Parcial
Karol Johana Zambrano Cruz
12/05/2021
Actividad
El Rpbs presenta el articulo propuesto para socializar y sustentar como parcial. El paquete de R “GWmodel” analizando la base de datos Dubvoter
Actividad articulo propuesto:
Presenta una colección de modelos locales, denominados modelos ponderados geográficamente (GW), que se pueden encontrar en el paquete GWmodel. Un modelo GW se adapta a situaciones en las que los datos espaciales están mal descritos por la forma global y, para algunas regiones, el ajuste localizado proporciona una mejor descripción
#1. Paquete GWmodel
#1.1. Datos DubVoter 1. Paquete GWmodel
GWmodel viene con cinco conjuntos de datos de ejemplo, estos son: (1) Georgia, (2) LondonHP, (3) DubVoter, (4) EWHP y (5) USelect. Para la presentación en este caso, se utiliza los datos DubVoter y USelect.
2 Exploración de datos
Para realizar los analisis debemos instalar el paquete
library(GWmodel)## Warning: package 'GWmodel' was built under R version 4.0.5## Loading required package: maptools## Loading required package: sp## Checking rgeos availability: TRUE## Loading required package: robustbase## Loading required package: Rcpp## Loading required package: spatialreg## Loading required package: spData## To access larger datasets in this package, install the spDataLarge
## package with: `install.packages('spDataLarge',
## repos='https://nowosad.github.io/drat/', type='source')`## Loading required package: Matrix## Registered S3 methods overwritten by 'spatialreg':
## method from
## residuals.stsls spdep
## deviance.stsls spdep
## coef.stsls spdep
## print.stsls spdep
## summary.stsls spdep
## print.summary.stsls spdep
## residuals.gmsar spdep
## deviance.gmsar spdep
## coef.gmsar spdep
## fitted.gmsar spdep
## print.gmsar spdep
## summary.gmsar spdep
## print.summary.gmsar spdep
## print.lagmess spdep
## summary.lagmess spdep
## print.summary.lagmess spdep
## residuals.lagmess spdep
## deviance.lagmess spdep
## coef.lagmess spdep
## fitted.lagmess spdep
## logLik.lagmess spdep
## fitted.SFResult spdep
## print.SFResult spdep
## fitted.ME_res spdep
## print.ME_res spdep
## print.lagImpact spdep
## plot.lagImpact spdep
## summary.lagImpact spdep
## HPDinterval.lagImpact spdep
## print.summary.lagImpact spdep
## print.sarlm spdep
## summary.sarlm spdep
## residuals.sarlm spdep
## deviance.sarlm spdep
## coef.sarlm spdep
## vcov.sarlm spdep
## fitted.sarlm spdep
## logLik.sarlm spdep
## anova.sarlm spdep
## predict.sarlm spdep
## print.summary.sarlm spdep
## print.sarlm.pred spdep
## as.data.frame.sarlm.pred spdep
## residuals.spautolm spdep
## deviance.spautolm spdep
## coef.spautolm spdep
## fitted.spautolm spdep
## print.spautolm spdep
## summary.spautolm spdep
## logLik.spautolm spdep
## print.summary.spautolm spdep
## print.WXImpact spdep
## summary.WXImpact spdep
## print.summary.WXImpact spdep
## predict.SLX spdep## Welcome to GWmodel version 2.2-3.
## The new version of GWmodel 2.2-5 now is ready##
## Attaching package: 'GWmodel'## The following objects are masked from 'package:stats':
##
## BIC, fittedLa librería GWmodel ‘Modelos ponderados geográficamente’ incluye funciones para realizar estadísticas de resumen GW, análisis de componentes principales de GW, análisis discriminante de GW y varias formas de regresión de GW; algunos de los cuales se proporcionan en formas básicas y robustas (resistentes a valores atípicos).
2.1 Datos DubVoter
Cargamos la base de datos seleccionada:
data("DubVoter")La base de datos DubVoter contiene: (A) la participación electoral en las elecciones del Dáil de 2004 en Irlanda y (B) ocho características de la estructura social (datos censales); para las Divisiones Electorales (ED) del Gran Dublín. Kavanagh, modeló estos datos mediante regresión GW; con participación electoral (GenEl2004) como variable dependiente. Las ocho variables independientes miden el porcentaje de población en cada SU, con respecto a:
• Migrantes hace un año (DiffAdd)
• inquilinos de la autoridad local (LARent)
• clase social alta (SC1)
• desempleados (Unempl)
• sin ningún tipo de educación formal (LowEduc)
• edad grupo de 18-24 (Age18_24)
• edad grupo de 25-44 (Age25_44)
• edad grupo de 45-64 (Age45_64)
visualizamos la clase de la base de datos:
class(Dub.voter)## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"Validando que es un SpatialPolygonsDataFrame “sp”
Vemos la dimensión de DubVoter
dim(Dub.voter)## [1] 322 12Viendo que se tienen 12 variables y 322 observaciones (toma en la cuenta como variables el ID, y los puntos (x,y), teniendo 9 variables)
Luego previsualizamos los 6 primeros datos del objeto espacial llamando a los datos @data
head(Dub.voter@data)Ahora vemos la clase, visualizando que tenemos ahora un data frame
class(Dub.voter@data)## [1] "data.frame"Realizamos la matriz de correlaciones de todas las variables
cor(Dub.voter@data[,4:12])## DiffAdd LARent SC1 Unempl LowEduc
## DiffAdd 1.000000000 0.2757630 0.37229879 0.005779965 -0.0318577039
## LARent 0.275763007 1.0000000 -0.29226328 0.668776169 0.1675897500
## SC1 0.372298795 -0.2922633 1.00000000 -0.591567897 -0.2727821424
## Unempl 0.005779965 0.6687762 -0.59156790 1.000000000 0.2828183109
## LowEduc -0.031857704 0.1675897 -0.27278214 0.282818311 1.0000000000
## Age18_24 0.335300415 0.2524328 -0.03295145 0.113380829 -0.0001270398
## Age25_44 0.703062428 0.3124497 0.09075550 0.131741285 0.0283071619
## Age45_64 -0.561283893 -0.4626929 0.08930325 -0.374268612 -0.0723867788
## GenEl2004 -0.308269272 -0.6806665 0.35172646 -0.682262679 -0.1978618688
## Age18_24 Age25_44 Age45_64 GenEl2004
## DiffAdd 0.3353004149 0.70306243 -0.56128389 -0.3082693
## LARent 0.2524327691 0.31244970 -0.46269291 -0.6806665
## SC1 -0.0329514460 0.09075550 0.08930325 0.3517265
## Unempl 0.1133808290 0.13174128 -0.37426861 -0.6822627
## LowEduc -0.0001270398 0.02830716 -0.07238678 -0.1978619
## Age18_24 1.0000000000 0.12619751 -0.21154554 -0.2597295
## Age25_44 0.1261975141 1.00000000 -0.69322998 -0.4268253
## Age45_64 -0.2115455410 -0.69322998 1.00000000 0.4836208
## GenEl2004 -0.2597295061 -0.42682529 0.48362078 1.0000000Se implementa la linea cor(Dub.voter@data[,4:12]), donde se especifica que se muestre desde la variable 4 hasta las 12, porque las primeras tres variables hacen referencia al ID, punto x, y.
