bw.gtwr | R Documentation |
A function for automatic bandwidth selection to calibrate a GTWR model
bw.gtwr(formula, data, obs.tv, approach="CV",kernel="bisquare",adaptive=FALSE,
p=2, theta=0, longlat=F,lamda=0.05,t.units = "auto",ksi=0, st.dMat,
verbose=T)
formula |
Regression model formula of a formula object |
data |
a Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp, or a sf object defined in package sf |
obs.tv |
a vector of time tags for each observation, which could be numeric or of POSIXlt class |
approach |
specified by CV for cross-validation approach or by AIC corrected (AICc) approach |
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 |
adaptive |
if TRUE calculate an adaptive kernel where the bandwidth (bw) corresponds to the number of nearest neighbours (i.e. adaptive distance); default is FALSE, where a fixed kernel is found (bandwidth is a fixed distance) |
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 |
longlat |
if TRUE, great circle distances will be calculated |
lamda |
an parameter between 0 and 1 for calculating spatio-temporal distance |
t.units |
character string to define time unit |
ksi |
an parameter between 0 and PI for calculating spatio-temporal distance, see details in Wu et al. (2014) |
st.dMat |
a pre-specified spatio-temporal distance matrix |
verbose |
logical variable to define whether show the selection procedure |
Returns the adaptive or fixed distance bandwidth
The function is developed according to the articles by Huang et al. (2010) and Wu et al. (2014).
Binbin Lu binbinlu@whu.edu.cn
Huang, B., Wu, B., & Barry, M. (2010). Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24, 383-401.
Wu, B., Li, R., & Huang, B. (2014). A geographically and temporally weighted autoregressive model with application to housing prices. International Journal of Geographical Information Science, 28, 1186-1204.
Fotheringham, A. S., Crespo, R., & Yao, J. (2015). Geographical and Temporal Weighted Regression (GTWR). Geographical Analysis, 47, 431-452.
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