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I thought I would post here since I have spent hours trying to figure this out. So I'm working with a csv file with Date and Closing return price. However, I can't get the file to be "timeBased." (timeBased function is from package xts). For example:
timeBased(dfx)
[1] FALSE
Here is what I have:
dfx = xts(aus$AUS, order.by=as.Date(aus$DATE))
and here's what the first 10 rows look like of the file:
DATE AUS
1 12/1/1988 -0.0031599720
2 12/2/1988 -0.0015724670
3 12/5/1988 -0.0000897619
4 12/6/1988 -0.0022670620
5 12/7/1988 0.0052895550
6 12/8/1988 -0.0048259860
7 12/9/1988 0.0106990910
8 12/12/1988 0.0033538810
9 12/13/1988 0.0118568700
10 12/14/1988 -0.0050105200
If anyone can help, I would appreciate it! I tried multiple codes using zoo and other edits, but nothing. Thank you!![enter image description here][1]
As Joshua Ulrich points out, using the timeBased function with an xts object should be expected to return FALSE. In addition to that, there may be another problem with your code. Assuming that your example displays the contents of aus, then aus$DATE is actually a factor or character data, not a Date object. To properly convert to an xts object, you'll have to specify the date format of the aus$DATE data. To convert and then test whether dfx is an xts object, you could use the following code:
dfx = xts(aus$AUS, order.by=as.Date(aus$DATE, "%m/%d/%Y"))
dfx
[,1]
1988-12-01 -0.0031599720
1988-12-02 -0.0015724670
1988-12-05 -0.0000897619
1988-12-06 -0.0022670620
timeBased(dfx)
[1] FALSE
is.xts(dfx)
[1] TRUE
I am following this tutorial by Rob Hyndman for initialization (additive).
Steps to calculate initial values are specified as:
I am running above steps manually (with pen/paper) on data set provided in Rob Hydman free online text book. Values I got after first two steps are:
I used same data set on "R", but seasonal output values in R are drastically different (screenshot below)
Not sure what I am doing wrong. Any help would be appreciated.
Another interesting thing I have observed just now is, initial level (l(t)) in text book is 33.8, but in R output it is : 48.24, which proves that I am missing something while calculating manually.
EDIT:
Here is how I am calculating Moving Averages Smooth (Based on formula used in Section 2 of this link. )
After calculating I have de-trended, means original value - smoothed value.
Then seasonal values: Which is
S1 =Average of Q1
S2 = Average of Q2
...
The first two values of your moving average are incorrect. You have assumed that the values prior to the first observation are zero. They are not zero, they are missing, which is quite different. It is impossible to compute the moving average for the first two observations for this reason.
The third and subsequent values of your moving average are only approximately correct because you have rounded the data to the first decimal point instead of using the data as provided in the fpp package in R.
The values obtained following this procedure are used as initial values in the optimization within ets(). So the output from ets() will not contain the initial values but the optimized values. The table in the book gives the optimized values. You will not be able to reproduce them using a simple procedure.
However, you can reproduce what is provided by HoltWinters because it does not do any optimization of initial values. Using HoltWinters, the initial seasonal values are given as:
> HoltWinters(y)$fitted[1:4,]
xhat level trend season
[1,] 43.73934 33.21330 1.207739 9.318302
[2,] 28.25863 35.65614 1.376490 -8.774002
[3,] 36.86581 37.57569 1.450688 -2.160566
[4,] 41.87604 38.83521 1.424568 1.616267
(The output in coefficients gives the final states not the initial states.)
The seasonal indices in the last column can be computed as follows:
y MAsmooth detrend detrend.adj
41.72746 NA NA NA
24.04185 NA NA NA
32.32810 34.41724 -2.089139 -2.160566
37.32871 35.64101 1.687695 1.616267
46.21315 36.82342 9.389730 9.318302
29.34633 38.04890 -8.702575 -8.774002
36.48291 NA NA NA
42.97772 NA NA NA
The last column is the adjusted detrended data (so they add to zero).
I get an error message when I attempt to use apply() conditional on a column of dates to return a set of coefficients.
