Getting following error while using prophet library:
Error in [<-(*tmp*, m$history$t >= m$changepoints.t[i], i, value =
1) : subscript out of bounds
Code : m <- prophet(data) this data I've loaded from csv file.
My dataset looks like this :
ds y
1 2017-05-23 08:07:00 21.16641
2 2017-05-23 08:07:10 16.79345
3 2017-05-23 08:07:20 16.40846
4 2017-05-23 08:07:30 16.24653
5 2017-05-23 08:07:40 16.14694
6 2017-05-23 08:07:50 15.89552
ds column is of following type :"POSIXct" "POSIXt"
y column is of following type :"numeric" (these are log values of some count values)
Being new to R, i don't have any clue on how to resolve this. Please help.
Your data does not have any change points (points of interest in your data series where there is change in the local trend direction). This error seems like a bug in the Prophet package which is not handling this situation gracefully. However you can fix this by setting the changepoint tuning parameters.
Quick fix: set changespoints to 0 by using param:
n.changepoints = 0
in your prophet call.
Related
I'm trying to model the impact of storms on sales patterns using the CausalImpact package. When I create a zoo object and pass it to the model I receive an error. I've read through the documentation and can't figure out what I'm doing differently from the examples in the documentation.
I'm working with the following data.frame:
> head(my.data)
date sales units
1 2014-10-17 71319.85 21436.64
2 2014-10-18 88598.26 26755.79
3 2014-10-19 95768.29 29823.86
4 2014-10-20 62303.04 19417.71
5 2014-10-21 56477.57 17562.21
6 2014-10-22 54890.39 16946.43
Then I'm converting it to a zoo object:
my.data<- zoo( my.data[ ,c('sales','units')], my.data[,'date'] )
> str(my.data)
‘zoo’ series from 2014-10-17 to 2017-04-13
Data: num [1:907, 1:2] 71320 88598 95768 62303 56478 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:2] "sales" "units"
Index: Date[1:907], format: "2014-10-17" "2014-10-18" "2014-10-19" ...
Then I set the pre and post periods and run the model:
pre.period <- as.Date(c('2015-10-17','2017-03-09'))
post.period <- as.Date(c('2017-03-10','2017-04-13'))
library(CausalImpact)
impact<- CausalImpact(data = my.data, pre.period = pre.period, post.period = post.period, alpha = .01)
But I'm receiving this error:
> impact<- CausalImpact(data = my.data, pre.period = pre.period, post.period = post.period, alpha = .05)
Error in bsts(formula, data = data, state.specification = ss, expected.model.size = kStaticRegressionExpectedModelSize, :
Caught exception with the following error message:
BregVsSampler did not start with a legal configuration.
Selector vector: 11
beta: 0 0
I've used this package successfully with univariate time series data, but cant identify why this isn't working.
Thank you for your help!
I ran into the same exact issue, after applying recent package updates (including CausalImpact). Everything was working fine previously.
While I don't have the exact cause/solution, I have discovered something that may help you.
In my data, I tried simply replacing the dates in the zoo object with a test sequence. So in your case it would be something like:
time.pts <- seq.Date(as.Date("2014-10-17"), by = 1, length.out = 907)
my.data<- zoo( my.data[ ,c('sales','units')], time.pts )
After doing this, the "BregVsSampler" exception did not occur. So I figured the issue must be related to the dates, and then put my original date series back into the zoo object. I then noticed that I had a gap between pre.period and post.period, i.e. see the gap between 3/9 and 3/20 below:
pre.period <- as.Date(c('2015-10-17','2017-03-09'))
post.period <- as.Date(c('2017-03-20','2017-04-13'))
When I adjusted the pre/post periods to remove the gap in dates, the problem again went away.
While you don't seem to have such a gap in the code you show above, you may want to look at your date series for any inconsistencies and/or try a different date range. Obviously there is a bug somewhere that needs to get fixed, but perhaps the above info will help you work around the issue in the interim.
I'm applying ddply to the following data frame. The point is to apply ecdf function to yearly_test_count value to rows that have the same country.
> head(test)
country yearly_test_count download_speed
1 AU 1 2.736704
2 AU 6 3.249486
3 AU 6 2.287267
4 AU 6 2.677241
5 AU 6 1.138213
6 AU 6 3.205364
This is the script I used:
house_total_year_ecdf <- ddply(test, c("country"), mutate,
ecdf_val = ecdf(yearly_test_count)(yearly_test_count)*length(yearly_test_count))
But I received the following error:
Error in eval(substitute(expr), envir, enclos) :
object 'yearly_test_count' not found
==================================================================
I tried using the function ecdf alone with yearly_test_count column and it works:
ecdf(test$yearly_test_count)(test$yearly_test_count)*length(test$yearly_test_count)
Anyone has any idea why this doesn't work when using ddply?
