SEMdeep (version 0.1.0)

getInputPvalue: Test for the significance of neural network inputs

Description

The function computes a formal test for the significance of neural network input nodes, based on a linear relationship between the observed output and the predicted values of an input variable, when all other input variables are maintained at their mean values, as proposed by Mohammadi (2018).

Usage

getInputPvalue(object, thr = NULL, verbose = FALSE, ...)

Value

A list od two object: (i) a data.frame including the connections together with their p-values, and (ii) the DAG with colored edges. If p-values > thr and t-test < 0, the edge is inhibited and it is highlighted in blue; otherwise, if p-values > thr and t-test > 0, the edge is activated and it is highlighted in red.

Arguments

object

A neural network object from SEMdnn() function.

thr

A value of the threshold to apply to input p-values. If thr=NULL (default), the threshold is set to thr=0.05.

verbose

A logical value. If FALSE (default), the processed graph will not be plotted to screen.

...

Currently ignored.

Author

Mario Grassi

Details

A neural network with an arbitrary architecture is trained, taking into account factors like the number of neurons, hidden layers, and activation function. Then, network's output is simulated to get the predicted values of the output variable, fixing all the inputs (with the exception of one nonconstant input variable) at their mean values; network’s predictions are saved, after doing this for each input variable. As last step, multiple regression analysis is applied node-wise (mapping the input DAG) on the observed output nodes with the predicted values of the input nodes as explanatory variables. The statistical significance of the coefficients is evaluated with the standard t-student critical values, which represent the importance of the input variables.

References

S. Mohammadi. A new test for the significance of neural network inputs. Neurocomputing 2018; 273: 304-322.

Examples

# \donttest{
if (torch::torch_is_installed()){

# load ALS data
ig<- alsData$graph
data<- alsData$exprs
data<- transformData(data)$data

dnn0 <- SEMdnn(ig, data, train=1:nrow(data), cowt = FALSE,
			#loss = "mse", hidden = 5*K, link = "selu",
			loss = "mse", hidden = c(10, 10, 10), link = "selu",
			validation = 0, bias = TRUE, lr = 0.01,
			epochs = 32, device = "cpu", verbose = TRUE)

res<- getInputPvalue(dnn0, thr=NULL, verbose=TRUE)
table(E(res$dag)$color)
}
# }