Predict method for ML objects.
# S3 method for class 'ML'
predict(object, newdata, newoutcome = NULL, ncores = 2, verbose = FALSE, ...)Arguments
- object
A model fitting object from
SEMml()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).
- ncores
number of cpu cores (default = 2)
- verbose
Print predicted out-of-sample MSE values (default = FALSE).
- ...
Currently ignored.
Value
A list of 3 objects:
"PE", vector of the amse = average MSE over all (sink and mediators) graph nodes; r2 = 1 - amse; and srmr= Standardized Root Means Squared Residual between the out-of-bag correlation matrix and the model correlation matrix.
"mse", vector of the Mean Squared Error (MSE) for each out-of-bag prediction of the sink and mediators graph nodes.
"Yhat", the matrix of continuous predicted values of graph nodes (excluding source nodes) based on out-of-bag samples.
Examples
# \donttest{
# Load Amyotrophic Lateral Sclerosis (ALS)
ig<- alsData$graph
data<- alsData$exprs
data<- transformData(data)$data
#> Conducting the nonparanormal transformation via shrunkun ECDF...done.
group<- alsData$group
#...with train-test (0.5-0.5) samples
set.seed(123)
train<- sample(1:nrow(data), 0.5*nrow(data))
start<- Sys.time()
# ... tree
res1<- SEMml(ig, data[train, ], algo="tree")
#> Running SEM model via ML...
#> done.
#>
#> TREE solver ended normally after 23 iterations
#>
#> logL:-45.080145 srmr:0.201877
mse1<- predict(res1, data[-train, ], verbose=TRUE)
#> amse r2 srmr
#> 0.8208979 0.1791021 0.2269379
# ... rf
res2<- SEMml(ig, data[train, ], algo="rf")
#> Running SEM model via ML...
#> done.
#>
#> RF solver ended normally after 23 iterations
#>
#> logL:-33.16687 srmr:0.086188
mse2<- predict(res2, data[-train, ], verbose=TRUE)
#> amse r2 srmr
#> 0.8260251 0.1739749 0.1760094
# ... xgb
res3<- SEMml(ig, data[train, ], algo="xgb")
#> Running SEM model via ML...
#> done.
#>
#> XGB solver ended normally after 23 iterations
#>
#> logL:70.10035 srmr:0.001439
mse3<- predict(res3, data[-train, ], verbose=TRUE)
#> amse r2 srmr
#> 0.8005846 0.1994154 0.1543352
# ... nn
res4<- SEMml(ig, data[train, ], algo="nn")
#> Running SEM model via ML...
#> done.
#>
#> NN solver ended normally after 23 iterations
#>
#> logL:-33.873642 srmr:0.135954
mse4<- predict(res4, data[-train, ], verbose=TRUE)
#> amse r2 srmr
#> NaN NaN 0.1844971
end<- Sys.time()
print(end-start)
#> Time difference of 2.424637 secs
#...with a categorical (as.factor) outcome
outcome <- factor(ifelse(group == 0, "control", "case")); table(outcome)
#> outcome
#> case control
#> 139 21
res5 <- SEMml(ig, data[train, ], outcome[train], algo="tree")
#> Running SEM model via ML...
#> done.
#>
#> TREE solver ended normally after 25 iterations
#>
#> logL:-48.72171 srmr:0.196861
pred <- predict(res5, data[-train, ], outcome[-train], verbose=TRUE)
#> amse r2 srmr
#> 0.7783295 0.2216705 0.2253818
yhat <- pred$Yhat[ ,levels(outcome)]; head(yhat)
#> case control
#> 1 0.4773726 -0.4773726
#> 2 0.4773726 -0.4773726
#> 3 0.4773726 -0.4773726
#> 4 0.4773726 -0.4773726
#> 5 0.4773726 -0.4773726
#> 6 -0.6137648 0.6137648
yobs <- outcome[-train]; head(yobs)
#> [1] case case case case case case
#> Levels: case control
classificationReport(yobs, yhat, verbose=TRUE)$stats
#> pred
#> yobs case control
#> case 57 17
#> control 2 4
#>
#> precision recall f1 accuracy mcc support
#> case 0.9661017 0.7702703 0.8571429 0.7625 0.261562 74
#> control 0.1904762 0.6666667 0.2962963 0.7625 0.261562 6
#> macro avg 0.5782889 0.7184685 0.5767196 0.7625 0.261562 80
#> weighted avg 0.9079298 0.7625000 0.8150794 0.7625 0.261562 80
#> support_prop
#> case 0.925
#> control 0.075
#> macro avg 1.000
#> weighted avg 1.000
# }