Ben Bolker wrote:
Ubuntu.Diego gmail.com> writes:
I'm trying to get some "easy coding" to reproduce the error. In the
meantime I have R code that run for 20 or more hours and suddenly i got
a "segfault 'memory not mapped'" error. I have to clarify that the error
not alway occurs and sometim
Ben Bolker wrote:
Lo, Ken roche.com> writes:
Hi all,
I am running into a snag using quantile function in stats. Basically, I
don't understand why the loop below throws the error that it does.
test.data <- rnorm(1000, 0, 1)
for (i in seq(0.1, 0.001, 0.1)){
test <- quantil
Hello,
I used the following codes to generate bivariate normal dependence structure
with unit Frechet margins.
Sigma <- matrix(c(1,.5*sqrt(1),.5*sqrt(1),1),2,2) # generate
y <- mvrnorm(Nsam, c(0,0), Sigma) # random
v <- cbind(pnorm(y[,1],mean = 0, sd = 1), pnorm(y[,2],mean = 0, sd =
On Fri, 2008-10-03 at 15:27 -0400, Lo, Ken wrote:
> Hi all,
>
> I am running into a snag using quantile function in stats. Basically, I
> don't understand why the loop below throws the error that it does.
>
> test.data <- rnorm(1000, 0, 1)
>
> for (i in seq(0.1, 0.001, 0.1)){
> te
Erik Iverson wrote:
Depending on the class, you might have another teaching opportunity.
right away after showing your students that (and why) x + 1 - x = 0
Example:
x <- 10^20
x + 1 - x
Uwe Ligges
See R FAQ 7.31 and the document it points to.
Adrian Teo wrote:
I was working on
Hello,everybody,
I used the following codes to generate bivariate normal dependence structure
with unit Frechet margins.
Sigma <- matrix(c(1,.5*sqrt(1),.5*sqrt(1),1),2,2) # generate
y <- mvrnorm(Nsam, c(0,0), Sigma) # random
v <- cbind(pnorm(y[,1],mean = 0, sd = 1), pnorm(y[,2],mean
Just thought I'd ask. For those who've never seen TK!Solver, I strongly
recommend taking a look. So far as I can tell, it's the only product of
its type available, retail or open source, for any platform.
What makes TK!Solver so cool is that it adaptively back-solves pretty
much any unknown fr
You may use kde2d from MASS, with estimates a 2d density
d <- kde2d(z1,z2,h=10^(-(2:9)/2))
plot(z1,z2)
contour(d,add=T)
hth.
biyeshejiqx schrieb:
Hello,everybody,
I used the following codes to generate bivariate normal dependence structure with unit Frechet margins.
Sigma <- matrix(c(
Sorry, mixed up where to place the desired level, it has to be done in
the plot part, as in
d <- kde2d(z1,z2)
contour(d, levels=10^(-(2:9)/2), add=T)
Eik Vettorazzi schrieb:
You may use kde2d from MASS, with estimates a 2d density
d <- kde2d(z1,z2,h=10^(-(2:9)/2))
plot(z1,z2)
contour(d,add=T
Hello,
On Thu, Oct 2, 2008 at 3:50 PM, Hanek Martin
<[EMAIL PROTECTED]> wrote:
> Our company has been looking at "SAS enterprise guide 4" (Insightful Miner is
> a similar product from Insightful, I believe) which seems to provide a nice
> graphical way of displaying/managing a process or project
For a non-log-scaled y-axis, I was able to change the appearance of the
y tick labels in an xyplot by using a custom function for
yscale.components. However I couldn't get that approach to work for when
scales=list(y=list(log=TRUE)).
What I'm trying to do is make the y-tick labels show up as some
Hi,
Is it possible to unload a COM component that is loaded through the
RDCOMClient?
many thanks,
Danny
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View this message in context:
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I haven't quite figured out how I can change the environment of a function.
I have a main function and want to use different auxillary functions, which I
supply as parameter (or names). What I want to do is something like this:
main.fun=function(aux.fun,dat){
x <- 1
fun.dat()
}
aux.fun.one
Thanks Stephen and Gabor. The zoo package is what I was looking for.
