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# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Library General Public License for more details.
#
# You should have received a copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA 02111-1307 USA
###############################################################################
# FUNCTION: REGRESSION MODELLING DESCRIPTION:
# regFit Wrapper Function for Regression Models
###############################################################################
test.lmFit <-
function()
{
# Simulate Artificial LM:
x <- regSim(model = "LM3", n = 50)
# Fit Parameters:
lmfit <- regFit(Y ~ X1 + X2 + X3, data = x, use = "lm")
lm <- stats::lm(Y ~ X1 + X2 + X3, data = x)
# Terms:
terms(lmfit@fit)
terms(lm)
# Return Value:
return()
}
# -----------------------------------------------------------------------------
test.rlmFit <-
function()
{
# Simulate Artificial LM:
x = regSim(model = "LM3", n = 50)
# Fit Parameters:
rlmfit <- regFit(Y ~ X1 + X2 + X3, data = x, use = "rlm")
rlm <- MASS::rlm(Y ~ X1 + X2 + X3, data = x)
# Terms:
terms(rlmfit@fit)
terms(rlm)
# Return Value:
return()
}
# -----------------------------------------------------------------------------
test.glmFit <-
function()
{
# Simulate Artificial LM:
x = regSim(model = "LM3", n = 50)
# Fit Parameters:
glmfit <- regFit(Y ~ X1 + X2 + X3, data = x, use = "glm")
glm <- stats::glm(Y ~ X1 + X2 + X3, data = x)
# Terms:
terms(glmfit@fit)
terms(glm)
# Return Value:
return()
}
# -----------------------------------------------------------------------------
test.gamFit <-
function()
{
# Simulate Artificial LM:
x = regSim(model = "LM3", n = 50)
# Fit Parameters:
gamfit <- regFit(Y ~ s(X1) + s(X2) + X3, data = x, use = "gam")
gam <- mgcv::gam(Y ~ X1 + X2 + X3, data = x)
# Terms:
terms(gamfit@fit)
terms(gam)
# Return Value:
return()
}
# -----------------------------------------------------------------------------
test.pprFit <-
function()
{
# Simulate Artificial LM:
x <- regSim(model = "LM3", n = 50)
# Fit Parameters:
pprfit <- regFit(Y ~ X1 + X2 + X3, data = x, use = "ppr")
ppr <- stats::ppr(Y ~ X1 + X2 + X3, data = x, nterms = 2)
# Terms:
terms(pprfit@fit)
terms(ppr)
# Return Value:
return()
}
# -----------------------------------------------------------------------------
test.nnetFit <-
function()
{
# Simulate Artificial LM:
x <- regSim(model = "LM3", n = 50)
# Fit Parameters:
nnetfit <- regFit(Y ~ X1 + X2 + X3, data = x, use = "nnet")
nnet <- nnet::nnet(Y ~ X1 + X2 + X3, data = x, trace = FALSE,
size = 2, linout = TRUE)
# Terms:
terms(nnetfit@fit)
terms(nnet)
# Return Value:
return()
}
# -----------------------------------------------------------------------------
test.polymarsFit <-
function()
{
# Simulate Artificial LM:
x = regSim(model = "LM3", n = 50)
# Fit Parameters:
polymarsfit <- regFit(Y ~ X1 + X2 + X3, data = x, use = "polymars")
polymars <- fRegression:::.polymars(Y ~ X1 + X2 + X3, data = x)
# Terms:
terms(polymarsfit@fit)
terms(polymars)
# Return Value:
return()
}
###############################################################################
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