I'm trying to validation test or to test reliability of multivariate autoregressive model estimated by MAR1 package.
As far as I understand, there is no such function in this package.
As one of the solution, I tried to use "plot(model,. plot/.type=model.resids.ytT)", which is introduced in users guideline of MARSS package, to confirm whether the model has convergence problems.
However, the output plot was the same as the plot of coefficients obtained by the function "plot(model$top.benefit)".
We would appreciate it if you could tell us the best way to do this.
Sincerely,
Related
I'm looking for methods to test the overall fit of a model, run model diagnostics to help with model selection and methods for model validation for binomial GAMs.
If knows of any way to do use this using R that would be extremely helpful as well (i.e packages and functions). I have heard of DHARMa, but am at a loss of how I would use the package.
Any links with more information would also be appreciated.
Currently, all I have been able to do is ROC curves and AUC values.
Thanks
I'm experimenting with Bayesian networks in R and have built some networks using the bnlearn package. I can use them to make predictions for new observations with predict(), however I would also like to have the posterior distribution over the possible classes. Is there a way of retrieving this information?
It seems like there is a prob-parameter that does this for the naive bayes implementation in the bnlearn package, but not for networks fitted with bn.fit.
Thankful for any help with this.
See the documentation of bnlearn.
predict function implements prob only for naive.bayes and TAN.
In short, because all other methods do not necessarily compute posterior probabilities.
[bnlearn] :: predict returns the predicted values for node given the data specified by data. Depending on the
value of method, the predicted values are computed as follows:
a)parents b)bayes-lw
When using bayes-lw , likelihood weighting simulations are performed for making predictions.
Hope this helps. :)
I can see how cv.glm work with a glm object, but what about fitted survival models?
I have a bunch of models (Weibull, Gompertz, lognormal, etc). I want to assess the prediction error using cross validation. Which package/function can do this in R?
SuperLearner can do V-fold cross-validation for a large library of underlying machine learning algorithms, not sure that it includes survival models. Alternatively, take a look at the cvTools package, which is designed to help do cross-validation of any prediction algorithm you give it.
I'm trying to do a hurdle model with random effects in either r or stata. I've looked at the glmmADMB package, but am running into problems getting it download in R and I can't find any documentation on the package in Cran. Is this package still available? Has anyone used it successfully to estimate a hurdle model with random effects?
Alternatively, is there a way to estimate this in stata? Is there a way to estimate random effects with any type of count data in stata?
Any advice would be greatly appreciated.
Jennifer
In Stata, xtnbreg and xtpoisson have the random effects estimator as the default option. You can always estimate the two parts separately by hand. See the count-data chapter of Cameron and Trivedi's Stata book for cross-sectional examples.
You also have the user-written hplogit and hnlogit for hurdle count models. These use a logit/probit for the first-stage and a zero-truncated poisson/negative binomial for the second stage. Also, a finite mixture model might be a nice approach (see user-written fmm). There's also ztpnm. All these are cross-sectional models.
I've been using the caret package in R to run some boosted regression tree and random forest models and am hoping to generate prediction intervals for a set of new cases using the inbuilt cross-validation routine.
The trainControl function allows you to save the hold-out predictions at each of the n-folds, but I'm wondering whether unknown cases can also be predicted at each fold using the built-in functions, or whether I need to use a separate loop to build the models n-times.
Any advice much appreciated
Check the R package quantregForest, available at CRAN. It can easily calculate prediction intervals for random forest models. There's a nice paper by the author of the package, explaining the backgrounds of the method. (Sorry, I can't say anything about prediction intervals for BRT models; I'm looking for them by myself...)