search for: technometr

Greetings, R-Wizards: I'm trying to find an extremum subject to a nonlinear constraint. (Yes, I have perused the archives but have found nothing positive.) The details of the problem are these: In a paper published some years ago in Technometrics, ("Confidence bands for cumulative distribution functions of continuous random variables" Technometrics, 25, 77-86. 1983), Cheng and Iles describe an ingenious method for placing confidence bounds on an entire cdf by defining the likelihood ratio confidence "ellipse" for the...

Hi all, Does anyone have an R implementation of the queplot (K.?M. Goldberg and B.?Iglewicz. Bivariate extensions of the boxplot. Technometrics, 34(3):pp. 307?320, 1992)? I'm struggling with the estimation of the asymmetry parameters. Hadley -- Assistant Professor / Dobelman Family Junior Chair Department of Statistics / Rice University http://had.co.nz/

Does anyone know of an R package that I can use to uncensor a normal or log-normal dataset? I'm particularly interested in the MLE method of Cohen (1959), "Simplified estimators for the normal distribution when samples are single censored or truncated," Technometrics, 1(3), 217-237. Of course, if there is anything better, I'd be glad to hear about that too. Thanks. Rick

Is there code available to decompose interactions involving at least one nominal factor with more than 2 levels as described, e.g., by Tukey or by Mandel (1971, Technometrics, 13: 1-18)? Tukey's model: E(y[i,j]) = mu0 + a[i] + b[j] + c*a[i]*b[j], estimating a, b, and c so sum(a) = sum(b)= 0. Mandel essentially describes a singular value decomposition of the interaction. Thanks, Spencer Graves

...st, I am looking for a function `Pseudo standard error' (PSE), which is available in S+ DOX (design of experiemnt) module - Is there a similar function available in R? Reference for PSE function is in the paper: 'Quick and easy analysis of unreplicated factorials' by Russell V. Lenth, Technometrics, 1989, 31, 4, 469-473. Thanks. -Nitin

...er component for the positive counts" if: a)to get true estimate of the relative mean abundance, the model multiply the relative mean abundance at a site by the probability that the relative mean abundance at a site is generated through a negative binomial distribution, as proposed by Lambert (Technometrics, 1992). By using this kind of mixture model, zeros arise from one or two processes and their related covariates. b) we have two independent models, where the first part is a binary outcome model and the second one is a negative binomial model, assuming that zeros arise from a single process and...

Hello Using R 2.2.1 on a Windows machine. Has anyone programmed the Welch test for equality of variances? I tried RSiteSearch, but this gave references to t test and oneway.test, which are not quite what I need.....I need the Welch test itself, for use in a meta-analysis (to determine if variances are equal). TIA Peter Peter L. Flom, PhD Assistant Director, Statistics and Data Analysis

Hi, I am trying to replicate Lambert (1992)'s simulation with zero-inflated Poisson models. The citation is here: @article{lambert1992zero, Author = {Lambert, D.}, Journal = {Technometrics}, Pages = {1--14}, Publisher = {JSTOR}, Title = {Zero-inflated {P}oisson regression, with an application to defects in manufacturing}, Year = {1992}} Specifically I am trying to recreate Table 2. But my estimates for Gamma are not working out. Any ideas why? Please cc me on a reply! Thanks, Chr...

Dear list, this is not a particular R question but perhaps someone can help. I am running a maximum likelihood estimation (competing risk duration model with unobserved heterogeneity) on 30 different datasets. The problem is that on 2 datasets the model does not converge. I am interested if there are any methods, based on the gradients or (an approximation of) the hessian which helps to

Hi there, during my work I have to use kernel smoothing methods for multivariate ordinal data. The R-package "KernSmooth" unfortunately includes only a version for continous scaled variables. Does anybody know whether there exists also a version for ordinal data? Thanks for help! --

Hello, I wonder if anybody has some idea about how to solve my problem : I am working , I would say trough an experimental design approach (perform experiments, get responses, make regression, sensitivity analysis, risk analysis ...). The problem is now that I have to face with not only a response but a vector or a matrix (typically a spatial distribution of a physical property ... pressure).

...imate models containing subsets of the coefficients. I therefore plan to compare alternative designs primarily in terms of their "estimation capacity" = percent of models of certain types that are actually estimable, following Li and Nachtsheim (2000) ?Model Robust Factorial Designs?, Technometrics, pp. 345-352. I propose to start with a half-fraction of a 12-run Plackett-Burman in 6 runs and a 2^3 in 8 runs, then move selected points to a middle value to obtain 3-level designs to compare in terms of estimation capacity. After I get the 3-factor design, then I can split each of those r...

Je suis étudiant en DEA et j'élabore un mémoir dans lequel j'applique le modèle STARMA , Pouvez vous ,s'il vous plait, m'envoyer l'algorithme de calcul ces STACF et STPACF de ce model afin de débuter mon estimation de ce modéle sur R Merci d'avance. --------------------------------- [[alternative HTML version deleted]]

...nd thus requiring stratification on covars. McLachlan (in his discriminant analysis book), on p. 347, differentiates between mixture sampling and separate sampling, but I can find a mention of what do when, under mixture sampling, we end up with all samples in only one group. Only Hirst (1996, Technometrics, 38 (4): 389--399) says that each bootstrap sample should include at least one observation for each group, and at least enough different observations from each group to allow estimation of the covariance matrix (he is referring to discriminant analysis), and thus he uses essentially stratifi...

Also Consider Bonferroni Hochberg Holm type procedures or . Dunn OJ. Multiple contrasts using rank sum tests. Technometrics 1964;6:241#/52. [[alternative HTML version deleted]]

...lips, G. D. A. (1980) Algorithm AS154. An algorithm for exact maximum likelihood estimation of autoregressive-moving average models by means of Kalman filtering. Applied Statistics 29, 311–322. - Jones, R. H. (1980) Maximum likelihood fitting of ARMA models to time series with missing observations. Technometrics 20 389–395. The first is used to fit the initial P0 matrix, and the second to do the forecasts. The AS154 implementation of P0 computation is O(r^4/8) in memory requirements, where r is roughly the period length. This is the origin of the ugly: src/library/stats/src/arima.c:838: if(r &g...

Hello, I am using the lmomco package (lmom.ub and pargev) to compute the GEV parameters (location, scale, and shape), which are used to estimate return values. I was wondering how/if I can calculate upper and lower confidence (CI_u, CI_l) intervals for each return frequency using the GEV parameters to fill-in the table below? Xi (location) = 35.396 Alpha (scale) = 1.726 Kappa (shape) =

Hello experts, as newcomer in pca, i have a question, concerning the princomp algorithm. With a dataset "r" containing 18 "input" parameters and 1 "output" parameter r[19], i got with the following fit ls <- lsfit(r[1:18],r[19]); lsdiag <- ls.diag(ls); lsdiag$std.dev a prediction error of: [1] 8.879561 what is quite reasonable. If i take only two

...e (was: Forward Stepwise regression based onpartial F test) Spencer, Obviously the problem is one of supersaturation. In view of that, are you aware of the following? A Two-Stage Bayesian Model Selection Strategy for Supersaturated Designs Authors: Beattie S. D; Fong D. K. H; Lin D. K. J Source: Technometrics, 1 February 2002, vol. 44, no. 1, pp. 55-63 And: Analysis Methods for Supersaturated Design: Some Comparisons Authors: Li R; Lin D. K. J Source: Journal of Data Sciences, 1, 2003, pp. 249-260 The latter is available for download in full (pdf) by googling for the title. HTH Mike -----Origi...

Dear R-users, I have two outliers related questions. I. I have a vector consisting of 69 values. mean = 0.00086 SD = 0.02152 The shape of EDA graphics (boxplots, density plots) is heavily distorted due to outliers. How to define the interval for outliers exception? Is <2SD - mean + 2SD> interval a correct approach? Or should I define 95% (or 99%) limit of agreement for data interval,