search for: gridpoint

...piration's line then interpolate.... ?? library(RQuantLib) library(quantmod) library(rgl) library(akima) library(ggplot2) library(plyr) GetIV <- function(type, value, underlying, strike,dividendYield, riskFreeRate, maturity, volatility, timeSteps=150, gridPoints=151) { AmericanOptionImpliedVolatility(type, value, underlying, strike,dividendYield, riskFreeRate, maturity, volatility, timeSteps=150, gridPoints=151)$impliedVol } GetDelta <- function(type, underlying, strike,...

...d and modified # =============================== library(MASS) kde2d.weighted <- function (x, y, w, h, n = n, lims = c(range(x), range(y))) { nx <- length(x) if (length(y) != nx) stop("data vectors must be the same length") gx <- seq(lims[1], lims[2], length = n) # gridpoints x gy <- seq(lims[3], lims[4], length = n) # gridpoints y if (missing(h)) h <- c(bandwidth.nrd(x), bandwidth.nrd(y)); if (missing(w)) w <- numeric(nx)+1; h <- h/4 ax <- outer(gx, x, "-")/h[1] # distance of each point to each grid point in x-direction a...

...mpute and store some frequently used matrices "on demand". The Matrix package already uses something like this for storing decompositions, but I don't know how to do it. The actual context is the following: A list has information about a basis of a B-spline space (nodes, order) and gridpoints at which the basis functions would be evaluated (not necessarily the nodes). Something like this: bsplinegrid <- list(nodes=1:8,order=4,grid=seq(2,5,by=.2)) I need the design matrix (computed by splineDesign) for various derivatives (not necessarily known in advance), to be calculated by the...

...would calculate multivarite (mostly 2d and 3d, tensor) cubic interpolating splines, so that I could evaluate these splines (and their derivatives) at many points (unkown at the time of calculating the spline polynomials) repeatedly. To make things concrete, I have an array V with dim(V) = k and gridpoint vectors grid=list(...), length(grid[[i]])==k[i], and would like to get some representation of the spline convenient for subsequent calculations. I have looked at a few spline packages in R, but didn't find what I was looking for. Could somebody please point me in the right direction? Thanks,...

...else { if (is.function(y)) y <- y(x) else if (length(y) != length(x)) stop("Mismatch in lengths of x and y.\n") s <- order(x) x <- x[s] ddd <- diff(x) if (any(ddd == 0)) stop("Gridpoints must be distinct.\n") equisp <- isTRUE(all.equal(diff(ddd), rep(0, length(ddd) - 1))) y <- y[s] } n <- length(x) - 1 if (equisp) { old.op <- options(warn = -1) on.exit(options(old.op)) M <- matrix(y, nrow = n + 2, ncol...

Hi, Let's play a little game. Forget all you know, or think you know. Without any context at all, what does the following pattern make you think of? <p> X -> X X -> X | ^ v | X <- X X <- X | ^ v | X X -> X X | ^ | ^ v | v | X -> X

Dear R community! I am trying to create a heatmap based on the following data. As you can see the diagonal (0,0 to 10,10) is always 0). If I run the heatmap command like i posted it I get a graph with horizontal lines. What is my mistake? > heatmap(activity.matrix, Rowv = NA, Colv = NA, scale="column", xlab="x Compounds", ylab="y Compounds",

I would like to integrate the area under a curve without any smoothing or the like- just on the raw numbers. I looked at integrate() but it requires a function which I assume means something like x+x^2+x^3 is there a built in function in R for this? #let's say x <- seq(1:50) y <- seq(1:50) plot(y~x) # the are would be 1250 # I would like to be able to do this but on more complicated

Hi, Does anyone know what the distribution for the product of two correlated normal? Say I have X~N(a, \sigma1^2) and Y~N(b, \sigma2^2), and the \rou(X,Y) is not equal to 0, I want to know the pdf or cdf of XY. Thanks a lot in advance. yu [[alternative HTML version deleted]]

Dear list, I'm writing a function to re-grid a data set from finer to coarser resolutions in R as follows (I use this function with sapply/apply): gridResize <- function(startVec = stop("What's your input vector"), to = stop("Missing 'to': How long do you want the fnial vector to be?")){ from <- length(startVec) shortVec<-numeric() tics <- from*to

Hello, I requested help a couple of weeks ago creating a dipole field in R but receieved no responses. Eventually I opted to create a 3d sinusoidal plot and concatenate this with its inverse as a means for a "next best" situation. It seems that this isn't sufficient for my needs and I'm really after creating a continuous 3d gaussian mesh with a "positive" and