Error in R-script: error in abs (alpha) non-numeric argument to mathematical function -


i trying reproduce results book "financial risk modelling , portfolio optimisation r" , error can't seem head around. following error in copposterior function:

error in abs(alpha) : non-numeric argument mathematical function

is able see why error?

the error following script:

library(urca) library(vars) library(fmultivar)  ## loading data set , converting zoo data(eustockmarkets) assets <- as.zoo(eustockmarkets) ## aggregating month-end series assetsm <- aggregate(assets, as.yearmon, tail, 1) head(assetsm) ## applying unit root tests sub-sample assetsmsub <- window(assetsm, start = start(assetsm),                      end = "jun 1996") ## levels adf <- lapply(assetsmsub, ur.df, type = "drift",               selectlags = "aic") ers <- lapply(assetsmsub, ur.ers) ## differences dadf <- lapply(diff(assetsmsub), ur.df, selectlags = "aic") ders <- lapply(diff(assetsmsub), ur.ers) ## vecm vec <- ca.jo(assetsmsub, ecdet = "none", spec = "transitory") summary(vec)  ## index of time stamps in test (extending window) idx <- index(assetsm)[-c(1:60)] anames <- colnames(assetsm) nassets <- ncol(assetsm) ## function return expectations f1 <- function(x, ci, percent = true){   data <- window(assetsm, start = start(assetsm), end = x)   lobs <- t(tail(data, 1))   vec <- ca.jo(data, ecdet = "none", spec = "transitory")   m <- vec2var(vec, r = 1)   fcst <- predict(m, n.ahead = 1, ci = ci)   lu <- matrix(unlist(fcst$fcst),                ncol = 4, byrow = true)[, c(2, 3)]   re <- rep(0, nassets)   pview <- lu[, 1] > lobs   nview <- lu[, 2] < lobs   re[pview] <- (lu[pview, 1] / lobs[pview, 1] - 1)   re[nview] <- (lu[nview, 1] / lobs[nview, 1] - 1)   names(re) <- anames   if(percent) re <- re * 100   return(re)                } returnest <- lapply(idx, f1, ci = 0.5) qv <- zoo(matrix(unlist(returnest),                  ncol = nassets, byrow = true), idx) colnames(qv) <- anames tail(qv)   library(blcop) library(fportfolio) ## computing returns , ew-benchmark returns r <- (assetsm / lag(assetsm, k = -1) -1.0) * 100  ## prior distribution ## fitting of skewed student's t distribution  mstfit <- mvfit(r, method = "st") mu <- c(mstfit@fit[["beta"]]) s <- mstfit@fit[["omega"]] skew <- c(mstfit@fit[["alpha"]]) df <- mstfit@fit[["df"]]  copprior <- mvdistribution("mvst", dim = nassets, mu = mu,                            omega = s, alpha = skew, df = df) ## pick matrix , view distributions last forecast retestcop <- returnest[[27]] retestcop pcop <- matrix(0, ncol = nassets, nrow = 3) colnames(pcop) <- anames pcop[1, anames[1]] <- 1 pcop[2, anames[2]] <- 1 pcop[3, anames[4]] <- 1 sds <- apply(r, 2, sd) retviews <- list(distribution("norm", mean = retestcop[1],                               sd = sds[1]),                  distribution("norm", mean = retestcop[2],                               sd = sds[2]),                  distribution("norm", mean = retestcop[4],                               sd = sds[4]) ) copviews <- copviews(pick = pcop, viewdist = retviews,                      confidences = rep(0.5, 3),                      assetnames = anames) ## simulation of posterior numsim <- 10000  coppost <- copposterior(copprior, copviews,                         numsimulations = numsim)  print(copprior)  print(copviews)  slotnames(coppost)

look @ structure of mstfit:

str(mstfit)

you can see if want estimated alpha value, need access via:

mstfit@fit$estimated[['alpha']]

rather than

mstfit@fit[['alpha']]

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