Geographically Weighted Regression
Yasna Cortés
2023-12-11
Paquetes que se utilizarán
library(spatialreg)
library(spdep)
library(maptools)
library(RColorBrewer)
library(leaflet)
library(dplyr)
library(ggplot2)
library(tmap)
library(tmaptools)
library(lmtest)
library(splm)
library(data.table)
library(sphet)
library(spgwr)
library(rgeos)
library(ade4)Geographically Weighted Regression Models (GWR)
Una regresión ponderada geográficamente (GWR) es una extensión de la regresión lineal que permite la variabilidad de los coeficientes a través del espacio geográfico. El modelo básico para un lugar \(s_{i}\) en un dominio de dos dimensiones es especificado como:
\[ y(s_{i}) = \sum_{k=1}^{K}x_{k}(s_{i}) \beta_{k}(s_{i}) + \epsilon (s_{i}), \qquad \epsilon (s_{i}) \sim N(0, \sigma^{2})\]
Donde \(y(s_{i})\) es la variable dependiente, \(x_{k}(s_{i})\) representa a un conjunto de variables explicativas \(k\) (incluyendo el intercepto), \(\epsilon (s_{i})\) es el término de error, que se distribuyen \(iid\).
Para estimar el vector de coeficientes \(\beta(s_{i})=[\beta_{1}(s_{i})...\beta_{K}(s_{i})]'\) en el lugar \(i\), las muestras son espacialmente ponderadas. El resultado del estimador GWR es:
\[ \hat{\beta} (s_{i}) = [X'G_{i}(b)X]^{-1}X'G_{i}(b)y \] Donde \(y\) representa al vector de la variable dependiente, y \(X\) es una matriz de variables explicativas. Por otro lado \(G_{i}(b)\) es una matriz diagonal cuyo elemento \(j\) es uno que está dado por el peso espacial \(g(s_{i}, s_{j};b)\) para la muestra \(j\). La ponderación es especificada a través de una función de kernel que decae con la distancia. Las funciones kernel se pueden definir como las siguientes:
Kernel Gausiano:
\[ g(s_{i}, s_{j};b) = \exp \bigg(-\dfrac{d(s_{i}, s_{j})^{2}}{b^{2}} \bigg) \]
Kernel Exponencial:
\[ g(s_{i}, s_{j};b) = \exp \bigg(-\dfrac{d(s_{i}, s_{j})}{b} \bigg) \]
Kernel Bi-cuadrado (bi-square)
\[ g(s_{i}, s_{j};b) = \bigg[1-\bigg(\dfrac{d(s_{i}, s_{j})}{b} \bigg)^{2} \bigg]^{2} \qquad if \quad d(s_{i}, s_{j})<b \]
Kernel Tricube
\[ g(s_{i}, s_{j};b) = \bigg[1-\bigg(\dfrac{d(s_{i}, s_{j})}{b} \bigg)^{3} \bigg]^{3} \qquad if \quad d(s_{i}, s_{j})<b \]
donde \(d(s_{i}, s_{j})\) es la distancia (euclideana) entre \(s_{i}\) y \(s_{j}\). \(b\) es el parámetro que determina la velocidad de decaimiento de cada kernel. Si \(b\) es pequeño, las ponderaciones se asignaran solo a las muestras cercanas, lo que resultará en una variación de los coeficientes en una escala pequeña. Cuando \(b\) se incrementa, mayor peso es asignado a las muestras más distantes, lo que resulta en un patrón de puntos que tiene una mayor escala (se acerca a los valores globales).
Estimación de los parámetros de regresión de GWR.
Para la estimación de los coeficientes de la GWR, se realiza de acuerdo con los siguientes pasos:
- Se selecciona un bandwidth.
- Los coeficientes de regresión para cada lugar son estimados sustituyendo el bandwidth estimado dentro del estimador de GWR.
Para seleccionar el bandwidth: El bandwidth puede ser estimado minimizando el cross-validation score (CV) el cual es formulado como sigue:
\[ CV \quad SCORE= \sum_{i=1}^{N} \Bigg( y(s_{i}) - \sum_{k=1}^{K}x_{k}(s_{i}) \hat{\beta}_{k}(s_{i})\Bigg)^{2} \] El coeficiente \(\hat{\beta}_{k}(s_{i})\) es estimado usando \(N-1\) muestras distintas a la muestra \(i\). Especificamente, \(\hat{\beta}(s_{-i})=[\hat{\beta}_{1}(s_{-i})...\hat{\beta}_{K}(s_{-i})]'\) es estimado usando la siguiente ecuación:
\[ \hat{\beta}(s_{-i}) = [X'G_{-i}(b)X]^{-1}X'G_{-i}(b)y, \] Donde \(G_{-i}(b)\) es igual a \(G_{i}(b)\) con su elemento \(i\) siendo reemplazado por cero. El LOOCV identifica el bandwidth optimo minimizando el CV score.
Determinantes precios de vivienda
Exploración espacial precios de vivienda
Para realizar este ejercicio, cargamos el archivo shape que contiene los precios de vivienda. En este caso, las manzanas que contiene la base de datos no son contiguas (puede hacer zoom al mapa y ver que los polígonos están separados). Por tanto, para facilitar el análisis, usaré los centroides de las manzanas (el punto más central) para desarrollar el ejercicio. Esto también limita el uso de matrices de peso espacial, ya que ahora los datos son de punto (en realidad, dado que las manzanas están aisladas, deberíamos pensar en una base de datos de punto siempre) usando una matriz de distancia o una matriz de k-vecinos.
De esta forma, el archivo shape que contiene los precios sería el siguiente:
data.afta<-st_read("ANTOFAGASTA_CENTROIDES_UV.shp")## Reading layer `ANTOFAGASTA_CENTROIDES_UV' from data source
## `C:\Users\Yasna\Dropbox\cursos\Cursos 2023\Econometría Espacial\Tareas Estudiantes\Tarea 2\ANTOFAGASTA_CENTROIDES_UV.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 3105 features and 29 fields
## Geometry type: MULTIPOINT
## Dimension: XY
## Bounding box: xmin: -70.44201 ymin: -23.73881 xmax: -70.37213 ymax: -23.53374
## Geodetic CRS: WGS 84data<-data.frame(data.afta)
names(data)## [1] "COMUNA" "MANZ_SII" "NOM_COMUNA" "CMN_MZ" "precio_viv"
## [6] "unidad_viv" "ln_pr_viv" "esc_priv" "esc_pub" "esc_parvu"
## [11] "farmacias" "buses" "plazas" "restau" "salud"
## [16] "super" "x_coord" "y_coord" "T_REG_CA" "T_REG_NOM"
## [21] "T_PROV_CA" "T_PROV_NOM" "T_COM" "COMUNA_2" "T_COM_NOM"
## [26] "T_UV_COD" "ID_UV" "T_UV_NOM" "CARTO" "geometry"attach(data)Construimos un mapa con los precios de vivienda (en logarítmo natural)
tm_shape(data.afta) +
tm_dots(col="ln_pr_viv", style="quantile", palette="Reds", title = "Precios Vivienda (logaritmo)") + tm_layout(legend.outside.size = 0.2)## Legend labels were too wide. The labels have been resized to 0.39, 0.39, 0.39, 0.39, 0.39. Increase legend.width (argument of tm_layout) to make the legend wider and therefore the labels larger.
Usamos el modo interactivo para corroborar la ubicación espacial de los datos:
tmap_mode("view")## tmap mode set to interactive viewingtm_shape(data.afta) +
tm_dots(col="ln_pr_viv", style="quantile", palette="Reds", title = "Precios Vivienda (logaritmo)") + tm_layout(legend.outside.size = 0.2)También, usamos matrices de peso espacial para determinar si los precios de vivienda presentan autocorrelación espacial.
Matriz W basada en los 10 Vecinos más cercanos
coords<-cbind(data$x_coord, data$y_coord)
kn5<-nb2listw(knn2nb(knearneigh(coords,k=10)), style = "W")## Warning in knearneigh(coords, k = 10): knearneigh: identical points found## Warning in knearneigh(coords, k = 10): knearneigh: kd_tree not available for
## identical pointskn5## Characteristics of weights list object:
## Neighbour list object:
## Number of regions: 3105
## Number of nonzero links: 31050
## Percentage nonzero weights: 0.3220612
## Average number of links: 10
## 2 disjoint connected subgraphs
## Non-symmetric neighbours list
##
## Weights style: W
## Weights constants summary:
## n nn S0 S1 S2
## W 3105 9641025 3105 575.16 12612.28Moran Global
Estimamos un índice de moran para comprobar la existencia de dependencia espacial:
moran.viv.kn5<-moran.test(ln_pr_viv, kn5, alternative="two.sided"); moran.viv.kn5##
## Moran I test under randomisation
##
## data: ln_pr_viv
## weights: kn5
##
## Moran I statistic standard deviate = 95.569, p-value < 2.2e-16
## alternative hypothesis: two.sided
## sample estimates:
## Moran I statistic Expectation Variance
## 7.359762e-01 -3.221649e-04 5.935682e-05Moran local
Moran Local: Precios de Vivienda
## Ii E.Ii Var.Ii Z.Ii Pr(z != E(Ii))
## 1 0.4638652 -2.631452e-04 0.081448158 1.626288 0.1038884366
## 2 0.1021922 -2.081322e-05 0.006443626 1.273330 0.2029011182
## 3 3.2845060 -2.736007e-03 0.844748574 3.576580 0.0003481188
## 4 1.8106444 -7.947567e-04 0.245860666 3.653248 0.0002589434
## 5 1.1489214 -5.377005e-04 0.166382254 2.817994 0.0048324748
## 6 1.1617684 -3.896352e-04 0.120583829 3.346730 0.0008177084
Significant Moran Local: Precios de Vivienda
Significant Moran Local: Precios de Vivienda
Estimación de un modelo hedónico para los precios de vivienda
Planteamos el siguiente modelo hedónico para los precios de vivienda de Antofagasta:
\[\ln(precios \quad vivienda) = X\beta + \epsilon\] Donde la variable dependiente es el logaritmo de los precios de vivienda. La matriz X contiene todas las amenidades consideradas en el estudio. Cabe resaltar que este modelo no considera características intrínsecas de la vivienda propiamente tal. Por tanto, estas variables no observadas podrían estar en el error.