Ahora visualizamos solamente la correlación entre las variables DiffAdd y LARent
cor(Dub.voter@data$DiffAdd,Dub.voter@data$LARent)## [1] 0.275763Obteniendo una correlación directa 0,27, osea que al aumentar los migrantes aumentan los inquilinos.
Si visualizamos la correlación entre las variables DiffAdd y LowEduc
cor(Dub.voter@data$DiffAdd,Dub.voter@data$LowEduc)## [1] -0.0318577Encontramos una correlación inversa al aumentar los migrantes disminuye la variable LowEduc ‘sin ningún tipo de educación formal’
Ajuste del modelo global
Donde los coeficientes estimados son considerados constantes estacionarios.
lm.global = lm(GenEl2004 ~ DiffAdd + LARent + SC1 + Unempl + LowEduc + Age18_24 + Age25_44 + Age45_64, data = Dub.voter)
summary(lm.global)##
## Call:
## lm(formula = GenEl2004 ~ DiffAdd + LARent + SC1 + Unempl + LowEduc +
## Age18_24 + Age25_44 + Age45_64, data = Dub.voter)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.9343 -3.3500 0.4952 3.4707 13.4373
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 77.70467 3.93928 19.726 < 2e-16 ***
## DiffAdd -0.08583 0.08594 -0.999 0.3187
## LARent -0.09402 0.01765 -5.326 1.92e-07 ***
## SC1 0.08637 0.07085 1.219 0.2238
## Unempl -0.72162 0.09387 -7.687 1.96e-13 ***
## LowEduc -0.13073 0.43022 -0.304 0.7614
## Age18_24 -0.13992 0.05480 -2.554 0.0111 *
## Age25_44 -0.35365 0.07450 -4.747 3.15e-06 ***
## Age45_64 -0.09202 0.09023 -1.020 0.3086
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.304 on 313 degrees of freedom
## Multiple R-squared: 0.6383, Adjusted R-squared: 0.629
## F-statistic: 69.03 on 8 and 313 DF, p-value: < 2.2e-16Se observa que las variables LARent, Unempl, Age18_24, Age25_44 son significativas
Ajuste del Modelo Local
Para realizar el ajuste del modelo local se deben considerar unos pasos: El primero es hallar el ancho de banda
bw.gwr.1 = bw.gwr(GenEl2004 ~ DiffAdd + LARent + SC1 + Unempl + LowEduc + Age18_24 + Age25_44 + Age45_64, data = Dub.voter, approach = "AICc",kernel = "bisquare", adaptive = TRUE)## Adaptive bandwidth (number of nearest neighbours): 206 AICc value: 1950.248
## Adaptive bandwidth (number of nearest neighbours): 135 AICc value: 1925.648
## Adaptive bandwidth (number of nearest neighbours): 90 AICc value: 1927.43
## Adaptive bandwidth (number of nearest neighbours): 161 AICc value: 1933.323
## Adaptive bandwidth (number of nearest neighbours): 116 AICc value: 1921.995
## Adaptive bandwidth (number of nearest neighbours): 107 AICc value: 1921.608
## Adaptive bandwidth (number of nearest neighbours): 99 AICc value: 1922.882
## Adaptive bandwidth (number of nearest neighbours): 109 AICc value: 1921.287
## Adaptive bandwidth (number of nearest neighbours): 113 AICc value: 1921.645
## Adaptive bandwidth (number of nearest neighbours): 109 AICc value: 1921.287bw.gwr.1## [1] 109Hallando el ancho de banda = 109
Ahora, se realiza el ajuste del modelo local y con la siguiente linea se puede visualizar el modelo global y el local:
gwr.res = gwr.basic(GenEl2004 ~ DiffAdd + LARent + SC1 + Unempl+ LowEduc + Age18_24 + Age25_44 + Age45_64, data = Dub.voter,bw = bw.gwr.1, kernel = "bisquare", adaptive = TRUE)
gwr.res## ***********************************************************************
## * Package GWmodel *
## ***********************************************************************
## Program starts at: 2021-05-24 23:40:03
## Call:
## gwr.basic(formula = GenEl2004 ~ DiffAdd + LARent + SC1 + Unempl +
## LowEduc + Age18_24 + Age25_44 + Age45_64, data = Dub.voter,
## bw = bw.gwr.1, kernel = "bisquare", adaptive = TRUE)
##
## Dependent (y) variable: GenEl2004
## Independent variables: DiffAdd LARent SC1 Unempl LowEduc Age18_24 Age25_44 Age45_64
## Number of data points: 322
## ***********************************************************************
## * Results of Global Regression *
## ***********************************************************************
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.9343 -3.3500 0.4952 3.4707 13.4373
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 77.70467 3.93928 19.726 < 2e-16 ***
## DiffAdd -0.08583 0.08594 -0.999 0.3187
## LARent -0.09402 0.01765 -5.326 1.92e-07 ***
## SC1 0.08637 0.07085 1.219 0.2238
## Unempl -0.72162 0.09387 -7.687 1.96e-13 ***
## LowEduc -0.13073 0.43022 -0.304 0.7614
## Age18_24 -0.13992 0.05480 -2.554 0.0111 *
## Age25_44 -0.35365 0.07450 -4.747 3.15e-06 ***
## Age45_64 -0.09202 0.09023 -1.020 0.3086
##
## ---Significance stars
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Residual standard error: 5.304 on 313 degrees of freedom
