search for: reweight

Dear all, When doing nonlinear regression, we normally use nls if e are iid normal. i learned that if the form of the variance of e is not completely known, we can use the IRWLS (Iteratively Reweighted Least Squares ) algorithm: for example, var e*i =*g0+g1*x*1 1. Start with *w**i = *1 2. Use least squares to estimate b. 3. Use the residuals to estimate g, perhaps by regressing e^2 on *x*. 4. Recompute the weights and goto 2. Continue until convergence i was wondering whether there is a...

...timate: > set.seed(221205) > y <- 7 + 3*rt(30,1) > library(MASS) > huber(y)$mu [1] 5.9117 > coefficients(rlm(y~1)) (Intercept) 5.9204 I was surprised to get two different results. The function huber() works directly with the definition whereas rlm() uses iteratively reweighted least squares. My surprise is because I vaguely remember @ARTICLE{hw77, author = {Holland, P. W. and Welsch, R. E.}, title = {Robust Regression using Iteratively Reweighted Least-Squares}, journal = {Communications in Statistics: Theory and Methods}, volume = {A6(9)}, number...

Dear R users, I use the glm() function to fit a generalized linear model with gamma distribution function and log link. I have read in the help page that the default method used by R is "glm.fit" (iteratively reweighted least squares, IWLS). Is it possible to use maximum likelihood method? Thanks Silvia Narduzzi Dipartimento di Epidemiologia ASL RM E Via di S. Costanza, 53 00198 Roma Tel +39 06 83060461 Mail narduzzi at asplazio.it

Hi! I tested two different implementations of the robust MCD estimator: cov.mcd from the MASS package and covMcd from the rrcov package. Tests were done on the hbk dataset included in the rrcov package. Unfortunately I get quite differing results -- so the question is whether this differences are justified or an error on my side or a bug? Here is, what I did: > require(MASS) >

...ver unbiased linear estimators. What I am trying to determine is whether or not it is necessary to check for normally-distributed errors in a GAM from mgcv. I know that the unsmoothed terms, if any, will be fitted by ordinary least-squares but I am unsure whether the default Penalized Iteratively Reweighted Least Squares method used in the package is also based upon this assumption or falls under any analogue to the Gauss-Markov Theorem. Thank you in advance for any help. Sincrely, Collin Lynch.

R-users E-mail: r-help@r-project.org I have a quenstion on "gam()" in "gam" package. The help of gam() says: 'gam' uses the _backfitting algorithm_ to combine different smoothing or fitting methods. On the other hand, lm.wfit(), which is a routine of gam.fit() contains: z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y *

...true, how to get it out of R I use the word "cult" on purpose -- an entire generation of users who believe in the efficacy of this incantation without having any idea what it actually does. In many particular instances the SAS type III corresponds to a survey sampling question, i.e., reweight the data so that it is balanced wrt factor A and then test factor B in the new sample. The three biggest problems with type III are that 1: the particular test has been hyped as "better" when in fact it sometimes is sensible and sometimes not, 2: SAS implemented it as a computational a...

Full_Name: Stephen Weigand Version: 1.9.0 OS: Mac OS X 10.3.4 Submission from: (NULL) (68.115.89.235) When running an iteratively reweighted least squares program R crashes and the following is written to the console.app (when using R GUI) or to stdout (when using R from the command line): Parameter 5 to routine DSTEIN was incorrect Mac OS BLAS parameter error in DSTEIN, parameter #0, (unavailable), is 0 In case it helps, here's...

Hello, I would like to know if there is any package that allow us to fit Generalized Linear Models via Maximum Likelihood and Linear Models using Generalized Least Squarse in R as the functions glim and gls, respectively, from S-Plus. Also, anybody know if there is any package that fit Log-Linear Models using Generalized Least Squares? Any help will be very useful. Thanks, -- Frederico

Dear R-users! I would like to fit exponential, Weibull and log-logistic via glm() like functions. Does anyone know a way to do this? Bellow is a bit longer description of my problem. Hm, could family() be adjusted/improved/added to allow for these distributions? SAS procedure GENMOD alows to specify deviance and variance functions to help in such cases. I have not tried that option and I do not

...my specific data and research problem at hand, I am fitting a gamma regression model to 13 000 lines of insurance claims data, which will be regressed against categorical variables such as Age Band, Gender, and Region. Perhaps my problem arises because the data set is too large and the iteratively reweighted least squares algorithm therefore cannot converge, in which case I perhaps need another GLM type. Or maybe the categorical explanatory variables can take on too many values (e.g. there are 15 Age Bands, 5 Regions). Any insights you could provide would be much appreciated. Thank you ever so much...

