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rebuild HSAR vignette
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@SpatLyu
SpatLyu committed Dec 20, 2024
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161 changes: 81 additions & 80 deletions vignettes/HSAR.Rmd
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Expand Up @@ -21,19 +21,20 @@ We start by loading the libraries that will be used.

``` r
library(sf)
## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
library(spdep)
## Loading required package: spData
library(tidyverse)
## ── Attaching core tidyverse packages ─────────────────────────────────────────── tidyverse 2.0.0 ──
## ── Attaching core tidyverse packages ─────────────────────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ───────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ───────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::expand() masks Matrix::expand()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ tidyr::pack() masks Matrix::pack()
## ✖ tidyr::unpack() masks Matrix::unpack()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(HSAR)
```
Expand Down Expand Up @@ -192,11 +193,11 @@ res.formula <- prpsqm ~ size + age + greensp + population + museums + airbnb
res <- hsar(res.formula,data=model.data,W=W,M=M,Delta=Delta,
burnin=500, Nsim=1000)
## Warning in spdep::mat2listw(W): style is M (missing); style should be set to a valid value
## Warning in sn2listw(df, style = style, zero.policy = zero.policy, from_mat2listw = TRUE): style is
## M (missing); style should be set to a valid value
## Warning in sn2listw(df, style = style, zero.policy = zero.policy, from_mat2listw = TRUE):
## style is M (missing); style should be set to a valid value
## Warning in spdep::mat2listw(W): style is M (missing); style should be set to a valid value
## Warning in sn2listw(df, style = style, zero.policy = zero.policy, from_mat2listw = TRUE): style is
## M (missing); style should be set to a valid value
## Warning in sn2listw(df, style = style, zero.policy = zero.policy, from_mat2listw = TRUE):
## style is M (missing); style should be set to a valid value
summary(res)
##
## Call:
Expand All @@ -206,43 +207,43 @@ summary(res)
##
## Coefficients:
## Mean SD
## (Intercept) 1.880878e+03 9.9085321261
## size 4.308784e+00 0.4857075117
## age -1.992973e+01 1.3218947297
## greensp 7.638049e-04 0.0006136726
## population -9.642519e-03 0.0022135920
## museums -4.488692e+01 9.5710324827
## airbnb 5.575054e-01 0.1645700213
## (Intercept) 1.880468e+03 9.835447e+00
## size 4.298802e+00 5.112041e-01
## age -1.995687e+01 1.304370e+00
## greensp 8.404794e-04 8.738884e-04
## population -9.940391e-03 2.273935e-03
## museums -4.515772e+01 1.008513e+01
## airbnb 6.022747e-01 2.496115e-01
##
## Spatial Coefficients:
## rho lambda
## [1,] 0.188356 0.14771
## rho lambda
## [1,] 0.196536 0.018432
##
## Diagnostics
## Deviance information criterion (DIC): 28196.61
## Effective number of parameters (pd): -1.524159
## Log likelihood: -14099.83
## Pseudo R squared: 0.3588787
## Deviance information criterion (DIC): 28193.01
## Effective number of parameters (pd): -1.66553
## Log likelihood: -14098.17
## Pseudo R squared: 0.3601049
##
## Impacts:
## direct indirect total
## (Intercept) 1.881439e+03 4.358260e+02 2.317264e+03
## size 4.310068e+00 9.984060e-01 5.308474e+00
## age -1.993567e+01 -4.618001e+00 -2.455368e+01
## greensp 7.640326e-04 1.769844e-04 9.410169e-04
## population -9.645393e-03 -2.234308e-03 -1.187970e-02
## museums -4.490030e+01 -1.040093e+01 -5.530124e+01
## airbnb 5.576716e-01 1.291819e-01 6.868535e-01