Estimamos el modelo propuesto vía OLS.
model<-ln_pr_viv ~ esc_pub+esc_priv+esc_parvu+farmacias+buses+plazas+restau+salud+super
VI_OLS<-lm(model, data=data)
summary(VI_OLS)##
## Call:
## lm(formula = model, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1510 -0.5024 -0.1216 0.4096 5.8438
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16.79972 0.03067 547.822 < 2e-16 ***
## esc_pub -15.52997 4.19620 -3.701 0.000219 ***
## esc_priv 6.64911 3.61756 1.838 0.066157 .
## esc_parvu -34.30646 3.10007 -11.066 < 2e-16 ***
## farmacias 46.13806 5.20942 8.857 < 2e-16 ***
## buses 5.68891 1.81639 3.132 0.001752 **
## plazas 3.87178 0.88138 4.393 1.16e-05 ***
## restau 0.64864 0.48727 1.331 0.183228
## salud -46.17014 4.94759 -9.332 < 2e-16 ***
## super -5.30006 1.54609 -3.428 0.000616 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7227 on 3095 degrees of freedom
## Multiple R-squared: 0.1178, Adjusted R-squared: 0.1153
## F-statistic: 45.93 on 9 and 3095 DF, p-value: < 2.2e-16Con esta información, comprobamos la existencia de autocorrelación espacial en los residuos de la regresión.
lm.morantest(VI_OLS, listw=kn5, alternative = "two.sided")##
## Global Moran I for regression residuals
##
## data:
## model: lm(formula = model, data = data)
## weights: kn5
##
## Moran I statistic standard deviate = 85.407, p-value < 2.2e-16
## alternative hypothesis: two.sided
## sample estimates:
## Observed Moran I Expectation Variance
## 6.525675e-01 -1.518931e-03 5.865203e-05Mapeamos los residuos para analizar su comportamiento espacial:
RES<-residuals(VI_OLS)
colours <- c("dark blue", "blue", "red", "dark red")
MAP.RES<-SpatialPointsDataFrame(data=data.frame(RES), coords=coords)
spplot(MAP.RES, cuts=quantile(RES), col.regions=colours, cex=1) 
Chequeamos si existe heterogeneidad espacial, a través del test de Chow.
Contraste /Test de Chow
El test de chow es un método que nos permite corroborar si la estructura del modelo de regresión difiere a través de grupos. La hipótesis nula y alternativa se plantea de la siguiente forma:
\[ H_{0} : y = X\beta + \epsilon \]
\[ H_{1}: \begin{bmatrix} X_{i} & 0 \\ 0 & X_{j} \end{bmatrix} + \epsilon\]
Note que la elección de la agrupación de datos es ex-ante.
Por tanto, estimamos una regresión lineal por grupos. En este caso, escogemos a las unidades vecinales como agrupamiento de datos.
model.ols.regime <- lm(ln_pr_viv ~ 0 +T_UV_COD/(esc_pub+esc_priv+esc_parvu+farmacias+buses+plazas+restau+salud+super), data = data)
summary(model.ols.regime)##
## Call:
## lm(formula = ln_pr_viv ~ 0 + T_UV_COD/(esc_pub + esc_priv + esc_parvu +
## farmacias + buses + plazas + restau + salud + super), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0130 -0.1393 0.0000 0.1220 4.0153
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## T_UV_COD1 1.574e+01 4.381e+00 3.593 0.000334 ***
