gtwr | R Documentation |
A function for calibrating a Geographically and Temporally Weighted Regression (GTWR) model.
gtwr(formula, data, regression.points, obs.tv, reg.tv, st.bw, kernel="bisquare",
adaptive=FALSE, p=2, theta=0, longlat=F,lamda=0.05,t.units = "auto",ksi=0,
st.dMat)
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 |
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 |
obs.tv |
a vector of time tags for each observation, which could be numeric or of POSIXlt class |
reg.tv |
a vector of time tags for each regression location, which could be numeric or of POSIXlt class |
st.bw |
spatio-temporal bandwidth used in the weighting function, possibly calculated by bw.gwr;fixed (distance) or adaptive bandwidth(number of nearest neighbours) |
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, and can be calculated via the function st.dist |
A list of class “gtwrm”:
GTW.arguments |
a list class object including the model fitting parameters for generating the report file |
GTW.diagnostic |
a list class object including the diagnostic information of the model fitting |
lm |
an object of class inheriting from “lm”, see lm. |
SDF |
a SpatialPointsDataFrame (may be gridded), or SpatialPolygonsDataFrame object (see package “sp”), or sf object (see package “sf”) integrated with regression.points, GTWR coefficient estimates, y value,predicted values, coefficient standard errors and t-values in its "data" slot. |
timings |
starting and ending time. |
this.call |
the function call used. |
The function implements GTWR model proposed 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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