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dear R experts: I want to define a function the calculates the black-scholes value. it takes 5 named parameters, BS <- function(S,K,dt,rf,sigma) {} . let's presume I want to be able to call this not only with my 5 numeric vectors BS( sigma=0.3, S=100, K=100, dt=1, rf=0.1 ) and BS( 100, 100, 1, 0.1, 0.3), but also with a data frame that contains the variables alll in a neat data frame

Hi, suppose that we have a triangular upper matrix A test <- matrix(ncol = 4, nrow = 4) test[1, ] <- c(NA,1,1,1) test[2, ] <- c(NA,NA,1,1) test[3, ] <- c(NA,NA,NA,1) test[4, ] <- c(NA,NA,NA,NA) I know how quickly set diagonal value diag(test) <- 1. But how quickly set down value i.e. matrix is symmetrical? Is there in r project any quickly function? Thanks, Best Marcin

Hello all, I have been working with b-splines and noted that the splines package is not available in CRAN. Does any body know what happened with it? Or, is there any package that replaces it? Thank you Felipe Parra [[alternative HTML version deleted]]

I am trying to apply methods used by Chaloupka & Limpus (1997) ( http://www.int-res.com/articles/meps/146/m146p001.pdf) to my own turtle growth data. I am having trouble with two things... 1) After the GAM is fit, the residuals are skewed. >m1 <- gam(growth~s(mean.size, bs="cr")+s(year,bs="cr",k=7)+s(cohort,bs="cr")+s(age,bs="cr"), data=grow,

Hallo, I am trying to define expectation as an integral and use uniroot to find the distribution parameter for a given expectation. However I fail to understand how to define properly the functions involved and pass the parameters correctly. Can anyone help me out? Thanks, Gerrit Draisma. This what I tried: ======= > # exponential density > g <- function(x,lambda){ lambda

Hi,all: I am running a nonlinear regression and there is a problem. There is a data frame: data p s x t 1 875.0 12392.5 11600 0.06967213 2 615.0 12332.5 12000 0.06967213 3 595.0 12332.5 12000 0.06967213 4 592.5 12337.0 12000 0.06967213 5 650.0 12430.0 12000 0.06967213 6 715.0 12477.5 12000 0.06967213 . . . . str(data): 'data.frame': 234 obs. of 4 variables:

Dear all I am a bit new to R so please keep your swords sheathed! I would simply like to bootstrap a covariance matrix from a multivariate gaussian density. At face value that seemed like a very straightforward problem to solve but I somehow could not get the boot package to work and did not really understand the documentation so I tried to do the bootstrap manually. Hence: x<-rmvnorm(n = 5,

Hi there, Here is a minimum working example: ---------------------------------------------------------------- lower = 0 upper = 1 n_bins = 50 interval = (upper - lower) / n_bins bins = vector(mode="numeric", length=n_bins) breaks = seq(from=lower + interval, to=upper, by=interval) for(idx in breaks) { bins[idx / interval] = idx } print(bins)

Hi again. I'm putting together a little project with R, python, and a website. So I have an HTML file, a py file, an R file. Here is the HTML file: <form action="/cgi-bin/radio4.py" method="post" target="_blank"> <input type="radio" name="subject" value="Integrate" /> Integrate <input type="radio"

Commit 72afcf450a78b7e58f65b4a7aaf94d71cd25fca5 was partially incorrect. If the guest userspace is expecting /selinux to exist, then we should bind-mount /sys/fs/selinux from the appliance kernel there. --- daemon/command.c | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/daemon/command.c b/daemon/command.c index 1aa1a52..939bf87 100644 --- a/daemon/command.c

Hello all R users I want to calculate this univariate integral: exp(-x)*sum(y^x) respect to x from 0 to 3 where y is a vector y=(2,3,5). In fact, the original y vector has a large number of elements but I propose with 3 elements. I know that I can resolve this problem doing fun <- function(x) exp(-x)*(2^x+3^x+5^x) integrate(fun,0,3) When the y vector has a large number of elements the

This patch adds some queue limit parameters into block drive. And inits them for sg block drive. Some interfaces are also added for accessing them. Signed-off-by: Cong Meng <mc at linux.vnet.ibm.com> --- block/raw-posix.c | 58 +++++++++++++++++++++++++++++++++++++++++++++++++++++ block_int.h | 4 +++ blockdev.c | 15 +++++++++++++ hw/block-common.h | 3 ++ 4 files

This patch adds some queue limit parameters into block drive. And inits them for sg block drive. Some interfaces are also added for accessing them. Signed-off-by: Cong Meng <mc at linux.vnet.ibm.com> --- block/raw-posix.c | 58 +++++++++++++++++++++++++++++++++++++++++++++++++++++ block_int.h | 4 +++ blockdev.c | 15 +++++++++++++ hw/block-common.h | 3 ++ 4 files

On 08/02/2012 05:00 AM, r-devel-request at r-project.org wrote: > Now I just have to grovel over the R code in ns() and bs() to figure > out how exactly they pick knots and handle boundary conditions, plus > there is some code that I don't understand in ns() that uses qr() to > postprocess the output from spline.des. I assume this is involved > somehow in imposing the boundary

Dear all, I'm trying to figure out the exact meaning of the B-spline coefficients generated by the R command bs(). After reading a lot of things, I still have no clue... Here's my data. > test time f0 1 1 94.76328 2 2 102.47954 3 3 105.01234 4 4 107.21387 5 5 108.63279 6 6 109.54507 7 7 113.87931 8 8 118.21356 9 9 121.08652 10

Dear R users, i try to integrate lgamma from 0 to Inf. But here i get the message "roundoff error is detected in the extrapolation table", if i use 1.0e120 instead of Inf the computation works, but this is against the suggestion of integrates help information to use Inf explicitly. Using stirlings approximation doesnt bring the solution too. ## Stirlings approximation lgammaApprox

hi Using a ts or tprs basis, I expected gcv to decrease when increasing the basis dimension, as I thought this would minimise gcv over a larger subspace. But gcv increased. Here's an example. thanks for any comments. greg #simulate some data set.seed(0) x1<-runif(500) x2<-rnorm(500) x3<-rpois(500,3) d<-runif(500) linp<--1+x1+0.5*x2+0.3*exp(-2*d)*sin(10*d)*x3

Dear R users, Can anyone tell me why I might get the error message "the array index is greater than the upper bound for t" in WinBUGS ? t is a vector I have defined. I have checked repeatedly that the array index for t does match with the upper bound till which the loop is run. I am facing this problem while calling the R2WinBUGS package and both in just WinBUGS as

First of all, I have GAMs noxd<-gam(newNOX~pressure+maxtemp+s(avetemp,bs="cr")+s(mintemp,bs="cr")+s(RH,bs="cr")+s(solar,bs="cr")+s(windspeed,bs="cr")+s(transport,bs="cr"),family=gaussian (link=log),groupD,methods=REML) Then I type " summary(noxd)". and show Family: gaussian Link function: log Formula: newNO2 ~ pressure

hello R users strange behavior of the integrate function! i assume this occurs because of the way in which the quadriture is set up! (any comments.) f=function(x){exp(-exp(-x)-5*x)/gamma(5)} xx=seq(from=-20, to=20, length.out=1000) plot(xx,f(xx),type="l") integrate(f, lower=-Inf, upper= 1) integrate(f, lower=-Inf, upper= 10) integrate(f, lower=-10, upper= 11) integrate(f,