Question

我有一个xts个对象的列表。对象是月度时间序列，我想将它们汇总到每季度。使用to.quarterly或apply.quarterly通过lapply失败：

l.qtr <- lapply(l.adj, function(x) apply.quarterly(x, mean))

结果：

Error in try.xts(x, error = "must be either xts-coercible or timeBased") : 
 must be either xts-coercible or timeBased

但是我可以使用另一个xts对象列表来调用lx。此lx列表将被发送，转换为ts个对象，X12已调整并重新标记为xts并已发回。不知怎的，当它回来时我得到了错误。我已经验证了对于大小为1的列表不存在的问题，即使在将其发送到X12调整后也是如此。

认为这是一个问题，就像在这个链接，但似乎不是？ Automatically plot (and save) list of xts objects

还有什么好帮手？这让我疯了。

更新对最初缺乏重点表示歉意。我一直试图将其缩小到一个严格的可重复的例子。它似乎是我的帮助函数SeasAdj（转换为ts，延迟转换并转换回xts）和我的data.frame初始转换为xts。但是，如果我运行通过xts quantmod's下载的getSymbols月度系列，我会得到所需的结果。这让我相信麻烦在于SeasAdj辅助函数在返回之前如何转换回xts。我最后有dput(lax)。在采购SeasAdj后，从那里运行这四条线：

lax.xts <- xts(lax$value, order.by=lax$date)
lax.adj <- SeasAdj(lax.xts)
lax.qtr <- apply.quarterly(lax.adj, mean)
lax.q <- apply.quarterly(lax.xts, mean)


SeasAdj  <- function(x) {

require(x12)
require(quantmod)

freq <- switch(periodicity(x)$scale,
             daily=365,
             weekly=52,
             monthly=12,
             quarterly=4,
             yearly=1)

## determine the start date from xts
pltStart <- as.POSIXlt(start(x))

# create 2 arg vector with year, month for start
Start <- c(pltStart$year+1900,pltStart$mon+1)

# capture xts series name
names <- dimnames(x)


 #pass info as args to create ts
 x.ts <- ts(x, start=Start, frequency=freq)

 ## use x12 automodel
 # do some seasonal adjustment using x12
 # return a TS object with data and seasonally adjusted data
 x12out <- x12work(x.ts,
            x12path="C:\\x12arima\\x12a.exe",
            transform.function="auto", 
            automdl=TRUE)

