similar to: matrix "Adjoint" function

Hello everybody When one is working with complex matrices, "transpose" very nearly always means *Hermitian* transpose, that is, A[i,j] <- Conj(A[j,i]). One often writes A^* for the Hermitian transpose. I have only once seen a "real-life" case where transposition does not occur simultaneously with complex conjugation. And I'm not 100% sure that that wasn't a

Hi, All: Is there a simple way to detect complex conjugates in the roots returned by 'polyroot'? The obvious comparison of each root with the complex conjugate of the next sometimes produces roundoff error, and I don't know how to bound its magnitude: (tst <- polyroot(c(1, -.6, .4))) tst[-1]-Conj(tst[-2]) [1] 3.108624e-15+2.22045e-16i

Hi, Man page for 'convolve' says: conj: logical; if 'TRUE', take the complex _conjugate_ before back-transforming (default, and used for usual convolution). The complex conjugate of 'x', of 'y', of both? In fact it seems that it takes the complex conjugate of 'y' only which is OK but might be worth mentioning because (1) conj=TRUE is the

Is there a function in R that returns the complex conjugate of a matrix (a la 'CONJ' in IDL or 'Conjugate' in Mathmatica)?

How do you calculate the percentage of data within 2SD, 3SD, 4SD, 5SD, and 6SD of the mean? I used the following link as the data I'm working with: nb10 <- read.table("http://www.adjoint-functors.net/su/web/314/R/NB10") if this helps answer my question. Can you please explain how to calculate the SD's? Please be specific in which part of the function changes when

Howdee, One of my student spotted something I can't explain: a probability >1 vs a normal probability density function. > dnorm(x=1, mean=1, sd=0.4) [1] 0.9973557 > dnorm(x=1, mean=1, sd=0.39) [1] 1.022929 > dnorm(x=1, mean=1, sd=0.3) [1] 1.329808 > dnorm(x=1, mean=1, sd=0.1) [1] 3.989423 > dnorm(x=1, mean=1, sd=0.01) [1] 39.89423 > dnorm(x=1, mean=1, sd=0.001) [1]

Okay, so I have a homework projecr for R, and we had to input the following link as some sort of data: nb10 <- read.table("http://www.adjoint-functors.net/su/web/314/R/NB10"). Afterwards, we have to use fivenum(nb10) to find max, min, quantiles, and sd, but I'm okay with this. The next question is where I'm stuck. The question is as follows; Compare the median (use the

thanks a lot Renaud. but i was interested in Finney's fiducial confidence intervals of LD50 so to obtain comparable results with SPSS. But your reply leads me to the next question: does anybody know what is the best method (asymptotic, bootstrap etc.) for calculating confidence intervals of LD50? i could "get rid" of Finney's fiducial confidence intervals but

Howdee, My question appears at #6 below: 1. I want to model the growth of each of a large number of individuals using a 4-parameter logistic growth curve. 2. nlme does not converge with the random structure that I want to use. 3. nlsList does not converge for some individuals. 4. I decided to go around nlsList using: t(sapply(split(data, list(data$id)), function(subd){coef(nls(mass ~

Dear all, I am trying to avoid the warnings produced by: > x <- -2:2 > log(x) [1] NaN NaN -Inf 0.0000000 0.6931472 Warning message: production de NaN in: log(x) I thought that using ifelse would be a solution, but it is not the case: > ifelse(test = x < 0, yes = NaN, no = log(x)) [1] NaN NaN -Inf 0.0000000 0.6931472 Warning message: production

Hi all I have run into a case where I don't understand why predict.lrm and predict.glm don't yield the same results. My data look like this: set.seed(1) library(Design); ilogit <- function(x) { 1/(1+exp(-x)) } ORDER <- factor(sample(c("mc-sc", "sc-mc"), 403, TRUE)) CONJ <- factor(sample(c("als", "bevor", "nachdem",

Dear All, Can R plot graphs like the one at http://www.mathwords.com/f/f_assets/floor_graph.gif with the balls at the discontinuity points? Thanks in advance, Paul

Is there a way to apply a function, say cor(), to each combination of some number of variables, and this, without using loops? For example, I have day, hour, var1 and var2. How could I compute cor(var1,var2) for each day*hour combination and obtain a matrix with day, hour and the cor value for each combination? Thanks for your time, Marc =================== Marc Bélisle Professeur adjoint

Hello, I am beginning with R and I would like to test a non linear model. But I do not find exactly wath I am looking for in nls packages (or I do not know where to search). I would like to try a model like this : y=b * x exp(n)/(a exp(n) + x exp (n)) Where a = a0 + a1z b= b0 + b1z x and z are variables y the variable that I am trying to modelise a0, a1, b0 and b1 are parameters to determine. I

Howdee, *** I know that the lmList() function exists, yet I don't want to use it. *** Would anyone be kind enough to tell how I can apply the function lm() to each level of a given factor so to obtain the intercept and slope for each factor level within a matrix? For instance, suppose a dataframe containing 3 variables: id, x and y. I want to compute the function lm() for each level

Hi: I create a hermitian matrix and then perform its singular value decomposition. But when I put it back, I don't get the original hermitian matrix. I am having the same problem with spectral value decomposition as well. I am using R 1.7.0 on Windows. Here is my code: X <- matrix(rnorm(16)+1i*rnorm(16),4) X <- X + t(X) X[upper.tri(X)] <- Conj(X[upper.tri(X)]) Y <-

Dear R-users, I am trying to translate a matlab code for calculating the Local Whittle estimator in time series with long memory originally written by Shimotsu and available free in his webpage ( http://www.econ.queensu.ca/pub/faculty/shimotsu/ ) The Matlab code is ======================================================================================= function[r] = whittle(d,x,m) % WHITTLE.M

Hello, Is there an accepted way to convey, for regression trees, something akin to R-squared? I'm developing regression trees for a continuous y variable and I'd like to say how well they are doing. In particular, I'm analyzing the results of a simulation model having highly non-linear behavior, and asking what characteristics of the inputs are related to a particular

Hi, all, Does anybody got the most general expression of a unitary matrix? I found one in the book, four entries of the matrix are: (cos\theta) exp(j\alpha); -(sin\theta)exp(j(\alpha-\Omega)); (sin\theta)exp(j(\beta+\Omega)); (cos\theta) exp(j\beta); where "j" is for complex. However, since for any two unitary matrices, their product should also be a unitary matrix. When I

On two laptops running 32-bit kubuntu, I have found that svd(), invoked within R 2.9.1 as supplied with the current ubuntu package, returns very incorrect results when presented with complex-valued input. One of the laptops is a Dell D620, the other a MacBook Pro. I've also verified the problem on a 32-bit desktop. On these same systems, R compiled from source provides apparently