitizer to check Julia (julialang.org), which uses (or at least will once I track down a few bugs) MCJIT for the code