Why not
Arima(tsdata, c(0, 0, 1), include.drift = TRUE)
?
Why do you say it should be an AR(2) model?
Hope this helps,
Rui Barradas
Às 06:43 de 10/05/19, Rui Barradas escreveu:
Hello,
This is just a typo, in R logical values ("true) are not character
strings. You must pass FALSE (the defa
Hello,
This is just a typo, in R logical values ("true) are not character
strings. You must pass FALSE (the default, can be omited) or TRUE.
fitdata <- Arima(tsdata, c(2, 0, 0), include.drift = TRUE)
From the help page ?logical
Details
TRUE and FALSE are reserved words denoting logical c
In future, always cc the list (unless it's personal,which this isn't). I
have done so here. As I am largely ignorant on the subject matter, others
will have to help, which is why you should cc the list.
Cheers,
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
Please start by reading and following the posting guide linked at the
bottom of this email. In particular:
1) Post in **plain text** on this plain text list so we don't get the
mangled html of your post.
2) Tell us what package Arima() is in.
Cheers,
Bert Gunter
On Thu, May 9, 2019 at 2:27 P
Hello everyone,
So this is my first post to this list, I'm trying to fit an Arima (2,0,0)
model and I think a drift term would help but I'm getting an error term
when I'm trying to include it. Here is my data:
-6.732172338
-2.868884273
-5.371585089
-6.512740463
-4.171062657
-5.738499071
-3.3439471
Hi,
I have created a function called myFn() which should be acting as below:
1. If function takes too long, then it will timeout and a specific message
will be displayed
2. This function may generate error on its own, so if it evaluates before
that specific time and fails then another specific me
I don't think previous responses have addressed the question, which appears
to be: "How does R know to look in the "data" object for the variable names
in the formula?" And, of course, I could be wrong -- in which case ignore
all the following.
My answer to that question is: it's quite complicated
Thanks, Martin. I missed the duplication. My apology -- old age is
asserting it's presence.
Then my response is: I think the documentation is correct as written:
> a <- matrix(rep(1:3,2), nr=3)
> a
[,1] [,2]
[1,]11
[2,]22
[3,]33
> duplicated(a)
[1] FALSE FALSE FALSE
Hello John,
Others have commented on the first half of your question, but the
second half of your question looks very much like R's built-in
predict() functions:
>?predict
>?predict.lm
Best Regards,
Bill.
W. Michels, Ph.D.
On Wed, May 8, 2019 at 6:23 PM Sorkin, John wrote:
>
> Can someone
> Bert Gunter
> on Thu, 9 May 2019 08:46:15 -0700 writes:
> Juan:
> No, I think there may be a bug:
>> duplicated(array(c(1,2,3,2,5,6),c(3,2)), MARGIN=1:2)
> [,1] [,2]
> [1,] FALSE TRUE
> [2,] FALSE FALSE
> [3,] FALSE FALSE
> ## This is wrong
>
Juan:
No, I think there may be a bug:
> duplicated(array(c(1,2,3,2,5,6),c(3,2)), MARGIN=1:2)
[,1] [,2]
[1,] FALSE TRUE
[2,] FALSE FALSE
[3,] FALSE FALSE
## This is wrong
## But if we first define the array...
> a <- array(c(1,2,3,4,5,6), c(3,2))
> duplicated(a, MARGIN = 1:2)
[,1] [
I think there is an error in duplicated() help page when it states that:
"The array method calculates for each element of the sub-array
specified by MARGIN if the remaining dimensions are identical to those
for an earlier (or later, when fromLast = TRUE) element (in row-major
order). "
Instead of:
I'd encourage you to install a full build system for your linux. That
way you are able to compile any contributed packages you may want.
Your linux package manager (apt, dnf, urpmi etc) may be able to
provide an R compiled package, which with the exception of
Debian/Ubuntu is likely to be several
Hello,
There is a "standard" deparse/substitute trick that gets the names of
the variables passed to a function. There are more sophisticated ways
but maybe that is what you are looking for.
myfunction <- function(y, x, dataframe){
y <- deparse(substitute(y))
x <- deparse(substitute(x))
HI, i am functional data analysis, using times series data to which i fit the
functional autoregressive model of order one using FAR package in R, know i
want to extend my model to order two as it is observed that the FAR package can
only do for order one., can any body help me that how to exte
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