I have a dataset (herein modified for simplicity, but reproducible):
ADataset <- data.table(Epoch = c("2007-11-15", "2007-11-16", "2007-11-17",
"2007-11-18", "2007-11-19", "2007-11-20", "2007-11-21"),
Distance = c("92336.22", "92336.23", "92336.22", "92336.20",
"92336.19", "92336.21", "92336.18))
ADataset
Epoch Distance
1: 2007-11-15 92336.22
2: 2007-11-16 92336.23
3: 2007-11-17 92336.22
4: 2007-11-18 92336.20
5: 2007-11-19 92336.19
6: 2007-11-20 92336.21
7: 2007-11-21 92336.18
The analysis begins with establishing start and end dates:
############## Establish dates for analysis
#4.Set date for center of duration
StartDate <- "2007-11-18"
as.numeric(as.Date(StartDate)); StartDate
EndDate <- as.Date(tail(Adataset$Epoch,1)); EndDate
Then I establish time durations for analysis:
#5.Quantify duration of time window
STDuration <- 1
LTDuration <- 3
Then I write functions to regress over both durations and return the slopes:
# Write STS and LTS functions, each with following steps
#6.Define time window- from StartDate less ShortTermDuration to
StartDate plus ShortTermDuration
#7.Define Short Term & Long Term datasets
#8. Run regression over dataset
my_STS_Function <- function (StartDate) {
STAhead <- as.Date(StartDate) + STDuration; STAhead
STBehind <- as.Date(StartDate) - STDuration; STBehind
STDataset <- subset(Adataset, as.Date(Epoch) >= STBehind & as.Date(Epoch)<STAhead)
STResults <- rlm( Distance ~ Epoch, data=STDataset); STResults
STSummary <- summary( STResults ); STSummary
# Return coefficient (Slope of regression)
STNum <- STResults$coefficients[2];STNum
}
my_LTS_Function <- function (StartDate) {
LTAhead <- as.Date(StartDate) + LTDuration; LTAhead
LTBehind <- as.Date(StartDate) - LTDuration; LTBehind
LTDataset <- subset(Adataset, as.Date(Epoch) >= LTBehind & as.Date(Epoch)<LTAhead)
LTResults <- rlm( Distance ~ Epoch, data=LTDataset); LTResults
LTSummary <- summary( LTResults ); LTSummary
# Return coefficient (Slope of regression)
LTNum <- LTResults$coefficients[2];LTNum
Then I test the function to make sure it works for a single date:
myTestResult <- my_STS_Function("2007-11-18")
It works, so I move on to apply the function over the range of dates in the dataset:
mySTSResult <- apply(Adataset, 1, my_STS_Function, seq(StartDate : EndDate))
...in which my desired result is a list or array or vector of mySTSResult (slopes) (and, subsequently, a separate list/array/vector of myLTSResults so then I can create a STSlope:LTSlope ratio over the duration), something like (mySTSResults fabricated)...
> Adataset
Epoch Distance mySTSResults
1: 2007-11-15 92336.22 3
2: 2007-11-16 92336.23 4
3: 2007-11-17 92336.22 5
4: 2007-11-18 92336.20 6
5: 2007-11-19 92336.19 7
6: 2007-11-20 92336.21 8
7: 2007-11-21 92336.18 9
Only I get this error:
Error in FUN(newX[, i], ...) : unused argument(s) (1:1185)
What is this telling me and how to do correct it? I've done some looking and cannot find the correction.
Hopefully I've explained this sufficiently. Please let me know if you need further details.
Ok, it seems the problem is in the additional arguments to my_STS_Function as stated in your apply function call (as you have defined it with only one parameter). The date range is being passed as an additional parameter to that function, and R is complaining that it is unused (a vector of 1185 elements it seems). Are you rather trying to pull a subset of the rows restricted by date range first, then wishing to apply the my_STS_Function? I'd have to think a bit on an exact solution to that.
Sorry - I did my working out in the comments there. A possible solution is this:
subSet <- Adataset[Adataset[,1] %in% seq(StartDate:EndDate),][order(na.exclude(match(Adataset[,1], seq(StartData,EndDate))),]
Adapted from the answer in this question:
R select rows in matrix from another vector (match, %in)
Adding this as a new answer as the previous one was getting confused. A previous commenter was correct, there are bugs in your code, but they aren't a sticking point.
My updated approach was to use seq.Date to generate the date sequence (only works if you have a data point for each day between the start and end - though you could use na.exclude as above):
dates = seq.Date(as.Date(StartDate),as.Date(EndDate),"days")
You then use this as the input to apply, with some munging of types to get things working correctly (I've done this with a lamda function):
mySTSResult <- apply(as.matrix(dates), 1, function(x) {class(x) <- "Date"; my_STS_Function(x)})
Then hopefully you should have a vector of the results, and you should be able to do something similar for LTS, and then manipulate that into another column in your original data frame/matrix.
