extractStData {telefit}

Basic extraction of SpatialGridDataFrame data for teleconnection analysis

Description

Basic extraction of SpatialGridDataFrame data for teleconnection analysis

Usage

extractStData(
  X,
  Y,
  Z,
  t = NULL,
  D.s,
  D.r,
  mask.s = NULL,
  mask.r = NULL,
  aggfact.s = NULL,
  aggfact.r = NULL,
  intercept = T,
  type.s = "response",
  type.r = "response",
  type.s.y = "response",
  X.lab = NULL,
  Y.lab = NULL,
  Z.lab = NULL,
  aspect = F,
  aspect.categories = 4,
  slope = F,
  colnames.X = NULL,
  formula = NULL
)

Arguments

X

SpatialGridDataFrame with local covariates. If X is a list, each SpatialGridDataFrame will be included as one covariate.

Y

SpatialGridDataFrame with response data

Z

SpatialGridDataFrame with remote covariates. If Z is a list, this function assumes each element of the list contains observations for the same covariate, but from different spatial regions. If Z is a list, D.r and mask.r must also be lists so that this function can know which regions to extract from each SpatialGridDataFrame

t

Timepoint from which to extract data from X, Y, and Z. If NULL, then all timepoints will be used.

D.s

c(xmin, xmax, ymin, ymax) region from which to extract data from X and Y, or a SpatialPolygonsXXX object containing boundaries of regions to extract areal data from.

D.r

c(xmin, xmax, ymin, ymax) region from which to extract data from Z

mask.s

SpatialGridDataFrame to be used as a mask when extracting data from X and Y. Locations in mask.s with NA values will be ignored when extracting data from X and Y.

mask.r

SpatialGridDataFrame to be used as a mask when extracting data from Z. Locations in mask.s with NA values will be ignored when extracting data from Z.

aggfact.s

If provided, will spatially average Y and X data

aggfact.r

If provided, will spatially average Z data

intercept

If TRUE, an intercept will be added to the design matrix

type.s

'response' 'anomaly' or 'std.anomaly' or a vector of these options depending on whether data extracted from X should be the observed data, anomalies, or standardized anomalies (where the climatology is computed from the observations as the pointwise temporal average)

type.r

'response' 'anomaly' or 'std.anomaly' or a vector of these options depending on whether data extracted from Z should be the observed data, anomalies, or standardized anomalies (where the climatology is computed from the observations as the pointwise temporal average)

type.s.y

'response' 'anomaly' or 'std.anomaly' depending on whether data extracted from Y should be the observed data, anomalies, or standardized anomalies (where the climatology is computed from the observations as the pointwise temporal average)

X.lab

name for X data (optional)

Y.lab

name for Y data (optional)

Z.lab

name for Z data (optional)

aspect

TRUE or vector of logicals (one for each X object) to return the aspect of the surface at each location instead of the value of the surface itself

aspect.categories

if aspect==TRUE, this specifies the number of discrete categories to divide aspect numbers (0-360) into. NULL if the original scale (0-360) should be kept. By design, the aspect categories will be centered on north in the first category.

slope

TRUE or vector of logicals (one for each X object) to return the slope of the surface at each location instead of the value of the surface itself

colnames.X

names of columns of X

formula

formula object to specify how to create the design matrix

Examples

# the extractRegion and extractStData methods create data matrices from 
# SpatialGridDataFrame objects

library(sp)

data("coprecip")
attach(coprecip)


#
# build SpatialGridDataFrame objects containing some of the coprecip data
#

gt = GridTopology(cellcentre.offset = apply(coords.s, 2, min),
                  cellsize = c(.5, .5), 
                  cells.dim = c(20, 12))

# Note: This is an example only; this grid will not match coprecip$coords.r
gt.Z = GridTopology(cellcentre.offset = apply(coords.r, 2, min),
                    cellsize = c(1.4, 1.4),
                    cells.dim = c(101, 52))

Xd = data.frame(`1981` = X[,2,1], `1982` = X[,2,2])
colnames(Xd) = gsub('X','', colnames(Xd))
sgdf.x = SpatialGridDataFrame(gt, Xd)


Yd = data.frame(`1981` = Y[,1], `1982` = Y[,2])
colnames(Yd) = gsub('X','', colnames(Yd))
sgdf.y = SpatialGridDataFrame(gt, Yd)


Zd = data.frame(`1981` = Z[,1], `1982` = Z[,2])
colnames(Zd) = gsub('X','', colnames(Zd))
sgdf.z = SpatialGridDataFrame(gt.Z, Zd)

# only extract a region of the coordinates
coprecip2 = extractStData(sgdf.x, sgdf.y, sgdf.z, 
                    D.s = c(-105, -103, 37, 41),
                    D.r = c(-160, -100, -15, 0))