diff --git a/articles/HSAR.html b/articles/HSAR.html index 9df3af9..bb072a6 100644 --- a/articles/HSAR.html +++ b/articles/HSAR.html @@ -233,43 +233,43 @@

Run the models## ## Coefficients: ## Mean SD -## (Intercept) 1.880461e+03 9.794419e+00 -## size 4.301935e+00 4.713561e-01 -## age -1.991273e+01 1.339089e+00 -## greensp 7.790633e-04 7.862559e-04 -## population -9.240542e-03 2.397341e-03 -## museums -4.531282e+01 1.033345e+01 -## airbnb 5.803532e-01 2.232756e-01 +## (Intercept) 1.880275e+03 10.451464146 +## size 4.329367e+00 0.513033746 +## age -1.999349e+01 1.324229710 +## greensp 1.025379e-03 0.001070125 +## population -1.020677e-02 0.002522363 +## museums -4.452665e+01 10.013196719 +## airbnb 5.915438e-01 0.268172791 ## ## Spatial Coefficients: ## rho lambda -## [1,] 0.201284 0.195202 +## [1,] 0.189246 0.095438 ## ## Diagnostics -## Deviance information criterion (DIC): 28197.36 -## Effective number of parameters (pd): -1.635173 -## Log likelihood: -14100.32 -## Pseudo R squared: 0.3587075 +## Deviance information criterion (DIC): 28198.27 +## Effective number of parameters (pd): -1.504082 +## Log likelihood: -14100.64 +## Pseudo R squared: 0.3620759 ## ## Impacts: ## direct indirect total -## (Intercept) 1.881108e+03 4.730904e+02 2.354198e+03 -## size 4.303415e+00 1.082290e+00 5.385706e+00 -## age -1.991958e+01 -5.009688e+00 -2.492927e+01 -## greensp 7.793313e-04 1.959984e-04 9.753297e-04 -## population -9.243721e-03 -2.324756e-03 -1.156848e-02 -## museums -4.532841e+01 -1.139990e+01 -5.672831e+01 -## airbnb 5.805528e-01 1.460065e-01 7.265593e-01 +## (Intercept) 1.880841e+03 4.382206e+02 2.319062e+03 +## size 4.330671e+00 1.009011e+00 5.339682e+00 +## age -1.999951e+01 -4.659722e+00 -2.465924e+01 +## greensp 1.025687e-03 2.389767e-04 1.264664e-03 +## population -1.020984e-02 -2.378809e-03 -1.258865e-02 +## museums -4.454006e+01 -1.037747e+01 -5.491752e+01 +## airbnb 5.917219e-01 1.378663e-01 7.295883e-01 ## ## Quantiles: ## 5% 25% 50% 75% 95% -## (Intercept) 1.863987e+03 1.874152e+03 1.881128e+03 1.887705e+03 1.894747e+03 -## size 3.501653e+00 3.996034e+00 4.328532e+00 4.625902e+00 5.033293e+00 -## age -2.205857e+01 -2.086760e+01 -1.991433e+01 -1.902301e+01 -1.773964e+01 -## greensp -2.238065e-04 2.536224e-04 6.319674e-04 1.162900e-03 2.560007e-03 -## population -1.313017e-02 -1.086792e-02 -9.282930e-03 -7.491176e-03 -5.489072e-03 -## museums -6.251765e+01 -5.195792e+01 -4.486279e+01 -3.794775e+01 -2.997397e+01 -## airbnb 1.780049e-01 4.327122e-01 6.070358e-01 7.323369e-01 9.083725e-01 +## (Intercept) 1.862427e+03 1.873212e+03 1.880350e+03 1.887635e+03 1.896350e+03 +## size 3.489378e+00 3.986492e+00 4.321245e+00 4.678758e+00 5.205164e+00 +## age -2.229396e+01 -2.091545e+01 -1.998110e+01 -1.907934e+01 -1.799405e+01 +## greensp -4.953357e-04 3.921906e-04 9.347588e-04 1.452994e-03 3.367750e-03 +## population -1.446168e-02 -1.192027e-02 -1.031386e-02 -8.534997e-03 -5.796541e-03 +## museums -6.060125e+01 -5.129784e+01 -4.486833e+01 -3.735308e+01 -2.862404e+01 +## airbnb 1.890712e-01 4.267859e-01 5.849742e-01 7.457998e-01 1.096826e+00

and the two simpler models defined for rho = 0 and lambda=0. So, firstly, assuming rho = 0 (no interaction effects at the lower level) we get

