数据必须是向量类型，was'；空'；R

我收到以下错误消息：

Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x),  : 
 'data' must be of a vector type, was 'NULL'

尝试运行此行时出现错误消息：

dcc.fit=dccfit（规格1，数据=r_t1，fit.control=list（比例=TRUE））

但是，当我运行下面的行时，它工作正常：

dcc.fit1 = dccfit(spec2, data = r_t1, fit.control=list(scale=TRUE), solver = "nlminb")

如果向量r_t1有问题，上面的代码行也不应该工作，所以这很奇怪。有人知道如何解决这个问题吗？ Multivariate3是我导入R的excel文件

我的全部代码如下所示：

install.packages('rmgarch', dependencies = TRUE)
library("rmgarch")
library("parallel")
library("quantmod")

Dat<-data.frame(Multivariate3$Bitcoin,Multivariate3$SP500,Multivariate3$DAX,Multivariate3$KS11,Multivariate3$VGLT,Multivariate3$Euro,Multivariate3$Franc,Multivariate3$Yen,Multivariate3$Oil,Multivariate3$Gold)

#Returns
retBTC<-diff(log(Multivariate3$Bitcoin))
retSP<-diff(log(Multivariate3$SP500))
retDAX<-diff(log(Multivariate3$DAX))
retKS11<-diff(log(Multivariate3$KS11))
retVGLT<-diff(log(Multivariate3$VGLT))
retEUR<-diff(log(Multivariate3$Euro))
retCFH<-diff(log(Multivariate3$Franc))
retYEN<-diff(log(Multivariate3$Yen))
retOil<-diff(log(Multivariate3$Oil))
retGLD<-diff(log(Multivariate3$Gold))

# univariate normal GARCH(1,1) for each series
####UNRESTRICTED MODEL#####
xspec = ugarchspec(mean.model = list(armaOrder = c(1, 1)), variance.model = list(garchOrder = c(1,1), model = 'sGARCH'), distribution.model = 'std')
####RESTRICTED MODEL#####
xspec1 = ugarchspec(mean.model = list(armaOrder = c(1, 1)), variance.model = list(garchOrder = c(1,1), model = 'sGARCH'), distribution.model = 'std',fixed.pars=list(alpha1 = 0, beta1 = 0))

#################################
uspec = multispec(replicate(10, xspec))
uspecx = multispec(replicate(10, xspec1))
spec1 = dccspec(uspec = uspec, dccOrder = c(1, 1), distribution = 'mvnorm')
spec2 = dccspec(uspec = uspecx, dccOrder = c(1, 1), distribution = 'mvnorm')

#return vector
r_t1=cbind(retBTC,retSP,retDAX,retKS11,retVGLT,retEUR,retCFH,retYEN,retOil,retGLD)
####URESTRICTED MODEL#####
dcc.fit = dccfit(spec1, data = r_t1, fit.control=list(scale=TRUE))
####RESTRICTED MODEL#####
dcc.fit1 = dccfit(spec2, data = r_t1, fit.control=list(scale=TRUE), solver = "nlminb")

install.packages（'rmgarch'，dependencies=TRUE）
图书馆（“rmgarch”）
图书馆（“平行”）
图书馆（“quantmod”）
Dat你能显示多变量数据吗？嗨，Priyanka。多变量3 excel文件现在已附加到问题中。您可以将这一行
标题（spec2）
添加到
#返回向量
行的正上方，并将输出添加到问题中吗？嗨，哈克曼。我现在已经在附件的链接下面添加了输出。由于某种原因，当我将excel文件中的观察数增加到313个观察数时，它起到了作用。而不是原来的176。我想这可能是我使用的软件包中的一些限制。如果有人发现为什么我的数据中没有176个观测值，我很乐意知道原因。
install.packages('rmgarch', dependencies = TRUE)
library("rmgarch")
library("parallel")
library("quantmod")

Dat<-data.frame(Multivariate3$Bitcoin,Multivariate3$SP500,Multivariate3$DAX,Multivariate3$KS11,Multivariate3$VGLT,Multivariate3$Euro,Multivariate3$Franc,Multivariate3$Yen,Multivariate3$Oil,Multivariate3$Gold)

#Returns
retBTC<-diff(log(Multivariate3$Bitcoin))
retSP<-diff(log(Multivariate3$SP500))
retDAX<-diff(log(Multivariate3$DAX))
retKS11<-diff(log(Multivariate3$KS11))
retVGLT<-diff(log(Multivariate3$VGLT))
retEUR<-diff(log(Multivariate3$Euro))
retCFH<-diff(log(Multivariate3$Franc))
retYEN<-diff(log(Multivariate3$Yen))
retOil<-diff(log(Multivariate3$Oil))
retGLD<-diff(log(Multivariate3$Gold))

# univariate normal GARCH(1,1) for each series
####UNRESTRICTED MODEL#####
xspec = ugarchspec(mean.model = list(armaOrder = c(1, 1)), variance.model = list(garchOrder = c(1,1), model = 'sGARCH'), distribution.model = 'std')
####RESTRICTED MODEL#####
xspec1 = ugarchspec(mean.model = list(armaOrder = c(1, 1)), variance.model = list(garchOrder = c(1,1), model = 'sGARCH'), distribution.model = 'std',fixed.pars=list(alpha1 = 0, beta1 = 0))

#################################
uspec = multispec(replicate(10, xspec))
uspecx = multispec(replicate(10, xspec1))
spec1 = dccspec(uspec = uspec, dccOrder = c(1, 1), distribution = 'mvnorm')
spec2 = dccspec(uspec = uspecx, dccOrder = c(1, 1), distribution = 'mvnorm')

#return vector
r_t1=cbind(retBTC,retSP,retDAX,retKS11,retVGLT,retEUR,retCFH,retYEN,retOil,retGLD)
####URESTRICTED MODEL#####
dcc.fit = dccfit(spec1, data = r_t1, fit.control=list(scale=TRUE))
####RESTRICTED MODEL#####
dcc.fit1 = dccfit(spec2, data = r_t1, fit.control=list(scale=TRUE), solver = "nlminb")