search for: decompositon

Dear R people, I do not have much knowledge about linear algebra but currently I need to understand what the function qr.qty is actually doing. The documentation states that it calculates t(Q) %*% y via a previously performed QR matrix decomposition. In order to do that, I tried following basic example: m<-matrix(c(1,0,0,0,1,0,0,0,1,0,0,1),ncol=3) # 4x3 matrix

Can we do Cholesky Decompositon in R for any matrix --------------------------------- [[alternative HTML version deleted]]

I'm new to the topic of wavelets. When I tried to use the mra function in the wavelets package, the data is not getting compressed. eg. if the original data has 500 values , the output data also has the same. However in MATLAB, depending on the level of decompositon, the data gets compressed. How do I implement this in R? -- View this message in context: http://r.789695.n4.nabble.com/wavelets-tp3642973p3642973.html Sent from the R help mailing list archive at Nabble.com.

...osition of time series. In R, when I fit the decomposition using the "stl" function, an object is returned when ask the summary of the fit, called STL.seasonal (%), STL.trend (%) and STL.remainder (%). Once the decomposition is additive, I thought that this would be some kind of decompositon of the variability of the time serie in terms of seasonal, trend and residual unexplained. Just like a factorial analysis. But, the sum os the %'s isn't one. In fact, in some cases the value of the STL.seasonal or STL.trend exceeds 100%. When I read the paper of the help of the fu...

...; Q 1) Is aov supposed to avoid qr decomposition and matrix inversion? > In S it is claimed to be faster than lm() for large datasets. Is > that due to avoidance of qr()? > (My main goal is to get the Error strata working.) My use of matrix manipulation to avoid qr decompositon takes more time than the complied solver build into lm.fit. It seems that the real reason for doing aov differently would be if the determination of Error strata and which factors to evaluate in any given error statum requires a different approach from lm.fit. I'm not going to worry about spee...