Hello,
I need to do a principal component analysis with EQUAMAX-rotation.
Unfortunately the function principal() I use normally for PCA does not offer
this rotation specification. I could find out that this might be possible
somehow with the package GPArotation but until now I could not figure out
That's what I was looking for, thank you very much!
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R
ut my equation is a little bit
different. Any help is welcome. I am thinking about to use RcppArmadillo,
but I am not sure that it is able to compute my equations.
Thank you everyone.
--
Wagner Hugo Bonat
LEG - Laboratório de Estatística e Geoinformação
UFPR - Universidade Federal do P
;- Matrix(0,nrow=nrow,ncol=ncol, sparse = TRUE)
## Restrições de borda
R[1,c(1,2)] <- c(1,-1)
R[ncol,c(ncol-1,ncol)] <- c(-1,1)
## Corpo da matriz
n <- ncol-1
for(i in 2:n){
R[i,c(i-1,i,i+1)] <- c(-1,2,-1)}
R <- as(R, "symmetricMatrix")
return(R)}
2014-08-25 18:29 G
)%*% solve(Wj,C)))
Any idea is welcome.
Thanks
--
Wagner Hugo Bonat
LEG - Laboratório de Estatística e Geoinformação
UFPR - Universidade Federal do Paraná
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Dear R experts,
Â
I need help to make my little program efficient which now takes 2hrs
to complete.
Â
Using arules package I developed set of rules consisted of 900 rules. Now I
want to check whether a lower rule is a subset of a higher premises rule. And
if it is a subset of higher premise
Dear R experts,
Â
I have a dataframe with 4 columns (variables). I want to redorder (or
reposition) these columns on the basis of a value in its last row. e.g.
Â
df1<-data.frame( v1= c(2,3,1,9,5), v2=c(8,5,12,4,11), v3=c(7,8,2,6,9),
v4=c(1,4,6,3,6))Â
Â
> df1
  v1 v2 v3 v4
1Â 2Â 8Â 7Â
 Dear R experts
Â
I generated rules using apriori method in arules package. Though I can access
rules using %in% function but I am unable to access a specific rule by its
unique identifier number. I want to use rule number for further analysis..
Â
Thanking you in advance.
Â
Daniel
Amsterdam
Â
Dear R users,
Â
I have a dataframe with lot of duplicate cases and I want to delete duplicate
ones which have low rank and keep that case which has highest rank.
e.g
Â
> df1
 cno     rank
1Â 1342Â Â Â 0.23
2Â 1342Â Â Â 0.14
3Â 1342Â Â Â 0.56
4Â Â 2568Â Â Â 0.15
5Â 2568Â Â Â 0.89
Please,
Would you kindly inform me if you have any course that includes "dynamic
factor models" ?
Thank you
Lisbon University
Portugal
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Hi all!
I'm using ssplot for drawing a map of Austria and colour the nine provinces
regarding their share of employment. Now I wanted to add the figures in each
province and failed miserably. Using the locator() and text() function
caused the error message "invalid graphics state".
Sigma in relation the
parameters par1, par2 and rho. Some idea ?
--
Wagner Hugo Bonat
LEG - Laboratório de EstatÃstica e Geoinformação
UFPR - Universidade Federal do Paraná
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R-help@r-project.org m
equation many times.
What is the better way to evaluate this equation in R ?
Note that I need only the diagonal, I think is possible to calculate only
the diagnonal, but how ??
--
Wagner Hugo Bonat
LEG - Laboratório de Estatística e Geoinformação
UFPR - Universidade Federal do Paraná
Hi!
For saving 3d plots I make use of the "rgl.snapshot" command provided within
the rgl-Package. So far there was no problem using Windows XP, but under
Windows 7 the result is a black image, however in the png-format (see
attachment http://r.789695.n4.nabble.com/file/n2844487/Koll_perf.png ).
Thanks, but your suggestions don't solve the problem. Though, the plot is
storable via the rgl.postscript command, but this increases computation time
and memory requirements.
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Sent from the R
Hello,
I would like to fit a linear model with autoregressive terms and independent
variables.
Since the largest AR-Term I am interested in is lag.max=336, the arima-function
(package stats) does not work (maximum for arima: lag.max=100).
The FitAR function (package FitAR) does allow model fitti
Hello!!, for linear models fit I use Gretl, but now I'm starting to use R,
I would like to know if is there some function to obtain a extended summary
like in Gretl.
I will write a example in Gretl
Modelo 1: MCO, usando las observaciones 1968-1982 (T = 15)
Variable dependiente: Invest
Coefici
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