I'm using GBM package for a regression problem. I would like to test the Huber loss function. I can use the "huberized" value for the distribution. However, how do you set the cutting edge parameter?
Maybe I missed it somewhere in the help.
Related
I'm using the сaret package from R to train Multilayer Perceptron (mlpML) model. But I don't see any parameter to set number of iterations of learning which is called epochs. Can someone help me? There's n.round parameter used for mxnet, I've tried to use it for mlpML but it seems like this parameter being ignored.
Any help would be good!
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$top.benefit)".
We would appreciate it if you could tell us the best way to do this.
Sincerely,
I am working on my dissertation and need to fit a path model, however, from using the MVN package in R, I found that all my variables are not multivariate normal.
Therefore,
does the lavaan package offer an argument to fit non-normal variables?
what non-parametric methods are there for such non-normal variables?
heard from a friend that I have to use asymptotic distribution for this. I found that an argument in lavaan does this: NACOV, but i am not sure how to use it.
Thank you.
I am trying to fit a transfer function model using R in order to apply the fitted model to a validation set of data, because SPSS doesn't allow me to (or I don't know how to) compute point forecasts just like the function Arima() from forecast package does. It does let me apply the model, but it does not use the dependet variable's lagged values, that's why I am trying R.
Anyone know how I could get those type of "updated" or validation forecasts using the arimax() function? I am not looking for the following type of predictions:
predict(vixari011, n.ahead=12)
But rather these:
Arima(test$VIX, model = vixari)
From what I have been reading there is no prediction function for the arimax() function, any ideas about how I could forecast to evaluate point-by-point performance? I can just think of computing manually using a spreadsheet...
I had the same problem. I know this post is old but this can help someone.
I used this it worked just fine
forecast(fitted(arimax_ts_model), h=11)
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. :)