## Multiple R-squared: 0.6383
## Adjusted R-squared: 0.629
## F-statistic: 69.03 on 8 and 313 DF, p-value: < 2.2e-16
## ***Extra Diagnostic information
## Residual sum of squares: 8805.251
## Sigma(hat): 5.245609
## AIC: 1999.15
## AICc: 1999.858
## BIC: 1772.641
## ***********************************************************************
## * Results of Geographically Weighted Regression *
## ***********************************************************************
##
## *********************Model calibration information*********************
## Kernel function: bisquare
## Adaptive bandwidth: 109 (number of nearest neighbours)
## Regression points: the same locations as observations are used.
## Distance metric: Euclidean distance metric is used.
##
## ****************Summary of GWR coefficient estimates:******************
## Min. 1st Qu. Median 3rd Qu. Max.
## Intercept 53.22830962 73.31782964 81.66277747 95.06908086 116.7660
## DiffAdd -0.72807886 -0.33380997 -0.15837712 0.15858002 0.5465
## LARent -0.19491170 -0.12060836 -0.08443575 -0.03691619 0.0940
## SC1 -0.15781787 0.03528402 0.30881760 0.42006638 0.8796
## Unempl -2.31794903 -1.14350301 -0.76487866 -0.47532711 -0.0925
## LowEduc -7.67491216 -0.73694598 0.53323357 1.80977357 3.4140
## Age18_24 -0.39700178 -0.25290301 -0.14571296 0.00076421 0.3669
## Age25_44 -1.09503913 -0.72092749 -0.45360107 -0.30484438 0.2184
## Age45_64 -0.92361942 -0.40984558 -0.11024847 0.04679070 0.4931
## ************************Diagnostic information*************************
## Number of data points: 322
## Effective number of parameters (2trace(S) - trace(S'S)): 79.90559
## Effective degrees of freedom (n-2trace(S) + trace(S'S)): 242.0944
## AICc (GWR book, Fotheringham, et al. 2002, p. 61, eq 2.33): 1921.287
## AIC (GWR book, Fotheringham, et al. 2002,GWR p. 96, eq. 4.22): 1826.147
## BIC (GWR book, Fotheringham, et al. 2002,GWR p. 61, eq. 2.34): 1799.906
## Residual sum of squares: 4516.821
## R-square value: 0.8144397
## Adjusted R-square value: 0.7529397
##
## ***********************************************************************
## Program stops at: 2021-05-24 23:40:03Almacenamos los resultados en SDF
names(gwr.res$SDF)## [1] "Intercept" "DiffAdd" "LARent" "SC1"
## [5] "Unempl" "LowEduc" "Age18_24" "Age25_44"
## [9] "Age45_64" "y" "yhat" "residual"
## [13] "CV_Score" "Stud_residual" "Intercept_SE" "DiffAdd_SE"
## [17] "LARent_SE" "SC1_SE" "Unempl_SE" "LowEduc_SE"
## [21] "Age18_24_SE" "Age25_44_SE" "Age45_64_SE" "Intercept_TV"
## [25] "DiffAdd_TV" "LARent_TV" "SC1_TV" "Unempl_TV"
## [29] "LowEduc_TV" "Age18_24_TV" "Age25_44_TV" "Age45_64_TV"
## [33] "Local_R2"y= valores observados (variable respuesta) yhat= valores ajustados TV= valor T SE = Desviaciones estandar
Visualizar distribución espacial de los coeficientes estimados de nivel educativo LowEduc
Regresion Geograficamente ponderada para coeficientes estimados de LowEduc
library(RColorBrewer)
mypalette.6 = brewer.pal(6, "Spectral")
spplot(gwr.res$SDF, "LowEduc", key.space = "right",col.regions = mypalette.6, at = c(-8, -6, -4, -2, 0, 2, 4),main = "Fig 1. Basic GW regression coefficient estimates for LowEduc")
Usamos la función gwss para encontrar Resumen de estadísticas de GW
gwss.1 <- gwss(Dub.voter,vars = c("GenEl2004", "LARent", "Unempl"),
kernel="bisquare", adaptive=TRUE, bw=109)
gwss.1## ***********************************************************************
## * Package GWmodel *
## ***********************************************************************
##
## ***********************Calibration information*************************
##
## Local summary statistics calculated for variables:
## GenEl2004 LARent Unempl
## Number of summary points: 322
## Kernel function: bisquare
## Summary points: the same locations as observations are used.
## Adaptive bandwidth: 109 (number of nearest neighbours)
## Distance metric: Euclidean distance metric is used.
##
## ************************Local Summary Statistics:**********************
## Summary information for Local means:
## Min. 1st Qu. Median 3rd Qu. Max.
## GenEl2004_LM 48.0465 52.8993 55.6610 57.9533 60.133
## LARent_LM 3.8073 7.6901 14.6109 21.8222 41.609
## Unempl_LM 3.7330 5.7940 8.2143 9.9256 11.465
## Summary information for local standard deviation :
## Min. 1st Qu. Median 3rd Qu. Max.
## GenEl2004_LSD 5.6304 7.1277 8.4525 9.4498 10.3377
## LARent_LSD 5.6866 13.8070 21.7104 30.2556 39.6789
## Unempl_LSD 1.7342 3.7683 5.2570 5.8944 6.7135
## Summary information for local variance :
## Min. 1st Qu. Median 3rd Qu. Max.
## GenEl2004_LVar 31.7014 50.8037 71.4451 89.2983 106.868
## LARent_LVar 32.3369 190.6384 471.3494 915.4378 1574.419
## Unempl_LVar 3.0074 14.2002 27.6356 34.7441 45.071
## Summary information for Local skewness:
## Min. 1st Qu. Median 3rd Qu. Max.
## GenEl2004_LSKe -2.16509 -0.69905 -0.44139 -0.15569 0.2031
## LARent_LSKe 0.48491 1.58241 2.01598 2.75989 5.7044
## Unempl_LSKe 0.48347 0.98123 1.32551 1.66473 3.6263
## Summary information for localized coefficient of variation:
## Min. 1st Qu. Median 3rd Qu. Max.
## GenEl2004_LCV 0.095026 0.121070 0.156576 0.178153 0.1986
## LARent_LCV 0.928542 1.233598 1.451255 1.708614 2.1704
## Unempl_LCV 0.416655 0.528853 0.609990 0.674724 0.7898
## Summary information for localized Covariance and Correlation between these variables:
## Min. 1st Qu. Median 3rd Qu.
## Cov_GenEl2004.LARent -332.20189 -205.52369 -141.21649 -58.94581
## Cov_GenEl2004.Unempl -50.56695 -38.55129 -27.57258 -16.29333
## Cov_LARent.Unempl 7.07572 40.75327 83.44055 112.54369
## Corr_GenEl2004.LARent -0.83758 -0.72930 -0.67662 -0.57214
## Corr_GenEl2004.Unempl -0.82693 -0.74806 -0.66825 -0.51930
## Corr_LARent.Unempl 0.37129 0.60127 0.70415 0.80542
## Spearman_rho_GenEl2004.LARent -0.86376 -0.78029 -0.73658 -0.63584
## Spearman_rho_GenEl2004.Unempl -0.81838 -0.76872 -0.68907 -0.55103
## Spearman_rho_LARent.Unempl 0.46822 0.71962 0.74490 0.77747
## Max.
## Cov_GenEl2004.LARent -16.5532
## Cov_GenEl2004.Unempl -5.3444
## Cov_LARent.Unempl 153.8304
## Corr_GenEl2004.LARent -0.3647
## Corr_GenEl2004.Unempl -0.3371
## Corr_LARent.Unempl 0.8987
## Spearman_rho_GenEl2004.LARent -0.4041
## Spearman_rho_GenEl2004.Unempl -0.3783
## Spearman_rho_LARent.Unempl 0.8514
##
## ************************************************************************También se realiza la pruebas de Monte Carlo correspondientes para las dos especificaciones de correlación GW.
Prueba Montecarlo La hipótesis de la aleatorización es que cualquier patrón visto en los datos ocurre por casualidad y, por lo tanto, cualquier permutación de los datos es igualmente probable.