 # assign adjusted and original series to vectors
 x.adj  <- as.ts(x12out$d11)
 x.orig  <- as.ts(x12out$a1)

 # convert to XTS 
 xts.adj <- as.xts(x.adj)

 # assign dimname back to series
 dimnames(xts.adj) <- names

 # return XTS object
 return(xts.adj)

}

这是一个松懈的命运 dput（不严）

structure(list(series_id = c("LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003", 
"LAUCN55063003", "LAUCN55063003", "LAUCN55063003", "LAUCN55063003"
), year = c(1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 1990L, 
1990L, 1990L, 1990L, 1990L, 1990L, 1991L, 1991L, 1991L, 1991L, 
1991L, 1991L, 1991L, 1991L, 1991L, 1991L, 1991L, 1991L, 1992L, 
1992L, 1992L, 1992L, 1992L, 1992L, 1992L, 1992L, 1992L, 1992L, 
1992L, 1992L, 1993L, 1993L, 1993L, 1993L, 1993L, 1993L, 1993L, 
1993L, 1993L, 1993L, 1993L, 1993L, 1994L, 1994L, 1994L, 1994L, 
1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1994L, 1995L, 
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 
1995L, 1995L, 1996L, 1996L, 1996L, 1996L, 1996L, 1996L, 1996L, 
1996L, 1996L, 1996L, 1996L, 1996L, 1997L, 1997L, 1997L, 1997L, 
1997L, 1997L, 1997L, 1997L, 1997L, 1997L, 1997L, 1997L, 1998L, 
1998L, 1998L, 1998L, 1998L, 1998L, 1998L, 1998L, 1998L, 1998L, 
1998L, 1998L, 1999L, 1999L, 1999L, 1999L, 1999L, 1999L, 1999L, 
1999L, 1999L, 1999L, 1999L, 1999L, 2000L, 2000L, 2000L, 2000L, 
2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2000L, 2001L, 
2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 2001L, 
2001L, 2001L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 
2002L, 2002L, 2002L, 2002L, 2002L, 2003L, 2003L, 2003L, 2003L, 
2003L, 2003L, 2003L, 2003L, 2003L, 2003L, 2003L, 2003L, 2004L, 
2004L, 2004L, 2004L, 2004L, 2004L, 2004L, 2004L, 2004L, 2004L, 
2004L, 2004L, 2005L, 2005L, 2005L, 2005L, 2005L, 2005L, 2005L, 
2005L, 2005L, 2005L, 2005L, 2005L, 2006L, 2006L, 2006L, 2006L, 
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2007L, 
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 
2007L, 2007L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 2008L, 
2008L, 2008L, 2008L, 2008L, 2008L, 2009L, 2009L, 2009L, 2009L, 
2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2009L, 2010L, 
2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 
2010L, 2010L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 2011L, 
2011L, 2011L, 2011L, 2011L, 2011L, 2012L, 2012L, 2012L, 2012L, 
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2013L, 
2013L, 2013L, 2013L, 2013L, 2013L), period = c("01", "02", "03", 
"04", "05", "06", "07", "08", "09", "10", "11", "12", "01", "02", 
"03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "01", 
"02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", 
"01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", 
"12", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", 
"11", "12", "01", "02", "03", "04", "05", "06", "07", "08", "09", 
"10", "11", "12", "01", "02", "03", "04", "05", "06", "07", "08", 
"09", "10", "11", "12", "01", "02", "03", "04", "05", "06", "07", 
