Hi,
I am trying to fit a multivariate time series model using DCC GARCH model
and forecast it.
The data looks like this:
> head(datax)
x vibration_x Speed
1 2017-05-16 17:53:00 -0.132 421.4189
2 2017-05-16 17:54:00 -0.296 1296.8882
3 2017-05-16 17:55:00 -
Hi all,
I have this code, but the marginal distribution plot doesn´t appear aligned
with the left plot.
I think could be something about layout or par() mar.
The code was programmed by me time ago.
Can anyone help me to get the marginal distribution on the center (more
higher centered)
id.txt
cumsum() seems to be what you need.
This can probably be done more elegantly, but ...
out <- aggregate(Q ~ wyr, data = Daily, which.max)
tbl <- table(Daily$wyr)
out$Q <- out$Q + cumsum(c(0,tbl[-length(tbl)]))
out
## yields
wyr Q
1 1990 4
2 1991 6
3 1992 9
4 1993 15
5 1994 18
I leave the
Using the dataset below, I got close to what I'm after, but not quite all
the way there. Any suggestions appreciated:
Daily <- read.table(textConnection(" Date wyrQ
1911-04-01 1990 4.530695
1911-04-02 1990 4.700596
1911-04-03 1990 4.898814
1911-04-04 1990 5.097032
1911-04-05 1991 5.2
Bert has suggested there are better ways to do what you want. But if you want
to continue down the path you have started and you want to decide whether to
quote based on the variable Class, something like this might work for you
myOptions <- with(myFunction1Vals, paste(Argument,
ifelse(Class
Since 2008, Microsoft (formerly Revolution Analytics) staff and guests
have written about R every weekday at the Revolutions blog
(http://blog.revolutionanalytics.com) and every month I post a summary
of articles from the previous month of particular interest to readers
of r-help.
In case you miss
Harold:
As a general rule, if you are using eval(parse(...)) you are doing it
poorly in R; cf
library("fortunes")
fortune(106)
Why is something like this not suitable:
fun1 <- function(a1,a2,a3 = c("hi","by"))
{
cat(a3,a1+a2,"\n")
}
> fun1 (1,2)
hi by 3
> fun1(1,2, a3 = "whoopee")
whoopee 3
If I understand you correctly what you are really asking is how to embed quotes
in a string so that it can be parse()'d as an expression. The answer would be:
escape the quotes.
R > myOptions <- "Hello"
R > eval(parse(text = paste( "print(", myOptions, ")" )))
Error in print(Hello) : object 'H
See package "glmnet".
-- Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Tue, Jun 6, 2017 at 8:10 AM, Ravi Varadhan wrote:
> More principled would be to
More principled would be to use a lasso-type approach, which combines selection
and estimation in one fell swoop!
Ravi
From: Ravi Varadhan
Sent: Tuesday, June 6, 2017 10:16 AM
To: r-help@r-project.org
Subject: Subject: [R] glm and stepAIC selects too many effec
Thank you Bert for your suggestion ;).
On Tue, Jun 6, 2017 at 8:19 AM, Bert Gunter wrote:
> Simple matrix indexing suffices without any fancier functionality.
>
> ## First convert M and N to character vectors -- which they should
> have been in the first place!
>
> M <- sort(as.character(M[,1]))
Simple matrix indexing suffices without any fancier functionality.
## First convert M and N to character vectors -- which they should
have been in the first place!
M <- sort(as.character(M[,1]))
N <- sort(as.character(N[,1]))
## This could be a one-liner, but I'll split it up for clarity.
res
I am writing a program where non-technical R users will read in a config file
and the config file will then parse the arguments found within the config and
pass them to respective functions. I'm having trouble (efficiently) writing a
piece of code to retain quotation marks around the argument wh
Thank you David. Using xtabs operation simplifies the code very much, many
thanks ;)
On Tue, Jun 6, 2017 at 7:44 AM, David Winsemius
wrote:
>
> > On Jun 6, 2017, at 4:01 AM, Jim Lemon wrote:
> >
> > Hi Bogdan,
> > Kinda messy, but:
> >
> > N <- data.frame(N=c("n1","n2","n3","n4"))
> > M <- data
> On Jun 6, 2017, at 4:01 AM, Jim Lemon wrote:
>
> Hi Bogdan,
> Kinda messy, but:
>
> N <- data.frame(N=c("n1","n2","n3","n4"))
> M <- data.frame(M=c("m1","m2","m3","m4","m5"))
> C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400))
> MN<-as.data.frame(matrix(NA,nrow=lengt
Thank you David for the code, as I am learning about xtabs operation. That
works great too ;)
On Tue, Jun 6, 2017 at 7:34 AM, David L Carlson wrote:
> Here's another approach:
>
> N <- data.frame(N=c("n1","n2","n3","n4"))
> M <- data.frame(M=c("m1","m2","m3","m4","m5"))
> C <- data.frame(n=c("n1
Here's another approach:
N <- data.frame(N=c("n1","n2","n3","n4"))
M <- data.frame(M=c("m1","m2","m3","m4","m5"))
C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400))
# Rebuild the factors using M and N
C$m <- factor(as.character(C$m), levels=levels(M$M))
C$n <- factor(as.c
Thank you Jim !
On Tue, Jun 6, 2017 at 4:01 AM, Jim Lemon wrote:
> Hi Bogdan,
> Kinda messy, but:
>
> N <- data.frame(N=c("n1","n2","n3","n4"))
> M <- data.frame(M=c("m1","m2","m3","m4","m5"))
> C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"),
> I=c(100,300,400))
> MN<-as.data.frame(mat
If AIC is giving you a model that is too large, then use BIC (log(n) as the
penalty for adding a term in the model). This will yield a more parsimonious
model. Now, if you ask me which is the better option, I have to refer you to
the huge literature on model selection.
Best,
Ravi
[[
Hi Bogdan,
Kinda messy, but:
N <- data.frame(N=c("n1","n2","n3","n4"))
M <- data.frame(M=c("m1","m2","m3","m4","m5"))
C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400))
MN<-as.data.frame(matrix(NA,nrow=length(N[,1]),ncol=length(M[,1])))
names(MN)<-M[,1]
rownames(MN)<-N[,1]
On Wed, 31-May-2017 at 10:05AM -0400, Martin Morgan wrote:
|> On 05/31/2017 04:38 AM, Patrick Connolly wrote:
|> >When I check out those directories in a terminal, there's a big diffrence:
|> >
|> >With R-3.4.0
|> >~ > ll /tmp/RtmpFUhtpY
|> >total 4
|> >drwxr-xr-x 2 hrapgc hrapgc 4096 May 31 10:4
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