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

Question

I have a list of xts objects. The objects are monthly time series and I'd like to aggregate them up to quarterly. Using the either to.quarterly or apply.quarterly fails through lapply:

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

results in:

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

However I can do it with another list of xts objects I call lx. This lx list is sent off, converted to ts objects, X12 adjusted and reclassed to xts and sent back. Somehow when it comes back I get the error. I verified the problem doesn't exist for a list of size one, even after sending it off to be X12 adjusted.

Thinking it was a problem like at this link but doesn't seem to be? Automatically plot (and save) list of xts objects

Anymore excellent help? This is driving me nuts.

UPDATE: My apologies for the initial lack of focus. I've been trying to narrow this down to a tight reproducible example. It appears to be an interaction between my helper function SeasAdj (which converts to ts, deseasonalizes and converts back to a xts) and the initial conversion of my data.frame to an xts. However If I run an xts monthly series downloaded through quantmod's getSymbols I have the desired outcome. It leads me to believe the trouble is in how the SeasAdj helper function converts back to an xts before returning. I have dput(lax) at the end. From there running these four lines after sourcing SeasAdj:

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)

}

Here is a dput of lax dput(lax)

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, 
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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", 
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"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, 
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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, 
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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("", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
"", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
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"S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", 
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"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, 
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0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 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")

Maybe you have nested lists or something? How about a [reproducible example](http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example)? — GSee, Aug 02 '13 at 23:00
I've tried to narrow it and make it reproducible. Thanks for your patience. — tjbrooks, Aug 04 '13 at 23:03
The solution was to comment out the `dimnames(xts.adj) <- names` from the SeasAdj function. — tjbrooks, Aug 05 '13 at 15:04

GSee · Accepted Answer · 2013-08-05T03:09:54.947

1

Works for me

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))

Edit

The code is still not reproducible (or minimal), but dimnames(xts.adj) <- names looks suspicious. It looks like you are setting dimnames to NULL (see lapply(split(lax.xts, "quarters"), dimnames))

If you set the dimnames of an xts to NULL, you end up turning it into something that as.xts cannot convert to an xts. I think this is probably not the intended behavior of xts:::`dimnames<-.xts`.

Since I couldn't run your code, I don't know for sure that this is the problem, but if it is, here is a much more concise reproducible example.

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

edited Aug 05 '13 at 03:09

answered Aug 02 '13 at 22:59

GSee

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This works for me as well, suggesting my problems lies elsewhere. I've updated the initial question to provide data and some reproducibility. – tjbrooks Aug 04 '13 at 23:03
`Gsee` thanks for your patience and help. Your suspicions were correct. Commenting out the `dimnames` in my helper function fixed the problem. I'm sorry, as it should have been obvious to me after my last edit as it was the one part of the difference between the `xts` objects I produced versus the ones that were downloaded as in your example. Thanks again. – tjbrooks Aug 05 '13 at 15:03
It looks like Joshua just patched [this bug](https://r-forge.r-project.org/tracker/index.php?func=detail&aid=4794&group_id=118&atid=516) in revision 784 on R-forge. – GSee Aug 05 '13 at 15:30
`GSee` thanks for the followup, and posting it to r-forge as a bug. – tjbrooks Aug 06 '13 at 16:11
@tjbrooks no problem. I'm glad you found it so it could get patched. I just wish it didn't take so much code to figure out the issue. ;-) – GSee Aug 06 '13 at 16:14
`GSee` I get it, and apologize for not honing in on the issue a little more before posting. I'll work a bit harder at it on my next post. – tjbrooks Aug 07 '13 at 17:52

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

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