interpolation: interpolation
In BradHubley/SpatialHub: Survey Design and Spatial Analysis of Fisheries Data

interpolation

R Documentation

interpolation

Description

preforms a spatial interpolation either inverse distance weighted or kriging.

Usage

interpolation(
  contour.dat,
  ticks,
  nstrata,
  str.max,
  str.min,
  place = 0,
  aspr,
  interp.method = "gstat",
  res = 0.01,
  maxdist = Inf,
  nmax = 8,
  idp = 0.5,
  mod.type = "Sph",
  smooth = F,
  smooth.fun = median,
  smooth.procedure = 1,
  sres = 1/60.1,
  no.data = "0",
  blank = T,
  blank.dist,
  blank.eff = 0,
  blank.type = 2,
  log.dat = F,
  covariate.dat = NULL,
  subset.poly = NULL,
  regrid = F,
  subset.eff = NA,
  subscale = res
)

Arguments

`contour.dat`	= undocumented
`ticks`	= contour lines or strata definitions, 'define' will determine them according to the f(x) rule from Cochran 1977 (see Hubley et al. 2009)
`nstrata`	= number of strata when ticks='define'
`str.max`	= maximum value for stratifying variable (all values greater will be set at maximum)
`str.min`	= minimum value for stratifying variable (all values lesser will be set to NA )
`place`	= rounding place for defining strata
`aspr`	= aspect ratio for for creating square grid, will determine from data if missing
`interp.method`	= 'gstat' = gstat (idw) function from gstat library, 'krige' = krige function from gstat library, 'none' = no interpolation function
`res`	= resolution for interpolation
`maxdist`	nmax, idp, mod.type = arguments to be passed to interpolation function (idp is inverse distance power)
`nmax`	= undocumented
`idp`	= undocumented
`mod.type`	= undocumented
`smooth`	= logical, TRUE calls smoothing function
`smooth.fun`	= applies smooth.fun to data over a grid
`smooth.procedure`	= undocumented
`sres`	= resolution for smoothing (grid)
`no.data`	= how to treat missing data when smoothing, default is to assume zero
`blank`	= TRUE calls blanking function, included blanking distance, beyond which if no data are present zeros are assigned or blank.eff
`blank.dist`	= blanking distance if missing will select the shortest distance to the most isolated point
`blank.eff`	= undocumented
`blank.type`	= how spaced out the zeros are, 1 = avg. nearest neighbour distance, 2 = blanking distance
`log.dat`	= logical, whether to log data
`covariate.dat`	= covariate data typically used in kriging
`subset.poly`	= inclusion polygon to subset spatially, 'square' used to close polygons (useful when not smoothing or blanking)
`regrid`	= undocumented
`subset.eff`	= sets values to this outside the subset.poly
`subscale`	= size of inset when subset.poly = 'square'