"08", "09", "10", "11", "12", "01", "02", "03", "04", "05", "06", 
"07", "08", "09", "10", "11", "12", "01", "02", "03", "04", "05", 
"06", "07", "08", "09", "10", "11", "12", "01", "02", "03", "04", 
"05", "06", "07", "08", "09", "10", "11", "12", "01", "02", "03", 
"04", "05", "06", "07", "08", "09", "10", "11", "12", "01", "02", 
"03", "04", "05", "06", "07", "08", "09", "10", "11", "12", "01", 
"02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12", 
"01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", 
"12", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", 
"11", "12", "01", "02", "03", "04", "05", "06", "07", "08", "09", 
"10", "11", "12", "01", "02", "03", "04", "05", "06", "07", "08", 
"09", "10", "11", "12", "01", "02", "03", "04", "05", "06", "07", 
"08", "09", "10", "11", "12", "01", "02", "03", "04", "05", "06", 
"07", "08", "09", "10", "11", "12", "01", "02", "03", "04", "05", 
"06", "07", "08", "09", "10", "11", "12", "01", "02", "03", "04", 
"05", "06", "07", "08", "09", "10", "11", "12", "01", "02", "03", 
"04", "05", "06", "07", "08", "09", "10", "11", "12", "01", "02", 
"03", "04", "05", "06"), value = c(4.7, 5, 4.8, 3.6, 3.4, 3.5, 
4, 3.4, 3.1, 3.1, 4, 4.1, 4.7, 5.2, 5.2, 4.1, 3.7, 4.5, 4.3, 
3.6, 3.3, 3.5, 3.9, 4.2, 4.7, 5.3, 5, 4.1, 4.5, 5.2, 4.7, 4.3, 
4, 3.6, 3.8, 3.9, 4.7, 5.2, 4.6, 3.9, 3.8, 4.4, 4.7, 4, 3.8, 
3.6, 3.5, 3.8, 4.9, 5.1, 4.8, 3.9, 3.4, 3.9, 4.2, 3.5, 3, 2.6, 
2.7, 2.8, 4.3, 4.7, 4.5, 4.2, 3.5, 4.1, 3.8, 3.5, 3, 2.9, 3.2, 
3.2, 4.1, 4.5, 4.3, 3.2, 2.9, 3, 2.8, 2.6, 2.2, 2.1, 2.5, 2.6, 
3.7, 3.6, 3.9, 3.1, 2.5, 2.8, 2.6, 2.5, 2.2, 2.1, 2.4, 2.4, 3.3, 
3.3, 3.6, 2.3, 2.3, 3, 2.3, 2.6, 2.4, 2.4, 2.4, 2.5, 3.3, 3.7, 
3.1, 2.6, 2.5, 3.1, 2.5, 3.2, 2.8, 2.7, 2.7, 3, 3.7, 3.9, 3.9, 
3, 2.8, 3.5, 3.1, 3.1, 2.6, 2.5, 2.8, 2.9, 3.8, 4.1, 4.5, 3.7, 
3.4, 3.9, 3.5, 3.6, 3.3, 3.2, 3.6, 3.8, 4.8, 5, 5.2, 4.7, 4.1, 
4.6, 4.2, 4.2, 3.6, 3.5, 4, 4, 5.2, 5.6, 5.3, 4.6, 4.3, 5, 4.5, 
4.4, 3.9, 3.8, 4, 3.9, 5.1, 5.1, 5.5, 4, 3.8, 4.4, 3.8, 3.8, 
3.5, 3.3, 3.6, 3.5, 4.7, 5.1, 4.9, 3.8, 3.8, 4.3, 3.9, 3.8, 3.7, 
3.3, 3.7, 3.8, 4.3, 4.5, 4.4, 3.8, 3.4, 4, 3.7, 3.6, 3.3, 3.1, 
3.4, 3.6, 4.6, 4.5, 4.3, 3.8, 3.6, 4.2, 3.8, 3.6, 3.4, 3.3, 3.3, 
3.6, 4, 4, 3.9, 3.2, 3.3, 4.2, 3.9, 3.9, 3.6, 3.7, 4.1, 4.8, 
6.2, 7, 7.5, 6.7, 6.7, 7.6, 7, 6.9, 6.5, 6.3, 6.3, 6.7, 7.8, 
7.7, 7.7, 6.3, 6.1, 6.4, 6.5, 6.2, 5.6, 5.4, 5.5, 5.4, 6.4, 6.6, 
6.3, 5.6, 5.5, 6.6, 6.1, 5.9, 5.3, 5.1, 5, 5, 5.9, 6.2, 5.8, 
4.9, 5.1, 6, 5.8, 5.5, 4.7, 4.6, 4.7, 4.9, 6.3, 6.3, 5.7, 5.3, 
5.1, 5.7), footnote_codes = c("", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", 
"S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", 
"S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "E", "E", "E", 
"E", "E", "E", "E", "E", "E", "E", "E", "E", "E", "E", "E", "E", 
"E", "E", "E", "E", "E", "E", "E", "E", "", "", "", "", "", "P"
), date = structure(list(sec = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
    min = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), 
