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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")
print(lmfit)
summary(lmfit)
# plot(lmfit)
fitted(lmfit)
slot(lmfit, "fitted")
residuals(lmfit)
slot(lmfit, "residuals")
coef(lmfit)
formula(lmfit)
predict(lmfit)
# 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")
print(rlmfit)
summary(rlmfit)
# plot(rlmfit)
fitted(rlmfit)
slot(rlmfit, "fitted")
residuals(rlmfit)
slot(rlmfit, "residuals")
coef(rlmfit)
formula(rlmfit)
predict(rlmfit)
utils::head(rlmfit@fit$model)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.glmFit <-
function()
{
# Simulate Artificial LM:
x <- regSim(model = "LM3", n = 50)
# Fit Parameters:
glmfit <- regFit(Y ~ X1 + X2 + X3, data = x, use = "glm")
print(glmfit)
summary(glmfit)
# plot(glmfit)
print(glmfit@fit)
summary(glmfit@fit)
fitted(glmfit)
slot(glmfit, "fitted")
residuals(glmfit)
slot(glmfit, "residuals")
coef(glmfit)
formula(glmfit)
predict(glmfit)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.gamFit <-
function()
{
# Simulate Artificial LM:
x <- regSim(model = "GAM3", n = 50)
# Fit Parameters:
gamfit <- regFit(Y ~ s(X1) + s(X2) + X3, data = x, use = "gam")
print(gamfit)
summary(gamfit)
# plot(gamfit)
print(gamfit@fit)
summary(gamfit@fit)
fitted(gamfit)
slot(gamfit, "fitted")
residuals(gamfit)
slot(gamfit, "residuals")
coef(gamfit)
formula(gamfit)
predict(gamfit)
gamfit@fit$terms
# 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 <- ppr(Y ~ X1 + X2 + X3, data = x, nterms = 2)
print(pprfit)
summary(pprfit)
# plot(pprfit)
print(pprfit@fit)
summary(pprfit@fit)
fitted(pprfit)
slot(pprfit, "fitted")
residuals(pprfit)
slot(pprfit, "residuals")
coef(pprfit)
formula(pprfit)
predict(pprfit)
pprfit@fit$terms
# Return Value:
return()
}
# ------------------------------------------------------------------------------
if (FALSE) {
test.nnetFit <-
function()
{
# Simulate Artificial LM:
x <- regSim(model = "LM3", n = 50)
# Fit Parameters:
nnetfit <- regFit(Y ~ X1 + X2 + X3, data = x, use = "nnet")
print(nnetfit)
summary(nnetfit)
# plot(nnetfit)
print(nnetfit@fit)
summary(nnetfit@fit)
fitted(nnetfit)
slot(nnetfit, "fitted")
residuals(nnetfit)
slot(nnetfit, "residuals")
coef(nnetfit)
formula(nnetfit)
predict(nnetfit)
nnetfit@fit$terms
# 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")
print(polymarsfit)
summary(polymarsfit)
fitted(polymarsfit)
slot(polymarsfit, "fitted")
residuals(polymarsfit)
slot(polymarsfit, "residuals")
coef(polymarsfit)
formula(polymarsfit)
predict(polymarsfit)
polymarsfit@fit$terms
# Return Value:
return()
}
################################################################################
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