## T_UV_COD10 4.695e+01 4.546e+01 1.033 0.301840
## T_UV_COD11 1.675e+01 7.484e+00 2.238 0.025291 *
## T_UV_COD12 1.626e+01 8.884e-01 18.297 < 2e-16 ***
## T_UV_COD13 1.169e+01 3.438e+01 0.340 0.733890
## T_UV_COD14 3.487e+01 1.446e+01 2.411 0.015985 *
## T_UV_COD15 3.067e+01 2.234e+01 1.373 0.169999
## T_UV_COD16 1.933e+01 2.382e+01 0.812 0.417019
## T_UV_COD17 7.999e+00 2.031e+01 0.394 0.693773
## T_UV_COD18 1.697e+01 1.397e+00 12.149 < 2e-16 ***
## T_UV_COD19 1.743e+01 4.908e-01 35.509 < 2e-16 ***
## T_UV_COD2 1.920e+01 9.193e-01 20.882 < 2e-16 ***
## T_UV_COD20 1.502e+01 2.625e+00 5.723 1.18e-08 ***
## T_UV_COD21 1.654e+01 3.710e+00 4.458 8.65e-06 ***
## T_UV_COD22 2.208e+01 7.844e+00 2.815 0.004914 **
## T_UV_COD23 6.679e+00 3.570e+00 1.871 0.061450 .
## T_UV_COD24 1.664e+01 3.565e+01 0.467 0.640797
## T_UV_COD25 2.286e+01 1.542e+01 1.483 0.138196
## T_UV_COD26 1.903e+01 4.905e-01 38.786 < 2e-16 ***
## T_UV_COD27 2.036e+01 8.875e-01 22.939 < 2e-16 ***
## T_UV_COD28 1.567e+01 7.744e+00 2.024 0.043102 *
## T_UV_COD29 1.791e+01 4.269e+01 0.420 0.674861
## T_UV_COD3 1.432e+02 1.136e+02 1.261 0.207458
## T_UV_COD30 2.584e+01 6.366e+00 4.060 5.07e-05 ***
## T_UV_COD31 1.404e+01 1.326e+00 10.587 < 2e-16 ***
## T_UV_COD32 1.736e+01 1.157e+00 15.003 < 2e-16 ***
## T_UV_COD33 5.018e+00 8.734e+00 0.575 0.565669
## T_UV_COD34 1.383e+01 2.405e+00 5.750 1.01e-08 ***
## T_UV_COD35 1.204e+01 9.281e+00 1.297 0.194774
## T_UV_COD36 1.554e+01 2.938e+00 5.290 1.34e-07 ***
## T_UV_COD38 1.798e+01 1.640e+00 10.966 < 2e-16 ***
## T_UV_COD39 7.417e+00 1.056e+01 0.703 0.482421
## T_UV_COD4 1.867e+01 3.262e-01 57.226 < 2e-16 ***
## T_UV_COD40 1.059e+01 6.807e+00 1.556 0.119872
## T_UV_COD41 1.528e+01 9.762e-01 15.651 < 2e-16 ***
## T_UV_COD42 1.575e+01 5.493e-01 28.673 < 2e-16 ***
## T_UV_COD43 1.679e+01 2.727e+00 6.158 8.65e-10 ***
## T_UV_COD44 -1.862e+01 8.384e+00 -2.221 0.026462 *
## T_UV_COD45 1.304e+01 1.601e+01 0.815 0.415390
## T_UV_COD46 1.045e+01 3.061e+00 3.414 0.000650 ***
## T_UV_COD47 1.599e+01 1.876e+00 8.525 < 2e-16 ***
## T_UV_COD48 1.293e+01 5.610e+00 2.305 0.021226 *
## T_UV_COD49 9.672e+00 3.034e+01 0.319 0.749889
## T_UV_COD5 1.714e+01 3.679e-01 46.602 < 2e-16 ***
## T_UV_COD50 1.059e+01 4.804e+00 2.204 0.027610 *
## T_UV_COD51 1.521e+01 1.751e+01 0.869 0.384895
## T_UV_COD52 -1.310e+03 3.665e+03 -0.357 0.720828
## T_UV_COD53 1.821e+01 1.707e+01 1.067 0.286069
## T_UV_COD54 1.511e+01 1.358e+00 11.131 < 2e-16 ***
## T_UV_COD55 1.654e+01 1.632e+00 10.134 < 2e-16 ***
## T_UV_COD56 -8.921e+00 5.976e+01 -0.149 0.881351
## T_UV_COD57 -1.103e+00 3.791e+01 -0.029 0.976793
## T_UV_COD58 1.751e+01 9.569e-01 18.294 < 2e-16 ***
## T_UV_COD59 1.922e+01 4.375e+00 4.393 1.17e-05 ***
## T_UV_COD60 1.534e+01 1.144e+00 13.407 < 2e-16 ***
## T_UV_COD61 1.531e+01 1.115e+00 13.728 < 2e-16 ***
## T_UV_COD62 1.614e+01 4.719e-01 34.200 < 2e-16 ***
## T_UV_COD63 1.591e+01 2.108e+00 7.549 6.25e-14 ***
## T_UV_COD64 1.484e+01 2.900e+00 5.115 3.39e-07 ***
## T_UV_COD65 1.642e+01 1.513e+00 10.848 < 2e-16 ***
## T_UV_COD66 1.689e+01 5.986e+00 2.822 0.004809 **
## T_UV_COD67 1.592e+01 1.209e+01 1.316 0.188294
## T_UV_COD68 1.644e+01 2.379e+00 6.912 6.11e-12 ***
## T_UV_COD69 1.623e+01 2.846e+00 5.703 1.33e-08 ***
## T_UV_COD7 1.838e+01 2.906e+00 6.327 2.98e-10 ***
## T_UV_COD70 1.654e+01 6.289e-01 26.294 < 2e-16 ***
## T_UV_COD71 1.373e+01 9.506e-01 14.442 < 2e-16 ***
## T_UV_COD72 1.757e+01 3.560e-01 49.353 < 2e-16 ***
## T_UV_COD73 9.839e+00 8.625e-01 11.408 < 2e-16 ***
## T_UV_COD74 1.556e+01 1.511e+00 10.297 < 2e-16 ***
## T_UV_COD75 1.538e+01 2.443e-01 62.962 < 2e-16 ***
## T_UV_COD76 1.485e+01 4.384e-01 33.868 < 2e-16 ***
## T_UV_COD77 1.733e+01 1.210e-01 143.196 < 2e-16 ***
## T_UV_COD78 1.825e+01 3.291e-01 55.472 < 2e-16 ***
## T_UV_COD8 1.693e+01 7.861e-01 21.541 < 2e-16 ***
## T_UV_COD9 1.768e+01 2.803e-01 63.062 < 2e-16 ***
## T_UV_COD99 1.857e+01 6.391e+01 0.291 0.771428
## T_UV_COD1:esc_pub 9.237e+04 1.511e+05 0.611 0.541117
## T_UV_COD10:esc_pub -1.028e+05 1.321e+05 -0.778 0.436769
## T_UV_COD11:esc_pub 3.781e+02 1.450e+03 0.261 0.794363
## T_UV_COD12:esc_pub 4.809e+01 9.478e+01 0.507 0.611945
## T_UV_COD13:esc_pub -4.228e+02 1.894e+03 -0.223 0.823423
## T_UV_COD14:esc_pub 2.371e+01 2.590e+02 0.092 0.927068
## T_UV_COD15:esc_pub 8.491e+01 7.382e+01 1.150 0.250126
## T_UV_COD16:esc_pub -2.514e+03 5.375e+03 -0.468 0.639992
## T_UV_COD17:esc_pub -1.765e+02 8.368e+02 -0.211 0.832982
## T_UV_COD18:esc_pub -1.246e+00 3.132e+01 -0.040 0.968271
## T_UV_COD19:esc_pub 1.607e+01 1.080e+01 1.488 0.136864
## T_UV_COD2:esc_pub 5.546e-01 3.235e+01 0.017 0.986322
## T_UV_COD20:esc_pub -6.969e+02 1.182e+03 -0.590 0.555361
## T_UV_COD21:esc_pub -5.518e+02 1.175e+03 -0.470 0.638716
## T_UV_COD22:esc_pub -5.189e+02 1.471e+03 -0.353 0.724312
## T_UV_COD23:esc_pub -1.540e+00 5.856e+02 -0.003 0.997901
## T_UV_COD24:esc_pub -3.199e+01 1.394e+02 -0.230 0.818498
## T_UV_COD25:esc_pub 2.383e+02 1.353e+03 0.176 0.860214
## T_UV_COD26:esc_pub -2.698e+01 2.240e+01 -1.204 0.228643
## T_UV_COD27:esc_pub -7.029e+00 3.127e+01 -0.225 0.822130
## T_UV_COD28:esc_pub -5.394e+02 6.853e+02 -0.787 0.431274
## T_UV_COD29:esc_pub 6.074e+04 2.219e+05 0.274 0.784342
## T_UV_COD3:esc_pub 2.492e+03 2.119e+03 1.176 0.239704
## T_UV_COD30:esc_pub 4.986e+02 7.082e+02 0.704 0.481451
## T_UV_COD31:esc_pub -9.804e+01 4.370e+01 -2.243 0.024965 *
## T_UV_COD32:esc_pub 1.902e+01 1.315e+01 1.446 0.148300
## T_UV_COD33:esc_pub 1.781e+03 7.971e+03 0.223 0.823216
## T_UV_COD34:esc_pub 2.650e+01 1.248e+02 0.212 0.831850
## T_UV_COD35:esc_pub -1.520e+03 1.981e+03 -0.767 0.443008
## T_UV_COD36:esc_pub -8.502e+00 1.325e+01 -0.642 0.521257
## T_UV_COD38:esc_pub -2.591e+01 2.768e+01 -0.936 0.349265
## T_UV_COD39:esc_pub -5.399e+01 4.442e+03 -0.012 0.990303
## T_UV_COD4:esc_pub -3.182e+02 2.129e+02 -1.494 0.135238
## T_UV_COD40:esc_pub -4.908e+01 2.746e+02 -0.179 0.858135
## T_UV_COD41:esc_pub 4.683e+00 2.105e+01 0.222 0.823974
## T_UV_COD42:esc_pub 1.472e+01 2.026e+01 0.726 0.467710
## T_UV_COD43:esc_pub -3.769e+02 3.427e+02 -1.100 0.271608
## T_UV_COD44:esc_pub 2.537e+03 8.569e+02 2.961 0.003096 **
## T_UV_COD45:esc_pub -4.985e+02 5.543e+02 -0.899 0.368591
## T_UV_COD46:esc_pub 1.433e+02 6.068e+01 2.362 0.018242 *
## T_UV_COD47:esc_pub -4.273e+00 3.740e+01 -0.114 0.909050
## T_UV_COD48:esc_pub 6.126e+01 1.380e+02 0.444 0.657074
## T_UV_COD49:esc_pub 1.132e+01 9.384e+01 0.121 0.904010
## T_UV_COD5:esc_pub 5.000e+01 1.944e+01 2.572 0.010177 *
## T_UV_COD50:esc_pub -1.212e+02 2.290e+02 -0.529 0.596776
## T_UV_COD51:esc_pub 1.232e+02 6.044e+02 0.204 0.838442
## T_UV_COD52:esc_pub -4.615e+04 1.188e+05 -0.389 0.697606
## T_UV_COD53:esc_pub -5.124e+00 3.024e+02 -0.017 0.986482
## T_UV_COD54:esc_pub -4.685e+02 1.482e+03 -0.316 0.751995
## T_UV_COD55:esc_pub 2.011e+01 5.830e+01 0.345 0.730217
## T_UV_COD56:esc_pub 3.850e+01 1.280e+02 0.301 0.763654
## T_UV_COD57:esc_pub 2.488e+02 2.322e+03 0.107 0.914676
## T_UV_COD58:esc_pub 1.058e+03 2.675e+02 3.953 7.93e-05 ***
## T_UV_COD59:esc_pub -3.588e+01 4.832e+01 -0.743 0.457779
## T_UV_COD60:esc_pub 2.842e+01 2.610e+01 1.089 0.276317
## T_UV_COD61:esc_pub 2.306e+01 5.382e+01 0.428 0.668367
## T_UV_COD62:esc_pub 3.705e+01 5.345e+01 0.693 0.488324
## T_UV_COD63:esc_pub 2.393e+03 2.804e+03 0.853 0.393590
## T_UV_COD64:esc_pub 4.417e+01 7.477e+01 0.591 0.554745
## T_UV_COD65:esc_pub 6.208e+01 2.136e+02 0.291 0.771379
## T_UV_COD66:esc_pub 2.875e+03 1.969e+04 0.146 0.883962
## T_UV_COD67:esc_pub 2.728e+03 8.590e+03 0.318 0.750835
## T_UV_COD68:esc_pub -4.408e+01 3.661e+02 -0.120 0.904171
## T_UV_COD69:esc_pub 1.155e+02 7.217e+02 0.160 0.872849
## T_UV_COD7:esc_pub -9.721e+01 1.862e+03 -0.052 0.958376
## T_UV_COD70:esc_pub 1.259e+01 2.697e+01 0.467 0.640507
## T_UV_COD71:esc_pub 2.801e+01 2.590e+01 1.082 0.279570
## T_UV_COD72:esc_pub -1.415e+02 7.468e+01 -1.895 0.058266 .
