I think your proposal of modifying plot.ts() to allow 'log' to be
vectorized would be the most natural solution here.
For what it's worth, the details of the implementation and the fact
that you can supply a panel function allows an ugly hack:
pfun <- function(...) {
e <- parent.frame()
e
Dear Sir or Madam
I’m trying to compile a collection of datasets that require use of the
following hypothesis tests.
Are there datasets within the R library that I can get access to?
Kind regards
James Carrigan
Hypothesis Testing
t.test(X,Y)
— performs a two sample t-test between X and Y
t.tes
dear members,
I am using arfima() from forecast package to model
a time series. The following is the code:
> LYGH[[202]]
[1] 45.40 3.25 6.50 2.15
> arfima(LYGH[[202]])
Error in .fdcov(x, fdf$d, h, nar = nar, nma = nma, hess = hess, fdf.work =
fdf$w) :
NA/NaN/Inf
I will try to explain in a bit more detail.
1. I want to have the labels on the right with a small size to be able
to read them
2. I would like not to have the labels on the left
3. I would like to be able to choose the number of principal ticks for
every sensor. That is to say the value of n
On 5/31/23 2:12 PM, Viechtbauer, Wolfgang (NP) wrote:
How about using the same 'mar' for all plots, but adding an outer margin?
DAX <- EuStockMarkets[, 'DAX']
DAX. <- cbind(DAX, diff(log(DAX)), diff(diff(log(DAX
colnames(DAX.) <- c("DAX", 'vel (%)', 'accel (%)')
head(DAX.)
par(mfrow=c(3,
How about using the same 'mar' for all plots, but adding an outer margin?
DAX <- EuStockMarkets[, 'DAX']
DAX. <- cbind(DAX, diff(log(DAX)), diff(diff(log(DAX
colnames(DAX.) <- c("DAX", 'vel (%)', 'accel (%)')
head(DAX.)
par(mfrow=c(3,1), mar=c(1,4.5,0,2), oma=c(3,0,1,0))
plot(DAX.[, 1], log=
On 5/31/23 9:20 AM, Eric Berger wrote:
I sent you an updated response to deal with the redundant copies of the x-axis.
Re-sending.
par(mfrow=c(3,1))
plot(DAX.[, 1], log='y', ylab='DAX', xaxt="n")
plot(DAX.[, 2], ylab='vel (%)', xaxt="n")
plot(DAX.[, 3], ylab='accel (%)')
I got that. Th
I sent you an updated response to deal with the redundant copies of the x-axis.
Re-sending.
par(mfrow=c(3,1))
plot(DAX.[, 1], log='y', ylab='DAX', xaxt="n")
plot(DAX.[, 2], ylab='vel (%)', xaxt="n")
plot(DAX.[, 3], ylab='accel (%)')
On Wed, May 31, 2023 at 4:27 PM Spencer Graves
wrote:
>
>
>
>
On 5/30/23 10:23 AM, Eric Berger wrote:
What if you just precede these commands as follows:
par(mfrow=c(3,1))
plot(DAX.[, 1], log='y', ylab='DAX')
plot(DAX.[, 2], ylab='vel (%)')
plot(DAX.[, 3], ylab='accel (%)')
Most of the space is consumed with two extraneous copies of the axis.
We
Slight modification to have the xaxt ticks and labels only appear on
the bottom chart
par(mfrow=c(3,1))
plot(DAX.[, 1], log='y', ylab='DAX', xaxt="n")
plot(DAX.[, 2], ylab='vel (%)', xaxt="n")
plot(DAX.[, 3], ylab='accel (%)')
On Tue, May 30, 2023 at 6:23 PM Eric Berger wrote:
>
> What if you j
Hello,
I've been enjoying using the "Mixture and Hidden Markov Models in R" by Visser
& Speekenbrink to learn how to apply these analyses to my own data using
depmixS4.
I currently have a fitted 4-state mixture model with three emissions variables
and one binomial covariate (HS). I am trying t
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