Como ejemplo de correlación GW, la prueba procede de la siguiente manera: (i) calcule el verdadero Correlación GW en todas las ubicaciones.
elegir aleatoriamente una permutación de los datos donde el las coordenadas se mantienen en los mismos pares, al igual que los pares de atributos elegidos.
calcular un correlación GW simulada en todas las ubicaciones utilizando los datos aleatorios de (ii);
repita los pasos (ii) y (iii), digamos 99 veces;
en cada ubicación i, clasifique la única correlación GW verdadera con la 99 correlaciones GW simuladas;
en cada ubicación i, si la verdadera correlación GW se encuentra en el 2.5% de la cola superior o inferior de la distribución clasificada, entonces se puede decir que la verdadera correlación GW ser significativamente diferente (al nivel del 95%) a dicha correlación GW encontrada por casualidad.
Se mapean los resultados de esta prueba de Monte Carlo. Para un GW PCA, se sigue un procedimiento similar donde la prueba evalúa si los valores propios locales varían significativamente en el espacio. Aquí las coordenadas emparejadas son sucesivamente aleatorizados entre el conjunto de datos variables y después de cada aleatorización, GW Se aplica PCA (con un ancho de banda reestimado de manera óptima) y la desviación estándar (SD) de un valor propio local dado se calcula. La verdadera SD del mismo valor propio local es entonces incluido en una distribución ordenada de SD. Su posición en esta distribución clasificada se relaciona con, si existe una variación (espacial) significativa en el valor propio local elegido.
#GWSS Monte Carlo test (GWSS:Estadísticas resumidas ponderadas geográficamente)
gwss.mc <- montecarlo.gwss(Dub.voter,vars = c("GenEl2004", "LARent",
"Unempl"), kernel="bisquare", adaptive=TRUE, bw=109)
gwss.mc## GenEl2004_LM LARent_LM Unempl_LM GenEl2004_LSD LARent_LSD Unempl_LSD
## 0 0.88 0.04 0.02 0.07 0.07 0.24
## 1 0.91 0.06 0.02 0.18 0.05 0.34
## 2 0.95 0.01 0.01 0.31 0.08 0.37
## 3 0.97 0.05 0.00 0.30 0.08 0.48
## 4 0.97 0.04 0.01 0.29 0.10 0.50
## 5 0.56 0.36 0.17 0.21 0.42 0.53
## 6 0.81 0.16 0.07 0.17 0.24 0.53
## 7 0.31 0.65 0.58 0.55 0.57 0.26
## 8 0.74 0.15 0.26 0.00 0.05 0.09
## 9 0.83 0.04 0.12 0.00 0.02 0.13
## 10 0.62 0.20 0.15 0.04 0.18 0.05
## 11 0.56 0.25 0.21 0.08 0.24 0.06
## 12 0.49 0.21 0.24 0.18 0.18 0.07
## 13 0.58 0.22 0.17 0.06 0.22 0.10
## 14 0.63 0.31 0.22 0.03 0.27 0.02
## 15 0.56 0.37 0.26 0.09 0.33 0.00
## 16 0.53 0.34 0.31 0.24 0.23 0.01
## 17 0.50 0.38 0.23 0.06 0.34 0.01
## 18 0.60 0.27 0.17 0.09 0.27 0.01
## 19 0.47 0.33 0.20 0.11 0.33 0.04
## 20 0.50 0.45 0.30 0.18 0.40 0.05
## 21 0.47 0.45 0.40 0.27 0.34 0.02
## 22 0.38 0.52 0.47 0.38 0.44 0.14
## 23 0.35 0.50 0.50 0.41 0.29 0.13
## 24 0.42 0.41 0.42 0.28 0.36 0.10
## 25 0.40 0.47 0.44 0.31 0.33 0.08
## 26 0.67 0.15 0.17 0.03 0.16 0.04
## 27 0.64 0.12 0.26 0.00 0.08 0.05
## 28 0.73 0.08 0.10 0.00 0.06 0.10
## 29 0.70 0.15 0.15 0.02 0.13 0.19
## 30 0.78 0.14 0.08 0.03 0.14 0.36
## 31 0.88 0.07 0.02 0.08 0.10 0.42
## 32 0.70 0.19 0.10 0.04 0.22 0.33
## 33 0.71 0.15 0.10 0.02 0.17 0.34
## 34 0.77 0.15 0.04 0.05 0.20 0.42
## 35 0.85 0.18 0.04 0.24 0.19 0.65
## 36 0.24 0.53 0.51 0.61 0.45 0.42
## 37 0.29 0.48 0.49 0.53 0.36 0.35
## 38 0.35 0.44 0.54 0.40 0.30 0.39
## 39 0.27 0.44 0.57 0.45 0.33 0.37
## 40 0.29 0.46 0.44 0.40 0.39 0.23
## 41 0.40 0.43 0.38 0.40 0.30 0.23
## 42 0.30 0.43 0.52 0.40 0.32 0.27
## 43 0.32 0.31 0.43 0.25 0.24 0.16
## 44 0.49 0.24 0.44 0.10 0.19 0.11
## 45 0.51 0.22 0.34 0.17 0.19 0.11
## 46 0.58 0.13 0.32 0.06 0.11 0.12
## 47 0.69 0.10 0.23 0.03 0.08 0.15
## 48 0.50 0.17 0.31 0.09 0.15 0.07
## 49 0.35 0.59 0.56 0.51 0.56 0.34
## 50 0.48 0.40 0.21 0.04 0.42 0.09
## 51 0.41 0.40 0.21 0.10 0.42 0.07
## 52 0.55 0.33 0.18 0.05 0.32 0.06
## 53 0.61 0.25 0.15 0.06 0.28 0.26
## 54 0.65 0.19 0.10 0.03 0.19 0.17
## 55 0.57 0.28 0.16 0.05 0.31 0.18
## 56 0.64 0.26 0.18 0.10 0.35 0.31
## 57 0.34 0.37 0.44 0.27 