## T_UV_COD73:esc_pub 9.686e+01 4.117e+01 2.353 0.018710 *
## T_UV_COD74:esc_pub -3.488e+00 1.628e+01 -0.214 0.830408
## T_UV_COD75:esc_pub 9.523e+00 1.493e+01 0.638 0.523555
## T_UV_COD76:esc_pub -3.857e+00 3.308e+01 -0.117 0.907190
## T_UV_COD77:esc_pub -1.541e+02 1.819e+01 -8.475 < 2e-16 ***
## T_UV_COD78:esc_pub -2.234e+02 7.648e+01 -2.921 0.003518 **
## T_UV_COD8:esc_pub 4.389e+00 1.039e+02 0.042 0.966293
## T_UV_COD9:esc_pub -1.888e+01 1.681e+01 -1.123 0.261482
## T_UV_COD99:esc_pub -4.554e+02 4.161e+03 -0.109 0.912870
## T_UV_COD1:esc_priv 3.600e+01 8.451e+01 0.426 0.670145
## T_UV_COD10:esc_priv 4.978e+04 6.717e+04 0.741 0.458684
## T_UV_COD11:esc_priv 1.880e+03 1.915e+03 0.982 0.326380
## T_UV_COD12:esc_priv -3.343e+02 3.870e+02 -0.864 0.387681
## T_UV_COD13:esc_priv -6.630e+02 1.000e+03 -0.663 0.507581
## T_UV_COD14:esc_priv -3.098e+03 2.095e+03 -1.478 0.139428
## T_UV_COD15:esc_priv -6.605e+01 1.503e+02 -0.440 0.660326
## T_UV_COD16:esc_priv 3.840e+01 1.639e+02 0.234 0.814750
## T_UV_COD17:esc_priv -2.327e+00 3.143e+01 -0.074 0.940992
## T_UV_COD18:esc_priv 2.634e+00 6.307e+01 0.042 0.966689
## T_UV_COD19:esc_priv -4.270e+00 7.860e+00 -0.543 0.586973
## T_UV_COD2:esc_priv -2.509e+02 1.247e+02 -2.011 0.044393 *
## T_UV_COD20:esc_priv 5.080e+02 2.229e+03 0.228 0.819694
## T_UV_COD21:esc_priv -9.248e+01 8.287e+02 -0.112 0.911154
## T_UV_COD22:esc_priv 2.076e+03 2.147e+03 0.967 0.333655
## T_UV_COD23:esc_priv 1.424e+01 7.655e+00 1.860 0.063015 .
## T_UV_COD24:esc_priv -9.057e+01 2.030e+04 -0.004 0.996440
## T_UV_COD25:esc_priv 6.887e+02 1.368e+04 0.050 0.959868
## T_UV_COD26:esc_priv -7.855e+00 5.761e+00 -1.363 0.172890
## T_UV_COD27:esc_priv -4.754e+01 1.731e+01 -2.746 0.006070 **
## T_UV_COD28:esc_priv -1.790e+02 1.297e+02 -1.381 0.167493
## T_UV_COD29:esc_priv -8.739e+04 2.583e+05 -0.338 0.735163
## T_UV_COD3:esc_priv -4.585e+04 7.719e+04 -0.594 0.552534
## T_UV_COD30:esc_priv -1.003e+04 5.327e+03 -1.883 0.059785 .
## T_UV_COD31:esc_priv 1.894e+02 2.802e+02 0.676 0.499056
## T_UV_COD32:esc_priv 1.196e+01 2.256e+01 0.530 0.595855
## T_UV_COD33:esc_priv 3.022e+03 5.847e+03 0.517 0.605300
## T_UV_COD34:esc_priv 5.573e+03 2.915e+03 1.912 0.056055 .
## T_UV_COD35:esc_priv 7.231e+03 1.022e+04 0.707 0.479366
## T_UV_COD36:esc_priv 5.579e+02 1.241e+03 0.450 0.653050
## T_UV_COD38:esc_priv -2.206e+02 3.573e+02 -0.617 0.536971
## T_UV_COD39:esc_priv 1.953e+03 3.093e+04 0.063 0.949650
## T_UV_COD4:esc_priv 7.963e+01 1.173e+02 0.679 0.497405
## T_UV_COD40:esc_priv 1.056e+04 1.334e+04 0.792 0.428606
## T_UV_COD41:esc_priv 1.559e+03 1.887e+03 0.826 0.408779
## T_UV_COD42:esc_priv 8.296e+02 6.354e+02 1.306 0.191785
## T_UV_COD43:esc_priv 1.532e+03 2.816e+03 0.544 0.586453
## T_UV_COD44:esc_priv 3.386e+04 7.461e+03 4.538 5.97e-06 ***
## T_UV_COD45:esc_priv 3.618e+04 4.515e+04 0.801 0.422956
## T_UV_COD46:esc_priv 1.385e+04 1.231e+04 1.125 0.260712
## T_UV_COD47:esc_priv -1.518e+03 5.073e+03 -0.299 0.764739
## T_UV_COD48:esc_priv 5.887e+03 7.514e+03 0.783 0.433415
## T_UV_COD49:esc_priv 1.260e+04 5.337e+04 0.236 0.813435
## T_UV_COD5:esc_priv -1.678e+01 2.710e+01 -0.619 0.535841
## T_UV_COD50:esc_priv 5.009e+03 6.671e+03 0.751 0.452836
## T_UV_COD51:esc_priv 9.659e+03 2.594e+04 0.372 0.709664
## T_UV_COD52:esc_priv 1.093e+07 3.021e+07 0.362 0.717647
## T_UV_COD53:esc_priv -1.792e+03 2.426e+04 -0.074 0.941118
## T_UV_COD54:esc_priv -1.909e+03 5.325e+03 -0.358 0.720003
## T_UV_COD55:esc_priv -1.785e+03 3.407e+03 -0.524 0.600408
## T_UV_COD56:esc_priv 6.105e+04 1.127e+05 0.542 0.587962
## T_UV_COD57:esc_priv 8.285e+04 1.027e+05 0.807 0.419900
## T_UV_COD58:esc_priv 2.095e+02 1.856e+02 1.129 0.259112
## T_UV_COD59:esc_priv -4.443e+01 9.487e+02 -0.047 0.962649
## T_UV_COD60:esc_priv 8.535e+03 5.603e+03 1.523 0.127840
## T_UV_COD61:esc_priv 1.603e+03 4.710e+03 0.340 0.733592
## T_UV_COD62:esc_priv 3.800e+02 4.860e+02 0.782 0.434346
## T_UV_COD63:esc_priv -7.044e+02 3.358e+03 -0.210 0.833874
## T_UV_COD64:esc_priv -5.542e+02 2.127e+03 -0.261 0.794413
## T_UV_COD65:esc_priv 1.315e+02 5.400e+02 0.244 0.807574
## T_UV_COD66:esc_priv 7.057e+02 4.528e+03 0.156 0.876172
## T_UV_COD67:esc_priv -2.315e+02 5.595e+03 -0.041 0.967001
## T_UV_COD68:esc_priv -9.605e+01 3.457e+02 -0.278 0.781156
## T_UV_COD69:esc_priv -3.951e+02 8.652e+02 -0.457 0.647977
## T_UV_COD7:esc_priv 5.060e+02 4.476e+03 0.113 0.909995
## T_UV_COD70:esc_priv -1.414e+01 1.594e+01 -0.887 0.374921
## T_UV_COD71:esc_priv 1.360e+02 2.927e+02 0.465 0.642278
## T_UV_COD72:esc_priv -7.226e+01 7.903e+01 -0.914 0.360636
## T_UV_COD73:esc_priv -2.962e+01 4.046e+01 -0.732 0.464110
## T_UV_COD74:esc_priv -8.669e+01 3.098e+02 -0.280 0.779661
## T_UV_COD75:esc_priv -3.134e+00 2.221e+01 -0.141 0.887798
## T_UV_COD76:esc_priv 2.682e+01 1.234e+01 2.173 0.029913 *
## T_UV_COD77:esc_priv -5.196e+01 1.062e+01 -4.893 1.06e-06 ***
## T_UV_COD78:esc_priv 3.601e+01 2.067e+01 1.742 0.081611 .
## T_UV_COD8:esc_priv 8.562e+01 2.498e+02 0.343 0.731801
## T_UV_COD9:esc_priv 1.686e+01 1.151e+01 1.464 0.143381
## T_UV_COD99:esc_priv -1.057e+03 2.031e+04 -0.052 0.958506
## T_UV_COD1:esc_parvu -7.482e+02 1.596e+03 -0.469 0.639265
## T_UV_COD10:esc_parvu 2.196e+01 7.637e+01 0.288 0.773726
## T_UV_COD11:esc_parvu 3.416e+01 2.166e+02 0.158 0.874692
## T_UV_COD12:esc_parvu -1.217e+02 5.468e+02 -0.223 0.823933
## T_UV_COD13:esc_parvu 1.054e+02 1.043e+03 0.101 0.919515
## T_UV_COD14:esc_parvu -1.092e+03 1.355e+03 -0.806 0.420130
## T_UV_COD15:esc_parvu -5.761e+03 7.568e+03 -0.761 0.446594
## T_UV_COD16:esc_parvu -5.507e+03 1.546e+04 -0.356 0.721642
## T_UV_COD17:esc_parvu 3.802e+03 1.227e+04 0.310 0.756708
## T_UV_COD18:esc_parvu -1.983e+02 1.115e+03 -0.178 0.858807
## T_UV_COD19:esc_parvu 2.506e+02 5.017e+02 0.500 0.617454
## T_UV_COD2:esc_parvu -1.941e+02 3.971e+02 -0.489 0.625007
## T_UV_COD20:esc_parvu 5.643e+02 8.847e+02 0.638 0.523660
## T_UV_COD21:esc_parvu -7.777e+03 9.185e+03 -0.847 0.397249
## T_UV_COD22:esc_parvu 6.493e+01 9.115e+02 0.071 0.943211
## T_UV_COD23:esc_parvu 2.420e+03 1.833e+03 1.320 0.186926
## T_UV_COD24:esc_parvu 4.484e+02 6.603e+02 0.679 0.497162
## T_UV_COD25:esc_parvu -1.541e+02 1.226e+03 -0.126 0.899979
## T_UV_COD26:esc_parvu -5.837e+02 1.230e+02 -4.747 2.18e-06 ***
## T_UV_COD27:esc_parvu -4.165e+02 3.333e+02 -1.250 0.211530
## T_UV_COD28:esc_parvu 1.639e+03 4.991e+03 0.328 0.742677
## T_UV_COD29:esc_parvu -2.481e+03 8.641e+04 -0.029 0.977100
## T_UV_COD3:esc_parvu 4.297e+04 7.578e+04 0.567 0.570782
## T_UV_COD30:esc_parvu 2.671e+02 1.973e+02 1.354 0.175921
## T_UV_COD31:esc_parvu 1.484e+01 3.579e+01 0.415 0.678385
## T_UV_COD32:esc_parvu 2.131e+03 1.068e+03 1.996 0.046019 *
## T_UV_COD33:esc_parvu -2.708e+02 2.821e+02 -0.960 0.337141
## T_UV_COD34:esc_parvu 2.650e+01 3.964e+01 0.669 0.503814
## T_UV_COD35:esc_parvu -1.351e+01 1.576e+03 -0.009 0.993162
## T_UV_COD36:esc_parvu 3.513e+01 4.809e+01 0.730 0.465169
## T_UV_COD38:esc_parvu 2.027e+03 1.563e+03 1.297 0.194810
## T_UV_COD39:esc_parvu 1.715e+03 1.219e+03 1.407 0.159516
## T_UV_COD4:esc_parvu -2.610e+01 6.490e+00 -4.022 5.95e-05 ***
## T_UV_COD40:esc_parvu 3.001e+01 3.772e+02 0.080 0.936598
## T_UV_COD41:esc_parvu 4.159e+01 5.294e+01 0.786 0.432195
## T_UV_COD42:esc_parvu 7.144e-01 8.115e+00 0.088 0.929854
## T_UV_COD43:esc_parvu -2.210e+01 3.434e+01 -0.643 0.519974
## T_UV_COD44:esc_parvu 2.597e+03 6.970e+02 3.725 0.000200 ***
## T_UV_COD45:esc_parvu 4.290e+01 6.901e+01 0.622 0.534226
## T_UV_COD46:esc_parvu 3.069e+00 2.064e+01 0.149 0.881791
## T_UV_COD47:esc_parvu 6.675e+00 1.822e+01 0.366 0.714096
## T_UV_COD48:esc_parvu 1.911e+01 2.964e+02 0.064 0.948609
## T_UV_COD49:esc_parvu -1.393e+02 1.804e+02 -0.772 0.440090
## T_UV_COD5:esc_parvu -1.905e+01 2.871e+02 -0.066 0.947120
## T_UV_COD50:esc_parvu -2.343e+02 3.027e+02 -0.774 0.439096
## T_UV_COD51:esc_parvu -3.058e+02 8.921e+02 -0.343 0.731780
## T_UV_COD52:esc_parvu 1.789e+05 5.133e+05 0.349 0.727404
## T_UV_COD53:esc_parvu 2.590e+00 1.218e+02 0.021 0.983042
## T_UV_COD54:esc_parvu 7.290e+02 6.612e+02 1.102 0.270378
## T_UV_COD55:esc_parvu 3.571e+00 2.565e+01 0.139 0.889305
## T_UV_COD56:esc_parvu -1.902e+01 1.029e+02 -0.185 0.853309
