Simulated Spatial Multisampling Data For Test (DataFrame)
mulsam.test.Rd
A simulation data set for testing use of spatial hierarchical structure and samples overlapping on certain locations.
Usage
data(mulsam.test)
Format
A list of three items called "data", "coords" and "beta". Item "data" is a data frame with 873 observations at 25 locations and the following 6 variables.
y
a numeric vector, dependent variable \(y\)
g1
a numeric vector, group level independent variable \(g_1\)
g2
a numeric vector, group level independent variable \(g_2\)
z1
a numeric vector, sample level independent variable \(z_1\)
x1
a numeric vector, sample level independent variable \(x_1\)
group
a numeric vector, group id of each sample
where g1
and g2
are used to estimate local fixed effects;
x1
is used to estimate global fixed effects
and z1
is used to estimate random effects.
Author
Yigong Hu yigong.hu@bristol.ac.uk
Examples
data(mulsam.test)
hgwr(formula = y ~ L(g1 + g2) + x1 + (z1 | group),
data = mulsam.test$data,
coords = mulsam.test$coords,
bw = 10, kernel = "bisquared")
#> Hierarchical and geographically weighted regression model
#> =========================================================
#> Formula: y ~ L(g1 + g2) + x1 + (z1 | group)
#> Method: Back-fitting and Maximum likelihood
#> Data: mulsam.test$data
#>
#> Fixed Effects
#> -------------
#> Intercept x1
#> 1.780635 1.967851
#>
#> Group-level Spatially Weighted Effects
#> --------------------------------------
#> Bandwidth: 10 (nearest neighbours)
#>
#> Coefficient estimates:
#> Coefficient Min 1st Quartile Median 3rd Quartile Max
#> Intercept -0.796101 -0.612570 -0.267630 0.301641 0.754773
#> g1 -0.084653 1.534803 1.711127 2.694849 6.206580
#> g2 0.687552 0.871403 1.827057 2.353679 4.074047
#>
#> Sample-level Random Effects
#> ---------------------------
#> Groups Name Std.Dev. Corr
#> group Intercept 1.030520
#> z1 1.030520 0.000000
#> Residual 1.030520
#>
#> Other Information
#> -----------------
#> Number of Obs: 873
#> Groups: group , 25