similar to: How to alpha entire plot?

Interesting problem. I would discretize the x-values and interleave them. Lines from one dataset still overlap, so you see high- density and low-density regions, but lines from the other dataset are drawn into the interval. Like so: interleave <- function(x, MIN, MAX, N, nChannel = 2, channel) { isp <- seq(MIN, MAX, length.out = N + 1) # interleave support points offset <-

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Hi useRs, I am trying to calculate the Sharpe ratio with "sharpe" of the library "tseries". The documentation requires the univariate time series to be a portfolio's cumulated returns. In this case, the example given data(EuStockMarkets) dax <- log(EuStockMarkets[,"FTSE"]) is however not the cumulated returns but rather the daily returns of the FTSE

Hi, I would like to use R to estimate the following model: X(t) = a + b1*X(t-1) + b2*X(t-2) + c1*Y(t) + c2*Y(t-1) + c3*Y(t-2) Is there any R function that performs this type of estimation? I know that if I only have one time series (i.e. lagged value of X) on the right hand side then there are R functions to do the estimation. I am thinking a work around by preparing X(t-1), X(t-2),Y(t),Y(t-1)

>>>>> Anupam Tyagi >>>>> on Tue, 9 Jul 2024 16:16:43 +0530 writes: > How can I do automatic knot selection while fitting piecewise linear > splines to two variables x and y? Which package to use to do it simply? I > also want to visualize the splines (and the scatter plot) with a graph. > Anupam NB: linear splines, i.e. piecewise

dear all, I apologize for my delay in replying you. Here my contribution, maybe just for completeness: Similar to "earth", "segmented" also fits piecewise linear relationships with the number of breakpoints being selected by the AIC or BIC (recommended). #code (example and code from Martin Maechler previous email) library(segmented) o<-selgmented(y, ~x, Kmax=20,

Does adjustcolor() help? cb8<- c("#000000", "#E69F00", "#56B4E9", "#009E73","#F0E442", "#0072B2", "#D55E00", "#CC79A7") plot(0,0,xlim=c(1,8),ylim=c(0,1)) points(1:8,rep(0.5,8),col=cb8,pch=19,cex=2) points(1:8,rep(0.75,8),col=adjustcolor(cb8, alpha.f = 0.3), pch=19,cex=2) On 2023-07-23 2:15 p.m., Nick Wray

Thanks That works nicely Nick On Sun, 23 Jul 2023 at 19:26, Ben Bolker <bbolker at gmail.com> wrote: > Does adjustcolor() help? > > cb8<- c("#000000", "#E69F00", "#56B4E9", "#009E73","#F0E442", "#0072B2", > "#D55E00", "#CC79A7") > plot(0,0,xlim=c(1,8),ylim=c(0,1)) >

Nick, I've also made colors transparent by pasting the hex equivalent of, say, 0.3*256 = 76.9 to the hex color code. e.q. for black it might be "#0000004d" and the 4d is 77 in hex. That way you don't need to convert back and forth so much. If col is "#000000" the transparent version is tcol <- paste0(col,"4d") This would work in one step on a whole

Just one addition which may or may not be useful: The color palette you use is also known as "Okabe-Ito" and it is the default set of colors in the palette.colors() function. This function also has an optional alpha argument. So if you want to generate these colors with an alpha of 0.3 you can also do: palette.colors(8, alpha = 0.3) or more explicitly palette.colors(8, palette =

I have the following simple for-loop, which makes R crash every time. The length of the vectors is about 1200 rows, 1 column. n = max(length(GSPC),length(FTSE)) for(i in 1:1000) { if (row.names(GSPC)[i]==row.names(FTSE)[i]){ } else { if (row.names(GSPC)[i]>row.names(FTSE)[i]){ GSPC<-rbind(GSPC[1:(i-1),],GSPC[(i-1):length(GSPC),]) row.names(GSPC)[i]=row.names(FTSE)[i] } else {

Dear All, There is an example for the priceIts function (the its package) which does not work for me as expected. > ?priceIts > x1 <- priceIts(instrument = c("^ftse"), start = "1998-01-01", + quote = "Close") Error in validObject(.Object) : invalid class "its" object: Missing values in dates > x2 <-

Dear all, I am looking for a way to work out if a file on the internet exists before attempting to download it using the function download.file(). For example, using a url that does not exist url <- "http://finance.yahoo.com/ftse.csv" destfile <- tempfile() download.file(url = url, destfile = destfile) # gives the following response ... trying URL

I'm trying to use garchFit from fSeries, with Student or Skewed Student conditionnal distribution. Let's say that eps (vector) is my series of daily log-returns: data(EuStockMarkets) eps = diff(log(EuStockMarkets[,"CAC"])) library(fSeries) g = garchFit(series = eps, formula.var = ~garch(2,2), cond.dist = "dstd") s = g at fit$series All the coefficients are ok

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Thanks for the question. Here is a sample of the code for my plot: Top = c(34, 39, 42, 45, 46, 41, 41, 40, 43, 38, 33, 33) Bottom = c(24, 29, 32, 36, 32, 34, 32,41, 40, 39, 29, 24) plot(1,1, col = "white", xlim = c(1.3,11.7), ylim = c(0,80), axis = FALSE, xaxt = "n") axis(1, at = c(1:12)) polygon(c(c(1:12),c(12:1)), c(top, bottom), col =

Hi I have a few problems with tail/head when applied to multiple time series. I'm not sure as whether I did not understand the function or whether it correspond to an unexpected behavior. When head(a,n) is applied on data.frame or matrix, it returns a data-frame or matrix with first n obs of *each* variable. When applied to a mts object, it returns first n obs of *first* variable only,

Dear list-reader, by running the following script: library(zoo) sessionInfo() search() packageDescription("zoo") data(EuStockMarkets) dax <- as.zoo(EuStockMarkets[1:10, "DAX"]) daxr <- diff(log(dax)) identical(as.vector(qnorm(daxr)), qnorm(coredata(daxr))) qqnorm(coredata(daxr)) qqnorm(daxr) qqnorm() produces an error: > qqnorm(daxr) Fehler in if (xi == xj) 0L

How can I do automatic knot selection while fitting piecewise linear splines to two variables x and y? Which package to use to do it simply? I also want to visualize the splines (and the scatter plot) with a graph. Anupam [[alternative HTML version deleted]]

Hello everyone, a problem with the plot.zoo function. In the parameters of the function, cex.lab is ignored. I tried to reduce the size of the yaxis labels by at least 50%. ------------------ Example: sample <- as.zoo(EuStockMarkets) par(las=1) plot.zoo(sample, plot.type="multiple", main="Time Series", xlab="Date", yaxt="n", cex.lab=0.5,