hour = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L
), mday = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), mon = c(0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 0L, 1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 0L, 1L, 2L, 3L, 
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 0L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 9L, 10L, 11L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 0L, 1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 0L, 1L, 2L, 3L, 
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 0L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 9L, 10L, 11L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 0L, 1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 0L, 1L, 2L, 3L, 
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 0L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 9L, 10L, 11L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 0L, 1L, 
2L, 3L, 4L, 5L), year = c(90L, 90L, 90L, 90L, 90L, 90L, 90L, 
90L, 90L, 90L, 90L, 90L, 91L, 91L, 91L, 91L, 91L, 91L, 91L, 
91L, 91L, 91L, 91L, 91L, 92L, 92L, 92L, 92L, 92L, 92L, 92L, 
92L, 92L, 92L, 92L, 92L, 93L, 93L, 93L, 93L, 93L, 93L, 93L, 
93L, 93L, 93L, 93L, 93L, 94L, 94L, 94L, 94L, 94L, 94L, 94L, 
94L, 94L, 94L, 94L, 94L, 95L, 95L, 95L, 95L, 95L, 95L, 95L, 
95L, 95L, 95L, 95L, 95L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 
96L, 96L, 96L, 96L, 96L, 97L, 97L, 97L, 97L, 97L, 97L, 97L, 
97L, 97L, 97L, 97L, 97L, 98L, 98L, 98L, 98L, 98L, 98L, 98L, 
98L, 98L, 98L, 98L, 98L, 99L, 99L, 99L, 99L, 99L, 99L, 99L, 
99L, 99L, 99L, 99L, 99L, 100L, 100L, 100L, 100L, 100L, 100L, 
100L, 100L, 100L, 100L, 100L, 100L, 101L, 101L, 101L, 101L, 
101L, 101L, 101L, 101L, 101L, 101L, 101L, 101L, 102L, 102L, 
102L, 102L, 102L, 102L, 102L, 102L, 102L, 102L, 102L, 102L, 
103L, 103L, 103L, 103L, 103L, 103L, 103L, 103L, 103L, 103L, 
103L, 103L, 104L, 104L, 104L, 104L, 104L, 104L, 104L, 104L, 
104L, 104L, 104L, 104L, 105L, 105L, 105L, 105L, 105L, 105L, 
105L, 105L, 105L, 105L, 105L, 105L, 106L, 106L, 106L, 106L, 
106L, 106L, 106L, 106L, 106L, 106L, 106L, 106L, 107L, 107L, 
107L, 107L, 107L, 107L, 107L, 107L, 107L, 107L, 107L, 107L, 
108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 108L, 
108L, 108L, 109L, 109L, 109L, 109L, 109L, 109L, 109L, 109L, 
109L, 109L, 109L, 109L, 110L, 110L, 110L, 110L, 110L, 110L, 
110L, 110L, 110L, 110L, 110L, 110L, 111L, 111L, 111L, 111L, 
111L, 111L, 111L, 111L, 111L, 111L, 111L, 111L, 112L, 112L, 
112L, 112L, 112L, 112L, 112L, 112L, 112L, 112L, 112L, 112L, 
113L, 113L, 113L, 113L, 113L, 113L), wday = c(1L, 4L, 4L, 
0L, 2L, 5L, 0L, 3L, 6L, 1L, 4L, 6L, 2L, 5L, 5L, 1L, 3L, 6L, 
1L, 4L, 0L, 2L, 5L, 0L, 3L, 6L, 0L, 3L, 5L, 1L, 3L, 6L, 2L, 
4L, 0L, 2L, 5L, 1L, 1L, 4L, 6L, 2L, 4L, 0L, 3L, 5L, 1L, 3L, 
6L, 2L, 2L, 5L, 0L, 3L, 5L, 1L, 4L, 6L, 2L, 4L, 0L, 3L, 3L, 
6L, 1L, 4L, 6L, 2L, 5L, 0L, 3L, 5L, 1L, 4L, 5L, 1L, 3L, 6L, 
1L, 4L, 0L, 2L, 5L, 0L, 3L, 6L, 6L, 2L, 4L, 0L, 2L, 5L, 1L, 
3L, 6L, 1L, 4L, 0L, 0L, 3L, 5L, 1L, 3L, 6L, 2L, 4L, 0L, 2L, 
