search for: parsimonious

...The global model has 52 estimable parameters and sample size is made of about 1500 observations. The global model seems not to have problems of parameters estimability nor get troubles with the convergence of these models. I have run all the models and at the end I get the global model as the more parsimonious one (least QAICc -> I have set c-hat=1.15 according to goodness of fit on the global model). This is the first time it occurs to me and I am somehow confused. I believe the data set is not that poor with respect to the number of parameters (it should not be the Friedman paradox) and the only thi...

Hi All, I am trying to use a neural network for my work, but I am not sure about my approach to select a parsimonious model. In R with nnet, the IAC has not been defined for a feed-forward neural network with a single hidden layer. Is this because it does not make sens mathematically in this case? For example, is this pseudo code sensible? Thanks in advance for your help. I am sorry if this has been answered bef...

If AIC is giving you a model that is too large, then use BIC (log(n) as the penalty for adding a term in the model). This will yield a more parsimonious model. Now, if you ask me which is the better option, I have to refer you to the huge literature on model selection. Best, Ravi [[alternative HTML version deleted]]

...els? 2.The effects of TREATMENT and TIME and their interaction were all significant (Table 5). Because of the significant interaction, the analysis was split by TIME. Comment; Given that you have interactions, you should do a model selection to show whether the interaction model is in fact more parsimonious. Can someone explain these and tell me how and when should I do model selection? Thanks, F [[alternative HTML version deleted]]

Dear listers, Here we have a strange result we can hardly cope with. We want to compare a null mixed model with a mixed model with one independent variable. > lmmedt1<-lme(mediane~1, random=~1|site, na.action=na.omit, data=bdd2) > lmmedt9<-lme(mediane~log(0.0001+transat), random=~1|site, na.action=na.omit, data=bdd2) Using the Akaike Criterion and selMod of the package pgirmess

> From: Peter Dalgaard BSA <p.dalgaard at biostat.ku.dk> > Date: 28 Sep 2000 13:58:22 +0200 > > Peter Dalgaard BSA <p.dalgaard at biostat.ku.dk> writes: > > > I think Occam/Ockham himself wrote in Latin. By my failing memory, the > > quote is > > > > "Entia non runt multiplicanda praeter necessitam" > > > > give or take

.... The structure of all data frames is the same, only the values are different. I then want to aggregate the various runs. Currently I use the following method (for three runs): means = (df.list[[1]]$variable + df.list[[2]]$variable + df.list[[3]]$variable)/3 I would like to do this in a more parsimonious way, for example using lapply or related commands, but I can't seem to figure out the magic touch. Any thoughts on the best way to accomplish this? Thanks and regards, Magnus

.../100) to get 4 or floor(x/10) to get 45, but I'd like to return only 5 or only 6, for example, in cases where I don't know what the numbers are going to be. I'm sure there is something very logical that I am missing, but my code is getting too complicated and I can't seem to find a parsimonious solution. Tyler -- View this message in context: http://r.789695.n4.nabble.com/truncate-integers-tp3041086p3041086.html Sent from the R help mailing list archive at Nabble.com.

...n a subscript out of range error. That I can't do this may speak to technical failings, but I suspect it is because Prof Harrell thinks/knows it injudicious. However, I can't invent a reason why nomograms should be restricted to the full models, if the purpose of fastbw is to generate parsimonious models with appropriate standard errors. I'd welcome comments on either the technical or the theoretical issues. Many thanks in advance, Rob James

...________ From: Ravi Varadhan Sent: Tuesday, June 6, 2017 10:16 AM To: r-help at r-project.org Subject: Subject: [R] glm and stepAIC selects too many effects If AIC is giving you a model that is too large, then use BIC (log(n) as the penalty for adding a term in the model). This will yield a more parsimonious model. Now, if you ask me which is the better option, I have to refer you to the huge literature on model selection. Best, Ravi [[alternative HTML version deleted]]

...because of its incredible statistical resources. My problem is this .........I am running a regression tree analysis using "rpart" and I need to run the calculation repeatedly (say n=50 times) to obtain a distribution of results from which I will pick the median one to represent the most parsimonious tree size. Unfortunately rpart does not contain this ability so it will have to be coded for. Could anyone help me with this? I have provided the code (and relevant output) for the analysis I am running. I need to run it n=50 times and from each output pick the appropriate tree size and post it to...