0.29 0.07
## 58 0.32 0.60 0.50 0.51 0.55 0.36
## 59 0.37 0.59 0.51 0.54 0.57 0.31
## 60 0.29 0.60 0.58 0.58 0.53 0.44
## 61 0.20 0.70 0.63 0.70 0.61 0.48
## 62 0.28 0.62 0.57 0.63 0.57 0.39
## 63 0.35 0.51 0.50 0.46 0.42 0.22
## 64 0.38 0.51 0.49 0.48 0.42 0.29
## 65 0.89 0.08 0.07 0.02 0.05 0.18
## 66 0.89 0.04 0.05 0.15 0.06 0.31
## 67 0.97 0.00 0.06 0.37 0.06 0.41
## 68 0.50 0.44 0.34 0.22 0.38 0.03
## 69 0.41 0.56 0.44 0.29 0.45 0.10
## 70 0.52 0.50 0.38 0.32 0.43 0.11
## 71 0.41 0.47 0.44 0.34 0.40 0.12
## 72 0.79 0.12 0.24 0.12 0.00 0.16
## 73 0.88 0.09 0.11 0.08 0.02 0.15
## 74 0.94 0.00 0.04 0.29 0.01 0.22
## 75 0.68 0.14 0.27 0.05 0.03 0.14
## 76 0.57 0.20 0.49 0.23 0.13 0.25
## 77 0.88 0.05 0.22 0.17 0.00 0.14
## 78 0.85 0.16 0.03 0.21 0.23 0.50
## 79 0.46 0.50 0.40 0.27 0.43 0.12
## 80 0.43 0.57 0.46 0.35 0.47 0.15
## 81 0.44 0.57 0.50 0.42 0.57 0.31
## 82 0.40 0.57 0.49 0.41 0.51 0.19
## 83 0.43 0.57 0.48 0.34 0.44 0.08
## 84 0.36 0.57 0.52 0.60 0.51 0.39
## 85 0.28 0.61 0.60 0.64 0.55 0.48
## 86 0.28 0.61 0.55 0.63 0.56 0.46
## 87 0.94 0.00 0.04 0.13 0.00 0.16
## 88 0.88 0.05 0.10 0.19 0.04 0.24
## 89 0.41 0.27 0.43 0.23 0.21 0.06
## 90 0.50 0.29 0.25 0.12 0.23 0.04
## 91 0.46 0.34 0.24 0.18 0.30 0.01
## 92 0.44 0.35 0.32 0.19 0.24 0.03
## 93 0.86 0.23 0.06 0.23 0.30 0.48
## 94 0.77 0.43 0.24 0.19 0.55 0.17
## 95 0.80 0.29 0.16 0.23 0.45 0.29
## 96 0.73 0.41 0.23 0.25 0.58 0.28
## 97 0.89 0.15 0.15 0.34 0.21 0.52
## 98 0.89 0.15 0.12 0.41 0.19 0.56
## 99 0.92 0.19 0.31 0.45 0.20 0.55
## 100 0.75 0.26 0.41 0.48 0.39 0.63
## 101 0.87 0.18 0.26 0.43 0.18 0.55
## 102 0.70 0.35 0.38 0.46 0.44 0.66
## 103 0.86 0.26 0.08 0.32 0.43 0.51
## 104 0.85 0.26 0.11 0.14 0.51 0.31
## 105 0.82 0.26 0.14 0.40 0.36 0.59
## 106 0.74 0.25 0.18 0.28 0.35 0.49
## 107 0.85 0.20 0.03 0.31 0.32 0.64
## 108 0.93 0.12 0.04 0.38 0.17 0.59
## 109 0.90 0.15 0.06 0.45 0.20 0.63
## 110 0.61 0.38 0.43 0.50 0.41 0.65
## 111 0.66 0.34 0.45 0.47 0.32 0.71
## 112 0.89 0.14 0.35 0.48 0.13 0.53
## 113 0.53 0.50 0.65 0.46 0.45 0.73
## 114 0.52 0.52 0.51 0.54 0.48 0.63
## 115 0.79 0.36 0.17 0.27 0.47 0.38
## 116 0.91 0.07 0.24 0.34 0.02 0.20
## 117 0.93 0.06 0.22 0.41 0.03 0.17
## 118 0.99 0.01 0.01 0.52 0.10 0.37
## 119 0.99 0.02 0.05 0.68 0.06 0.48
## 120 0.97 0.05 0.07 0.60 0.09 0.44
## 121 0.98 0.06 0.19 0.68 0.06 0.45
## 122 0.95 0.12 0.30 0.67 0.12 0.48
## 123 0.94 0.07 0.11 0.50 0.09 0.43
## 124 0.58 0.24 0.63 0.30 0.19 0.34
## 125 0.76 0.11 0.60 0.40 0.08 0.39
## 126 0.44 0.39 0.79 0.72 0.28 0.74
## 127 0.33 0.62 0.86 0.72 0.48 0.80
## 128 0.64 0.18 0.70 0.53 0.14 0.56
## 129 0.85 0.13 0.48 0.41 0.04 0.26
## 130 0.82 0.18 0.71 0.55 0.14 0.51
## 131 0.90 0.10 0.46 0.48 0.03 0.35
## 132 0.47 0.66 0.82 0.70 0.57 0.81
## 133 0.84 0.16 0.60 0.60 0.10 0.50
## 134 0.57 0.44 0.74 0.60 0.30 0.68
## 135 0.65 0.47 0.74 0.66 0.32 0.74
## 136 0.81 0.22 0.63 0.50 0.15 0.49
## 137 0.94 0.16 0.44 0.59 0.14 0.47
## 138 0.84 0.21 0.50 0.57 0.18 0.52
## 139 0.70 0.27 0.80 0.67 0.22 0.77
## 140 0.63 0.37 0.73 0.54 0.29 0.72
## 141 0.63 0.48 0.71 0.57 0.35 0.71
## 142 0.81 0.24 0.60 0.49 0.17 0.58
## 143 0.99 0.02 0.13 0.47 0.04 0.25
## 144 0.98 0.03 0.16 0.55 0.03 0.26
## 145 0.93 0.11 0.26 0.57 0.07 0.31
## 146 0.90 0.06 0.30 0.34 0.01 0.17
## 147 0.73 0.32 0.56 0.43 0.24 0.57
## 148 0.70 0.40 0.57 0.38 0.35 0.57
## 149 0.39 0.69 0.70 0.67 0.63 0.69
## 150 0.31 0.68 0.69 0.68 0.66 0.71
## 151 0.91 0.10 0.02 0.41 0.23 0.64
## 152 0.99 0.02 0.02 0.39 0.08 0.42
## 153 0.99 0.05 0.03 0.67 0.11 0.59
## 154 0.91 0.10 0.14 0.61 0.12 0.63
## 155 0.88 0.19 0.12 0.50 0.22 0.57
## 156 0.88 0.21 0.11 0.58 0.27 0.58
## 157 0.59 0.50 0.42 0.41 0.53 0.56
## 158 0.70 0.41 0.34 0.46 0.43 0.57
## 159 0.59 0.41 0.35 0.43 0.41 0.60
## 160 0.99 0.01 0.06 0.53 0.01 0.34
## 161 0.95 0.10 0.38 0.69 0.09 0.46
## 162 0.58 0.68 0.62 0.55 0.69 0.60
## 163 0.04 0.95 0.82 0.86 0.95 0.82
## 164 0.34 0.86 0.78 0.67 0.84 