## (Intercept) 1.881082e+03 4.592335e+02 2.340316e+03
## size 4.300207e+00 1.049821e+00 5.350028e+00
## age -1.996339e+01 -4.873715e+00 -2.483711e+01
## greensp 8.407540e-04 2.052555e-04 1.046009e-03
## population -9.943639e-03 -2.427567e-03 -1.237121e-02
## museums -4.517248e+01 -1.102808e+01 -5.620055e+01
## airbnb 6.024715e-01 1.470830e-01 7.495545e-01
##
## Quantiles:
## 5% 25% 50% 75% 95%
## (Intercept) 1.865108e+03 1.873915e+03 1.880482e+03 1.887116e+03 1.899108e+03
## size 3.474374e+00 3.985585e+00 4.313592e+00 4.660815e+00 5.054106e+00
## age -2.199631e+01 -2.079239e+01 -1.997034e+01 -1.906072e+01 -1.767202e+01
## greensp -2.965393e-04 3.974262e-04 7.551037e-04 1.132355e-03 1.722759e-03
## population -1.320664e-02 -1.116246e-02 -9.764824e-03 -8.101470e-03 -5.738580e-03
## museums -6.031927e+01 -5.151993e+01 -4.444317e+01 -3.817029e+01 -3.079498e+01
## airbnb 2.722792e-01 4.595209e-01 5.553124e-01 6.682065e-01 8.220889e-01
## (Intercept) 1.864369e+03 1.874059e+03 1.880361e+03 1.887328e+03 1.896080e+03
## size 3.436255e+00 3.956910e+00 4.291712e+00 4.628642e+00 5.155081e+00
## age -2.215863e+01 -2.075471e+01 -1.991798e+01 -1.911688e+01 -1.792525e+01
## greensp -3.546526e-04 2.905312e-04 7.376810e-04 1.261277e-03 2.657171e-03
## population -1.382163e-02 -1.159143e-02 -9.920309e-03 -8.353503e-03 -6.404121e-03
## museums -6.148110e+01 -5.224350e+01 -4.495013e+01 -3.850940e+01 -2.847943e+01
## airbnb 1.776907e-01 4.578970e-01 6.114716e-01 7.457536e-01 1.010643e+00
```

and the two simpler models defined for rho = 0 and lambda=0.
Expand All @@ -252,8 +253,8 @@ So, firstly, assuming rho = 0 (no interaction effects at the lower level) we ge
``` r
res_1 <- hsar(res.formula,data=model.data,W=NULL,M=M,Delta=Delta,burnin=500, Nsim=1000)
## Warning in spdep::mat2listw(W): style is M (missing); style should be set to a valid value
## Warning in sn2listw(df, style = style, zero.policy = zero.policy, from_mat2listw = TRUE): style is
## M (missing); style should be set to a valid value
## Warning in sn2listw(df, style = style, zero.policy = zero.policy, from_mat2listw = TRUE):
## style is M (missing); style should be set to a valid value
summary(res_1)
##
## Call:
Expand All @@ -263,33 +264,33 @@ summary(res_1)
##
## Coefficients:
## Mean SD
## (Intercept) 1.881134e+03 1.002537e+01
## size 4.315024e+00 4.218945e-01
## age -2.002619e+01 1.287527e+00
## greensp 7.226078e-04 5.564728e-04
## population -7.121008e-03 1.120733e-03
## museums -4.585389e+01 1.025743e+01
## airbnb 6.433655e-01 1.604121e-01
## (Intercept) 1.880592e+03 1.010450e+01
## size 4.329009e+00 4.214029e-01
## age -2.004845e+01 1.258780e+00
## greensp 6.812935e-04 6.843458e-04
## population -6.877688e-03 1.100394e-03
## museums -4.585817e+01 9.479326e+00
## airbnb 6.288388e-01 2.222552e-01
##
## Spatial Coefficients:
## lambda
## -0.026046
## -0.130888
##
## Diagnostics
## Deviance information criterion (DIC): 28189.43
## Effective number of parameters (pd): -1.792601
## Log likelihood: -14096.51
## Pseudo R squared: 0.3578193
## Deviance information criterion (DIC): 28196.24
## Effective number of parameters (pd): -1.988165
## Log likelihood: -14100.11
## Pseudo R squared: 0.3587056
##
## Quantiles:
## 5% 25% 50% 75% 95%
## (Intercept) 1.864436e+03 1.874832e+03 1.881650e+03 1.887990e+03 1.896975e+03
## size 3.644688e+00 4.041712e+00 4.329345e+00 4.602022e+00 5.023365e+00
## age -2.219428e+01 -2.086044e+01 -2.001777e+01 -1.921052e+01 -1.781857e+01
## greensp -5.217643e-05 3.655865e-04 6.542170e-04 1.009572e-03 1.786107e-03
## population -8.760368e-03 -7.847762e-03 -7.202664e-03 -6.476834e-03 -5.157352e-03
## museums -6.156313e+01 -5.381063e+01 -4.567536e+01 -3.870049e+01 -2.860034e+01
## airbnb 3.738831e-01 5.369197e-01 6.492991e-01 7.430972e-01 9.062610e-01
## (Intercept) 1.863327e+03 1.874280e+03 1.880446e+03 1.887451e+03 1.897526e+03
## size 3.648867e+00 4.026734e+00 4.331703e+00 4.647616e+00 5.040309e+00
## age -2.217344e+01 -2.090501e+01 -2.002926e+01 -1.916105e+01 -1.800675e+01
## greensp -5.464395e-04 3.468840e-04 7.498473e-04 1.081365e-03 1.660609e-03
## population -8.718853e-03 -7.542340e-03 -6.897453e-03 -6.200430e-03 -4.988964e-03
## museums -6.078241e+01 -5.205170e+01 -4.542701e+01 -3.995038e+01 -3.145765e+01
## airbnb 2.844778e-01 4.953275e-01 6.164306e-01 7.496283e-01 9.872352e-01
```

and secondly, given lambda = 0 (no interaction at the higher level) we get
Expand All @@ -298,8 +299,8 @@ and secondly, given lambda = 0 (no interaction at the higher level) we get
``` r
res_2 <- hsar(res.formula,data=model.data,W=W,M=NULL,Delta=Delta,burnin=500, Nsim=1000)
## Warning in spdep::mat2listw(W): style is M (missing); style should be set to a valid value
## Warning in sn2listw(df, style = style, zero.policy = zero.policy, from_mat2listw = TRUE): style is
## M (missing); style should be set to a valid value
## Warning in sn2listw(df, style = style, zero.policy = zero.policy, from_mat2listw = TRUE):
## style is M (missing); style should be set to a valid value
summary(res_2)
##
## Call:
Expand All @@ -309,31 +310,31 @@ summary(res_2)
##
## Coefficients:
## Mean SD
## (Intercept) 1.881433e+03 9.7787251890
## size 4.320724e+00 0.5145981502
## age -2.006309e+01 1.3671298243
## greensp 9.191971e-04 0.0006723134
## population -9.097623e-03 0.0022338151
## museums -4.564746e+01 9.6967267461
## airbnb 5.445758e-01 0.2287821477
## (Intercept) 1.880293e+03 9.970170e+00
## size 4.271588e+00 4.581010e-01
## age -1.995568e+01 1.295576e+00
## greensp 9.672711e-04 6.581401e-04
## population -9.434666e-03 2.083913e-03
## museums -4.545286e+01 1.035321e+01
## airbnb 5.415177e-01 1.964108e-01
##
## Spatial Coefficients:
## rho
## 0.173088
## rho
## 0.19122
##
## Diagnostics
## Deviance information criterion (DIC): 28198.6
## Effective number of parameters (pd): -1.704043
## Log likelihood: -14101
## Pseudo R squared: 0.3623617
## Deviance information criterion (DIC): 28196.99
## Effective number of parameters (pd): -1.752399
## Log likelihood: -14100.25
## Pseudo R squared: 0.3597633
##
## Quantiles:
## 5% 25% 50% 75% 95%
## (Intercept) 1.865152e+03 1.875127e+03 1.881216e+03 1.887290e+03 1.897579e+03
## size 3.458822e+00 3.993053e+00 4.325490e+00 4.657485e+00 5.126591e+00
## age -2.231385e+01 -2.093929e+01 -2.011275e+01 -1.917424e+01 -1.780022e+01
## greensp -1.478657e-04 4.673335e-04 9.304327e-04 1.372135e-03 1.963982e-03
## population -1.280245e-02 -1.064503e-02 -9.044582e-03 -7.595252e-03 -5.335030e-03
## museums -6.121924e+01 -5.253706e+01 -4.566362e+01 -3.913182e+01 -2.949665e+01
## airbnb 1.861284e-01 3.930978e-01 5.466702e-01 6.996989e-01 9.133508e-01
## (Intercept) 1.864332e+03 1.873364e+03 1.880298e+03 1.886796e+03 1.896416e+03
## size 3.550689e+00 3.944261e+00 4.254099e+00 4.559823e+00 5.073155e+00
## age -2.192657e+01 -2.079846e+01 -1.995871e+01 -1.909676e+01 -1.780104e+01
## greensp 3.886899e-05 5.256176e-04 8.835621e-04 1.378462e-03 2.197482e-03
## population -1.282486e-02 -1.074985e-02 -9.589800e-03 -8.107401e-03 -6.107883e-03
## museums -6.271483e+01 -5.293104e+01 -4.448211e+01 -3.801137e+01 -2.948482e+01
## airbnb 2.208243e-01 4.100049e-01 5.411334e-01 6.705060e-01 8.635070e-01
```

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