This is weird since the script worked before, now I run the script again and encounter the mentioned error. I'm not sure if this issue is related to different in versions of R or versions of the package?
Any help is much appreciated ! :)
One option would be using ave from base R
test$ecdf_val <- with(test, ave(yearly_test_count, country,
FUN = function(x) ecdf(x)(x)*length(x)))
Can someone help me even phrase what I am trying to do? (I am new to this.)
I am trying out Machine Learning in R now that I have it nailed in Matlab. R is just a passion of mine at the moment.
Data:
> head(newzap1209, n=5)
buoy_douglas avgtopsum avgstdwin1 stddiff2
1 3 -12.097720 410.4747 410.6323
2 2 -10.462240 260.7213 263.2085
3 2 -11.539432 357.1802 362.3258
4 2 -9.524074 234.8285 234.8571
5 3 -11.498597 356.4736 359.4485
Code:
library(mda)
fit<-mda(buoy_douglas~.,data=newzap1209)
summary(fit)
predictions<-predict(fit,newzap1209[,2:4])
table(predictions,newzap1209$buoy_douglas)
Error message:
Error in table(predictions, newzap1209$buoy_douglas) : all arguments must have the same length
Everything works except the table!
Same goes for the confusion matrix.
The error is saying that predictions and newzap1209 have mismatching lengths (nrows). Which should be impossible since you generated fit from newzap1209[,2:4].
Check the length of each and debug why they mismatch.
I have data showing when an animal came to a survey station. example csv file here The first few lines of data look like this:
Site_ID DateTime HourOfDay MinTemp LunarPhase Habitat
F1 6/12/2013 14:01:00 14 -1 0 river
F1 6/12/2013 14:23:00 14 -1 0 river
F2 6/13/2013 1:21:00 1 3 1 upland
F2 6/14/2013 1:33:00 1 4 2 upland
F3 6/14/2013 1:48:00 1 4 2 river
F3 6/15/2013 11:08:00 11 0 0 river
I would like to perform a circular-linear regression in R to determine peak activity times. The dependent variable could be DateTime or HourOfDay, whichever is easier. I would like to incorporate the covariates Site_ID (random effect), plus MinTemp, LunarPhase, and Habitat into a mixed-effects model.
I have tried using the lm.circular function of program circular, and have the following code:
data<-read.csv("StackOverflowExampleData.csv")
data$DateTime<-as.POSIXct(as.character(data$DateTime), format = "%m/%d/%Y %H:%M:%S")
data$LunarPhase<-as.factor(data$LunarPhase)
str(data)
library(circular)
y<-data$DateTime
y<-circular(y, units ="hours",template = "clock24",rotation = "clock")
x<-data[,c(1,4,5,6)]
lm.circular(y=y, x=x, init=c(1,1,1,1), type='c-l', verbose=TRUE)
I keep getting the error:
Error in Ops.POSIXt(x, 12) : '/' not defined for "POSIXt" objects
Apparently this is a known bug, but I was confused by this threat about it and could not determine an appropriate work-around. Suggestions?
Also, my ultimate goal with this data was to run a circular-linear version of a glm, and then test several models against one another using AIC or some other information theoretics method. The model I'm seeking would be a circular-linear version of something like this:
glmer(HourOfDay~MinTemp+LunarPhase+Habitat+(1|Site_ID),family=binomial,data=data)
Perhaps this is an inappropriate application of the circular package. If so, I'm open to other suggestions of models and/or graphics that would investigate peak activity using the data and covariates.
Note: I did search for related discussions and found this somewhat relevant thread, but it was never answered, did not request a solution in R, and was of a different scope.
The specific problem is caused by conversion.circular. There, a POSIXlt object is divided by 12. This is an operation that has a non-defined outcome:
> as.POSIXlt('2005-07-16') / 2
Error in Ops.POSIXt(as.POSIXlt("2005-07-16"), 2) :
'/' not defined for "POSIXt" objects
So, it seems that you cannot use data of this class as input for the circular package. I could not find any mention of POSIXlt data in the examples. Maybe you need to specify the timestamps simply as a number, not as a POSIXlt object.
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).