## T_UV_COD57:esc_parvu 3.435e+01 8.545e+02 0.040 0.967935
## T_UV_COD58:esc_parvu -4.703e+01 2.385e+01 -1.972 0.048696 *
## T_UV_COD59:esc_parvu -6.493e+01 8.710e+01 -0.745 0.456114
## T_UV_COD60:esc_parvu 2.406e+01 2.017e+01 1.193 0.232848
## T_UV_COD61:esc_parvu -1.479e+01 4.059e+01 -0.364 0.715665
## T_UV_COD62:esc_parvu 1.390e+01 6.892e+01 0.202 0.840153
## T_UV_COD63:esc_parvu -2.159e+03 2.309e+03 -0.935 0.349938
## T_UV_COD64:esc_parvu 2.552e+02 1.235e+03 0.207 0.836384
## T_UV_COD65:esc_parvu -3.238e+01 2.034e+02 -0.159 0.873505
## T_UV_COD66:esc_parvu 6.506e+03 4.448e+04 0.146 0.883717
## T_UV_COD67:esc_parvu 2.538e+03 5.092e+03 0.498 0.618189
## T_UV_COD68:esc_parvu 6.815e+01 3.403e+02 0.200 0.841298
## T_UV_COD69:esc_parvu 2.178e+02 1.309e+03 0.166 0.867865
## T_UV_COD7:esc_parvu -1.631e+00 1.595e+01 -0.102 0.918543
## T_UV_COD70:esc_parvu -8.156e+00 9.986e+00 -0.817 0.414178
## T_UV_COD71:esc_parvu -1.037e+02 5.145e+01 -2.016 0.043921 *
## T_UV_COD72:esc_parvu 3.112e+00 6.406e+00 0.486 0.627174
## T_UV_COD73:esc_parvu 1.457e+02 2.228e+01 6.540 7.54e-11 ***
## T_UV_COD74:esc_parvu 2.405e+00 3.456e+01 0.070 0.944522
## T_UV_COD75:esc_parvu 1.960e+01 6.175e+00 3.174 0.001523 **
## T_UV_COD76:esc_parvu 1.554e+01 1.477e+01 1.052 0.292894
## T_UV_COD77:esc_parvu -4.191e+01 6.191e+00 -6.768 1.64e-11 ***
## T_UV_COD78:esc_parvu 1.351e+02 2.030e+02 0.666 0.505620
## T_UV_COD8:esc_parvu 5.439e+01 9.166e+01 0.593 0.552974
## T_UV_COD9:esc_parvu 1.212e+02 1.610e+02 0.753 0.451373
## T_UV_COD99:esc_parvu -4.324e+01 1.042e+03 -0.041 0.966902
## T_UV_COD1:farmacias 2.045e+05 1.548e+05 1.321 0.186656
## T_UV_COD10:farmacias 7.346e+04 8.550e+04 0.859 0.390340
## T_UV_COD11:farmacias -1.516e+03 5.604e+03 -0.271 0.786736
## T_UV_COD12:farmacias -6.486e+01 9.988e+01 -0.649 0.516138
## T_UV_COD13:farmacias -9.575e+02 4.955e+04 -0.019 0.984586
## T_UV_COD14:farmacias -1.170e+03 6.883e+03 -0.170 0.865082
## T_UV_COD15:farmacias -9.265e+03 1.048e+04 -0.884 0.376750
## T_UV_COD16:farmacias 1.588e+04 4.575e+04 0.347 0.728548
## T_UV_COD17:farmacias 2.390e+03 4.670e+03 0.512 0.608929
## T_UV_COD18:farmacias 1.121e+02 4.016e+02 0.279 0.780106
## T_UV_COD19:farmacias 9.761e+00 5.916e+00 1.650 0.099065 .
## T_UV_COD2:farmacias 4.904e+03 2.521e+03 1.945 0.051864 .
## T_UV_COD20:farmacias 2.676e+02 5.794e+03 0.046 0.963173
## T_UV_COD21:farmacias 4.694e+03 5.503e+03 0.853 0.393688
## T_UV_COD22:farmacias -9.374e+03 9.144e+03 -1.025 0.305431
## T_UV_COD23:farmacias 1.490e+03 7.360e+02 2.025 0.042977 *
## T_UV_COD24:farmacias -5.046e+03 1.192e+05 -0.042 0.966238
## T_UV_COD25:farmacias -3.799e+03 3.685e+04 -0.103 0.917899
## T_UV_COD26:farmacias 3.126e+02 1.165e+02 2.683 0.007340 **
## T_UV_COD27:farmacias -1.552e+01 1.792e+02 -0.087 0.930996
## T_UV_COD28:farmacias 1.819e+02 1.548e+02 1.175 0.240302
## T_UV_COD29:farmacias -1.975e+04 2.354e+05 -0.084 0.933139
## T_UV_COD3:farmacias 3.265e+05 3.452e+05 0.946 0.344422
## T_UV_COD30:farmacias 2.510e+03 3.850e+03 0.652 0.514491
## T_UV_COD31:farmacias 1.299e+03 9.283e+02 1.400 0.161752
## T_UV_COD32:farmacias 1.437e+02 1.091e+02 1.317 0.187902
## T_UV_COD33:farmacias 2.040e+04 1.900e+04 1.073 0.283224
## T_UV_COD34:farmacias -1.117e+03 1.109e+03 -1.007 0.314239
## T_UV_COD35:farmacias 5.836e+02 1.380e+03 0.423 0.672316
## T_UV_COD36:farmacias 8.093e+02 8.367e+02 0.967 0.333507
## T_UV_COD38:farmacias 4.562e+02 1.269e+03 0.360 0.719216
## T_UV_COD39:farmacias 1.514e+04 1.960e+04 0.773 0.439702
## T_UV_COD4:farmacias 4.145e+01 2.929e+01 1.415 0.157223
## T_UV_COD40:farmacias 1.018e+03 4.418e+03 0.230 0.817862
## T_UV_COD41:farmacias 3.301e+00 3.034e+02 0.011 0.991320
## T_UV_COD42:farmacias -7.323e+01 4.807e+01 -1.523 0.127796
## T_UV_COD43:farmacias -5.105e+01 8.505e+01 -0.600 0.548442
## T_UV_COD44:farmacias NA NA NA NA
## T_UV_COD45:farmacias -7.167e+03 2.608e+04 -0.275 0.783467
## T_UV_COD46:farmacias -1.220e+03 1.631e+03 -0.748 0.454409
## T_UV_COD47:farmacias 1.629e+02 3.966e+02 0.411 0.681339
## T_UV_COD48:farmacias -3.592e+02 1.120e+03 -0.321 0.748433
## T_UV_COD49:farmacias 6.865e+03 2.567e+04 0.267 0.789134
## T_UV_COD5:farmacias 5.538e+01 2.340e+01 2.367 0.018005 *
## T_UV_COD50:farmacias 4.606e+03 3.634e+03 1.268 0.205065
## T_UV_COD51:farmacias -7.122e+03 1.316e+04 -0.541 0.588553
## T_UV_COD52:farmacias -7.163e+06 1.988e+07 -0.360 0.718683
## T_UV_COD53:farmacias -4.990e+02 3.842e+03 -0.130 0.896667
## T_UV_COD54:farmacias 2.696e+03 3.071e+03 0.878 0.380086
## T_UV_COD55:farmacias 8.305e+02 9.980e+02 0.832 0.405400
## T_UV_COD56:farmacias -1.863e+04 3.442e+04 -0.541 0.588407
## T_UV_COD57:farmacias -8.010e+03 1.204e+04 -0.666 0.505745
## T_UV_COD58:farmacias -2.156e+03 1.235e+03 -1.746 0.080936 .
## T_UV_COD59:farmacias -1.898e+03 3.133e+03 -0.606 0.544729
## T_UV_COD60:farmacias -8.179e+02 1.612e+03 -0.507 0.611909
## T_UV_COD61:farmacias -5.328e+02 9.892e+02 -0.539 0.590172
## T_UV_COD62:farmacias 2.389e+01 4.993e+01 0.478 0.632358
## T_UV_COD63:farmacias 6.034e+01 9.998e+01 0.604 0.546175
## T_UV_COD64:farmacias 2.099e+03 6.101e+03 0.344 0.730844
## T_UV_COD65:farmacias -2.458e+02 1.356e+03 -0.181 0.856174
## T_UV_COD66:farmacias -1.411e+05 9.521e+05 -0.148 0.882186
## T_UV_COD67:farmacias 1.143e+03 3.139e+03 0.364 0.715880
## T_UV_COD68:farmacias -1.373e+01 2.149e+02 -0.064 0.949035
## T_UV_COD69:farmacias 3.321e+02 1.497e+03 0.222 0.824434
## T_UV_COD7:farmacias -3.830e+02 3.406e+03 -0.112 0.910487
## T_UV_COD70:farmacias 6.095e-01 1.417e+01 0.043 0.965703
## T_UV_COD71:farmacias 2.957e+02 2.741e+02 1.079 0.280781
## T_UV_COD72:farmacias -1.747e+01 3.032e+01 -0.576 0.564577
## T_UV_COD73:farmacias -1.392e+01 2.218e+01 -0.628 0.530323
## T_UV_COD74:farmacias 3.315e+02 8.681e+02 0.382 0.702606
## T_UV_COD75:farmacias 2.464e+02 2.106e+02 1.170 0.241977
## T_UV_COD76:farmacias 9.547e+02 3.744e+02 2.550 0.010832 *
## T_UV_COD77:farmacias 2.127e+02 7.819e+01 2.720 0.006577 **
## T_UV_COD78:farmacias 9.017e+01 2.953e+01 3.054 0.002286 **
## T_UV_COD8:farmacias -1.154e+02 3.236e+02 -0.357 0.721407
## T_UV_COD9:farmacias -3.931e+01 1.751e+01 -2.245 0.024862 *
## T_UV_COD99:farmacias -5.846e+02 1.690e+05 -0.003 0.997240
## T_UV_COD1:buses -8.838e+00 1.241e+01 -0.712 0.476573
## T_UV_COD10:buses -5.770e+03 9.125e+03 -0.632 0.527214
## T_UV_COD11:buses 3.980e+02 1.297e+03 0.307 0.759086
## T_UV_COD12:buses -1.047e+01 2.547e+01 -0.411 0.681018
## T_UV_COD13:buses 5.054e+02 6.372e+02 0.793 0.427764
## T_UV_COD14:buses 9.875e+02 9.298e+02 1.062 0.288331
## T_UV_COD15:buses -6.067e+01 7.397e+01 -0.820 0.412197
## T_UV_COD16:buses -1.700e+00 5.296e+02 -0.003 0.997439
## T_UV_COD17:buses -2.760e+01 1.283e+02 -0.215 0.829734
## T_UV_COD18:buses -9.796e+00 1.878e+01 -0.522 0.601949
## T_UV_COD19:buses -1.514e+01 8.980e+00 -1.686 0.091836 .
## T_UV_COD2:buses 3.386e+00 6.112e+00 0.554 0.579590
## T_UV_COD20:buses 2.238e+02 5.501e+02 0.407 0.684177
## T_UV_COD21:buses 3.542e+00 5.080e+01 0.070 0.944422
## T_UV_COD22:buses -1.313e+00 5.119e+02 -0.003 0.997954
## T_UV_COD23:buses 5.324e+01 3.278e+01 1.624 0.104444
## T_UV_COD24:buses -5.223e+02 7.571e+02 -0.690 0.490409
## T_UV_COD25:buses 4.690e+01 2.959e+02 0.158 0.874089
## T_UV_COD26:buses 1.508e+00 7.612e+00 0.198 0.842944
## T_UV_COD27:buses 1.191e+01 1.046e+01 1.139 0.254965
## T_UV_COD28:buses -2.197e+01 9.018e+00 -2.436 0.014910 *
## T_UV_COD29:buses -1.192e+03 3.125e+03 -0.382 0.702830
## T_UV_COD3:buses 2.583e+02 5.127e+02 0.504 0.614497
## T_UV_COD30:buses 8.410e+01 3.758e+01 2.238 0.025306 *
## T_UV_COD31:buses 6.687e+01 3.389e+01 1.973 0.048609 *
## T_UV_COD32:buses -2.191e+01 1.021e+01 -2.145 0.032084 *
## T_UV_COD33:buses -1.106e+03 3.099e+03 -0.357 0.721253
## T_UV_COD34:buses -1.755e+00 2.822e+01 -0.062 0.950428
## T_UV_COD35:buses 3.296e+01 1.746e+01 1.887 0.059237 .
## T_UV_COD36:buses -9.088e+00 1.404e+01 -0.647 0.517519
## T_UV_COD38:buses 2.211e+01 1.208e+01 1.830 0.067451 .
