ddst.normubounded.test | R Documentation |
Performs data driven smooth test for composite hypothesis of normality.
Null density is given by
f(z;\gamma)=1/(\sqrt{2 \pi}\gamma_2) \exp(-(z-\gamma_1)^2/(2 \gamma_2^2))
for z \in R
.
ddst.normubounded.test(
x,
d.n = 20,
e.0,
v.0,
r.alpha,
s.n.alpha,
alpha = 0.05,
nr = 10000,
compute.cv = TRUE
)
ddst.normunbounded.bias(n = 100, d.n = 20, nr = 10000)
x |
a (non-empty) numeric vector of data values |
d.n |
an integer specifying the maximum dimension considered, only for advanced users |
e.0 |
a (non-empty) numeric vector being the Monte Carlo estimate of the mean of the vector (C2; ... ;Cd.n) calculated using the function |
v.0 |
a (non-empty) numeric vector being the Monte Carlo estimate of the variance of the vector (C2; ... ;Cd.n) calculated using the function |
r.alpha |
a critical value of the alpha level R.n test |
s.n.alpha |
a penalty in the auxiliary model selection rule |
alpha |
a significance level |
nr |
an integer specifying the number of runs for a critical value computation |
compute.cv |
a logical value indicating whether to compute a critical value corresponding to the significance level alpha or not |
n |
sample size |
Ledwina, T., Wyłupek, G. (2015) Detection of non-Gaussianity by Ledwina and Wyłupek Journal of Statistical Computation and Simulation 17, 3480-3497.
set.seed(7)
# H0 is true
z <- rnorm(100)
# let's look on first 20 coordinates
d.n <- 20
## Not run:
# calculate finite sample corrections
# see 6.2. Composite null hypothesis H in the appendix materials
e.v <- ddst.normunbounded.bias(n = length(z))
e.v
# simulated 1-alpha qunatiles, s(n, alpha) and s.o(n, alpha)
# see Table 1 in the JSCS article
s <- 4.4
r.alpha <- 2.708
t <- ddst.normubounded.test(z, d.n, e.v$e.0, e.v$v.0, r.alpha, s)
t
plot(t)
# for Tephra data
z <- c(-1.748789, -1.75753, -1.740102, -1.740102, -1.731467, -1.765523,
-1.761521, -1.72522, -1.80371, -1.745624, -1.872957, -1.729121,
-1.81529, -1.888637, -1.887761, -1.881645, -1.91518, -1.849769,
-1.755141, -1.665687, -1.764721, -1.736171, -1.736956, -1.737742,
-1.687537, -1.804534, -1.790593, -1.808661, -1.784081, -1.729903,
-1.711263, -1.748789, -1.772755, -1.72756, -1.71358, -1.821116,
-1.839588, -1.839588, -1.830321, -1.807835, -1.747206, -1.788147,
-1.759923, -1.786519, -1.726779, -1.738528, -1.754345, -1.781646,
-1.641949, -1.755936, -1.775175, -1.736956, -1.705103, -1.743255,
-1.82613, -1.826967, -1.780025, -1.684504, -1.751168)
# calculate finite sample corrections
e.v <- ddst.normunbounded.bias(n = length(z))
e.v
s <- 3.3
so <- 3.6
r.alpha <- 2.142
t <- ddst.normubounded.test(z, d.n, e.v$e.0, e.v$v.0, r.alpha, s)
t
plot(t)
## End(Not run)
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