5L, 1L, 1L, 4L, 6L, 2L, 4L, 0L, 3L, 5L, 1L, 3L, 6L, 2L, 3L, 
6L, 1L, 4L, 6L, 2L, 5L, 0L, 3L, 5L, 1L, 4L, 4L, 0L, 2L, 5L, 
0L, 3L, 6L, 1L, 4L, 6L, 2L, 5L, 5L, 1L, 3L, 6L, 1L, 4L, 0L, 
2L, 5L, 0L, 3L, 6L, 6L, 2L, 4L, 0L, 2L, 5L, 1L, 3L, 6L, 1L, 
4L, 0L, 1L, 4L, 6L, 2L, 4L, 0L, 3L, 5L, 1L, 3L, 6L, 2L, 2L, 
5L, 0L, 3L, 5L, 1L, 4L, 6L, 2L, 4L, 0L, 3L, 3L, 6L, 1L, 4L, 
6L, 2L, 5L, 0L, 3L, 5L, 1L, 4L, 4L, 0L, 2L, 5L, 0L, 3L, 6L, 
1L, 4L, 6L, 2L, 5L, 6L, 2L, 4L, 0L, 2L, 5L, 1L, 3L, 6L, 1L, 
4L, 0L, 0L, 3L, 5L, 1L, 3L, 6L, 2L, 4L, 0L, 2L, 5L, 1L, 1L, 
4L, 6L, 2L, 4L, 0L, 3L, 5L, 1L, 3L, 6L, 2L, 2L, 5L, 0L, 3L, 
5L, 1L, 4L, 6L, 2L, 4L, 0L, 3L, 4L, 0L, 2L, 5L, 0L, 3L, 6L, 
1L, 4L, 6L, 2L, 5L, 5L, 1L, 3L, 6L), yday = c(0L, 31L, 59L, 
90L, 120L, 151L, 181L, 212L, 243L, 273L, 304L, 334L, 0L, 
31L, 59L, 90L, 120L, 151L, 181L, 212L, 243L, 273L, 304L, 
334L, 0L, 31L, 60L, 91L, 121L, 152L, 182L, 213L, 244L, 274L, 
305L, 335L, 0L, 31L, 59L, 90L, 120L, 151L, 181L, 212L, 243L, 
273L, 304L, 334L, 0L, 31L, 59L, 90L, 120L, 151L, 181L, 212L, 
243L, 273L, 304L, 334L, 0L, 31L, 59L, 90L, 120L, 151L, 181L, 
212L, 243L, 273L, 304L, 334L, 0L, 31L, 60L, 91L, 121L, 152L, 
182L, 213L, 244L, 274L, 305L, 335L, 0L, 31L, 59L, 90L, 120L, 
151L, 181L, 212L, 243L, 273L, 304L, 334L, 0L, 31L, 59L, 90L, 
120L, 151L, 181L, 212L, 243L, 273L, 304L, 334L, 0L, 31L, 
59L, 90L, 120L, 151L, 181L, 212L, 243L, 273L, 304L, 334L, 
0L, 31L, 60L, 91L, 121L, 152L, 182L, 213L, 244L, 274L, 305L, 
335L, 0L, 31L, 59L, 90L, 120L, 151L, 181L, 212L, 243L, 273L, 
304L, 334L, 0L, 31L, 59L, 90L, 120L, 151L, 181L, 212L, 243L, 
273L, 304L, 334L, 0L, 31L, 59L, 90L, 120L, 151L, 181L, 212L, 
243L, 273L, 304L, 334L, 0L, 31L, 60L, 91L, 121L, 152L, 182L, 
213L, 244L, 274L, 305L, 335L, 0L, 31L, 59L, 90L, 120L, 151L, 
181L, 212L, 243L, 273L, 304L, 334L, 0L, 31L, 59L, 90L, 120L, 
151L, 181L, 212L, 243L, 273L, 304L, 334L, 0L, 31L, 59L, 90L, 
120L, 151L, 181L, 212L, 243L, 273L, 304L, 334L, 0L, 31L, 
60L, 91L, 121L, 152L, 182L, 213L, 244L, 274L, 305L, 335L, 
0L, 31L, 59L, 90L, 120L, 151L, 181L, 212L, 243L, 273L, 304L, 
334L, 0L, 31L, 59L, 90L, 120L, 151L, 181L, 212L, 243L, 273L, 
304L, 334L, 0L, 31L, 59L, 90L, 120L, 151L, 181L, 212L, 243L, 
273L, 304L, 334L, 0L, 31L, 60L, 91L, 121L, 152L, 182L, 213L, 
244L, 274L, 305L, 335L, 0L, 31L, 59L, 90L, 120L, 151L), isdst = c(0L, 
0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 
1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 
0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 
1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 
0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 
1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 
0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 
0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 
0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L)), .Names = c("sec", 
"min", "hour", "mday", "mon", "year", "wday", "yday", "isdst"
), class = c("POSIXlt", "POSIXt"))), .Names = c("series_id", 
