Description Usage Arguments Details Value Examples
Compute the non-parametric mixing distrbution with components fixed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | npnormfc.ll(
data,
mu0 = 0,
pi0 = 0,
stdev = NULL,
mix = NULL,
order = FALSE,
maxit = 1000,
tol = 1e-06
)
npnormfc.sq(
data,
mu0 = 0,
pi0 = 0,
stdev = NULL,
mix = NULL,
order = FALSE,
maxit = 100,
tol = 1e-06
)
npnormfc.cvm(
data,
mu0 = 0,
pi0 = 0,
stdev = NULL,
mix = NULL,
order = FALSE,
maxit = 100,
tol = 1e-06
)
npnormfc.ad(
data,
mu0 = 0,
pi0 = 0,
stdev = NULL,
mix = NULL,
order = FALSE,
maxit = 100,
tol = 1e-06
)
|
data |
the vector of observation to compute the mixing distribution |
mu0 |
the vector of fixed support points |
pi0 |
the vector of probabilities corresponding to support points |
stdev |
the standard deviation of the component density |
mix |
the initial mixing distribution |
order |
the digit to round down the observations. If FALSE, no binning is performed. |
maxit |
the maximum iterations allowed. |
tol |
the tolerence. Stop if the directional derivative is less than tol |
This function uses the non-parametric method to compute the mixing distribution based on various losses with components fixed when the density is normal.
npnormfc.sq
computes the mixing distribution under squared error loss.
npnormfc.cvm
computes the mixing distribution under cramer-von Mises loss
npnormfc.ll
computes the mixing distribution under KL divergence (Maximum likelihood)
npnormfc.ad
computes the mixing distribution under Anderson-Darling loss.
the nspmix object with ll meaning the loss.
1 2 3 4 5 6 7 8 9 | data = rnorm(100, c(0, 2))
npnormfc.ll(data, mu0 = 0, pi0 = 0.2)
npnormfc.sq(data, mu0 = 0, pi0 = 0.2)
npnormfc.cvm(data, mu0 = 0, pi0 = 0.2)
npnormfc.ad(data, mu0 = 0, pi0 = 0.2)
datalarge = rnorm(1e5, c(0, 2))
npnormfc.cvm(datalarge, order = -2)
npnormfc.ad(datalarge, order = -2)
npnormfc.ll(datalarge, order = -2)
|
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