0.79
## 165 0.70 0.41 0.28 0.29 0.57 0.45
## 166 0.83 0.49 0.34 0.28 0.62 0.22
## 167 0.76 0.42 0.31 0.23 0.58 0.23
## 168 0.83 0.34 0.21 0.26 0.52 0.41
## 169 0.90 0.34 0.21 0.24 0.55 0.22
## 170 0.81 0.45 0.37 0.23 0.52 0.13
## 171 0.77 0.43 0.29 0.26 0.55 0.27
## 172 0.03 0.98 0.85 0.78 0.99 0.79
## 173 0.22 0.93 0.75 0.73 0.82 0.75
## 174 0.25 0.89 0.73 0.74 0.76 0.74
## 175 0.25 0.82 0.73 0.71 0.81 0.74
## 176 0.83 0.40 0.29 0.20 0.59 0.16
## 177 0.80 0.45 0.35 0.23 0.56 0.25
## 178 0.77 0.41 0.40 0.28 0.52 0.29
## 179 0.74 0.52 0.42 0.33 0.59 0.29
## 180 0.85 0.24 0.10 0.09 0.40 0.19
## 181 0.74 0.31 0.16 0.10 0.46 0.15
## 182 0.54 0.67 0.59 0.52 0.73 0.52
## 183 0.22 0.94 0.73 0.74 0.78 0.73
## 184 0.14 0.90 0.73 0.77 0.81 0.74
## 185 0.05 0.96 0.87 0.76 0.95 0.76
## 186 0.11 0.91 0.79 0.72 0.89 0.74
## 187 0.16 0.84 0.82 0.79 0.81 0.82
## 188 0.72 0.55 0.49 0.45 0.63 0.45
## 189 0.41 0.68 0.60 0.62 0.75 0.61
## 190 0.70 0.52 0.41 0.37 0.64 0.41
## 191 0.76 0.50 0.35 0.39 0.68 0.45
## 192 0.39 0.70 0.66 0.68 0.74 0.65
## 193 0.73 0.58 0.47 0.46 0.68 0.51
## 194 0.57 0.65 0.57 0.56 0.76 0.58
## 195 0.63 0.54 0.41 0.40 0.63 0.47
## 196 0.50 0.64 0.62 0.56 0.73 0.64
## 197 0.60 0.56 0.51 0.51 0.62 0.52
## 198 0.48 0.64 0.60 0.61 0.67 0.60
## 199 0.54 0.61 0.51 0.50 0.65 0.45
## 200 0.56 0.59 0.47 0.42 0.68 0.51
## 201 0.38 0.73 0.67 0.70 0.75 0.63
## 202 0.24 0.86 0.72 0.71 0.77 0.74
## 203 0.43 0.61 0.61 0.59 0.62 0.62
## 204 0.42 0.67 0.63 0.66 0.69 0.63
## 205 0.25 0.77 0.69 0.75 0.76 0.73
## 206 0.19 0.81 0.75 0.79 0.80 0.77
## 207 0.31 0.75 0.66 0.66 0.70 0.67
## 208 0.50 0.49 0.48 0.54 0.52 0.55
## 209 0.46 0.59 0.60 0.64 0.58 0.67
## 210 0.57 0.51 0.45 0.55 0.57 0.52
## 211 0.62 0.45 0.33 0.16 0.39 0.00
## 212 0.28 0.74 0.75 0.86 0.77 0.76
## 213 0.18 0.82 0.80 0.91 0.86 0.81
## 214 0.26 0.63 0.57 0.65 0.58 0.54
## 215 0.51 0.52 0.43 0.30 0.46 0.17
## 216 0.45 0.64 0.63 0.67 0.63 0.56
## 217 0.29 0.72 0.71 0.86 0.77 0.72
## 218 0.65 0.35 0.15 0.11 0.48 0.35
## 219 0.76 0.46 0.32 0.23 0.62 0.34
## 220 0.63 0.45 0.36 0.17 0.58 0.30
## 221 0.68 0.37 0.25 0.16 0.41 0.28
## 222 0.75 0.40 0.33 0.22 0.56 0.36
## 223 0.67 0.46 0.28 0.18 0.56 0.33
## 224 0.70 0.42 0.26 0.16 0.51 0.27
## 225 0.68 0.45 0.33 0.27 0.46 0.36
## 226 0.76 0.30 0.22 0.08 0.47 0.25
## 227 0.70 0.27 0.13 0.05 0.43 0.40
## 228 0.46 0.65 0.69 0.71 0.68 0.56
## 229 0.42 0.67 0.62 0.63 0.67 0.56
## 230 0.68 0.44 0.19 0.07 0.39 0.07
## 231 0.42 0.67 0.66 0.71 0.66 0.67
## 232 0.37 0.68 0.68 0.80 0.69 0.69
## 233 0.23 0.79 0.77 0.86 0.80 0.77
## 234 0.18 0.71 0.65 0.73 0.71 0.66
## 235 0.68 0.58 0.46 0.34 0.56 0.24
## 236 0.56 0.55 0.48 0.38 0.48 0.31
## 237 0.73 0.46 0.42 0.20 0.57 0.27
## 238 0.35 0.69 0.67 0.77 0.71 0.69
## 239 0.50 0.59 0.57 0.54 0.55 0.44
## 240 0.36 0.77 0.71 0.59 0.68 0.41
## 241 0.35 0.66 0.59 0.62 0.63 0.47
## 242 0.35 0.64 0.59 0.59 0.61 0.48
## 243 0.31 0.69 0.69 0.71 0.68 0.68
## 244 0.28 0.72 0.66 0.77 0.74 0.69
## 245 0.21 0.69 0.64 0.72 0.65 0.52
## 246 0.59 0.58 0.46 0.32 0.50 0.11
## 247 0.59 0.63 0.51 0.32 0.57 0.20
## 248 0.36 0.72 0.68 0.71 0.66 0.46
## 249 0.46 0.58 0.54 0.40 0.51 0.27
## 250 0.46 0.69 0.58 0.39 0.58 0.18
## 251 0.70 0.35 0.15 0.14 0.36 0.23
## 252 0.56 0.45 0.26 0.18 0.42 0.03
## 253 0.02 0.97 0.95 0.83 0.97 0.95
## 254 0.04 0.95 0.93 0.80 0.95 0.93
## 255 0.03 0.96 0.90 0.72 0.96 0.92
## 256 0.04 0.97 0.97 0.81 0.97 0.97
## 257 0.03 0.98 0.94 0.78 0.96 0.93
## 258 0.00 0.99 0.98 0.83 0.99 0.98
## 259 0.13 0.84 0.87 0.91 0.90 0.89
## 260 0.26 0.75 0.93 0.80 0.70 0.88
## 261 0.11 0.88 0.87 0.99 0.87 0.91
## 262 0.16 0.87 0.92 0.89 0.78 0.93
## 263 0.15 0.79 0.96 0.86 0.75 0.92
## 264 0.13 0.87 0.87 0.94 0.83 0.85
## 265 0.13 0.83 0.85 0.97 0.85 0.86
## 266 0.15 0.84 0.84 0.89 0.80 0.83
## 267 0.10 0.88 0.90 0.96 0.86 0.84
## 268 0.13 0.86 0.85 0.87 0.76 0.82
## 269 0.10 0.90 0.95 0.90 0.83 0.95
## 270 0.12 0.87 0.86 0.88 0.89 0.87
