ebps | R Documentation |
empirical Bayes Poisson smoothing
ebps(
x,
s = NULL,
g_init = NULL,
q_init = NULL,
init_control = list(),
general_control = list(),
smooth_control = list()
)
x, s |
data vector and scaling factor. s can be a vector of the same length as x, or a scalar. |
g_init |
a list of initial value of sigma2, and g_smooth. g_smooth is the initial prior g of the smoothing method. Can be NULL. |
q_init |
a list of initial values of m, smooth. m is the posterior mean of mu, smooth the posterior mean of b. See the details below. |
init_control |
See function ebps_init_control_default |
general_control |
See function ebps_general_control_default |
smooth_control |
See function ebps_smooth_control_default |
The problem is
x_i\sim Poisson(\lambda_i,
\lambda_i = \exp(\mu_i)),
\mu_i\sim N(b_i,\sigma^2),
\b_i\sim g(.).
The init_control
argument is a list in which any of the following
named components will override the default algorithm settings (as
defined by ebps_init_control_default
):
m_init_method
'vga' or 'smash_poi'
The general_control
argument is a list in which any of the following
named components will override the default algorithm settings (as
defined by ebps_general_control_default
):
est_sigma2
whether estiamte sigma2 or fix it
maxiter
max iteration of the main algorithm, default is 100
maxiter_vga
max iteration of the vga step
vga_tol
tolerance for vga step stopping
verbose
print progress?
tol
tolerance for stopping the main algorithm
convergence_criteria
'objabs' or 'nugabs'
make_power_of_2
'reflect' or 'extend'
plot_updates
internal use only
The smooth_control
argument is a list in which any of the following
named components will override the default algorithm settings (as
defined by ebps_smooth_control_default
):
wave_trans
'dwt' or 'ndwt'
ndwt_method
'smash' or 'ti.thresh'
ebnm_params
parameters for ebnm used in wavelet smoothing
warmstart
init posterior using last iteration's results
W
DWT matrix for non-haar wavelet basis
set.seed(12345)
n=2^9
sigma=0.5
mu=c(rep(0.3,n/4), rep(3, n/4), rep(10, n/4), rep(0.3, n/4))
x = rpois(n,exp(log(mu)+rnorm(n,sd=sigma)))
fit = ebps(x)
plot(x,col='grey80')
lines(fit$posterior$mean_smooth)
fit$sigma2
plot(fit$elbo_trace)
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