seffEst: STFIT Spatial Effect Estimation

In stfit: Spatio-Temporal Functional Imputation Tool

STFIT Spatial Effect Estimation

Description

STFIT Spatial Effect Estimation

Usage

seffEst(
  rmat,
  img.nrow,
  img.ncol,
  h.cov = 2,
  h.sigma2 = 2,
  weight.cov = NULL,
  weight.sigma2 = NULL,
  nnr,
  method = c("lc", "emp"),
  partial.only = TRUE,
  pve = 0.99,
  msk = NULL,
  msk.tol = 0.95,
  var.est = FALSE
)

Arguments

rmat	residual matrix
img.nrow	image row dimension
img.ncol	image column dimension
h.cov	bandwidth for spatial covariance estimation; ignored if weight.cov is supplied
h.sigma2	bandwidth for sigma2 estimation
weight.cov	weight matrix for spatial covariance estimation
weight.sigma2	weight vector for spatial variance estimation
nnr	maximum number of nearest neighbor pixels to use for spatial covariance estimation
method	"lc" for local constant covariance estimation and "emp" for empirical covariance estimation
partial.only	calculate the spatical effect for partially observed images only, default is TRUE
pve	percent of variance explained of the selected eigen values. Default is 0.99.
msk	an optional logistic vector. TRUE represent the corresponding pixel is always missing.
msk.tol	if 'msk' is not given, the program will determine the mask using getMask function. If the percentage of missing values for a pixel over time is greater than this
var.est	Whether to estimate the variance of the temporal effect. Default is FALSE.

Value

List of length 3 with entries:

• seff_mat: estimated spatial effect matrix of the same shape as rmat.

• seff_var_mat: estimated spatial effect variance matrix of the same shape as rmat.

• idx: a list of two entries:

  • idx.allmissing: index of the completely missing images.

  • idx.imputed: index of the partially observed images, where spatial effects are estimated.

stfit documentation built on Oct. 18, 2022, 5:07 p.m.