this one is not a false alarm like my previous message.
i have cut and paste the code below so if anyone could run it would be
appreciated. basically,
my question is why the horizontal axis of the acf plot is labelled with
such huge numbers when
the labels should be 1 through 10 since may lag.max = 10 ?
i looked at the cdoe of acf but it was pretty much beyond me. i think it
has something to
do with the way the lag.max variable in the acf function is getting
calculated.
if i do aggfxdata<-as.ts(aggfxdata) before the call to acf, the same
thing happens so the problem is not
caused by the fact that aggfxdata is a zoo object. it's not such a big
deal because i know the lags are actually 1 thoruh 10
but if someone knows how to get them outputted on the horizontal axis of
the acf plot, that would be great. thanks.
also, just in case people are wondering , the aggfxdata structure below
has been aggregated so that there are no duplicates so this is
not the problem either.
again, you would need the zoo and chron libraries installed for the code
below to run.
#-----------------------------------------------------------------------
------------------------------------------------------------------------
---------------------------------
aggfxdata<-structure(c(118.48, 118.476, 118.46, 118.436666666667,
118.417142857143,
118.381, 118.38, 118.368888888889, 118.373333333333, 118.36,
118.328571428571, 118.347142857143, 118.350714285714, 118.366666666667,
118.372857142857, 118.37, 118.365833333333, 118.338333333333,
118.348571428571, 118.36, 118.36, 118.36, 118.363333333333, 118.36,
118.3575, 118.36, 118.385, 118.40875, 118.398, 118.4, 118.401428571429,
118.41, 118.402857142857, 118.394615384615, 118.4, 118.411666666667,
118.410833333333, 118.412, 118.40875, 118.405, 118.415714285714,
118.431, 118.440666666667, 118.44, 118.424, 118.43, 118.425,
118.42, 118.415, 118.43, 118.49, 118.486, 118.47, 118.453333333333,
118.43, 118.3965, 118.393333333333, 118.387777777778, 118.388333333333,
118.374615384615, 118.344285714286, 118.361428571429, 118.365,
118.38, 118.387142857143, 118.385, 118.378333333333, 118.353333333333,
118.364285714286, 118.375, 118.373333333333, 118.375, 118.378333333333,
118.37, 118.3775, 118.38, 118.3975, 118.41875, 118.41, 118.415,
118.418571428571, 118.421666666667, 118.415714285714, 118.403076923077,
118.42, 118.426666666667, 118.4275, 118.426, 118.42625, 118.42,
118.428571428571, 118.45, 118.457333333333, 118.4575, 118.44,
118.44, 118.44, 118.44, 118.44, 118.44), .Dim = c(50, 2), .Dimnames =
list(
c("1144713660", "1144713780", "1144713900", "1144713960",
"1144714020", "1144714080", "1144714140", "1144714200",
"1144714260",
"1144714320", "1144714380", "1144714440", "1144714500",
"1144714560",
"1144714620", "1144714680", "1144714740", "1144714800",
"1144714860",
"1144714980", "1144715040", "1144715100", "1144715160",
"1144715280",
"1144715400", "1144715460", "1144715520", "1144715580",
"1144715640",
"1144715700", "1144715760", "1144715820", "1144715880",
"1144715940",
"1144716000", "1144716120", "1144716180", "1144716240",
"1144716300",
"1144716360", "1144716420", "1144716480", "1144716540",
"1144716600",
"1144716660", "1144716720", "1144716780", "1144716840",
"1144716900",
"1144716960"), c("bid", "ask")), index = structure(c(1144713660,
1144713780, 1144713900, 1144713960, 1144714020, 1144714080, 1144714140,
1144714200, 1144714260, 1144714320, 1144714380, 1144714440, 1144714500,
1144714560, 1144714620, 1144714680, 1144714740, 1144714800, 1144714860,
1144714980, 1144715040, 1144715100, 1144715160, 1144715280, 1144715400,
1144715460, 1144715520, 1144715580, 1144715640, 1144715700, 1144715760,
1144715820, 1144715880, 1144715940, 1144716000, 1144716120, 1144716180,
1144716240, 1144716300, 1144716360, 1144716420, 1144716480, 1144716540,
1144716600, 1144716660, 1144716720, 1144716780, 1144716840, 1144716900,
1144716960), class = c("POSIXt", "POSIXct")), class = "zoo")
acf(aggfxdata[,"bid"],lag.max=10,plot=TRUE,na.action=na.pass)
#-----------------------------------------------------------------------
------------------------------------------------------------------------
--------------------------------------------
--------------------------------------------------------
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