similar to: Inverse Student t-value

Hello, My response variable seems to be distributed according to Student t with df=4. I have 320 observations and about 20 variables. I am wondering whether there is a way to fit glm with Student t for error distribution. Student t is not one of the family choices in glm function. How should I proceed to fit glm with Student t? I know that Student t is the Inverse Gamma with shape parameter

Hi, This is more of a general question than a pure R one, but I hope that is OK. I want to combine one-tailed independent p-values using the weighted version of fisher's inverse chi-square method. The unweighted version is pretty straightforward to implement. If x is a vector with p-values, then I guess that this will do for the unweighted version: statistic <- -2*sum(log(x)) comb.p <-

Hello all: Is there a function in R to estimate the Inverse t-distribution(tif in Systat).If so how can I see an example on how is used? Thanks Felipe D. Carrillo Fishery Biologist Department of the Interior US Fish & Wildlife Service California, USA

Hello all... I am really new to statistics and I am trying to figure out a way to apply Chauvenet's criterion using the t-distribution on a set of numbers in perl. I was unable to find a TDIST and TINV function for perl. I am getting these functions from Excel. So, I figured that I would install R and call it from perl, overkill for what I need I know. I am having a hard time figuring

Dear R-users! I?m faced with following problem: Given is a sample where the sample size is 12, the sample mean is 30, and standard deviation is 4.1. Based on a Student-t distribution i?d like to simulate randomly 500 possible mean values within a two-tailed 95% confidence interval. Calculation of the lower and upper limit of the two-tailed confidence interval is the easy part. m <- 30 #sample

Dear All, My question is simple but I need someone to help me out. Suppose I have a positive definite matrix A. The funtion chol() gives matrix L, such that A = L'L. The inverse of A, say A.inv, is also positive definite and can be factorized as A.inv = M'M. Then A = inverse of (A.inv) = inverse of (M'M) = (inverse of M) %*% (inverse of M)' = ((inverse of

I'm rusty, but not *that* rusty here, I hope. If W (=Z*Z' in your case) is singular, it can not have inverse, which by definition also mean that nothing multiply by it will produce the identity matrix (for otherwise it would have an inverse and thus nonsingular). The definition of a generalized inverse is something like: If A is a non-null matrix, and G satisfy AGA = A, then G is called

Generalised Inverse: The Moore-Penrose Generalisied Inverse is probably better defined as a pseudo-Inverse that arises in solving least squares problems. Another well known pseudo-Inverse is the so-called Drazin pseudo-Inverse. If memory serves (and it's been 10-12 years!) it can be obtained via a diagonalisation. Anyway, I dare say Prof. Ripley (among others) probably has "all the

>I'm rusty, but not *that* rusty here, I hope. > >If W (=Z*Z' in your case) is singular, it can not have >inverse, which by >definition also mean that nothing multiply by it will >produce the identity >matrix (for otherwise it would have an inverse and >thus nonsingular). > >The definition of a generalized inverse is something >like: If A is a >non-null

Dear R-users, I have one question about using SVD to get an inverse matrix of covariance matrix Sometimes I met many singular values d are close to 0: look this example $d [1] 4.178853e+00 2.722005e+00 2.139863e+00 1.867628e+00 1.588967e+00 [6] 1.401554e+00 1.256964e+00 1.185750e+00 1.060692e+00 9.932592e-01 [11] 9.412768e-01 8.530497e-01 8.211395e-01 8.077817e-01 7.706618e-01 [16]

Hello, I've been attempting to fit the data below with an inverse gamma distribution. The reason for this is outside proprietary software (@Risk) kicked back a Pearson5 (inverse gamma) as the best fitting distribution with a Chi-Sqr goodness-of-fit roughly 40% better than with a log-normal fit. Looking up "Inverse gamma" on this forum led me the following post:

Hi, I've been trying to figure out a special set of covariance matrices that causes some symmetric zero elements in the inverse covariance matrix but am having trouble figuring out if that is possible. Say, for example, matrix a is a 4x4 covariance matrix with equal variance and zero covariance elements, i.e. [,1] [,2] [,3] [,4] [1,] 4 0 0 0 [2,] 0 4

I have a one-variable data set in R. The plot of histogram of my numerical variable suggests an inverse gaussian distribution. How can I obtain best estimation for the two parameters of inverse gaussian based on my data? Thanks. -- View this message in context: http://n4.nabble.com/estimate-inverse-gaussian-in-R-tp949692p949692.html Sent from the R help mailing list archive at Nabble.com.

Dear R-devel: I could use some advice about matrix calculations and steps that might make for faster computation of generalized inverses. It appears in some projects there is a bottleneck at the use of svd in calculation of generalized inverses. Here's some Rprof output I need to understand. > summaryRprof("Amelia.out") $by.self self.time self.pct

Hello Everyone Both the MCMCpack and the bayesm libraries allow us to make draws from the Inverse Wishart distribution. But I wanted to find out how exactly is the Inverse Wishart distribution parameterized in these libraries. The reason I ask is the following: Now its generally standard to express Inverse Wishart as IW(0.5 * DOF,0.5* Scale). (DOF-> Degree of freedom, Scale -> Scale

Thorsten is right. There is a direct formula for computing the Moore-Penrose inverse using the singular value composition of a matrix. This is incorporated in the following: mpinv <- function(A, eps = 1e-13) { s <- svd(A) e <- s$d e[e > eps] <- 1/e[e > eps] return(s$v %*% diag(e) %*% t(s$u)) } Hope it helps. Dietrich

I am looking fpr a algo to find matrix inverse. Till time I am aware of Gauss-Jordan Elimination procedure to find the same. Are there any other algo. as well? What does R use to find the inverse? -- View this message in context: http://www.nabble.com/Algo.-for-matrix-inverse-tp20182285p20182285.html Sent from the R help mailing list archive at Nabble.com.

I've been looking for an IACF() procedure in R for a long time (it's a very convenient function to check for overdifferencing time series), and eventually decided to write my own function. Here's what I came up with : 3 web-pages helped me estimate it : http://www.xycoon.com/inverse_autocorrelations.htm

Hi all, I am using R trying to get a inverse matrix of (X^T)X , but I keep getting the error message like: no b argument and no default value for sprintf(gettext(fmt, domain = domain), ...) . -------------------------------------------------------------------------------------------- # my code X<-Matrix(rep(1,500),100,5) X[lower.tri(X)]<-1-10^-7 XtX<- t(X)%*% X XtXu<-lu(XtX)

Hi, We are two students doing project with Vorbis audio compression for our B.Sc studies. We have two questions: Our first question related to the floor1 inverse dB lookup table. As we understood the floor values encoded as offset integers (Y) from 1 to 256 and then used as index in the floor1_inverse_dB_table to get an encoded value. This value (denoted as X) is inverse of linear frequency so