predict.DNN: SEM-based out-of-sample prediction using layer-wise DNN

In SEMdeep: Structural Equation Modeling with Deep Neural Network and Machine Learning

SEM-based out-of-sample prediction using layer-wise DNN

Description

Predict method for DNN objects.

Usage

## S3 method for class 'DNN'
predict(object, newdata, newoutcome = NULL, verbose = FALSE, ...)

Arguments

object	A model fitting object from SEMdnn() function.
newdata	A matrix containing new data with rows corresponding to subjects, and columns to variables.
newoutcome	A new character vector (as.factor) of labels for a categorical output (target) (default = NULL).
verbose	Print predicted out-of-sample MSE values (default = FALSE).
...	Currently ignored.

Value

A list of three objects:

1. "PE", vector of the amse = average MSE over all (sink and mediators) graph nodes; r2 = 1 - amse; and srmr= Standardized Root Means Square Residual between the out-of-bag correlation matrix and the model correlation matrix.

2. "mse", vector of the Mean Squared Error (MSE) for each out-of-bag prediction of the sink and mediators graph nodes.

3. "Yhat", the matrix of continuous predicted values of graph nodes (excluding source nodes) based on out-of-bag samples.

Author(s)

Mario Grassi [e-mail]

Examples

if (torch::torch_is_installed()){

# Load Amyotrophic Lateral Sclerosis (ALS)
ig<- alsData$graph
data<- alsData$exprs
data<- transformData(data)$data
group<- alsData$group 

#...with train-test (0.5-0.5) samples
set.seed(123)
train<- sample(1:nrow(data), 0.5*nrow(data))
#ncores<- parallel::detectCores(logical = FALSE)

start<- Sys.time()
dnn0 <- SEMdnn(ig, data[train, ],
			# hidden = 5*K, link = "selu", bias = TRUE, 
			hidden = c(10,10,10), link = "selu", bias = TRUE, 
			validation = 0, epochs = 32, ncores = 2)
end<- Sys.time()
print(end-start)
pred.dnn <- predict(dnn0, data[-train, ], verbose=TRUE)

# SEMrun vs. SEMdnn MSE comparison
sem0 <- SEMrun(ig, data[train, ], algo="ricf", n_rep=0)
pred.sem <- predict(sem0, data[-train,], verbose=TRUE)

#...with a categorical (as.factor) outcome
outcome <- factor(ifelse(group == 0, "control", "case")); table(outcome) 

start<- Sys.time()
dnn1 <- SEMdnn(ig, data[train, ], outcome[train],
			#hidden = 5*K, link = "selu", bias = TRUE,
			hidden = c(10,10,10), link = "selu", bias = TRUE,
			validation = 0,  epochs = 32, ncores = 2)
end<- Sys.time()
print(end-start)

pred <- predict(dnn1, data[-train, ], outcome[-train], verbose=TRUE)
yhat <- pred$Yhat[ ,levels(outcome)]; head(yhat)
yobs <- outcome[-train]; head(yobs)
classificationReport(yobs, yhat, verbose=TRUE)$stats
}

SEMdeep documentation built on April 12, 2025, 2:24 a.m.