I am working with data, 1st two columns are dates, 3rd column is symbol, and 4th and 5th columns are prices.
So, I created a subset of the data as follows:
test.sub<-subset(test,V3=="GOOG",select=c(V1,V4)
and then I try to plot a time series chart using the following
as.ts(test.sub)
plot(test.sub)
well, it gives me a scatter plot - not what I was looking for.
so, I tried plot(test.sub[1],test.sub[2])
and now I get the following error:
Error in xy.coords(x, y, xlabel, ylabel, log) :
'x' and 'y' lengths differ
To make sure the no. of rows were same, I ran nrow(test.sub[1]) and nrow(test.sub[2]) and they both return equal rows, so as a newcomer to R, I am not sure what the fix is.
I also ran plot.ts(test.sub) and that works, but it doesn't show me the dates in the x-axis, which it was doing with plot(test.sub) and which is what I would like to see.
test.sub[1]
V1
1107 2011-Aug-24
1206 2011-Aug-25
1307 2011-Aug-26
1408 2011-Aug-29
1510 2011-Aug-30
1613 2011-Aug-31
1718 2011-Sep-01
1823 2011-Sep-02
1929 2011-Sep-06
2035 2011-Sep-07
2143 2011-Sep-08
2251 2011-Sep-09
2359 2011-Sep-13
2470 2011-Sep-14
2581 2011-Sep-15
2692 2011-Sep-16
2785 2011-Sep-19
2869 2011-Sep-20
2965 2011-Sep-21
3062 2011-Sep-22
3160 2011-Sep-23
3258 2011-Sep-26
3356 2011-Sep-27
3455 2011-Sep-28
3555 2011-Sep-29
3655 2011-Sep-30
3755 2011-Oct-03
3856 2011-Oct-04
3957 2011-Oct-05
4059 2011-Oct-06
4164 2011-Oct-07
4269 2011-Oct-10
4374 2011-Oct-11
4479 2011-Oct-12
4584 2011-Oct-13
4689 2011-Oct-14
str(test.sub)
'data.frame': 35 obs. of 2 variables:
$ V1:Class 'Date' num [1:35] NA NA NA NA NA NA NA NA NA NA ...
$ V4: num 0.475 0.452 0.423 0.418 0.403 ...
head(test.sub) V1 V4
1212 <NA> 0.474697
1313 <NA> 0.451907
1414 <NA> 0.423184
1516 <NA> 0.417709
1620 <NA> 0.402966
1725 <NA> 0.414264
Now that this is working, I'd like to add a 3rd variable to plot a 3d chart - any suggestions how I can do that. thx!
So I think there are a few things going on here that are worth talking through:
first, some example data:
test <- data.frame(End = Sys.Date()+1:5,
Start = Sys.Date()+0:4,
tck = rep("GOOG",5),
EndP= 1:5,
StartP= 0:4)
test.sub = subset(test, tck=="GOOG",select = c(End, EndP))
First, note that test and test.sub are both data frames, so calls like test.sub[1] don't really "mean" anything to R.** It's more R-ish to write test.sub[,1] by virtue of consistency with other R structures. If you compare the results of str(test.sub[1]) and str(test.sub[,1]) you'll see that R treats them slightly differently.
You said you typed:
as.ts(test.sub)
plot(test.sub)
I'd guess you have extensive experience with some sort of OO-language; and while R does have some OO flavor to it, it doesn't apply here. Rather than transforming test.sub to something of class ts, this just does the transformation and throws it away, then moves on to plot the data frame you started with. It's an easy fix though:
test.sub.ts <- as.ts(test.sub)
plot(test.sub.ts)
But, this probably isn't what you were looking for either. Rather, R creates a time series that has two variables called "End" (which is the date now coerced to an integer) and "EndP". Funny business like this is part of the reason time series packages like zoo and xts have caught on so I'll detail them instead a little further down.
(Unfortunately, to the best of my understanding, R doesn't keep date stamps with its default ts class, choosing instead to keep start and end dates as well as a frequency. For more general time series work, this is rarely flexible enough)
You could perhaps get what you wanted by typing
plot(test.sub[,1], test.sub[,2])
instead of
plot(test.sub[1], test.sub[2])
since the former runs into trouble given that you are passing two sub-data frames instead of two vectors (even though it looks like you would be).*
Anyways, with xts (and similarly for zoo):
library(xts) # You may need to install this
xtemp <- xts(test.sub[,2], test.sub[,1]) # Create the xts object
plot(xtemp)
# Dispatches a xts plot method which does all sorts of nice time series things
Hope some of this helps and sorry for the inline code that's not identified as such: still getting used to stack overflow.