## T_UV_COD39:buses 4.057e+01 3.373e+01 1.203 0.229191
## T_UV_COD4:buses -5.404e+00 3.901e+00 -1.385 0.166101
## T_UV_COD40:buses -8.290e+00 2.512e+01 -0.330 0.741425
## T_UV_COD41:buses 5.722e+00 7.146e+00 0.801 0.423338
## T_UV_COD42:buses 7.460e+00 1.035e+01 0.721 0.471148
## T_UV_COD43:buses 2.952e+00 2.366e+01 0.125 0.900746
## T_UV_COD44:buses NA NA NA NA
## T_UV_COD45:buses 2.948e+01 1.529e+02 0.193 0.847182
## T_UV_COD46:buses 1.053e+01 1.476e+01 0.714 0.475494
## T_UV_COD47:buses 7.853e+00 9.034e+00 0.869 0.384808
## T_UV_COD48:buses -8.255e+00 2.073e+01 -0.398 0.690463
## T_UV_COD49:buses -3.002e+02 5.148e+02 -0.583 0.559905
## T_UV_COD5:buses -1.764e+01 1.043e+01 -1.691 0.090876 .
## T_UV_COD50:buses 2.164e+01 2.212e+01 0.978 0.328048
## T_UV_COD51:buses 1.468e+01 6.354e+01 0.231 0.817319
## T_UV_COD52:buses 1.496e+03 4.291e+03 0.349 0.727390
## T_UV_COD53:buses -1.255e+00 1.360e+01 -0.092 0.926471
## T_UV_COD54:buses -2.556e+01 2.793e+01 -0.915 0.360212
## T_UV_COD55:buses 5.490e+00 7.738e+00 0.709 0.478090
## T_UV_COD56:buses -1.479e+01 7.359e+01 -0.201 0.840740
## T_UV_COD57:buses 4.145e+01 4.724e+01 0.877 0.380338
## T_UV_COD58:buses -9.917e+00 1.546e+01 -0.641 0.521268
## T_UV_COD59:buses 1.314e+02 1.989e+02 0.661 0.508806
## T_UV_COD60:buses -4.674e+00 2.391e+01 -0.195 0.845051
## T_UV_COD61:buses -2.630e+00 8.574e+00 -0.307 0.759053
## T_UV_COD62:buses -2.264e+01 2.307e+01 -0.982 0.326405
## T_UV_COD63:buses 5.552e+00 8.106e+00 0.685 0.493492
## T_UV_COD64:buses 1.198e+01 2.594e+01 0.462 0.644197
## T_UV_COD65:buses 3.446e-01 1.591e+01 0.022 0.982717
## T_UV_COD66:buses -1.954e+03 1.254e+04 -0.156 0.876202
## T_UV_COD67:buses 2.204e+02 4.297e+02 0.513 0.608071
## T_UV_COD68:buses 5.537e-01 1.869e+01 0.030 0.976362
## T_UV_COD69:buses 1.126e+01 5.279e+01 0.213 0.831186
## T_UV_COD7:buses 7.413e+01 2.598e+01 2.853 0.004365 **
## T_UV_COD70:buses 6.477e+00 8.948e+00 0.724 0.469231
## T_UV_COD71:buses -5.428e+01 2.465e+01 -2.203 0.027720 *
## T_UV_COD72:buses 3.123e+01 1.428e+01 2.187 0.028845 *
## T_UV_COD73:buses 5.718e+01 1.160e+01 4.930 8.82e-07 ***
## T_UV_COD74:buses 1.454e+00 1.369e+01 0.106 0.915472
## T_UV_COD75:buses 9.538e-01 7.662e+00 0.124 0.900945
## T_UV_COD76:buses 4.385e+00 5.295e+00 0.828 0.407666
## T_UV_COD77:buses 1.456e+01 4.401e+00 3.309 0.000951 ***
## T_UV_COD78:buses -7.905e+00 8.064e+00 -0.980 0.327034
## T_UV_COD8:buses 1.720e-01 8.740e+00 0.020 0.984297
## T_UV_COD9:buses 2.281e+00 7.818e+00 0.292 0.770531
## T_UV_COD99:buses 3.297e+02 4.582e+03 0.072 0.942642
## T_UV_COD1:plazas 1.038e+00 9.944e-01 1.044 0.296722
## T_UV_COD10:plazas -4.588e+02 6.876e+02 -0.667 0.504640
## T_UV_COD11:plazas -1.356e+02 6.875e+01 -1.972 0.048671 *
## T_UV_COD12:plazas 9.629e+01 1.631e+02 0.590 0.554920
## T_UV_COD13:plazas -2.091e+02 4.554e+02 -0.459 0.646088
## T_UV_COD14:plazas 1.526e+02 3.446e+02 0.443 0.657887
## T_UV_COD15:plazas 1.568e+02 2.463e+02 0.636 0.524516
## T_UV_COD16:plazas 4.439e+00 1.053e+02 0.042 0.966366
## T_UV_COD17:plazas 3.753e+01 5.026e+01 0.747 0.455261
## T_UV_COD18:plazas -5.955e+00 2.455e+01 -0.243 0.808373
## T_UV_COD19:plazas 9.318e-01 1.491e+01 0.062 0.950173
## T_UV_COD2:plazas 1.728e+00 3.830e+00 0.451 0.651926
## T_UV_COD20:plazas -2.959e+00 9.811e+01 -0.030 0.975945
## T_UV_COD21:plazas -2.903e+02 4.338e+02 -0.669 0.503388
## T_UV_COD22:plazas 6.285e+00 2.417e+01 0.260 0.794854
## T_UV_COD23:plazas 3.194e+03 9.387e+02 3.403 0.000678 ***
## T_UV_COD24:plazas -2.200e+01 7.096e+01 -0.310 0.756556
## T_UV_COD25:plazas 5.883e+00 1.896e+01 0.310 0.756344
## T_UV_COD26:plazas -1.317e+01 1.295e+01 -1.017 0.309230
## T_UV_COD27:plazas -1.548e+01 1.710e+01 -0.905 0.365364
## T_UV_COD28:plazas 1.421e+01 1.656e+01 0.858 0.390914
## T_UV_COD29:plazas 1.682e+04 3.516e+04 0.478 0.632507
## T_UV_COD3:plazas -1.153e+02 5.265e+01 -2.191 0.028579 *
## T_UV_COD30:plazas 9.858e+02 4.046e+02 2.437 0.014889 *
## T_UV_COD31:plazas -6.303e+00 3.727e+02 -0.017 0.986507
## T_UV_COD32:plazas 4.108e+02 1.315e+02 3.123 0.001812 **
## T_UV_COD33:plazas -2.444e+01 3.585e+02 -0.068 0.945664
## T_UV_COD34:plazas 2.176e+01 1.104e+01 1.971 0.048827 *
## T_UV_COD35:plazas 7.968e+00 4.180e+01 0.191 0.848816
## T_UV_COD36:plazas -5.189e+01 1.055e+02 -0.492 0.622851
## T_UV_COD38:plazas -8.983e-02 6.823e+00 -0.013 0.989497
## T_UV_COD39:plazas -2.452e+01 2.496e+01 -0.983 0.325923
## T_UV_COD4:plazas -9.509e-02 3.006e+00 -0.032 0.974770
## T_UV_COD40:plazas 7.672e+01 1.627e+02 0.472 0.637189
## T_UV_COD41:plazas 1.236e-01 3.162e+01 0.004 0.996882
## T_UV_COD42:plazas 7.510e+01 5.441e+01 1.380 0.167649
## T_UV_COD43:plazas 1.995e+00 4.054e+01 0.049 0.960754
## T_UV_COD44:plazas NA NA NA NA
## T_UV_COD45:plazas 3.917e+01 3.570e+02 0.110 0.912655
## T_UV_COD46:plazas -1.754e+01 1.605e+02 -0.109 0.913008
## T_UV_COD47:plazas 2.094e+01 5.414e+01 0.387 0.698975
## T_UV_COD48:plazas 9.424e+00 2.296e+01 0.410 0.681500
## T_UV_COD49:plazas 2.228e+03 3.954e+03 0.563 0.573188
## T_UV_COD5:plazas -2.843e+00 1.343e+01 -0.212 0.832361
## T_UV_COD50:plazas -1.780e+02 2.199e+02 -0.809 0.418492
## T_UV_COD51:plazas -1.891e+01 1.317e+02 -0.144 0.885814
## T_UV_COD52:plazas NA NA NA NA
## T_UV_COD53:plazas -1.425e-01 3.156e+00 -0.045 0.963994
## T_UV_COD54:plazas -1.486e+02 3.443e+02 -0.432 0.666034
## T_UV_COD55:plazas 6.205e+00 1.478e+01 0.420 0.674740
## T_UV_COD56:plazas 2.903e+01 3.649e+02 0.080 0.936594
## T_UV_COD57:plazas 1.505e+01 1.229e+02 0.122 0.902514
## T_UV_COD58:plazas -2.594e+00 1.262e+01 -0.206 0.837164
## T_UV_COD59:plazas -5.698e+00 2.825e+01 -0.202 0.840155
## T_UV_COD60:plazas -2.055e+02 9.094e+02 -0.226 0.821282
## T_UV_COD61:plazas 2.108e+02 2.700e+02 0.781 0.434994
## T_UV_COD62:plazas 1.859e+00 2.913e+01 0.064 0.949134
## T_UV_COD63:plazas 1.075e+00 1.599e+01 0.067 0.946430
## T_UV_COD64:plazas -6.190e+02 3.563e+03 -0.174 0.862107
## T_UV_COD65:plazas 2.986e+02 8.723e+02 0.342 0.732178
## T_UV_COD66:plazas 1.223e+05 8.236e+05 0.148 0.881968
## T_UV_COD67:plazas -4.417e+01 8.469e+01 -0.522 0.602005
## T_UV_COD68:plazas 2.098e+00 8.531e+01 0.025 0.980385
## T_UV_COD69:plazas 7.006e+01 7.146e+02 0.098 0.921902
## T_UV_COD7:plazas -2.862e+01 2.058e+01 -1.390 0.164569
## T_UV_COD70:plazas 3.820e+00 6.427e+00 0.594 0.552352
## T_UV_COD71:plazas 5.675e+02 1.897e+02 2.992 0.002798 **
## T_UV_COD72:plazas -8.894e+00 8.977e+00 -0.991 0.321898
## T_UV_COD73:plazas 4.985e+00 2.892e+00 1.724 0.084879 .
## T_UV_COD74:plazas -5.056e+01 9.230e+01 -0.548 0.583886
## T_UV_COD75:plazas 1.329e+01 1.114e+01 1.193 0.232980
## T_UV_COD76:plazas -8.744e+00 1.044e+01 -0.837 0.402546
## T_UV_COD77:plazas -1.155e+01 2.699e+00 -4.280 1.94e-05 ***
## T_UV_COD78:plazas -1.435e+00 8.017e-01 -1.790 0.073552 .
## T_UV_COD8:plazas -2.619e+00 2.861e+01 -0.092 0.927080
## T_UV_COD9:plazas -1.287e+01 1.635e+01 -0.787 0.431094
## T_UV_COD99:plazas 5.838e+00 4.854e+01 0.120 0.904288
## T_UV_COD1:restau -8.254e+00 1.119e+01 -0.738 0.460778
## T_UV_COD10:restau 1.416e+04 1.787e+04 0.792 0.428290
## T_UV_COD11:restau -2.917e+01 4.358e+02 -0.067 0.946639
## T_UV_COD12:restau 2.308e+01 2.571e+01 0.898 0.369487
## T_UV_COD13:restau -4.955e+00 1.433e+02 -0.035 0.972414
## T_UV_COD14:restau -9.178e+01 9.545e+01 -0.962 0.336366
## T_UV_COD15:restau 1.550e+03 1.475e+03 1.051 0.293238
## T_UV_COD16:restau 1.258e+03 4.582e+03 0.275 0.783668
## T_UV_COD17:restau -1.319e+01 3.868e+02 -0.034 0.972797
## T_UV_COD18:restau 8.390e+00 9.632e+00 0.871 0.383790
## T_UV_COD19:restau 1.331e+00 6.927e+00 0.192 0.847601
## T_UV_COD2:restau 1.908e+00 4.586e+00 0.416 0.677412
## T_UV_COD20:restau -4.930e+00 1.092e+01 -0.451 0.651873
## T_UV_COD21:restau -1.274e+01 4.715e+01 -0.270 0.787014
## T_UV_COD22:restau -1.511e+01 1.057e+02 -0.143 0.886390
## T_UV_COD23:restau 1.220e+01 2.363e+01 0.516 0.605807
## T_UV_COD24:restau 1.322e+02 5.434e+02 0.243 0.807728
## T_UV_COD25:restau 7.040e+01 8.672e+02 0.081 0.935303
## T_UV_COD26:restau -3.379e+01 7.379e+00 -4.580 4.89e-06 ***
## T_UV_COD27:restau 5.721e+00 2.078e+01 0.275 0.783091
## T_UV_COD28:restau 2.004e+01 9.240e+00 2.169 0.030174 *
## T_UV_COD29:restau -2.364e+04 2.124e+05 -0.111 0.911371
## T_UV_COD3:restau 6.857e+02 1.228e+03 0.558 0.576590
## T_UV_COD30:restau 1.875e+02 1.584e+02 1.183 0.236779
## T_UV_COD31:restau 5.809e+01 3.328e+01 1.745 0.081047 .
