arfimafit-methods(rugarch)
arfimafit-methods()所属R语言包：rugarch
function: ARFIMA Fit
功能：ARFIMA适合
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Method for fitting an ARFIMA models.
装修的ARFIMA模型的方法。
用法----------Usage----------
arfimafit(spec, data, out.sample = 0, solver = "solnp", solver.control = list(),
fit.control = list(fixed.se = 0, scale = 0), ...)
参数----------Arguments----------
参数：data
A univariate data object. Can be a numeric vector, matrix, data.frame, zoo, xts, timeSeries, ts or irts object.
一元数据对象。可以是一个数值向量，矩阵，数据框，动物园，XTS，时间序列，TS或IRTS对象。
参数：spec
An ARFIMA spec object of class ARFIMAspec.
一个的ARFIMA规范对象的类ARFIMAspec。
参数：out.sample
A positive integer indicating the number of periods before the last to keep for out of sample forecasting (see details).
一个正整数，表示前的最后期间保持了样本外预测（见详情）。
参数：solver
One of either “nlminb”, “solnp”, “gosolnp” or “nloptr”.
其中一个任一“nlminb”中，“solnp”，的的“gosolnp”或“nloptr”。
参数：solver.control
Control arguments list passed to optimizer.
控制参数列表传递给优化。
参数：fit.control
Control arguments passed to the fitting routine. The fixed.se argument controls whether standard errors should be calculated for those parameters which were fixed (through the fixed.pars argument of the arfimaspec function). The scale parameter controls whether the data should be scaled before being submitted to the optimizer.
控制参数传递给装修程序。的参数控制标准误差是否应计算这些参数是固定的（通过论点的arfimaspec function). The scale parameter controls whether the data should be scaled before being submitted to the optimizer.“的fixed.pars的fixed.se
参数：...
.
。
Details
详细信息----------Details----------
The ARFIMA optimization routine first calculates a set of feasible starting points which are used to initiate the ARFIMA Maximum Likelihood recursion. The main part of the likelihood calculation is performed in C-code for speed.<br> The out.sample option is provided in order to carry out forecast performance testing against actual data. A minimum of 5 data points are required for these tests. If the out.sample option is positive, then the routine will fit only N - out.sample (where N is the total data length) data points, leaving out.sample points for forecasting and testing using the forecast performance measures. In the arfimaforecast routine the n.ahead may also be greater than the out.sample number resulting in a combination of out of sample data points matched against actual data and some without, which the forecast performance tests will ignore.<br> The “gosolnp” solver allows for the initialization of multiple restarts of the solnp solver with randomly generated parameters (see documentation in the Rsolnp-package for details of the strategy used). The solver.control list then accepts the following additional (to the solnp) arguments: “n.restarts” is the number of solver restarts required (defaults to 1), “parallel” and “parallel.control” for use of the parallel functionality, “rseed” is the seed to initialize the random number generator, and “n.sim” is the number of simulated parameter vectors to generate per n.restarts.
的的ARFIMA优化程序首先计算出了一套可行的出发点，这是用于启动ARFIMA最大似然递归。似然度计算的主要部分的C代码进行的速度。<br>该out.sample选项设置，以便进行测试的实际数据的预测性能。这些测试所需的最低的5个数据点。如果out.sample选项是正的，那么程序将适合：仅N - out.sample（其中N是总的数据长度）的数据点，用于预测和测试使用的预测性能的措施留下out.sample点。在arfimaforecast例程n.ahead也可以是更大的，比满分样本数据点的组合的实际数据和一些不相匹配，导致的out.sample数目的预测性能测试将忽略。<BR >“gosolnp”求解器可以初始化多个重新启动的solnp求解随机生成的参数（在Rsolnp包使用的策略的详细信息，请参阅文档）。该solver.control名单，然后接受下列额外的（solnp）参数：“n.restarts”是求解器重新启动需要使用（默认为1），“平行”和“parallel.control”平行的功能，“rseed”是初始化的随机数发生器的种子，并，“n.sim”是数字模拟的参数矢量，以产生每n.restarts。
值----------Value----------
A ARFIMAfit object containing details of the ARFIMA fit.
AARFIMAfit对象，详细介绍的ARFIMA适合。
（作者）----------Author(s)----------
Alexios Ghalanos
实例----------Examples----------
## Not run: [＃不运行：]
data(sp500ret)
fit = vector(mode = "list", length = 9)
dist = c("norm", "snorm", "std", "sstd", "ged", "sged", "nig", "ghyp", "jsu")
for(i in 1:9){
spec = arfimaspec(mean.model = list(armaOrder = c(1,1), include.mean = TRUE,
arfima = FALSE), distribution.model = dist[i])
fit[[i]] = arfimafit(spec = spec, data = sp500ret, solver = "solnp",
fit.control = list(scale = 1))
}
cfmatrix = matrix(NA, nrow = 9, ncol = 7)
colnames(cfmatrix) = c("mu", "ar1", "ma1", "sigma", "skew", "shape", "ghlambda")
rownames(cfmatrix) = dist
for(i in 1:9){
cf = coef(fit[[i]])
cfmatrix[i, match(names(cf), colnames(cfmatrix))] = cf
}
sk = ku = rep(0, 9)
for(i in 1:9){
cf = coef(fit[[i]])
if(fit[[i]]@model$modelinc[16]>0) sk[i] = dskewness(distribution = dist[i],
skew = cf["skew"], shape = cf["shape"], lambda = cf["ghlambda"])
if(fit[[i]]@model$modelinc[17]>0) ku[i] = dkurtosis(distribution = dist[i],
skew = cf["skew"], shape = cf["shape"], lambda = cf["ghlambda"])
}
hq = sapply(fit, FUN = function(x) infocriteria(x)[4])
cfmatrix = cbind(cfmatrix, sk, ku, hq)
colnames(cfmatrix)=c(colnames(cfmatrix[,1:7]), "skewness", "ex.kurtosis","HQIC")
print(cfmatrix, digits = 4)
# notice that for the student distribution kurtosis is NA since shape (dof) < 4.[请注意，对于学生分布峭度是不适用的，因为形状（DOF）4。]
cat("\nas.data.frame method:\n")
head(as.data.frame(fit[[1]]))
cat("\ncoef method:\n")
coef(fit[[1]])
cat("\nfitted method:\n")
head(fitted(fit[[1]]))
cat("\ninfocriteria method:\n")
infocriteria(fit[[1]])
cat("\nlikelihood method:\n")
likelihood(fit[[1]])
cat("\nresiduals method:\n")
# Note that we the package will always return the full length residuals and [需要注意的是，我们的包将总是返回全长残差]
# fitted object irrespective of the lags (i.e. since this is an ARMA(1,1) [嵌合对象无关的滞后（即，因为这是为ARMA（1,1）]
# i.e. max lag = 1, the first row is zero and should be discarded).[即最大滞后= 1中，第一行是零，应该被丢弃）。]
head(residuals(fit[[1]]))
cat("\nuncmean method:\n")
uncmean(fit[[1]])
cat("\nuncmean method (by simulation):\n")
uncmean(fit[[1]], method = "simulation", n.sim = 100000, rseed = 100)
cat("\nsummary method:\n")
show(fit[[1]])
## End(Not run)[＃（不执行）]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-28 23:32:25