@@ -287,33 +287,33 @@

Run the models## ## Coefficients: ## Mean SD -## (Intercept) 1.880969e+03 1.004751e+01 -## size 4.299105e+00 4.215697e-01 -## age -1.996865e+01 1.310667e+00 -## greensp 5.304726e-04 6.544811e-04 -## population -6.654456e-03 1.060707e-03 -## museums -4.511823e+01 9.175141e+00 -## airbnb 7.234227e-01 2.314932e-01 +## (Intercept) 1.880563e+03 9.2553985121 +## size 4.326023e+00 0.4325595027 +## age -1.997018e+01 1.2471979927 +## greensp 5.829718e-04 0.0006813374 +## population -6.584233e-03 0.0011227284 +## museums -4.534275e+01 9.5807150591 +## airbnb 6.574944e-01 0.1764067282 ## ## Spatial Coefficients: ## lambda -## 0.070876 +## 0.111888 ## ## Diagnostics -## Deviance information criterion (DIC): 28198.46 -## Effective number of parameters (pd): -1.870333 -## Log likelihood: -14101.1 -## Pseudo R squared: 0.3582752 +## Deviance information criterion (DIC): 28193.07 +## Effective number of parameters (pd): -2.092455 +## Log likelihood: -14098.63 +## Pseudo R squared: 0.3569871 ## ## Quantiles: ## 5% 25% 50% 75% 95% -## (Intercept) 1.864880e+03 1.874552e+03 1.880957e+03 1.887456e+03 1.897001e+03 -## size 3.610783e+00 4.006603e+00 4.322592e+00 4.583691e+00 4.983597e+00 -## age -2.207850e+01 -2.087071e+01 -1.995724e+01 -1.901799e+01 -1.790105e+01 -## greensp -6.921436e-04 1.703272e-04 5.736104e-04 9.622126e-04 1.489968e-03 -## population -8.359189e-03 -7.311060e-03 -6.666792e-03 -5.998892e-03 -4.847390e-03 -## museums -6.003316e+01 -5.108847e+01 -4.522155e+01 -3.899018e+01 -2.960428e+01 -## airbnb 3.630902e-01 5.791529e-01 7.000081e-01 8.588804e-01 1.159867e+00 +## (Intercept) 1.865244e+03 1.874265e+03 1.881036e+03 1.886741e+03 1.895768e+03 +## size 3.641719e+00 4.033250e+00 4.310376e+00 4.628015e+00 5.048434e+00 +## age -2.200615e+01 -2.083570e+01 -1.996753e+01 -1.911833e+01 -1.802003e+01 +## greensp -6.439277e-04 2.933380e-04 6.549234e-04 1.027634e-03 1.541188e-03 +## population -8.302344e-03 -7.287608e-03 -6.676006e-03 -5.995643e-03 -4.711512e-03 +## museums -6.123372e+01 -5.128380e+01 -4.534965e+01 -3.955427e+01 -2.992627e+01 +## airbnb 3.592684e-01 5.376341e-01 6.620247e-01 7.804008e-01 9.272886e-01

and secondly, given lambda = 0 (no interaction at the higher level) we get

@@ -329,34 +329,34 @@ 
Run the models## Type:  hsar with lambda = 0  
 ## 
 ## Coefficients:
-##                      Mean          SD
-## (Intercept)  1.880360e+03 9.638392003
-## size         4.274640e+00 0.475946778
-## age         -2.000767e+01 1.416531702
-## greensp      8.151624e-05 0.001316126
-## population  -8.668497e-03 0.002604684
-## museums     -4.511405e+01 9.814353408
-## airbnb       6.302068e-01 0.318353574
+##                      Mean           SD
+## (Intercept)  1.880265e+03 10.543140981
+## size         4.308513e+00  0.525275852
+## age         -1.986549e+01  1.319366170
+## greensp      1.371616e-03  0.001140316
+## population  -9.124421e-03  0.002675214
+## museums     -4.476371e+01  9.550375854
+## airbnb       4.911532e-01  0.225534492
 ## 
 ##  Spatial Coefficients:
 ##      rho 
-## 0.189216 
+## 0.173638 
 ## 
 ##  Diagnostics 
-## Deviance information criterion (DIC): 28196.66 
-## Effective number of parameters (pd): -1.67705 
-## Log likelihood: -14100.01 
-## Pseudo R squared: 0.3591423 
+## Deviance information criterion (DIC): 28194.37 
+## Effective number of parameters (pd): -1.627506 
+## Log likelihood: -14098.81 
+## Pseudo R squared: 0.3590682 
 ## 
 ##  Quantiles:
 ##                        5%           25%           50%           75%           95%
-## (Intercept) 1864.24515875  1.873775e+03  1.880736e+03  1.886645e+03  1.895744e+03
-## size           3.50820040  3.921107e+00  4.282089e+00  4.580065e+00  5.067648e+00
-## age          -22.32636764 -2.097454e+01 -1.997216e+01 -1.902378e+01 -1.763772e+01
-## greensp       -0.00297142 -2.411191e-04  5.260136e-04  9.078372e-04  1.450913e-03
-## population    -0.01257212 -1.045538e-02 -8.815213e-03 -7.081089e-03 -4.074837e-03
-## museums      -61.04617221 -5.171848e+01 -4.530204e+01 -3.812907e+01 -2.869411e+01
-## airbnb         0.20185017  4.330003e-01  5.837671e-01  7.511525e-01  1.288780e+00
+## (Intercept) 1.863706e+03 1.873326e+03 1.879689e+03 1.887368e+03 1.897511e+03 +## size 3.490029e+00 3.923317e+00 4.296004e+00 4.663584e+00 5.184349e+00 +## age -2.200291e+01 -2.074799e+01 -1.982105e+01 -1.895658e+01 -1.769628e+01 +## greensp 1.420867e-05 6.256490e-04 9.982406e-04 1.942325e-03 3.742835e-03 +## population -1.315579e-02 -1.102766e-02 -9.232202e-03 -7.360224e-03 -4.491216e-03 +## museums -5.960091e+01 -5.079269e+01 -4.475590e+01 -3.875467e+01 -2.831173e+01 +## airbnb 6.098123e-02 3.628817e-01 5.108585e-01 6.384128e-01 8.167038e-01