Dear Tom,
Running R 3.2.2 on Ubuntu 15.04, if I run dev.list(), I get NULL. And
I guess it is the expected behavior, as per the help page, it "returns
the numbers of all open devices, except device 1, the null device".
So, if I run
x11()
dev.list()
I get
X11cairo
2
HTH,
Pascal
On Mon,
All,
I have previous built R from source many times, generally, without
problems. However on my new Ubuntu 15.04 Linux system with R 3.2.2 when I
run the command dev.list() I get:
> dev.list()
NULL
At the completion of running ./configure, I have
R is now configured for x86_64-pc-linux-gnu
S
Thank you to Dennis and Jeff,
The scales = "free_y" did exactly what I needed. Just in case some one
else has the same problem, the code is below.
Take Care
David
p <- ggplot(data = SS, aes(x=Year, y=Sulfate, col=Detections)) +
geom_point(aes(shape=Detections)) +
##sets the colors
scale
> On Nov 8, 2015, at 4:05 PM, Val wrote:
>
> HI all,
>
> DF <- read.table(textConnection(" X1 X2 X3 TIME
> Alex1 0 0 1960
> Alexa 0 01920
> Abbot 0 0 0
> Smith Alex1 Alexa2012
> Carla Alex1 01996
> Jacky
HI all,
DF <- read.table(textConnection(" X1 X2 X3 TIME
Alex1 0 0 1960
Alexa 0 01920
Abbot 0 0 0
Smith Alex1 Alexa2012
Carla Alex1 01996
JackySmith Abbot2013
Jack 0 Jacky2014
Alm
While I fully agree with Jim's comments, you may also need to understand the
notion of time complexity in algorithm analysis. All the mentioned speed-ups
are basically linear, in the sense that they accelerate a single step of your
algorithm. However if your algorithm has combinatorial complexit
You need to take a close look at the function incomb that you are
creating. I see what appears to be a constant value ("*(
gamma((1/beta)+1))*((alpha)^(-(1/beta)))") being computed that you might
only have to compute once before the function. You are also referencing
many variables (m, LED, j, ..
Hi Julian,
As I don't have access to "datos", I had to make it up. The following does
what I expected.
library(scatterplot3d)
#datos<-read.csv("C:\\prueba.csv",sep=",",header=TRUE)
#str(datos)
datos<-data.frame(Bx=runif(40),e=runif(40),t=runif(40))
scatterplot3d(datos)
s3d<- scatterplot3d(datos, t
Thanks all for replying.
In fact I've used the the Rprof() function and found out that the incomb()
function (in my code above) takes about 80% of the time, but I didn't
figure out which part of the function is causing the delay. So I thought
that this may be due to the for() loops.
I MUST run th
Hi all,
I've seen recently this great post by Nikita Murzintcev
http://rpubs.com/nikita-moor/107657. If I understood correctly, according
to Griffiths (2004) I should select 11 topics? But, it seems that other
metrics suggest quite different number of topics?
I mean, 11 topics is about the right
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