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)Returns the adaptive or fixed distance bandwidth
Regression model formula of a formula object
a Spatial*DataFrame, i.e. SpatialPointsDataFrame or SpatialPolygonsDataFrame as defined in package sp, or a sf object defined in package sf
a vector of time tags for each observation, which could be numeric or of POSIXlt class
specified by CV for cross-validation approach or by AIC corrected (AICc) approach
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
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)
the power of the Minkowski distance, default is 2, i.e. the Euclidean distance
an angle in radians to rotate the coordinate system, default is 0
if TRUE, great circle distances will be calculated
an parameter between 0 and 1 for calculating spatio-temporal distance
character string to define time unit
an parameter between 0 and PI for calculating spatio-temporal distance, see details in Wu et al. (2014)
a pre-specified spatio-temporal distance matrix
logical variable to define whether show the selection procedure
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.