## 271 0.11 0.84 0.80 0.90 0.93 0.88
## 272 0.07 0.89 0.88 0.85 0.95 0.95
## 273 0.08 0.85 0.87 0.90 0.92 0.90
## 274 0.17 0.90 0.93 0.76 0.82 0.85
## 275 0.26 0.82 0.81 0.73 0.71 0.77
## 276 0.06 0.95 0.89 0.79 0.94 0.84
## 277 0.24 0.74 0.88 0.72 0.68 0.79
## 278 0.22 0.89 0.90 0.75 0.75 0.77
## 279 0.21 0.77 0.96 0.85 0.70 0.86
## 280 0.32 0.74 0.82 0.77 0.63 0.79
## 281 0.36 0.63 0.81 0.70 0.48 0.80
## 282 0.03 0.97 0.98 0.87 0.86 0.98
## 283 0.21 0.86 0.92 0.78 0.70 0.80
## 284 0.17 0.81 0.80 0.95 0.89 0.85
## 285 0.18 0.78 0.78 0.99 0.87 0.82
## 286 0.19 0.78 0.79 0.99 0.83 0.84
## 287 0.21 0.73 0.73 0.99 0.85 0.83
## 288 0.22 0.72 0.73 0.99 0.80 0.80
## 289 0.03 0.97 0.96 0.89 0.97 0.97
## 290 0.01 0.96 0.99 0.80 0.94 0.99
## 291 0.01 0.99 0.99 0.82 0.99 0.99
## 292 0.08 0.92 0.94 0.82 0.89 0.91
## 293 0.10 0.96 0.96 0.81 0.84 0.96
## 294 0.16 0.79 0.76 0.91 0.82 0.79
## 295 0.25 0.74 0.75 0.97 0.78 0.81
## 296 0.11 0.89 0.89 0.99 0.88 0.86
## 297 0.12 0.85 0.89 0.94 0.83 0.84
## 298 0.15 0.80 0.85 0.95 0.81 0.84
## 299 0.17 0.78 0.80 0.99 0.86 0.85
## 300 0.14 0.83 0.84 0.92 0.90 0.90
## 301 0.17 0.82 0.78 0.92 0.83 0.80
## 302 0.19 0.80 0.77 0.98 0.84 0.81
## 303 0.17 0.78 0.83 0.90 0.83 0.81
## 304 0.18 0.81 0.80 0.89 0.84 0.81
## 305 0.06 0.89 0.92 0.89 0.95 0.94
## 306 0.09 0.84 0.85 0.87 0.93 0.93
## 307 0.06 0.89 0.91 0.89 0.92 0.92
## 308 0.08 0.86 0.91 0.89 0.93 0.93
## 309 0.05 0.94 0.94 0.79 0.95 0.96
## 310 0.10 0.86 0.85 0.94 0.95 0.89
## 311 0.14 0.83 0.81 0.91 0.86 0.87
## 312 0.12 0.86 0.83 0.85 0.89 0.91
## 313 0.17 0.79 0.76 0.83 0.92 0.87
## 314 0.16 0.76 0.75 0.88 0.92 0.91
## 315 0.07 0.92 0.96 0.90 0.90 0.95
## 316 0.03 0.93 0.98 0.87 0.94 0.96
## 317 0.05 0.86 0.88 0.89 0.94 0.88
## 318 0.04 0.93 0.94 0.94 0.95 0.95
## 319 0.09 0.91 0.96 0.78 0.78 0.95
## 320 0.04 0.92 0.95 0.95 0.92 0.94
## 321 0.02 0.98 0.94 0.75 0.98 0.90
## GenEl2004_LVar LARent_LVar Unempl_LVar GenEl2004_LSKe LARent_LSKe
## 0 0.07 0.07 0.24 0.08 0.95
## 1 0.18 0.05 0.34 0.03 0.94
## 2 0.31 0.08 0.37 0.01 0.96
## 3 0.30 0.08 0.48 0.05 0.92
## 4 0.29 0.10 0.50 0.04 0.93
## 5 0.21 0.42 0.53 0.76 0.52
## 6 0.17 0.24 0.53 0.45 0.73
## 7 0.55 0.57 0.26 0.91 0.25
## 8 0.00 0.05 0.09 0.35 0.89
## 9 0.00 0.02 0.13 0.18 0.96
## 10 0.04 0.18 0.05 0.76 0.77
## 11 0.08 0.24 0.06 0.82 0.67
## 12 0.18 0.18 0.07 0.70 0.72
## 13 0.06 0.22 0.10 0.69 0.76
## 14 0.03 0.27 0.02 0.85 0.58
## 15 0.09 0.33 0.00 0.91 0.47
## 16 0.24 0.23 0.01 0.79 0.62
## 17 0.06 0.34 0.01 0.84 0.56
## 18 0.09 0.27 0.01 0.83 0.65
## 19 0.11 0.33 0.04 0.81 0.53
## 20 0.18 0.40 0.05 0.84 0.45
## 21 0.27 0.34 0.02 0.87 0.40
## 22 0.38 0.44 0.14 0.84 0.38
## 23 0.41 0.29 0.13 0.77 0.48
## 24 0.28 0.36 0.10 0.78 0.52
## 25 0.31 0.33 0.08 0.80 0.50
## 26 0.03 0.16 0.04 0.63 0.83
## 27 0.00 0.08 0.05 0.39 0.88
## 28 0.00 0.06 0.10 0.31 0.93
## 29 0.02 0.13 0.19 0.46 0.83
## 30 0.03 0.14 0.36 0.35 0.85
## 31 0.08 0.10 0.42 0.12 0.92
## 32 0.04 0.22 0.33 0.63 0.79
## 33 0.02 0.17 0.34 0.42 0.84
## 34 0.05 0.20 0.42 0.42 0.82
## 35 0.24 0.19 0.65 0.27 0.82
## 36 0.61 0.45 0.42 0.71 0.33
## 37 0.53 0.36 0.35 0.81 0.38
## 38 0.40 0.30 0.39 0.84 0.51
## 39 0.45 0.33 0.37 0.82 0.54
## 40 0.40 0.39 0.23 0.78 0.47
## 41 0.40 0.30 0.23 0.77 0.44
## 42 0.40 0.32 0.27 0.79 0.49
## 43 0.25 0.24 0.16 0.79 0.56
## 44 0.10 0.19 0.11 0.52 0.79
## 45 0.17 0.19 0.11 0.68 0.78
## 46 0.06 0.11 0.12 0.62 0.88
## 47 0.03 0.08 0.15 0.35 0.93
## 48 0.09 0.15 0.07 0.67 0.83
## 49 0.51 0.56 0.34 0.83 0.26
## 50 0.04 0.42 0.09 0.86 0.42
## 51 0.10 0.42 0.07 0.90 0.34
## 52 0.05 0.32 0.06 0.89 0.56
## 53 0.06 0.28 0.26 0.76 0.56
## 54 0.03 0.19 0.17 0.62 0.79
## 55 0.05 0.31 0.18 0.71 0.57
## 56 0.10 0.35 0.31 0.74 0.51
## 57 0.27 0.29 0.07 0.78 0.62
## 58 0.51 0.55 0.36 0.86 0.22
## 59 0.54 0.57 0.31 0.91 0.21
## 60 0.58 0.53 0.44 0.88 0.22
## 61 0.70 0.61 0.48 0.85 0.21
## 62 0.63 0.57 0.39 0.80 0.19
## 63 0.46 0.42 0.22 