Description Usage Arguments Value References See Also Examples

The function fit geographically weighted elliptical regression model to explore the non-stationarity for a certain bandwidth and weighting function.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
gwer(
formula,
data,
regression.points,
bandwidth,
kernel = "bisquare",
p = 2,
theta = 0,
adapt = NULL,
hatmatrix = FALSE,
family = Normal,
longlat = NULL,
dMat,
weights,
dispersion = NULL,
subset,
na.action = "na.fail",
method = "gwer.fit",
control = glm.control(epsilon = 1e-04, maxit = 100, trace = F),
model = FALSE,
x = FALSE,
y = TRUE,
contrasts = NULL,
offset,
spdisp = TRUE,
parplot = FALSE,
...
)
``` |

`formula` |
regression model formula as in |

`data` |
model data frame, or may be a SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp. |

`regression.points` |
a Spatial*DataFrame object, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp; Note that no diagnostic information will returned if it is assigned. |

`bandwidth` |
value of the selected bandwidth used in the weighting function (see |

`kernel` |
function chosen as follows: gaussian: wgt = exp(-.5*(vdist/bw)^2); exponential: wgt = exp(-vdist/bw); bisquare: wgt = (1-(vdist/bw)^2)^2 if vdist < bw, wgt=0 otherwise; tricube: wgt = (1-(vdist/bw)^3)^3 if vdist < bw, wgt=0 otherwise; boxcar: wgt=1 if dist < bw, wgt=0 otherwise |

`p` |
the power of the Minkowski distance, default is 2, i.e. the Euclidean distance |

`theta` |
an angle in radians to rotate the coordinate system, default is 0 |

`adapt` |
defines the type of bandwidth used. either NULL (default) or a proportion between 0 and 1 of observations to include in weighting scheme (k-nearest neighbours). |

`hatmatrix` |
if TRUE, return the hatmatrix as a component of the result. |

`family` |
a description of the error distribution to be used in the model (see |

`longlat` |
TRUE if point coordinates are longitude-latitude decimal degrees, in which case distances are measured in kilometers. If x is a SpatialPoints object, the value is taken from the object itself. |

`dMat` |
a pre-specified distance matrix, it can be calculated by the function gw.dist |

`weights` |
an optional numeric vector of weights to be used in the fitting process. |

`dispersion` |
an optional fixed value for dispersion parameter. |

`subset` |
an optional numeric vector specifying a subset of observations to be used in the fitting process. |

`na.action` |
a function which indicates what should happen when the data contain NAs (see |

`method` |
the method to be used in fitting local models. The default method "bw.gwer" uses Fisher's scoring method. The alternative "model.frame" returns the model frame and does no fitting. |

`control` |
a list of parameters for controlling the fitting process. For |

`model` |
a logical value indicating whether model frame should be included as a component of the return. |

`x` |
a logical value indicating whether the response vector used in the fitting process should be returned as components of the return. |

`y` |
a logical value indicating whether model matrix used in the fitting process should be returned as components of the return. |

`contrasts` |
an optional list. See the |

`offset` |
this can be used to specify an a priori known component to be included in the linear predictor during fitting as in |

`spdisp` |
if TRUE dispersion parameter varies geographically. |

`parplot` |
if TRUE the parameters boxplots are plotted. |

`...` |
arguments to be used to form the default control argument if it is not supplied directly. |

returns an object of class “gwer”, a list with follow components:

`SDF` |
a SpatialPointsDataFrame (may be gridded) or SpatialPolygonsDataFrame object (see package sp) with fit.points, weights, GWR coefficient estimates, dispersion and the residuals in its |

`coef` |
the matrices of coefficients, standard errors and significance values for parameters hypothesis test. |

`dispersion` |
either the supplied argument or the estimated dispersion with standard error. |

`hat` |
hat matrix of the geographically weighted elliptical model. |

`lm` |
elliptical global regression on the same model formula. |

`results` |
a list of results values for fitted geographically weighted elliptical model. |

`bandwidth` |
the bandwidth used in geographical weighting function. |

`fitted` |
the fitted mean values of the geographically weighted elliptical model. |

`hatmatrix` |
a logical value indicating if hatmatrix was considered |

`gweights` |
a matrix with the geographical weighting for all local elliptical models. |

`family` |
the |

`flm` |
a matrix with the fitted values for all local elliptical models. |

`adapt` |
the |

`kernel` |
the |

`spdisp` |
the |

`this.call` |
the function call used. |

`longlat` |
the |

Brunsdon, C., Fotheringham, A. S. and Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical analysis, 28(4), 281-298. doi: 10.1111/j.1538-4632.1996.tb00936.x

Cysneiros, F. J. A., Paula, G. A., and Galea, M. (2007). Heteroscedastic symmetrical linear models. Statistics & probability letters, 77(11), 1084-1090. doi: 10.1016/j.spl.2007.01.012

Fang, K. T., Kotz, S. and NG, K. W. (1990, ISBN:9781315897943). Symmetric Multivariate and Related Distributions. London: Chapman and Hall.

`bw.gwer`

, `elliptical`

, `family.elliptical`

1 2 3 4 5 6 7 | ```
data(georgia, package = "spgwr")
fit.formula <- PctBach ~ TotPop90 + PctRural + PctFB + PctPov
gwer.bw.t <- bw.gwer(fit.formula, data = gSRDF, family = Student(3), adapt = TRUE)
gwer.fit.t <- gwer(fit.formula, data = gSRDF, family = Student(3), bandwidth = gwer.bw.t,
adapt = TRUE, parplot = FALSE, hatmatrix = TRUE, spdisp = TRUE,
method = "gwer.fit")
print(gwer.fit.t)
``` |

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