Create a covariance parameter initial object that specifies
initial and/or known values to use while estimating specific covariance parameters
with ssn_lm()
or ssn_glm()
. See spmodel::randcov_initial()
for documentation regarding
random effect covariance parameter initial objects.
Usage
tailup_initial(tailup_type, de, range, known)
taildown_initial(taildown_type, de, range, known)
euclid_initial(euclid_type, de, range, rotate, scale, known)
nugget_initial(nugget_type, nugget, known)
Arguments
- tailup_type
The tailup covariance function type. Available options include
"linear"
,"spherical"
,"exponential"
,"mariah"
,"epa"
, and"none"
.- de
The spatially dependent (correlated) random error variance. Commonly referred to as a partial sill.
- range
The correlation parameter.
- known
A character vector indicating which covariance parameters are to be assumed known. The value
"given"
is shorthand for assuming all covariance parameters given to*_initial()
are assumed known.- taildown_type
The taildown covariance function type. Available options include
"linear"
,"spherical"
,"exponential"
,"mariah"
,"epa"
, and"none"
.- euclid_type
The euclidean covariance function type. Available options include
"spherical"
,"exponential"
,"gaussian"
,"cosine"
,"cubic"
,"pentaspherical"
,"wave"
,"jbessel"
,"gravity"
,"rquad"
,"magnetic"
, and"none"
.- rotate
Anisotropy rotation parameter (from 0 to \(\pi\) radians) for the euclidean portion of the covariance. A value of 0 (the default) implies no rotation.
- scale
Anisotropy scale parameter (from 0 to 1) for the euclidean portion of the covariance. A value of 1 (the default) implies no scaling.
- nugget_type
The nugget covariance function type. Available options include
"nugget"
or"none"
.- nugget
The spatially independent (not correlated) random error variance. Commonly referred to as a nugget.
References
Peterson, E.E. and Ver Hoef, J.M. (2010) A mixed-model moving-average approach to geostatistical modeling in stream networks. Ecology 91(3), 644--651.
Ver Hoef, J.M. and Peterson, E.E. (2010) A moving average approach for spatial statistical models of stream networks (with discussion). Journal of the American Statistical Association 105, 6--18. DOI: 10.1198/jasa.2009.ap08248. Rejoinder pgs. 22--24.
Examples
tailup_initial("exponential", de = 1, range = 20, known = "range")
#> $initial
#> de range
#> 1 20
#>
#> $is_known
#> de range
#> FALSE TRUE
#>
#> attr(,"class")
#> [1] "tailup_exponential"
tailup_initial("exponential", de = 1, range = 20, known = "given")
#> $initial
#> de range
#> 1 20
#>
#> $is_known
#> de range
#> TRUE TRUE
#>
#> attr(,"class")
#> [1] "tailup_exponential"
euclid_initial("spherical", de = 2, range = 4, scale = 0.8, known = c("range", "scale"))
#> $initial
#> de range scale
#> 2.0 4.0 0.8
#>
#> $is_known
#> de range scale
#> FALSE TRUE TRUE
#>
#> attr(,"class")
#> [1] "euclid_spherical"
dispersion_initial("nbinomial", dispersion = 5)
#> $initial
#> dispersion
#> 5
#>
#> $is_known
#> dispersion
#> FALSE
#>
#> attr(,"class")
#> [1] "nbinomial"