On Fri, Oct 3, 2008 at 5:44 PM, Gabor Grothendieck
<[EMAIL PROTECTED]>wrote:
> Check out the zoo package and its three vignettes and
> ?aggregate.zoo in particular.
>
> Also have a look at the article on dates and times in R New
On 03/10/2008 7:19 PM, Tomas Lanczos wrote:
Thank You for Your answer, Duncan,
Duncan Murdoch wrote:
On 03/10/2008 4:33 AM, Tomas Lanczos wrote:
hello,
I wish to create some 3d scatter diagrams visualising different
grouped data set by a given field in the database. I tried the
scatterplot3
Please provide complete examples that include test drivers.
Here is main.fun reworked to eliminate the error in the arg
list and also changing the environment of aux.fun:
main.fun <- function(aux.fun, dat) {
x <- 1
environment(aux.fun) <- environment()
aux.fun()
}
aux.fun.one <- function
What's wrong with explicitly passing the variables as arguments to the function?
aux.fun.one <- function(dat, x){
median(dat) - x
}
Hadley
On Sat, Oct 4, 2008 at 11:50 AM, René Holst <[EMAIL PROTECTED]> wrote:
> I haven't quite figured out how I can change the environment of a function.
> I ha
Rene,
What you have below does not make much sense. You have not provided a
definition for fun.dat() nor have you given an example with which to run
any of your functions.
The issue you raise about passing 'dat variable as a parameter as it can
get pretty large' is not an issue.
Observ
Excel has a solver and Ryacas has limited ability to
do symbolic solving:
> library(Ryacas)
> x <- Sym("x")
> y <- Sym("y")
> Solve(List(x + y == 1, x - y == 1), List(x, y))
[1] "Starting Yacas!"
expression(list(list(x == 1 - y, y == 0)))
On Sat, Oct 4, 2008 at 10:07 AM, Carl Witthoft <[EMAIL PR
Hi,
I would like to estimate an error correction model with lm() but I don't find
the correct syntax for that.
The model (leaving out the time indices) looks like:
dY = a0 - a1 * (Y - b1*X) + b0*dX + e
the problem is the term - a1 * (Y - b1*X). How can I restrict a1 to be the same
for both Y a
On 04/10/2008 3:29 PM, Werner Wernersen wrote:
Hi,
I would like to estimate an error correction model with lm() but I don't find
the correct syntax for that.
The model (leaving out the time indices) looks like:
dY = a0 - a1 * (Y - b1*X) + b0*dX + e
the problem is the term - a1 * (Y - b1*X). H
Werner: what you want done is more easily done using a time series
framework. I think there are examples for doing what you want to in the urca
package or , if not there, then in the associated book of Bernhard Pfaff.
If you send your question to R-Sig-Finance , that may illicit other ideas
besides
Hi ggplot experts,
I need to plot two time series of stacked data: a barchart with bars for
each month. To compare the data of two years I need to combine both time
series with in a single graph via position=doge.
How should I do that?
I tried the following scenario:
I added two layers with t
Dear R users,
I used sm.density function in the sm package and kde2d() in the MASS package
to estimate the bivariate density. Then I calculated the Kullback leibler
divergence meassure between a distribution and the each of the estimated
densities, but the asnwers are different. Is there any diff
Peter Dalgaard biostat.ku.dk> writes:
[snip discussion of overrun in seq()]
> > If I don't hear otherwise I will submit this as
> > a bug to r-devel ...
> >
> It IS deliberate We discussed this when R was still in the toddler
> stages.
>
> If you don't allow slight overruns like tha
Gabor Grothendieck wrote:
Mark did not post his response so I don't know what it
is.
This is Mark's proposal. Sorry, I was speaking about it as if posted to
the list.
