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> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/tests.html
> # * https://testthat.r-lib.org/reference/test_package.html#special-files
> 
> library(testthat)
> library(hgwrr)
Loading required package: sf
Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
> 
> test_check("hgwrr")
Hierarchical and geographically weighted regression model
=========================================================
Formula: y ~ L(g1 + g2) + x1 + (z1 | group)
 Method: Back-fitting and Maximum likelihood
   Data: multisampling$data

Global Fixed Effects
-------------------
 Intercept        x1 
  2.799593  0.966264 

Local Fixed Effects
-------------------
Bandwidth: 10 (nearest neighbours)
 Coefficient        Min  1st Quartile     Median  3rd Quartile        Max 
   Intercept  -2.228035     -1.317095  -1.033530     -0.619987  -0.253209 
          g1   3.480131      5.185006   7.250047      8.885822  12.724435 
          g2  -1.961020      0.705599   1.579516      1.813527   2.433680 

Random Effects
--------------
   Groups       Name  Std.Dev.      Corr 
    group  Intercept  1.915502           
                  z1  1.915502  0.000000 
 Residual             1.915502           

Other Information
-----------------
Number of Obs: 484
       Groups: group , 16
Hierarchical and geographically weighted regression model
=========================================================
Formula: y ~ L(g1 + g2) + x1 + (z1 | group)
 Method: Back-fitting and Maximum likelihood
   Data: multisampling$data

Global Fixed Effects
-------------------
| Intercept |       x1 |
| --------- | -------- |
|  2.799593 | 0.966264 |

Local Fixed Effects
-------------------
Bandwidth: 10 (nearest neighbours)
| Coefficient |       Min | 1st Quartile |    Median | 3rd Quartile |       Max |
| ----------- | --------- | ------------ | --------- | ------------ | --------- |
|   Intercept | -2.228035 |    -1.317095 | -1.033530 |    -0.619987 | -0.253209 |
|          g1 |  3.480131 |     5.185006 |  7.250047 |     8.885822 | 12.724435 |
|          g2 | -1.961020 |     0.705599 |  1.579516 |     1.813527 |  2.433680 |

Random Effects
--------------
|   Groups |      Name | Std.Dev. |     Corr |
| -------- | --------- | -------- | -------- |
|    group | Intercept | 1.915502 |          |
|          |        z1 | 1.915502 | 0.000000 |
| Residual |           | 1.915502 |          |

Other Information
-----------------
Number of Obs: 484
       Groups: group , 16
Hierarchical and geographically weighted regression model
=========================================================
Formula: y ~ L(g1 + g2) + x1 + (z1 | group)
 Method: Back-fitting and Maximum likelihood
   Data: multisampling$data

Parameter estimates
-------------------
Fixed effects:
|           | Estimated |    Sd. Err |    t.val | Pr(>|t|) |     |
| --------- | --------- | ---------- | -------- | -------- | --- |
| Intercept | 2.7995933 | 0.25449128 | 11.00074 |        0 | *** |
|        x1 | 0.9662643 | 0.04676336 | 20.66285 |        0 | *** |

Local fixed effects:
|           |       Min | 1st Quartile |    Median | 3rd Quartile |       Max |
| --------- | --------- | ------------ | --------- | ------------ | --------- |
| Intercept | -2.228035 |    -1.317095 | -1.033530 |    -0.619987 | -0.253209 |
|        g1 |  3.480131 |     5.185006 |  7.250047 |     8.885822 | 12.724435 |
|        g2 | -1.961020 |     0.705599 |  1.579516 |     1.813527 |  2.433680 |

Diagnostics
-----------
| rsquared | 0.647098 |
| -------- | -------- |
|   logLik |      NaN |
|      AIC |      NaN |

Scaled residuals
----------------
|       Min |        1Q |    Median |       3Q |      Max |
| --------- | --------- | --------- | -------- | -------- |
| -5.447895 | -1.220145 | -0.067784 | 1.253786 | 5.313347 |

Other Information
-----------------
Number of Obs: 484
       Groups: group , 16
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 23 ]
> 
> proc.time()
   user  system elapsed 
   3.18    0.31    3.50