"year", "period", "value", "footnote_codes", "date"), row.names = c(40841L, 
40842L, 40843L, 40844L, 40845L, 40846L, 40847L, 40848L, 40849L, 
40850L, 40851L, 40852L, 40854L, 40855L, 40856L, 40857L, 40858L, 
40859L, 40860L, 40861L, 40862L, 40863L, 40864L, 40865L, 40867L, 
40868L, 40869L, 40870L, 40871L, 40872L, 40873L, 40874L, 40875L, 
40876L, 40877L, 40878L, 40880L, 40881L, 40882L, 40883L, 40884L, 
40885L, 40886L, 40887L, 40888L, 40889L, 40890L, 40891L, 40893L, 
40894L, 40895L, 40896L, 40897L, 40898L, 40899L, 40900L, 40901L, 
40902L, 40903L, 40904L, 40906L, 40907L, 40908L, 40909L, 40910L, 
40911L, 40912L, 40913L, 40914L, 40915L, 40916L, 40917L, 40919L, 
40920L, 40921L, 40922L, 40923L, 40924L, 40925L, 40926L, 40927L, 
40928L, 40929L, 40930L, 40932L, 40933L, 40934L, 40935L, 40936L, 
40937L, 40938L, 40939L, 40940L, 40941L, 40942L, 40943L, 40945L, 
40946L, 40947L, 40948L, 40949L, 40950L, 40951L, 40952L, 40953L, 
40954L, 40955L, 40956L, 40958L, 40959L, 40960L, 40961L, 40962L, 
40963L, 40964L, 40965L, 40966L, 40967L, 40968L, 40969L, 40971L, 
40972L, 40973L, 40974L, 40975L, 40976L, 40977L, 40978L, 40979L, 
40980L, 40981L, 40982L, 40984L, 40985L, 40986L, 40987L, 40988L, 
40989L, 40990L, 40991L, 40992L, 40993L, 40994L, 40995L, 40997L, 
40998L, 40999L, 41000L, 41001L, 41002L, 41003L, 41004L, 41005L, 
41006L, 41007L, 41008L, 41010L, 41011L, 41012L, 41013L, 41014L, 
41015L, 41016L, 41017L, 41018L, 41019L, 41020L, 41021L, 41023L, 
41024L, 41025L, 41026L, 41027L, 41028L, 41029L, 41030L, 41031L, 
41032L, 41033L, 41034L, 41036L, 41037L, 41038L, 41039L, 41040L, 
41041L, 41042L, 41043L, 41044L, 41045L, 41046L, 41047L, 41049L, 
41050L, 41051L, 41052L, 41053L, 41054L, 41055L, 41056L, 41057L, 
41058L, 41059L, 41060L, 41062L, 41063L, 41064L, 41065L, 41066L, 
41067L, 41068L, 41069L, 41070L, 41071L, 41072L, 41073L, 41075L, 
41076L, 41077L, 41078L, 41079L, 41080L, 41081L, 41082L, 41083L, 
41084L, 41085L, 41086L, 41088L, 41089L, 41090L, 41091L, 41092L, 
41093L, 41094L, 41095L, 41096L, 41097L, 41098L, 41099L, 41101L, 
41102L, 41103L, 41104L, 41105L, 41106L, 41107L, 41108L, 41109L, 
41110L, 41111L, 41112L, 41114L, 41115L, 41116L, 41117L, 41118L, 
41119L, 41120L, 41121L, 41122L, 41123L, 41124L, 41125L, 41127L, 
41128L, 41129L, 41130L, 41131L, 41132L, 41133L, 41134L, 41135L, 
41136L, 41137L, 41138L, 41140L, 41141L, 41142L, 41143L, 41144L, 
41145L), class = "data.frame")

Answer 1

为我工作

library(quantmod)
e <- new.env()
s <- c("SPY", "DIA", "GLD")
getSymbols(s, env=e)
l.adj <- eapply(e, Ad)[s]
l.qtr <- lapply(l.adj, function(x) apply.quarterly(x, mean))

修改

代码仍然不可重复（或最小），但dimnames(xts.adj) <- names看起来很可疑。看起来您将dimnames设置为NULL（请参阅lapply(split(lax.xts, "quarters"), dimnames)）

如果将xts的dimnames设置为NULL，则最终会将其变为as.xts无法转换为xts的内容。我认为这可能不是xts:::`dimnames<-.xts`的预期行为。

由于我无法运行您的代码，我不确定这是否是问题，但如果是，则这是一个更简洁，可重现的示例。

x <- xts(1:5, .POSIXct(0)+1:5)
dimnames(x) <- dimnames(x)
as.xts(x)
#Error in as.xts.matrix(x) : 
#  order.by must be either 'rownames()' or otherwise specified

try.xts出错（x，错误=“必须是xts-coercible或timeBased”）

1 个答案: