similar to: CADFtest difference between max.lag.y with criterion and without criterion

Hello: I am a rookie in using R. When I used the unit root test in "CADFtest", I got the different t-test statistics between using criterion="BIC" and no using criterion. But when I checked the result with eviews, I find out that no using criterion is correct. Why after using criterion="BIC", I got the different result? Paul > data(Canada) > ADFt

Hello, I am using the function stepAIC (library MASS) to run a backward elimination on my linear regression. The new model stepAIC calculates contains coefficients that have a Pr(>|t|) value below 0.1, but I'd like to have only coefficients with 0.001 or below. How can I change the stop criterion for stepAIC, so that it is more strict? There is a parameter "steps", but it is

Dear Dr. Gilbert, Dr. Varadhan and all R-help list members, I'm using the function BBsolve( ) and I have some questions on the stopping criterion "maxit" and "noimp" specified in the option "control". Here are two such examples I'm having problem with. In these two examples, the function BBsolve( ) always stops at iteration 100, overlooking the values

Hi, I have data for one dependent variable and multiple independent variables y = b0 + b1*x1 + b2*x2 + ... I want to a list of all models that have some subset of the independents (just x1 x2, and not x3, etc.) and their corresponding BIC values. Is there a pre-existing function that does this? I saw that you can calculate individual BIC values using 'lm' and something like AIC(lm1, k

Hi, As an example, how can I get the data such that field a of ab, ab["a"], equals 3? I expect the answer to be the union of 2 and 4, as Thanks, > a<-c(1,3,4,3,5,6,5) > b<-c(2,4,6,7,3,1,2) > ab<-data.frame(a,b) > ab a b 1 1 2 2 3 4 3 4 6 4 3 7 5 5 3 6 6 1 7 5 2 > ab[a==3] Error in `[.data.frame`(ab, a == 3) : undefined columns selected >

I'm interested in writing some model selection functions (for linear regression models, as a start), which incorporate the PRESS criterion since it, to my knowledge, is not currently implemented in any available model selection procedure. I thought it would be simplest to build on already existing functions like regsubsets in package leaps. It's easy enough to calculate the PRESS

Dear R users, I'm using the rpart package to build classification trees. I'm interested in implementing the ordered Twoing as a splitting criterion. Does anyone have experience with this task ? Thank you for your help Paolo Paolo Radaelli Dipartimento di Metodi Quantitativi per le Scienze Economiche ed Aziendali Facolt? di Economia Universit? degli Studi di Milano-Bicocca Via Bicocca

Yes, paraphrasing Murphy I can say of myself: "Nothing seems to be able to stop a stupid thought in its pathway from the brain to the keyboard". :-) Sorry once again and thank for your patience. Stefano > -----Messaggio originale----- > Da: Prof Brian Ripley [mailto:ripley at stats.ox.ac.uk] > Inviato: venerd?? 23 luglio 2004 16.30 > A: Guazzetti Stefano > Cc: Luis

Please can you help me I have the following problem: I have selected an lm model through the step procedure which visualize for each step the AIC value; then I have calculated for the initial model and the selected one the AIC using the funnction AIC. The results are different.What's happened? Emilia Rocco Dipartimento di Statistica "G. Parenti" Università di Firenze e-mail:

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Dear all, is there any functions which allow to calculate Student t-criterion using means, their SE and sample size? I've seek for, but bulit-in t-criterion works only with initial sample... Best regards, A.Morkovin

Hi everybody, I was wondering if you can help me about a module. In fact, I'm looking for a package or module about Wilks' lambda criterion in R environment. I didn't find it in R website ( http://cran.cict.fr/web/packages/index.html#available-packages-W or http://search.cpan.org/faq.html). If this module exists, could you show me the command line. Thank you very much for your help,

Dear list I would like to compute a Mantel-Haenszel chi-square in which the matching variable is a continuous variable. The MH chi-square is used to assess the relationship between two categorical variables at each level or strata defined by a third variable. Specifically I would like to know if there is a straightforward way to divide the matching variable into levels, in which each level has a

Hi, I would like to cluster a dataset with the ward algorithm. That works fine. But I can't find a method to plot the structure chart to estimate the "elbow crterion" for the number of clusters. Can someone tell me how I can do it? Thanks for your help. Dominik

Dear Rers, is there a way to color counties on a full US map based on a criterion one establishes (i.e., all counties I assign the same number should be the same color)? I explored a bit and looks like the package "maps" might be of help. library(maps) One could get a map of the US: map('usa') One could get countries within a US state: map('county', 'iowa', fill

Dear R users (r-help@r-project.org), The definition of AIC (Akaike information criterion) for normal error models has just been changed. Please refer to the paper below on this matter. Eq.(22) is the new definition. The essential part is RSS(n+q+1)/(n-q-3); it is close to GCV. The paper is temporarily available at the "Papers In Press" place. Kunio Takezawa(2012): A Revision of

Hello all, I would like to rigorously test whether observations in my dataset are outliers. I guess all the main tests in R (Grubbs) impose the assumption of normality. My data is surely not normal, so I would like to use something else. As far as I can tell from wikipedia, Peirce's criterion is just that. The data I am interested in testing is: 1) Continuous on the unit interval 2)

Still wrong: > x <- data.frame(a = sample(letters[1:3], 10, replace=T), b=1:10) > x a b 1 a 1 2 a 2 3 b 3 4 b 4 5 a 5 6 a 6 7 b 7 8 b 8 9 b 9 10 c 10 > subset(x, a == c("b", "c")) a b 3 b 3 7 b 7 9 b 9 10 c 10 > subset(x, a %in% c("b", "c")) a b 3 b 3 4 b 4 7 b 7 8 b 8 9 b 9 10 c 10 What matters

Dear R-help members, I'd like to run a binomial logistic stepwise regression with ten explanatory variables and as many interaction terms as R can handle. I'll come up with the right R command sooner or later, but my real question is whether and how the criterion for the evaluation of the different models can be set to be the probability of the residual deviance in the Chi-Square

sorry for my previus (WRONG) answer, as someone already pointed out a solution could be subset(data, PUNTAR==c("IX49","IX48")) > -----Messaggio originale----- > Da: Guazzetti Stefano > Inviato: venerd?? 23 luglio 2004 15.55 > A: 'Luis Rideau Cruz'; r-help at stat.math.ethz.ch > Oggetto: R: [R] retrieve rows from frame assuming criterion > >