I'm getting unexpected results from the coxph function when using weights from counter-matching. For example, the following code produces a parameter estimate of -1.59 where I expect 0.63: d2 = structure(list(x = c(1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1), wt = c(5, 42, 40, 4, 43, 4, 42, 4, 44, 5, 38, 4, 39, 4, 4, 37, 40, 4, 44, 5, 45, 5, 44, 5), riskset =

...regression B = ROBUSTFIT(X,Y) returns the vector B of regression coefficients, obtained by performing robust regression to estimate the linear model Y = Xb. X is an n-by-p matrix of predictor variables, and Y is an n-by-1 vector of observations. The algorithm uses iteratively reweighted least squares with the bisquare weighting function. By default, ROBUSTFIT adds a column of ones to X, corresponding to a constant term in the first element of B. Do not enter a column of ones directly into the X matrix. B = ROBUSTFIT(X,Y,'WFUN',TUNE) uses the weighting...

> ... with assignments inside of loops like this: > > reweight = function(iter, w, Q) { > for (i in 1:iter) { > wT = w * Q > } > } > ... before the RHS is executed, the LHS allocation would be added > to a small fixed length list of available space which is checked > before future allocations. If the same size is requested before...

...o have small or zero > frequencies. > In addition, adding a new variable to the model would at least double the > number > of cells, spreading/thinning out the data even more. > >> >> Perhaps my problem arises because the data set is too large and the >> iteratively reweighted least squares algorithm therefore cannot converge, >> in >> which case I perhaps need another GLM type. Or maybe the categorical >> explanatory variables can take on too many values (e.g. there are 15 Age >> Bands, 5 Regions). >> > > If your response is continu...

I'm trying to improve the performance of the update loop within a logistic regression, and am struggling against the overhead of memory allocation and garbage collection. The main issue I'd like to solve is with assignments inside of loops like this: reweight = function(iter, w, Q) { for (i in 1:iter) { wT = w * Q } } If the matrix Q is large I can get a significant gain in performance if I can reuse the same allocation for wT rather than making a new allocation on each loop iteration. While I can solve this problem in this case with an Rcpp f...

Hi all, Trying to understand the logistic regression performed by glm (i.e. when family='binomial'), and I'm curious to know how it treats perfect success. That is, lets say I have the following summary data x=c(1,2,3,4,5,6) y=c(0,.04,.26,.76,.94,1) w=c(100,100,100,100,100,100) where x is y is the probability of success at each value of x, calculated across w observations.

Dear community, I'm trying to track down a quote, but can't recall the source or the exact structure - not very helpful, I know - something along the lines that: 80% of [applied] statistics is linear regression ... ? Does this ring a bell for anyone? Thanks, Andrew -- Andrew Robinson Department of Mathematics and Statistics Tel: +61-3-8344-9763 University of

Dear R users: I am using lmer fo fit binomial data with a probit link function: > fer_lmer_PQL<-lmer(fer ~ gae + ctipo + (1|perm) -1, + family = binomial(link="probit"), + method = 'PQL', + data = FERTILIDAD, + msVerbose= True) The output look like this: > fer_lmer_PQL Generalized linear mixed model fit

...sampling weights (selection probability weights) I'll add: * inverse-variance weights, where var(y for observation) = 1/weight (as opposed to just being inversely proportional to the weight) * weights used as part of an algorithm (e.g. for robust estimation, or glm's using iteratively-reweighted least-squares). For linear regression, the type of weights don't affect regression coefficient calculation, but do affect inferences such as standard errors for the regression coefficients, degrees of freedom for variance estimates, etc. lm() inferences assume the first type. Other form...