Train an equivalent of a 1 hidden unit neural network with a defined nonlinear activation function
using optim
s.NLA(x, y = NULL, x.test = NULL, y.test = NULL,
activation = softplus, b_o = mean(y), W_o = 1, b_h = 0,
W_h = 0.01, optim.method = "BFGS", control = list(),
lower = -Inf, upper = Inf, x.name = NULL, y.name = NULL,
save.func = TRUE, print.plot = TRUE, plot.fitted = NULL,
plot.predicted = NULL, plot.theme = getOption("rt.fit.theme",
"lightgrid"), question = NULL, rtclass = NULL, verbose = TRUE,
trace = 0, outdir = NULL, save.mod = ifelse(!is.null(outdir), TRUE,
FALSE), ...)
Numeric vector or matrix / data frame of features i.e. independent variables
Numeric vector of outcome, i.e. dependent variable
Numeric vector or matrix / data frame of testing set features
Columns must correspond to columns in x
Numeric vector of testing set outcome
Function: Activation function to use. Default = softplus
Float, vector (length y): Output bias. Defaults to mean(y)
Float: Output weight. Defaults to 1
Float: Hidden layer bias. Defaults to 0
Float, vector (length NCOL(x)
): Hidden layer weights. Defaults to 0
String: Optimization method to use: "Nelder-Mead", "BFGS", "CG", "L-BFGS-B",
"SANN", "Brent". See stats::optim
for more details. Default = "BFGS"
Character: Name for feature set
Character: Name for outcome
Logical: if TRUE, produce plot using mplot3
Takes precedence over plot.fitted
and plot.predicted
Logical: if TRUE, plot True (y) vs Fitted
Logical: if TRUE, plot True (y.test) vs Predicted.
Requires x.test
and y.test
String: "zero", "dark", "box", "darkbox"
String: the question you are attempting to answer with this model, in plain language.
String: Class type to use. "S3", "S4", "RC", "R6"
Logical: If TRUE, print summary to screen.
Integer: If higher than 0, will print more information to the console. Default = 0
Path to output directory.
If defined, will save Predicted vs. True plot, if available,
as well as full model output, if save.mod
is TRUE
Logical. If TRUE, save all output as RDS file in outdir
save.mod
is TRUE by default if an outdir
is defined. If set to TRUE, and no outdir
is defined, outdir defaults to paste0("./s.", mod.name)
Additional arguments to be passed to sigreg
Object of class rtemis
Since we are using optim
, results will be sensitive to the combination of
optimizer method (See optim::method
for details),
initialization values, and activation function.
elevate for external cross-validation
Other Supervised Learning: s.ADABOOST
,
s.ADDTREE
, s.BART
,
s.BAYESGLM
, s.BRUTO
,
s.C50
, s.CART
,
s.CTREE
, s.DA
,
s.ET
, s.EVTREE
,
s.GAM.default
, s.GAM.formula
,
s.GAMSEL
, s.GAM
,
s.GBM3
, s.GBM
,
s.GLMNET
, s.GLM
,
s.GLS
, s.H2ODL
,
s.H2OGBM
, s.H2ORF
,
s.IRF
, s.KNN
,
s.LDA
, s.LM
,
s.MARS
, s.MLRF
,
s.MXN
, s.NBAYES
,
s.NLS
, s.NW
,
s.POLYMARS
, s.PPR
,
s.PPTREE
, s.QDA
,
s.QRNN
, s.RANGER
,
s.RFSRC
, s.RF
,
s.SGD
, s.SPLS
,
s.SVM
, s.TFN
,
s.XGBLIN
, s.XGB