Print description of a hgwrm
object.
Usage
# S3 method for class 'hgwrm'
print(x, decimal.fmt = "%.6f", ...)
Arguments
- x
An
hgwrm
object returned byhgwr()
.- decimal.fmt
The format string passing to
base::sprintf()
.- ...
Arguments passed on to
print.table.md
col.sep
Column separator. Default to
""
.header.sep
Header separator. Default to
"-"
. Ifheader.sep
only contains one character, it will be repeated for each column. If it contains more than one character, it will be printed below the first row.row.begin
Character at the beginning of each row. Default to
col.sep
.row.end
Character at the ending of each row. Default to
col.sep
.table.before
Characters to be printed before the table.
table.after
Characters to be printed after the table.
table.style
Name of pre-defined style. Possible values are
"plain"
,"md"
,"latex"
, or"booktabs"
. Default to"plain"
.
Examples
data(multisampling)
model <- hgwr(formula = y ~ L(g1 + g2) + x1 + (z1 | group),
data = multisampling$data,
coords = multisampling$coords,
bw = 10)
print(model)
#> Hierarchical and geographically weighted regression model
#> =========================================================
#> Formula: y ~ L(g1 + g2) + x1 + (z1 | group)
#> Method: Back-fitting and Maximum likelihood
#> Data: multisampling$data
#>
#> Fixed Effects
#> -------------
#> Intercept x1
#> 2.844509 0.966059
#>
#> Group-level Spatially Weighted Effects
#> --------------------------------------
#> Bandwidth: 10 (nearest neighbours)
#>
#> Coefficient estimates:
#> Coefficient Min 1st Quartile Median 3rd Quartile Max
#> Intercept -1.430985 -1.350693 -1.140097 -0.911629 -0.727237
#> g1 5.969397 6.328338 7.136143 7.447083 8.481628
#> g2 -0.683021 0.043467 0.887209 1.174541 1.521918
#>
#> Sample-level Random Effects
#> ---------------------------
#> Groups Name Std.Dev. Corr
#> group Intercept 1.921255
#> z1 1.921255 0.000000
#> Residual 1.921255
#> Groups Name Std.Dev. Corr
#> group Intercept 1.921255
#> z1 1.921255 0.000000
#> Residual 1.921255
#>
#> Other Information
#> -----------------
#> Number of Obs: 484
#> Groups: group , 16
print(model, table.style = "md")
#> Hierarchical and geographically weighted regression model
#> =========================================================
#> Formula: y ~ L(g1 + g2) + x1 + (z1 | group)
#> Method: Back-fitting and Maximum likelihood
#> Data: multisampling$data
#>
#> Fixed Effects
#> -------------
#>
#> | Intercept | x1 |
#> | --------- | -------- |
#> | 2.844509 | 0.966059 |
#>
#> Group-level Spatially Weighted Effects
#> --------------------------------------
#> Bandwidth: 10 (nearest neighbours)
#>
#> Coefficient estimates:
#>
#> | Coefficient | Min | 1st Quartile | Median | 3rd Quartile | Max |
#> | ----------- | --------- | ------------ | --------- | ------------ | --------- |
#> | Intercept | -1.430985 | -1.350693 | -1.140097 | -0.911629 | -0.727237 |
#> | g1 | 5.969397 | 6.328338 | 7.136143 | 7.447083 | 8.481628 |
#> | g2 | -0.683021 | 0.043467 | 0.887209 | 1.174541 | 1.521918 |
#>
#> Sample-level Random Effects
#> ---------------------------
#>
#> | Groups | Name | Std.Dev. | Corr |
#> | -------- | --------- | -------- | -------- |
#> | group | Intercept | 1.921255 | |
#> | | z1 | 1.921255 | 0.000000 |
#> | Residual | | 1.921255 | |
#>
#> | Groups | Name | Std.Dev. | Corr |
#> | -------- | --------- | -------- | -------- |
#> | group | Intercept | 1.921255 | |
#> | | z1 | 1.921255 | 0.000000 |
#> | Residual | | 1.921255 | |
#>
#> Other Information
#> -----------------
#> Number of Obs: 484
#> Groups: group , 16