You could also consider
isTRUE(all.equal(FUN, mean))
> isTRUE(all.equal(mean, mean))
[1] TRUE
> isTRUE(all.equal(mean, median))
[1] FALSE
HTH
Keith J
"Patrick Burns" wrote in message
news:4c28777f.1040...@pburns.seanet.com...
> If I understand the problem properly,
> you want something li
If I understand the problem properly,
you want something like this:
function(FUN, ...)
{
FunName <- deparse(substitute(FUN))
if(FunName == "mean") {
...
} else if(FunName == "median") {
...
}
}
Using 'switch' is an alternative to 'i
Hey,
I am using the ets() function in the forecast package to find out the best
fit parameters for my time-series. I have about 50 sets of time series data.
I'm currently using the function as follows:
ets(x,model="AZZ",opt.crit="mse")
As to my observation about 5-10 of them have been identifie
Hello everybody,
I'm trying to use a if-statment on a function. For a better
understanding I want to present a small example:
FUN=mean # could also be median,sd or
any other function
if (FUN == mean)
plot(...)
if (FUN == median)
plot(...)
Dear R Users,
Is there a package that performs multiple-treatment meta-analysis in R?
Also, most of the articles I read that apply MTM are interested in combining
direct and indirect effects. I am interested in adjusting for multiple
comparisons within the same study (multiple compari
Yes,
I believe I did something along your lines.
See the code snippet at the end of the email which sorts everything out
as far as I am concerned.
Cheers
Lorenzo
#
library(Cairo)
library(plotrix)
set.seed(1234)
myseq <- abs(
Dear all, I am looking for some interactive study materials on Principal
component analysis. Basically I would like to know what we are actually
doing with PCA? What is happening within the dataset at the time of doing
PCA.
Probably a 3-dimensional interactive explanation would be best for me.
I
Hey,
I have a few doubts with regard to the usage of the auto.arima function from
the forecast package in R.
*Background:*
I have a set of about 50 time-series for which I would like to estimate the
best autroregressive model. (I want to estimate the coefficients and order
of p). Each of the serie
Christian Jebsen writes:
> Dear R-help users,
>
> I'd like to use the R-package "pls" and want to extract the explained
> Y-variance to identify the important (PLS-) principal components in my
> model, related to the y-data. For explained X-variance there is a function:
> "explvar()". If I under
basicly I am using standart shape package in R, no need additional code for
analysis
http://cran.r-project.org/web/packages/shapes/index.html
http://cran.r-project.org/web/packages/shapes/shapes.pdf
anf the main reference is Statistical Shape Analysis
(http://www.amazon.com/Statistical-Shape-An
101 - 110 of 110 matches
Mail list logo