## T_UV_COD32:restau 3.026e+01 2.149e+01 1.408 0.159273
## T_UV_COD33:restau 1.353e+03 2.123e+03 0.637 0.523988
## T_UV_COD34:restau -1.585e+01 1.129e+01 -1.403 0.160693
## T_UV_COD35:restau -1.502e+00 7.928e+00 -0.189 0.849743
## T_UV_COD36:restau -7.866e+00 9.130e+00 -0.861 0.389067
## T_UV_COD38:restau 5.126e+01 2.920e+01 1.756 0.079256 .
## T_UV_COD39:restau -3.733e+02 3.435e+03 -0.109 0.913462
## T_UV_COD4:restau -1.750e+00 3.256e+00 -0.538 0.590940
## T_UV_COD40:restau -5.527e+01 1.028e+02 -0.538 0.590782
## T_UV_COD41:restau -1.125e+00 5.650e+00 -0.199 0.842157
## T_UV_COD42:restau 4.281e+00 1.023e+01 0.418 0.675662
## T_UV_COD43:restau -2.635e-01 7.165e+00 -0.037 0.970671
## T_UV_COD44:restau NA NA NA NA
## T_UV_COD45:restau 4.682e+02 1.742e+03 0.269 0.788096
## T_UV_COD46:restau -7.212e+00 7.011e+01 -0.103 0.918073
## T_UV_COD47:restau -7.678e+00 1.144e+01 -0.671 0.502127
## T_UV_COD48:restau 1.696e+01 3.585e+01 0.473 0.636150
## T_UV_COD49:restau 2.768e+03 4.437e+03 0.624 0.532782
## T_UV_COD5:restau 1.718e+00 4.219e+00 0.407 0.683861
## T_UV_COD50:restau -4.252e+00 2.684e+01 -0.158 0.874166
## T_UV_COD51:restau 4.924e+02 5.398e+03 0.091 0.927332
## T_UV_COD52:restau NA NA NA NA
## T_UV_COD53:restau 1.364e+02 5.466e+03 0.025 0.980100
## T_UV_COD54:restau 1.493e+01 2.309e+01 0.647 0.517901
## T_UV_COD55:restau 1.583e+01 3.138e+02 0.050 0.959785
## T_UV_COD56:restau -1.387e+03 3.593e+03 -0.386 0.699552
## T_UV_COD57:restau -1.825e+02 2.079e+02 -0.878 0.379988
## T_UV_COD58:restau -2.958e+00 1.042e+01 -0.284 0.776410
## T_UV_COD59:restau -5.820e+00 5.899e+00 -0.987 0.323942
## T_UV_COD60:restau -9.373e+00 3.356e+01 -0.279 0.780061
## T_UV_COD61:restau 2.924e+01 1.851e+01 1.580 0.114211
## T_UV_COD62:restau -1.267e+00 1.572e+01 -0.081 0.935757
## T_UV_COD63:restau -1.087e+01 2.287e+01 -0.476 0.634440
## T_UV_COD64:restau -6.331e+02 1.803e+03 -0.351 0.725498
## T_UV_COD65:restau 6.965e+00 7.496e+01 0.093 0.925984
## T_UV_COD66:restau -9.752e+02 6.621e+03 -0.147 0.882912
## T_UV_COD67:restau -2.770e+01 9.871e+01 -0.281 0.779001
## T_UV_COD68:restau -5.460e+00 2.055e+01 -0.266 0.790528
## T_UV_COD69:restau 8.834e+01 1.580e+02 0.559 0.576017
## T_UV_COD7:restau 1.400e+01 3.227e+01 0.434 0.664377
## T_UV_COD70:restau -4.007e+00 5.347e+00 -0.749 0.453676
## T_UV_COD71:restau -4.538e+00 1.037e+01 -0.438 0.661715
## T_UV_COD72:restau -7.184e+00 3.957e+00 -1.815 0.069576 .
## T_UV_COD73:restau -7.481e-01 1.022e+01 -0.073 0.941675
## T_UV_COD74:restau 1.664e+01 1.143e+02 0.146 0.884243
## T_UV_COD75:restau 8.768e-01 7.743e+00 0.113 0.909854
## T_UV_COD76:restau 3.937e+00 7.809e+00 0.504 0.614140
## T_UV_COD77:restau 2.060e-01 2.827e-01 0.729 0.466275
## T_UV_COD78:restau -9.670e+00 5.501e+00 -1.758 0.078913 .
## T_UV_COD8:restau 2.547e+00 1.069e+01 0.238 0.811786
## T_UV_COD9:restau 2.888e+00 5.931e+00 0.487 0.626337
## T_UV_COD99:restau 1.418e+02 1.370e+03 0.103 0.917616
## T_UV_COD1:salud -1.721e+05 2.086e+05 -0.825 0.409548
## T_UV_COD10:salud -9.436e+04 1.261e+05 -0.748 0.454390
## T_UV_COD11:salud -1.663e+03 1.289e+04 -0.129 0.897391
## T_UV_COD12:salud 1.643e+03 1.771e+03 0.928 0.353590
## T_UV_COD13:salud 5.144e+03 1.320e+04 0.390 0.696675
## T_UV_COD14:salud -1.249e+04 2.315e+04 -0.540 0.589535
## T_UV_COD15:salud -1.365e+03 9.019e+03 -0.151 0.879728
## T_UV_COD16:salud -5.568e+03 2.198e+04 -0.253 0.800055
## T_UV_COD17:salud 1.944e+03 4.696e+03 0.414 0.678957
## T_UV_COD18:salud 1.215e+02 3.696e+02 0.329 0.742376
## T_UV_COD19:salud 4.437e+01 1.871e+01 2.372 0.017783 *
## T_UV_COD2:salud -1.096e+04 5.691e+03 -1.927 0.054145 .
## T_UV_COD20:salud -3.713e+00 6.899e+00 -0.538 0.590448
## T_UV_COD21:salud 1.290e+03 1.622e+03 0.795 0.426599
## T_UV_COD22:salud -2.149e+01 5.151e+01 -0.417 0.676570
## T_UV_COD23:salud -1.285e+03 7.459e+02 -1.723 0.084971 .
## T_UV_COD24:salud -1.052e+02 1.082e+04 -0.010 0.992241
## T_UV_COD25:salud -5.324e+03 7.075e+03 -0.752 0.451868
## T_UV_COD26:salud -4.412e+02 1.387e+02 -3.180 0.001490 **
## T_UV_COD27:salud -7.760e+02 2.201e+02 -3.525 0.000431 ***
## T_UV_COD28:salud 1.832e+03 1.671e+03 1.096 0.273138
## T_UV_COD29:salud 2.412e+04 6.909e+04 0.349 0.727034
## T_UV_COD3:salud -8.964e+05 9.010e+05 -0.995 0.319896
## T_UV_COD30:salud -4.914e+03 2.239e+03 -2.195 0.028271 *
## T_UV_COD31:salud 7.161e+02 7.140e+02 1.003 0.316018
## T_UV_COD32:salud -6.612e+03 2.767e+03 -2.389 0.016955 *
## T_UV_COD33:salud -5.537e+03 9.468e+03 -0.585 0.558752
## T_UV_COD34:salud -1.464e+01 1.680e+01 -0.871 0.383781
## T_UV_COD35:salud 3.515e+02 5.664e+02 0.621 0.534903
## T_UV_COD36:salud 1.242e+02 6.982e+02 0.178 0.858819
## T_UV_COD38:salud -5.878e+03 5.883e+03 -0.999 0.317788
## T_UV_COD39:salud -3.029e+03 1.798e+04 -0.169 0.866190
## T_UV_COD4:salud -7.443e+02 2.923e+02 -2.546 0.010954 *
## T_UV_COD40:salud -1.153e+00 6.305e+02 -0.002 0.998541
## T_UV_COD41:salud -2.390e+00 1.666e+01 -0.143 0.885982
## T_UV_COD42:salud 5.071e+01 7.888e+01 0.643 0.520375
## T_UV_COD43:salud 8.822e+01 6.375e+02 0.138 0.889954
## T_UV_COD44:salud NA NA NA NA
## T_UV_COD45:salud -4.199e+03 1.030e+04 -0.408 0.683446
## T_UV_COD46:salud 4.699e+02 9.700e+02 0.484 0.628099
## T_UV_COD47:salud 7.976e+01 4.136e+01 1.928 0.053920 .
## T_UV_COD48:salud 1.008e+02 1.968e+02 0.512 0.608448
## T_UV_COD49:salud -1.038e+04 1.563e+04 -0.664 0.506922
## T_UV_COD5:salud 2.315e+02 2.886e+02 0.802 0.422532
## T_UV_COD50:salud 1.859e+01 5.354e+02 0.035 0.972297
## T_UV_COD51:salud 1.032e+03 4.249e+03 0.243 0.808148
## T_UV_COD52:salud NA NA NA NA
## T_UV_COD53:salud 1.999e+01 5.609e+03 0.004 0.997156
## T_UV_COD54:salud -8.997e+02 2.348e+03 -0.383 0.701646
## T_UV_COD55:salud 2.806e+01 5.948e+02 0.047 0.962372
## T_UV_COD56:salud 4.041e+03 1.403e+04 0.288 0.773280
## T_UV_COD57:salud -5.519e+04 6.151e+04 -0.897 0.369644
## T_UV_COD58:salud 1.405e+02 1.170e+03 0.120 0.904455
## T_UV_COD59:salud -3.703e+02 3.096e+03 -0.120 0.904810
## T_UV_COD60:salud -6.300e+03 3.942e+03 -1.598 0.110106
## T_UV_COD61:salud -8.333e+02 2.816e+03 -0.296 0.767338
## T_UV_COD62:salud -3.081e+02 6.626e+02 -0.465 0.642034
## T_UV_COD63:salud 1.970e+03 4.194e+03 0.470 0.638558
## T_UV_COD64:salud 3.337e+02 1.241e+03 0.269 0.788048
## T_UV_COD65:salud -5.287e+01 1.671e+02 -0.316 0.751667
## T_UV_COD66:salud NA NA NA NA
## T_UV_COD67:salud -3.302e+03 1.661e+04 -0.199 0.842454
## T_UV_COD68:salud 1.950e+02 1.324e+03 0.147 0.882864
## T_UV_COD69:salud -1.287e+02 2.320e+03 -0.055 0.955774
## T_UV_COD7:salud -2.750e+03 4.019e+03 -0.684 0.493952
## T_UV_COD70:salud -1.124e+02 3.402e+02 -0.330 0.741195
## T_UV_COD71:salud 2.641e+02 1.128e+03 0.234 0.814989
## T_UV_COD72:salud -3.628e+02 7.086e+01 -5.120 3.30e-07 ***
## T_UV_COD73:salud 7.437e+03 8.477e+02 8.774 < 2e-16 ***
## T_UV_COD74:salud 2.494e+02 1.353e+03 0.184 0.853755
## T_UV_COD75:salud 1.572e+01 1.680e+01 0.936 0.349475
## T_UV_COD76:salud 3.579e+00 1.865e+01 0.192 0.847857
## T_UV_COD77:salud -4.741e+01 1.539e+01 -3.080 0.002096 **
## T_UV_COD78:salud -1.477e+03 3.976e+02 -3.715 0.000208 ***
## T_UV_COD8:salud 5.773e+01 3.584e+02 0.161 0.872054
## T_UV_COD9:salud -2.058e+00 3.357e+01 -0.061 0.951118
## T_UV_COD99:salud -7.362e+02 4.890e+03 -0.151 0.880345
## T_UV_COD1:super -1.254e+05 1.815e+05 -0.691 0.489737
## T_UV_COD10:super 7.316e+03 1.329e+04 0.551 0.581902
## T_UV_COD11:super -1.537e+01 5.661e+01 -0.272 0.785995
## T_UV_COD12:super -2.813e+01 2.385e+01 -1.179 0.238326
## T_UV_COD13:super 1.569e+03 6.253e+03 0.251 0.801890
## T_UV_COD14:super -8.672e-01 4.234e+01 -0.020 0.983661
## T_UV_COD15:super -3.395e+01 5.266e+01 -0.645 0.519148
## T_UV_COD16:super -2.091e+02 9.594e+02 -0.218 0.827478
## T_UV_COD17:super -9.004e+00 2.438e+01 -0.369 0.711879
## T_UV_COD18:super -7.884e+00 3.187e+01 -0.247 0.804663
## T_UV_COD19:super -5.748e+00 4.093e+00 -1.405 0.160278
## T_UV_COD2:super -2.607e+01 3.480e+01 -0.749 0.453777
## T_UV_COD20:super -3.435e+01 2.320e+02 -0.148 0.882312
## T_UV_COD21:super 1.432e+02 1.222e+02 1.171 0.241610
## T_UV_COD22:super -3.469e+00 1.932e+02 -0.018 0.985675
## T_UV_COD23:super 7.731e+01 3.201e+01 2.415 0.015820 *
## T_UV_COD24:super 2.297e+03 6.334e+03 0.363 0.716831
## T_UV_COD25:super -3.721e+02 6.853e+02 -0.543 0.587140
## T_UV_COD26:super -1.924e+01 1.464e+01 -1.314 0.188882
## T_UV_COD27:super -1.015e+02 3.028e+01 -3.354 0.000809 ***
## T_UV_COD28:super -3.895e+02 1.542e+02 -2.527 0.011580 *
## T_UV_COD29:super 1.188e+02 8.745e+02 0.136 0.891947
## T_UV_COD3:super NA NA NA NA
## T_UV_COD30:super -3.308e-01 5.644e+00 -0.059 0.953264
## T_UV_COD31:super -3.637e+01 3.285e+01 -1.107 0.268366
## T_UV_COD32:super -8.559e+01 1.261e+02 -0.679 0.497488
## T_UV_COD33:super 1.865e+02 1.828e+03 0.102 0.918728
## T_UV_COD34:super 2.756e+01 1.444e+01 1.909 0.056425 .