...is by far the best on all counts. I've read postings that xfs excells with very large files, e.g. movies, but I couldn't see any difference - reiserfs was just as fast). It's incredibly fast in directory manipulations, especially for very large directories with lakhs of files. Very parsimonious too. It doesn't compress data, not yet, but it doesn't waste space like other fs's. A full reiserfs volume could probably not be restored in an equally large ext3 volume. It works since about a year impeccably. > As I am about to upgrade our nt4 domain, and this is > the time...

Dear all, I'm modeling growth curve of some ecosystems with respect to their rainfall-productivity relationship using a simple linear regression (ANPP(t)=a+b*Rain(t)) and a modified version of the Brody Model ANPP(t)=a*(1-exp(-b*rain(t))) I would like to know why the "best model" is function of the criteria that I use (maximizing the fit using R2 or testing the Null hypothesis with

Dear all, Please excuse my ignorance, but I am having difficulty with this, and am unable to find help on the website/Google. I have a series of explanatory variables that I am aiming to get parsimony out of. For example, if I have 10 variables, a-j, I am initially looking at the linear relationships amongst them: my.lm1 <- lm(a ~ b+c+d+e+f+g+h+i+j, data=my.data) summary(my.lm1) my.lm2

...tree and rpart to build a classification tree to predict a 0/1 outcome. The package rpart has the advantage that the function plotcp gives a visual representation of the cross-validation results with a horizontal line indicating the 1 standard error rule, i.e. the recommendation to select the most parsimonious model (the smallest tree) whose error is not more than one standard error above the error of the best model. However, in the rpart package I am not getting trees of all sizes but for example three sizes are 1,2,5 in one example I am working with, while with cv.tree in package tree it gives 1,2,3,4...

...my models that worked fine with lme. I have problems with the pdMat classes. Below a toy dataset with a fixed effect F and a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious model with pdDiag replacing pdLogChol I cannot reproduce with lmer. The obvious choice for me would be my model lmer2, but this is yielding different result. Somebody any idea? Thanks, Joris I am using R version 2.1.0 for Linux and the most recent downloads of Matrix and nlme #dataset from Mc...

Dear R-users, dear Ron I use pls from the pls.pcr package for classification. Since I need to know which variables are most influential onto the classification performance, what criteria shall I look at: a) B, the array of regression coefficients for a certain model (means a certain number of latent variables) (and: squared or absolute values?) OR b) the weight matrix RR (or R in the De

...;Statistical computing" one may consider to supply "traditional" ANOVA tables as an additional explanation for the reader (e.g. field biologists). An example: one has fitted 5 models m1..m5 and after: >anova(m1,m2,m3,m4,m5) # giving AIC and LRT-tests he selects m3 as the most parsimonious model and calls anova with the best model (Wald-test): >anova(m3) # the additional explanatory table My questions: * Do people outside the S-PLUS/R world still understand us? * Is it wise to add such an explanatory table (in particular when the results are the same) to make th...

Dear List, I would greatly appreciate help on the following matter: The RuleFit program of Professor Friedman uses partial dependence plots to explore the effect of an explanatory variable on the response variable, after accounting for the average effects of the other variables. The plot method [plot(summary(rq(y ~ x1 + x2, t=seq(.1,.9,.05))))] of Professor Koenker's quantreg program

...> > The 0-length case I don't think we should change as I do find > NA (is logical!) to be an appropriate logical answer. Can you explain your reasoning a bit more here? I'd like to understand the general principle, because from my perspective it's more parsimonious to say that the inputs to || and && must be length 1, rather than to say that inputs could be length 0 or length 1, and in the length 0 case they are replaced with NA. Hadley I would say the value NA would cause warnings later on, that are easy to track down, so a retu...