R CRAN Neural Network Package compute vs prediction - r

I am using R along with the neuralnet package see docs (https://cran.r-project.org/web/packages/neuralnet/neuralnet.pdf). I have used the neural network function to build and train my model.
Now I have built my model I want to test it on real data. Could someone explain if I should use the compute or prediction function? I have read the documentation and it isnt clear, both functions seem to do similar?
Thanks


The short answer is to use compute to do predictions.
You can see an example of using compute on the test set here. We can also see that compute is the right one from the documentation:
compute, a method for objects of class nn, typically produced by neuralnet. Computes the outputs
of all neurons for specific arbitrary covariate vectors given a trained neural network.
The above says that you can use covariate vectors in order to compute the output of the neural network i.e. make a prediction.
On the other hand prediction does what is mentioned in the title in the documentation:
Summarizes the output of the neural network, the data and the fitted
values of glm objects (if available)
Moreover, it only takes two arguments: the nn object and a list of glm models so there isn't a way to pass in the test set in order to make a prediction.

Related

Equation for Neural Network from R output

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.

R - Party package: is cforest really bagging?

I'm using the "party" package to create random forest of regression trees.
I've created a ForestControl class in order to limit my number of trees (ntree), of nodes (maxdepth) and of variables I use to fit a tree (mtry).
One thing I'm not sure of is if the cforest algo is using subsets of my training set for each tree it generates or not.
I've seen in the documentation that it is bagging so I assume it should. But I'm not sure to understand well what the "subset" input is in that function.
I'm also puzzled by the results I get using ctree: when plotting the tree, I see that all my variables of my training set are classified in the different terminal tree nodes while I would have exepected that it only uses a subset here too.
So my question is, is cforest doing the same thing as ctree or is it really bagging my training set?
Thanks in advance for you help!
Ben

random forest package in R

I use random forest package in R for regression, it gives me two kind of information: Mean of squared residuals and % Var explained. But I wanna calculate the RMSE and R^2 of the training and test sets, can anyone help me how can I find these kind of information?

Sorry this is not a specific answer, but I do not have enough cred to leave a comment.
It is tough to say how you may get at what you want without a reproducible example. However, if you used the xtest= and ytest= arguments in the call to randomForest (assuming you are using the "randomForest" package), then what you are looking for should be a part of the resulting randomForest object. What you want to look in is the test part of the resulting random forest list.
An attempted example:
rf.results <- randomForest( whatever arguments )
rf.results$test$mse # mse (maybe you can take the square root to get rmse)
rf.results$test$rsq # pseudo-R2 for random forest
If you have the random forest package loaded you can validate this information as well as do some exploration yourself with ?randomForest. The "Value" section of the documentation details the object that results from a call to randomForest and where you can find various performance metrics.

R - How to get one "summary" prediction map instead for 5 when using 5-fold cross-validation in maxent model?

I hope I have come to the right forum. I'm an ecologist making species distribution models using the maxent (version 3.3.3, http://www.cs.princeton.edu/~schapire/maxent/) function in R, through the dismo package. I have used the argument "replicates = 5" which tells maxent to do a 5-fold cross-validation. When running maxent from the maxent.jar file directly (the maxent software), an html file with statistics will be made, including the prediction maps. In R, an html file is also made, but the prediction maps have to be extracted afterwards, using the function "predict" in the dismo package in r. When I do this, I get 5 maps, due to the 5-fold cross-validation setting. However, (and this is the problem) I want only one output map, one "summary" prediction map. I assume this is possible, although I don't know how maxent computes it. The maxent tutorial (see link above) says that:
"...you may want to avoid eating up disk space by turning off the “write output grids” option, which will suppress writing of output grids for the replicate runs, so that you only get the summary statistics grids (avg, stderr etc.)."
A list of arguments that can be put into R is found in this forum https://groups.google.com/forum/#!topic/maxent/yRBlvZ1_9rQ.
I have tried to use the argument "outputgrids=FALSE" both in the maxent function itself, and in the predict function, but it doesn't work. I still get 5 maps, even though I don't get any errors in R.
So my question is: How do I get one "summary" prediction map instead of the five prediction maps that results from the cross-validation?
I hope someone can help me with this, I am really stuck and haven't found any answers anywhere on the internet. Not even a discussion about this. Hope my question is clear. This is the R-script that I use:
model1<-maxent(x=predvars, p=presence_points, a=target_group_absence, path="//home//...//model1", args=c("replicates=5", "outputgrids=FALSE"))
model1map<-predict(model1, predvars, filename="//home//...//model1map.tif", outputgrids=FALSE)
Best regards,
Kristin

Sorry to be the bearer of bad news, but based on the source code, it looks like Dismo's predict function does not have the ability to generate a summary map.
Nitty-gritty details for those who care: When you call maxent with replicates set to something greater than 1, the maxent function returns a MaxEntReplicates object, rather than a normal MaxEnt object. When predict receives a MaxEntReplicates object, it just iterates through all of the models that it contains and calls predict on them individually.
So, what next? Fortunately, all is not lost! The reason that Dismo doesn't have this functionality is that for most kinds of model-building, there isn't actually a valid way to average parameters across your cross-validation models. I don't want to go so far as to say that that's definitely the case for MaxEnt specifically, but I suspect it is. As such, cross-validation is usually used more as a way of checking that your model building methodology works for your data than as a way of building your model directly (see this question for further discussion of that point). After verifying via cross-validation that models built using a given procedure seem to be accurate for the phenomenon you're modelling, it's customary to build a final model using all of your data. In theory this new model should only be better than models trained on a subset of your data.
So basically, assuming your cross-validated models look reasonable, you can run MaxEnt again with only one replicate. Your final result will be a model accuracy estimate based on the cross-validation and a map based on the second run with all of your data lumped together. Depending on what exactly your question is, there might be other useful summary statistics from the cross-validation that you want to use, but those are all things you've already seen in the html output.

I may have found this a couple of years later. But you could do something like this:
xm <- maxent(predictors, pres_train) # basically the maxent model
px <- predict(predictors, xm, ext=ext, progress= '' ) #prediction
px2 <- predict(predictors, xm2, ext=ext, progress= '' ) #prediction #02
models <- stack(px,px2) # create a stack of prediction from all the models
final_map <- mean(px,px2) # Take a mean of all the prediction
plot(final_map) #plot the averaged map
xm1,xm2,.. would be the maxent models for each partitions in cross-validation, and px, px2,.. would be the predicted maps.

Output posterior distribution from bayesian network in R (bnlearn)

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. :)

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