## T_UV_COD35:super 7.802e+00 6.175e+00 1.263 0.206554
## T_UV_COD36:super 2.524e+00 1.675e+01 0.151 0.880190
## T_UV_COD38:super -3.923e+00 1.397e+01 -0.281 0.778859
## T_UV_COD39:super -1.995e+03 2.164e+03 -0.922 0.356547
## T_UV_COD4:super -2.450e+00 1.291e+00 -1.897 0.057890 .
## T_UV_COD40:super -2.644e+02 4.720e+02 -0.560 0.575452
## T_UV_COD41:super -2.269e+00 3.937e+00 -0.576 0.564533
## T_UV_COD42:super 1.336e+01 1.587e+01 0.842 0.400023
## T_UV_COD43:super -1.947e+01 2.215e+01 -0.879 0.379602
## T_UV_COD44:super NA NA NA NA
## T_UV_COD45:super -5.495e+01 7.974e+02 -0.069 0.945064
## T_UV_COD46:super -2.408e+01 2.147e+01 -1.122 0.262127
## T_UV_COD47:super 1.858e+02 1.618e+02 1.148 0.250898
## T_UV_COD48:super 1.471e+02 3.506e+02 0.420 0.674820
## T_UV_COD49:super -1.343e+01 3.229e+01 -0.416 0.677518
## T_UV_COD5:super -1.645e+00 7.438e+00 -0.221 0.825015
## T_UV_COD50:super 8.465e+01 1.561e+02 0.542 0.587671
## T_UV_COD51:super -2.556e+02 1.779e+03 -0.144 0.885747
## T_UV_COD52:super NA NA NA NA
## T_UV_COD53:super 1.185e+01 3.433e+02 0.035 0.972471
## T_UV_COD54:super -4.442e+00 1.118e+01 -0.397 0.691244
## T_UV_COD55:super 3.222e+00 1.780e+01 0.181 0.856353
## T_UV_COD56:super -1.530e+03 2.405e+03 -0.636 0.524767
## T_UV_COD57:super -4.785e+02 2.585e+03 -0.185 0.853164
## T_UV_COD58:super -3.551e+01 2.530e+01 -1.403 0.160638
## T_UV_COD59:super -1.146e+01 1.432e+01 -0.801 0.423340
## T_UV_COD60:super -2.925e+00 1.649e+01 -0.177 0.859217
## T_UV_COD61:super 1.576e+01 2.336e+01 0.675 0.499975
## T_UV_COD62:super 1.319e+01 2.360e+01 0.559 0.576294
## T_UV_COD63:super -3.998e+02 6.026e+02 -0.664 0.507069
## T_UV_COD64:super -7.895e+01 4.801e+02 -0.164 0.869398
## T_UV_COD65:super -1.485e+02 9.737e+02 -0.153 0.878799
## T_UV_COD66:super NA NA NA NA
## T_UV_COD67:super -1.340e+03 1.939e+03 -0.691 0.489430
## T_UV_COD68:super -2.522e-01 3.112e+01 -0.008 0.993534
## T_UV_COD69:super -4.891e+02 8.866e+02 -0.552 0.581271
## T_UV_COD7:super 1.249e+03 3.253e+02 3.839 0.000127 ***
## T_UV_COD70:super -2.176e+00 1.160e+01 -0.188 0.851224
## T_UV_COD71:super 1.787e+01 1.230e+01 1.453 0.146490
## T_UV_COD72:super 1.921e+01 1.495e+01 1.285 0.198941
## T_UV_COD73:super 3.982e+01 1.743e+01 2.285 0.022383 *
## T_UV_COD74:super 6.674e-01 2.006e+01 0.033 0.973461
## T_UV_COD75:super 2.382e+00 6.570e+00 0.363 0.716951
## T_UV_COD76:super -2.999e+00 6.436e+00 -0.466 0.641261
## T_UV_COD77:super -3.941e+01 5.104e+00 -7.721 1.69e-14 ***
## T_UV_COD78:super 1.445e+01 8.313e+00 1.738 0.082326 .
## T_UV_COD8:super -9.304e+00 2.081e+01 -0.447 0.654819
## T_UV_COD9:super 6.283e+00 5.962e+00 1.054 0.292067
## T_UV_COD99:super 2.467e+02 2.263e+03 0.109 0.913209
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3882 on 2348 degrees of freedom
## Multiple R-squared: 0.9996, Adjusted R-squared: 0.9995
## F-statistic: 7599 on 757 and 2348 DF, p-value: < 2.2e-16Chequeamos si existe heterogeneidad espacial
#create a function that runs the spatial chow test (Anselin, 2007)
chow.test <- function(rest,unrest) {
#extracts residuals from the regime and regular regression models
er <- residuals(rest)
eu <- residuals(unrest)
#sum of squared errors
er2 <- sum(er^2)
eu2 <- sum(eu^2)
#calculates degrees of freedom
k <- rest$rank
n2k <- rest$df.residual - k
#calculates chow statistic
c <- ((er2 - eu2)/k) / (eu2 / n2k)
#pvalue from F distribution
pc <- pf(c,k,n2k,lower.tail=FALSE)
#returns chow stat, pvalue, rank (number of estimated parameters) and degrees of freedom
list(c,pc,k,n2k)
}
chow.test(VI_OLS, model.ols.regime)## [[1]]
## [1] 1100.683
##
## [[2]]
## [1] 0
##
## [[3]]
## [1] 10
##
## [[4]]
## [1] 3085Como se rechaza la hipótesis nula, podemos seguir con la implementación de la GWR.
Implementamos GWR
Estimamos el Bandwidth
coords<-cbind(data$x_coord, data$y_coord)
bw<-gwr.sel(model, coords = coords, gweight = gwr.Gauss, adapt = TRUE)## Adaptive q: 0.381966 CV score: 1373.344
## Adaptive q: 0.618034 CV score: 1586.513
## Adaptive q: 0.236068 CV score: 1215.905
## Adaptive q: 0.145898 CV score: 1080.035
## Adaptive q: 0.09016994 CV score: 954.8208
## Adaptive q: 0.05572809 CV score: 852.9641
## Adaptive q: 0.03444185 CV score: 762.6164
## Adaptive q: 0.02128624 CV score: 689.1336
## Adaptive q: 0.01315562 CV score: 630.3444
## Adaptive q: 0.008130619 CV score: 592.9394
## Adaptive q: 0.005024999 CV score: 567.6022
## Adaptive q: 0.00310562 CV score: 547.4683
## Adaptive q: 0.001919379 CV score: 551.5837
## Adaptive q: 0.002898419 CV score: 548.5378
## Adaptive q: 0.003838757 CV score: 558.6618
## Adaptive q: 0.003385654 CV score: 551.0251
## Adaptive q: 0.00306493 CV score: 547.6295
## Adaptive q: 0.003212583 CV score: 547.3255
## Adaptive q: 0.003171893 CV score: 547.3393
## Adaptive q: 0.003253273 CV score: 548.0844
## Adaptive q: 0.003212583 CV score: 547.3255Estimamos GWR
MOD.GWR<-gwr(model, coords = coords, adapt = bw, hatmatrix = TRUE)
MOD.GWR## Call:
## gwr(formula = model, coords = coords, adapt = bw, hatmatrix = TRUE)
## Kernel function: gwr.Gauss
## Adaptive quantile: 0.003212583 (about 9 of 3105 data points)
## Summary of GWR coefficient estimates at data points:
## Min. 1st Qu. Median 3rd Qu. Max.
## X.Intercept. -2.6263e+03 1.5215e+01 1.6086e+01 1.7276e+01 2.3476e+02
## esc_pub -1.6390e+07 -2.7834e+01 4.9904e+00 4.3469e+01 3.0823e+08
## esc_priv -1.8296e+05 -3.3382e+01 9.0995e+00 2.0955e+02 1.6569e+05
## esc_parvu -7.7724e+04 -3.5610e+01 -1.9006e+00 2.0328e+01 2.0055e+07
## farmacias -3.0488e+08 -2.7402e+01 4.6649e+01 3.8117e+02 2.8642e+06
## buses -3.6780e+02 -6.2756e+00 7.1443e-01 6.7800e+00 3.4718e+02
## plazas -8.4064e+02 -5.7277e+00 1.3075e+00 1.1786e+01 4.0532e+03
## restau -8.2702e+03 -6.8910e+00 -5.2987e-01 5.6692e+00 6.1219e+03
## salud -5.4247e+06 -3.7038e+02 -3.5314e+00 1.0045e+02 3.5927e+08
## super -3.5150e+08 -1.0538e+01 -1.3466e+00 5.5810e+00 1.7847e+07
## Global
## X.Intercept. 16.7997
## esc_pub -15.5300
## esc_priv 6.6491
## esc_parvu -34.3065
## farmacias 46.1381
## buses 5.6889
## plazas 3.8718
## restau 0.6486
## salud -46.1701
## super -5.3001
## Number of data points: 3105
## Effective number of parameters (residual: 2traceS - traceS'S): 835.0064
## Effective degrees of freedom (residual: 2traceS - traceS'S): 2269.994
## Sigma (residual: 2traceS - traceS'S): 0.3533437
## Effective number of parameters (model: traceS): 662.45
## Effective degrees of freedom (model: traceS): 2442.55
## Sigma (model: traceS): 0.340634
## Sigma (ML): 0.3021196
## AICc (GWR p. 61, eq 2.33; p. 96, eq. 4.21): 3066.812
## AIC (GWR p. 96, eq. 4.22): 2041.108
## Residual sum of squares: 283.4127
## Quasi-global R2: 0.8453322Mapeamos los resultados obtenidos
tmap_mode("view")## tmap mode set to interactive viewingtm_shape(MOD.GWR$SDF) +
tm_dots(c("esc_pub", "esc_priv", "esc_parvu", "farmacias", "buses"), midpoint = 0, style = "quantile") +
tm_style("col_blind")+
tm_layout(legend.position = c("right", "top"))## legend.postion is used for plot mode. Use view.legend.position in tm_view to set the legend position in view mode.