# MAKE POSIXct OBJECTS FROM CHARACTER STRINGS
temp1 <- as.POSIXct(strptime("2007-02-02","%Y-%m-%d"))
temp2 <- as.POSIXct(
On 10/4/08, Desany, Brian <[EMAIL PROTECTED]> wrote:
> For a non-log-scaled y-axis, I was able to change the appearance of the
> y tick labels in an xyplot by using a custom function for
> yscale.components. However I couldn't get that approach to work for when
> scales=list(y=list(log=TRUE)).
>
Assuming the first row is the comparison row convert
to Date class and calculate:
DF[] <- lapply(DF, as.Date, format = "%Y/%m/%d")
pmin(DF[[2]], DF[1, 2]) - pmax(DF[[1]], DF[1,1])
Please be explicit next time in formulating your queries
as requested in the last line of every message to r-help.
O
Thanks.
Gabor Grothendieck wrote:
Assuming the first row is the comparison row convert
to Date class and calculate:
DF[] <- lapply(DF, as.Date, format = "%Y/%m/%d")
pmin(DF[[2]], DF[1, 2]) - pmax(DF[[1]], DF[1,1])
Please be explicit next time in formulating your queries
as requested in the las
I'm trying to get the VAR method of the VARS package to work and I get
this weird (probably internal) error:
> usvardata
CBR CDR RGDPGrowthRate
1610416 8.6 -1.98
16105 15.8 8.6 1.62
...
16123 14.4 8.6 3.55
16124 14.39 8.52 2.75
> usvar <- V
Hi Elena,
Currently, there's no way to combine stacking and dodging in a single
graphic. However, you can often use faceting to get a similar effect
to dodging. Could you explain your problem in a little more detail?
Thanks,
Hadley
On Sat, Oct 4, 2008 at 4:22 PM, Elena Schulz <[EMAIL PROTECTE
Thanks a lot! It works, however it is less smooth than I had expected.
And I have a further question:
What if I have my own 2-d density function as:
f <- function(x1,x2,parameters)
...
and there is no way how to generate random variates (x1,x2) from f. But I'd
still like to get the contour
Frankly, I would do it Duncan's way as that represents it simply as
a linear model but if you insist you could use nls.
nls(dY ~ a0 - a1 * (Y - b1*X) + b0*dX, DF, start = whatever)
On Sat, Oct 4, 2008 at 4:06 PM, Duncan Murdoch <[EMAIL PROTECTED]> wrote:
> On 04/10/2008 3:29 PM, Werner Wernersen
Hi all,
I am kind of stuck of using Predict function in R to make prediction
for a model with continuous variable and categorial variables. i have
no problem making the model, the model is e.g.
cabbage.lm2<- lm(VitC ~ HeadWt + Date + Cult)
HeadWt is a continuous variable, Date and Culte are fac
Hi
I have two columns of data with time in form of HH:MM:SS - representing
start time and end time of an activity. I am trying to calculate the time
difference (duration of the activity).
(1) I first tried
> difftime(btime, etime, units = "mins")
This however gave me the error - Error in
as.POSIX
Hi all,
I am kind of stuck of using Predict function in R to make prediction
for a model with continuous variable and categorial variables. i have
no problem making the model, the model is e.g.
cabbage.lm2<- lm(VitC ~ HeadWt + Date + Cult)
HeadWt is a continuous variable, Date and Culte are fac
You seem to have omitted a left quotation mark in (2): %H:%M:%S".
Regards,
Yihui
--
Yihui Xie <[EMAIL PROTECTED]>
Phone: +86-(0)10-82509086 Fax: +86-(0)10-82509086
Mobile: +86-15810805877
Homepage: http://www.yihui.name
School of Statistics, Room 1037, Mingde Main Building,
Renmin University of Ch
You used variable names 'Date' and 'Cult' in lm2, but *different*
names 'Cultc52' and 'Dated16' for prediction.
Regards,
Yihui
--
Yihui Xie <[EMAIL PROTECTED]>
Phone: +86-(0)10-82509086 Fax: +86-(0)10-82509086
Mobile: +86-15810805877
Homepage: http://www.yihui.name
School of Statistics, Room 1037,
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