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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:
# polymars Polymars Regression
# .polymars Polymars regress from package polspline
# .polymarsDefault Internal Function
# .polymarsVormula Internal Function
# .predict.polymars Internal Function
################################################################################
test.polymars <-
function()
{
# Simulate Artificial LM:
x <- regSim(model = "LM3", n = 50)
# Original Polymars:
fit <- polspline::polymars(responses = x[,1], predictors = x[,2:4])
# Model Fitting:
fit$fitting
# Model Produced:
fit$model
fit$coef
# Summary:
# Note print.polymars = summary.polymars
polspline::summary.polymars(fit)
# Predict:
ans <- polspline::predict.polymars(object = fit, x = x[,-1])
as.vector(ans)
as.vector(fit$fitted)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.polymarsDefault <-
function()
{
# Simulate Artificial LM:
set.seed(4711)
x = regSim(model = "LM3", n = 50)
# Polymars Wrapper:
fit1 = fRegression:::.polymarsDefault(responses = x[,1], predictors = x[, 2:4])
class(fit1)
names(fit1)
# Note, this fails:
# fit1 = .polymars(responses = x[,1], predictors = x[,2:4])
# Model Fitting:
fit1$fitting
# Model Produced:
# fit1$model reserved for model series, use ...
fit1$coef
# Summary:
print(fit1)
# Print:
summary(fit1)
# Predict:
ans <- polspline::predict.polymars(object = fit1, x = x[,-1])
as.vector(ans)
as.vector(fit1$fitted)
# Check:
fit1$ranges.and.medians
# Return Value:
return()
}
# -----------------------------------------------------------------------------
test.polymarsFormula <-
function()
{
# Simulate Artificial LM:
set.seed(4711)
x <- regSim(model = "LM3", n = 50)
# Polymars Formula Wrapper:
fit2 <- fRegression:::.polymarsFormula(formula = Y ~ X1 + X2 + X3, data = x)
fit2 <- fRegression:::.polymars(formula = Y ~ X1 + X2 + X3, data = x)
class(fit2)
names(fit2)
# Model Fitting:
fit2$fitting
# Model Produced:
# fit$model reserved for model series, use ...
fit2$coef
# Summary:
print(fit2)
# Print:
summary(fit2)
# Predict:
fit2$model <- fit2$coef
ans <- polspline::predict.polymars(object = fit2, x = x[,-1])
as.vector(ans)
as.vector(fit2$fitted)
# Check:
fit2$ranges.and.medians
# Return Value:
return()
}
# -----------------------------------------------------------------------------
test.regFit.polymars <-
function()
{
# Simulate Artificial LM:
x <- regSim(model = "LM3", n = 50)
# Polymars Formula Wrapper:
fit <- regFit(formula = Y ~ X1 + X2 + X3, data = x, use = "polymars")
class(fit)
# Model Fitting:
fit@fit$fitting
# Model Produced:
# fit$model reserved for model series, use ...
fit@fit$coef
# Summary:
print(fit)
# Print:
summary(fit)
# Return Value:
return()
}
# -----------------------------------------------------------------------------
test.regFit.polymars.methods <-
function()
{
# Simulate Artificial LM:
x <- regSim(model = "LM3", n = 20)
# Polymars Formula Wrapper:
polymarsfit <- regFit(
formula = Y ~ X1 + X2 + X3, data = x, use = "polymars")
# Print:
print(polymarsfit)
# Summary:
summary(polymarsfit)
# Fitted Values:
fitted(polymarsfit)
slot(polymarsfit, "fitted")
# Residuals:
residuals(polymarsfit)
slot(polymarsfit, "residuals")
# Coefficients:
coef(polymarsfit)
# Formula
formula(polymarsfit)
# Return Value:
return()
}
# -----------------------------------------------------------------------------
test.regFit.polymars.predict <-
function()
{
# Simulate Artificial LM:
x <- regSim(model = "LM3", n = 50)
# regFit / Polymars Formula Wrapper:
fit <- regFit(formula = Y ~ X1 + X2 + X3, data = x, use = "polymars")
class(fit)
fit@fit$cmd
# Predict from predict.polymars:
object <- fit@fit
class(object) = "polymars"
object
object$model = object$coef
ans <- polspline::predict.polymars(object = object, x = x[,-1])
as.vector(ans)
as.vector(fit@fitted)
# Predict from predict.fREG:
ans <- predict(object = fit, newdata = x)
as.vector(ans)
as.vector(fit@fitted)
# Return Value:
return()
}
###############################################################################
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