Michael
**In reality, they access the lists that are used to structure a data frame internally, but that's more a code nuance than something worth relying on.
***The nitty-gritty is that when you pass plot(test.sub[1], test.sub[2]) to R, it dispatches the method plot.data.frame which takes a single data frame and tries to interpret the second data frame as an additional plot parameter which gets misinterpreted somewhere way down the line, giving your error.
The reason that you get the Error about different x and y lengths is immediately apparent if you do a traceback immediately upon raising the error:
> plot(test.sub[1],test.sub[2])
Error in xy.coords(x, y, xlabel, ylabel, log) :
'x' and 'y' lengths differ
> traceback()
6: stop("'x' and 'y' lengths differ")
5: xy.coords(x, y, xlabel, ylabel, log)
4: plot.default(x1, ...)
3: plot(x1, ...)
2: plot.data.frame(test.sub[1], test.sub[2])
1: plot(test.sub[1], test.sub[2])
The problems in your call are manifold. First, as mentioned by #mweylandt test.sub[1] is a data frame with the single component, not a vector comprised of the contents of the first component of test.sub.
From the traceback, we see that the plot.data.frame method was called. R is quite happy to plot a data frame as long as it has at least two columns. R took you at your word and passed test.sub[1] (as a data.frame) on to plot() - test.sub[2] never gets a look in. test.sub[1] is eventually passed on to xy.coords() which correctly informs you that you have lots of rows for x but 0 rows for y because test.sub[1] only contains a single component.
It would have worked if you'd done plot(test.sub[,1], test.sub[,2], type = "l") or used the formula interface to name the variables plot(V4 ~ V1, data = test.sub, type = "l") as I show in my other Answer.
Surely it is easier to use the formula interface:
> test <- data.frame(End = Sys.Date()+1:5,
+ Start = Sys.Date()+0:4,
+ tck = rep("GOOG",5),
+ EndP= 1:5,
+ StartP= 0:4)
>
> test.sub = subset(test, tck=="GOOG",select = c(End, EndP))
> head(test.sub)
End EndP
1 2011-10-19 1
2 2011-10-20 2
3 2011-10-21 3
4 2011-10-22 4
5 2011-10-23 5
> plot(EndP ~ End, data = test.sub, type = "l")
I work extensively with time series type data and rarely, if ever, have any need for the "ts" class of objects. Packages zoo and xts are very useful, but if all you want to do is plot the data, i) get the date/time information correctly formatted/set-up as a "Date" or "POSIXt" class object, and then ii) just plot it using standard graphics and type = "l" (or type = "b" or type = "o" if you want to see the observation times).
Build an xts object with two rows.
library(xts)
junk<-xts(c(1,2),as.Date(c("2010-01-01","2010-05-01")))
junk
> [,1]
> 2010-01-01 1
> 2010-05-01 2
Why doesn't the following change the index for the first row?
time(junk[1])<-as.Date("2010-02-01")
junk
> [,1]
> 2010-01-01 1
> 2010-05-01 2
I realize that the following works, but why doesn't the above work?
time(junk)[1]<-as.Date("2010-02-01")
junk
> [,1]
> 2010-02-01 1
> 2010-05-01 2
Thanks,
Bill
Direct answer to the post is that the magic is inside of attr<- as Josh says. Subsetting the object first simply creates a new object that gets promptly disposed of once time<- is finished.
In addition you can see the 'internals' of the index via the .index() function. Essentially an vector of type double or integer that maps to POSIXct time - with some attributes attached. The class you are assigning is automatically coerced back and forth. This makes the internals easier to maintain - and lets you do things with any time class you need outside of it.
In general, Date will be the cleanest way to keep TZ and secs trouble out of the mix, but keep in mind that the cost of this hidden aspect is the function index(). This will have to recreate the object you expect.
time(junk[1]) <- as.Date("2010-02-01")
The above doesn't change the index of the first row of junk because subsetting creates a new object--with no reference to junk--and time<-.xts replaces the index of the new object.
The dates in time series are not referenced with "[". They are more like rownames in dataframes. They are stored in the "index" leaf of the attributes list. In addition to that, they are not of Date class but rather the DateTime class so you need to may use POSIXct:
> attributes(junk)$index[1] <- as.POSIXct("2010-02-01")
> junk
[,1]
2010-02-01 1
2010-05-01 2
Edit: more accurately the attribute$index is internally in seconds but the time method will accept a variety of assignment classes.