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################################################################################
# FUNCTION: NORMALITY TESTS:
# fHTEST Class Representation
# show.fHTEST S4 Print Method
# FUNCTION: DESCRIPTION:
# jbTable Finite sample p values for the Jarque Bera test
# FUNCTION: PVALUE AND STATISTICS TABLES:
# pPlot General finite sample probability plot
# pTable Interpolated probabilities from finite sample table
# .pTable Utility function called by the function 'pTable'
# qTable Interpolated quantiles from finite sample table
# .qTable Utility function called by the function 'qTable'
# FUNCTION: INTERNAL FUNCTIONS:
# .interpTable.old 'akima' interpolation utility function
# .interpTable.new 'akima' interpolation utility function
################################################################################
test.helpFile =
function()
{
# Help File:
helpFile = function() {
example(HypothesisTesting); return() }
checkIdentical(
target = class(try(helpFile())),
current = "NULL")
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.jbTable.LM =
function()
{
# Plot Parameters:
par(ask = FALSE)
par(mfrow = c(1, 1))
# Interpolated plot of Small Jarque Bera Table:
X = jbTable(type = "LM", size = "small")
# List of finite Sizes:
colnames(X)
# List of Probabilities:
rownames(X)
# 3D-Plot Probability Table:
pPlot(X, linear = TRUE, logStat = TRUE, main = "JB LM", cex = 0.5)
pPlot(X, linear = TRUE, logStat = TRUE, fill = TRUE, cex = 0.5)
pPlot(X, linear = FALSE, logStat = TRUE, cex = 0.5)
pPlot(X, linear = FALSE, logStat = TRUE, fill = TRUE, cex = 0.5)
# 2D-Plot Probability for Fixed Size:
p = (1:99)/100
plot(qTable(X, p, N = 100), p, type = "b")
grid()
# 2D-Plot Statistics for Fixed Size:
Stat = seq(0.01, 15, length = 100)
plot(Stat, pTable(X, Stat, N = 100), type = "b")
grid()
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.jbTable.ALM =
function()
{
# Plot Parameters:
par(ask = FALSE)
par(mfrow = c(1, 1))
# Interpolated plot of Small Jarque Bera Table:
X = jbTable(type = "ALM", size = "small")
# List of finite Sizes:
colnames(X)
# List of Probabilities:
rownames(X)
# 3D-Plot Probability Table:
pPlot(X, linear = TRUE, logStat = TRUE, main = "JB ALM", cex = 0.5)
pPlot(X, linear = TRUE, logStat = TRUE, fill = TRUE, cex = 0.5)
pPlot(X, linear = FALSE, logStat = TRUE, cex = 0.5)
pPlot(X, linear = FALSE, logStat = TRUE, fill = TRUE, cex = 0.5)
# 2D-Plot Probability for Fixed Size:
p = (1:99)/100
plot(qTable(X, p, N = 100), p, type = "b")
grid()
# 2D-Plot Statistics for Fixed Size:
Stat = seq(0.01, 15, length = 100)
plot(Stat, pTable(X, Stat, N = 100), type = "b")
grid()
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.adfTable =
function()
{
# Plot Parameters:
par(ask = FALSE)
par(mfrow = c(1, 1))
# Dickey-Fuller Tables:
# type = "ns"
adfTable = cbind(
c(-2.66, -2.26, -1.95, -1.60, +0.92, +1.33, +1.70, +2.16),
c(-2.62, -2.25, -1.95, -1.61, +0.91, +1.31, +1.66, +2.08),
c(-2.60, -2.24, -1.95, -1.61, +0.90, +1.29, +1.64, +2.03),
c(-2.58, -2.23, -1.95, -1.62, +0.89, +1.29, +1.63, +2.01),
c(-2.58, -2.23, -1.95, -1.62, +0.89, +1.28, +1.62, +2.00),
c(-2.58, -2.23, -1.95, -1.62, +0.89, +1.28, +1.62, +2.00))
colnames(adfTable) = c(25, 50, 100, 250, 500, 100000)
rownames(adfTable) = c(0.01, 0.025, 0.05, 0.1, 0.9, 0.95, 0.975, 0.99)
print(adfTable)
# 25 50 100 250 500 1e+05
# 0.01 -2.66 -2.62 -2.60 -2.58 -2.58 -2.58
# 0.025 -2.26 -2.25 -2.24 -2.23 -2.23 -2.23
# 0.05 -1.95 -1.95 -1.95 -1.95 -1.95 -1.95
# 0.1 -1.60 -1.61 -1.61 -1.62 -1.62 -1.62
# 0.9 0.92 0.91 0.90 0.89 0.89 0.89
# 0.95 1.33 1.31 1.29 1.29 1.28 1.28
# 0.975 1.70 1.66 1.64 1.63 1.62 1.62
# 0.99 2.16 2.08 2.03 2.01 2.00 2.00
# Table:
X = adfTable
# List of finite Sizes:
colnames(X)
# List of Probabilities:
rownames(X)
# 3D Probability Plot:
pPlot(X)
ans = pPlot(X, 10, 10, fill = TRUE)$z
# Probabilities:
Stat = c(-3, -1.5, 0, 1.5, 3)
N = 400
p = pTable(X = adfTable, Stat, N)
names(p) = paste("q=", Stat, sep = "")
attr(p, "N")<-N
p
# Quantiles:
p = c(0.001, 0.01, 0.02, 0.06, 0.11)
N = 400
q = qTable(X = adfTable, p, N)
names(q) = paste("p=", p, sep = "")
attr(q, "N")<-N
q
# Return Value:
return()
}
# ------------------------------------------------------------------------------
if (FALSE) {
require(RUnit)
testResult <- runTestFile("C:/Rmetrics/SVN/trunk/fBasics/test/runit5A.R")
printTextProtocol(testResult)
}
################################################################################
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