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!
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I'd like to simulate a\ periodic ARMA(1,1) using R. I found the R package perARMA but I don't understand how to use it.
There is the function makeparma that permit to simulated the parma(1,1). But I don't understand the input parameters and the model used to simulate the periodic stuff.
This is the source code provided by the package trying to simulate a parma(2,1):
T=12
nlen=480
p=1
a=matrix(0,T,p)
q=1
b=matrix(0,T,q)
a[1,1]=.8
a[2,1]=.3
phia<-ab2phth(a)
phi0=phia$phi
phi0=as.matrix(phi0)
b[1,1]=-.7
b[2,1]=-.6
thetab<-ab2phth(b)
theta0=thetab$phi
theta0=as.matrix(theta0)
del0=matrix(1,T,1)
PARMA21<-makeparma(nlen,phi0,theta0,del0)
parma<-PARMA21$y
I don't understand why we should specify two beta value. And why del0 is a matrix.
I solved using the R package sarima and for the simulation I used the function prepareSimSarima
Hi I am a newbie in Depp learning fields.
I ran a neural network model (regression) with 2 hidden layers in R (neuralnet Package). then I used the the compute function to get the predicted probabilities.Now I want to regenerate predicted output using the equation used in the neural net. for example, following are weights received from the model object
Intercept.to.1layhid1 4.55725020215
Var1.to.1layhid1 -13.61221477737
VAr2.to.1layhid1 0.30686384857
var1.to.1layhid2 0.23527690062
var2.to.1layhid2 0..67345678
1layhid.1.to.target 1.95414397785
1layhid.2.to.target 3.68009136857
Can any one help me derive a equation with the above weights so that I can replicate the output
Thanks
In order to get the output for new data, you can always use predict function using the fitted model, which is the returned object from neuralnet function.
For instance, if your model is the following:
neuralFit = neuralnet(trainData)
Then you reproduce the output with the following:
predict(neuralFit,newdata)
Otherwise, you'll need to compute the result manually. But you need to understand your network architecture first.
I am trying to fit a transfer function model using R in order to apply the fitted model to a validation set of data, because SPSS doesn't allow me to (or I don't know how to) compute point forecasts just like the function Arima() from forecast package does. It does let me apply the model, but it does not use the dependet variable's lagged values, that's why I am trying R.
Anyone know how I could get those type of "updated" or validation forecasts using the arimax() function? I am not looking for the following type of predictions:
predict(vixari011, n.ahead=12)
But rather these:
Arima(test$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 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.
I'm using randomForest in order to find out the most significant variables. I was expecting some output that defines the accuracy of the model and also ranks the variables based on their importance. But I am a bit confused now. I tried randomForest and then ran importance() to extract the importance of variables.
But then I saw another command rfcv (Random Forest Cross-Valdidation for feature selection), which should be the most appropriate for this purpose I suppose, but the question I have regarding this is: how to get the list of the most important variables? How to see the output after running it? Which command to use?
Another thing: What is the difference between randomForest and predict.randomForest?
I am not very familiar with randomforest and R therefore any help would be appreciated.
Thank you in advance!
After you have made a randomForest model you use predict.randomForest to use the model you created on new data e.g. build a random forest with training data then run your validation data through that model with predict.randomForest.
As for the rfcv there is an option recursive which (from the help):
whether variable importance is (re-)assessed at each step of variable
reduction
Its all in the help file