0.79 0.35
## 64 0.48 0.42 0.29 0.69 0.38
## 65 0.02 0.05 0.18 0.11 0.94
## 66 0.15 0.06 0.31 0.05 0.98
## 67 0.37 0.06 0.41 0.00 0.98
## 68 0.22 0.38 0.03 0.83 0.42
## 69 0.29 0.45 0.10 0.82 0.35
## 70 0.32 0.43 0.11 0.83 0.35
## 71 0.34 0.40 0.12 0.86 0.33
## 72 0.12 0.00 0.16 0.17 0.95
## 73 0.08 0.02 0.15 0.10 0.92
## 74 0.29 0.01 0.22 0.01 0.99
## 75 0.05 0.03 0.14 0.27 0.89
## 76 0.23 0.13 0.25 0.49 0.84
## 77 0.17 0.00 0.14 0.07 0.95
## 78 0.21 0.23 0.50 0.36 0.76
## 79 0.27 0.43 0.12 0.86 0.36
## 80 0.35 0.47 0.15 0.88 0.32
## 81 0.42 0.57 0.31 0.90 0.34
## 82 0.41 0.51 0.19 0.83 0.30
## 83 0.34 0.44 0.08 0.89 0.31
## 84 0.60 0.51 0.39 0.85 0.26
## 85 0.64 0.55 0.48 0.82 0.21
## 86 0.63 0.56 0.46 0.74 0.23
## 87 0.13 0.00 0.16 0.01 0.99
## 88 0.19 0.04 0.24 0.02 0.98
## 89 0.23 0.21 0.06 0.76 0.72
## 90 0.12 0.23 0.04 0.79 0.64
## 91 0.18 0.30 0.01 0.92 0.46
## 92 0.19 0.24 0.03 0.84 0.65
## 93 0.23 0.30 0.48 0.39 0.64
## 94 0.19 0.55 0.17 0.54 0.56
## 95 0.23 0.45 0.29 0.43 0.62
## 96 0.25 0.58 0.28 0.54 0.55
## 97 0.34 0.21 0.52 0.23 0.80
## 98 0.41 0.19 0.56 0.19 0.80
## 99 0.45 0.20 0.55 0.09 0.80
## 100 0.48 0.39 0.63 0.25 0.67
## 101 0.43 0.18 0.55 0.15 0.80
## 102 0.46 0.44 0.66 0.36 0.41
## 103 0.32 0.43 0.51 0.38 0.60
## 104 0.14 0.51 0.31 0.42 0.62
## 105 0.40 0.36 0.59 0.34 0.66
## 106 0.28 0.35 0.49 0.46 0.54
## 107 0.31 0.32 0.64 0.30 0.75
## 108 0.38 0.17 0.59 0.16 0.82
## 109 0.45 0.20 0.63 0.32 0.79
## 110 0.50 0.41 0.65 0.23 0.38
## 111 0.47 0.32 0.71 0.19 0.55
## 112 0.48 0.13 0.53 0.06 0.86
## 113 0.46 0.45 0.73 0.38 0.31
## 114 0.54 0.48 0.63 0.31 0.33
## 115 0.27 0.47 0.38 0.47 0.54
## 116 0.34 0.02 0.20 0.05 0.94
## 117 0.41 0.03 0.17 0.07 0.93
## 118 0.52 0.10 0.37 0.02 0.96
## 119 0.68 0.06 0.48 0.03 0.97
## 120 0.60 0.09 0.44 0.04 0.92
## 121 0.68 0.06 0.45 0.06 0.93
## 122 0.67 0.12 0.48 0.03 0.89
## 123 0.50 0.09 0.43 0.05 0.90
## 124 0.30 0.19 0.34 0.60 0.78
## 125 0.40 0.08 0.39 0.33 0.88
## 126 0.72 0.28 0.74 0.55 0.59
## 127 0.72 0.48 0.80 0.61 0.21
## 128 0.53 0.14 0.56 0.44 0.79
## 129 0.41 0.04 0.26 0.25 0.86
## 130 0.55 0.14 0.51 0.30 0.84
## 131 0.48 0.03 0.35 0.17 0.93
## 132 0.70 0.57 0.81 0.31 0.19
## 133 0.60 0.10 0.50 0.20 0.83
## 134 0.60 0.30 0.68 0.45 0.40
## 135 0.66 0.32 0.74 0.36 0.32
## 136 0.50 0.15 0.49 0.20 0.81
## 137 0.59 0.14 0.47 0.03 0.84
## 138 0.57 0.18 0.52 0.19 0.78
## 139 0.67 0.22 0.77 0.23 0.62
## 140 0.54 0.29 0.72 0.32 0.42
## 141 0.57 0.35 0.71 0.15 0.41
## 142 0.49 0.17 0.58 0.12 0.78
## 143 0.47 0.04 0.25 0.02 0.97
## 144 0.55 0.03 0.26 0.06 0.98
## 145 0.57 0.07 0.31 0.09 0.90
## 146 0.34 0.01 0.17 0.10 0.96
## 147 0.43 0.24 0.57 0.16 0.55
## 148 0.38 0.35 0.57 0.29 0.44
## 149 0.67 0.63 0.69 0.31 0.22
## 150 0.68 0.66 0.71 0.42 0.10
## 151 0.41 0.23 0.64 0.13 0.85
## 152 0.39 0.08 0.42 0.02 0.93
## 153 0.67 0.11 0.59 0.04 0.93
## 154 0.61 0.12 0.63 0.07 0.90
## 155 0.50 0.22 0.57 0.12 0.78
## 156 0.58 0.27 0.58 0.22 0.75
## 157 0.41 0.53 0.56 0.50 0.33
## 158 0.46 0.43 0.57 0.33 0.45
## 159 0.43 0.41 0.60 0.38 0.37
## 160 0.53 0.01 0.34 0.01 0.98
## 161 0.69 0.09 0.46 0.06 0.88
## 162 0.55 0.69 0.60 0.34 0.67
## 163 0.86 0.95 0.82 0.60 0.06
## 164 0.67 0.84 0.79 0.34 0.40
## 165 0.29 0.57 0.45 0.58 0.52
## 166 0.28 0.62 0.22 0.46 0.73
## 167 0.23 0.58 0.23 0.46 0.84
## 168 0.26 0.52 0.41 0.52 0.66
## 169 0.24 0.55 0.22 0.49 0.68
## 170 0.23 0.52 0.13 0.48 0.69
## 171 0.26 0.55 0.27 0.51 0.78
## 172 0.78 0.99 0.79 0.60 0.22
## 173 0.73 0.82 0.75 0.56 0.16
## 174 0.74 0.76 0.74 0.47 0.11
## 175 0.71 0.81 0.74 0.48 0.33
## 176 0.20 0.59 0.16 0.45 0.80
## 177 0.23 0.56 0.25 0.48 0.74
## 178 0.28 0.52 0.29 0.49 0.79
## 179 0.33 0.59 0.29 0.59 0.85
## 180 0.09 0.40 0.19 0.48 0.69
## 181 0.10 0.46 0.15 0.58 0.57
## 182 0.52 0.73 0.52 0.69 0.66
## 183 0.74 0.78 0.73 0.48 0.06
## 184 0.77 0.81 0.74 0.56 0.08
## 185 0.76 0.95 0.76 0.69 0.02
## 186 0.72 0.89 0.74 0.60 0.06
## 187 0.79 0.81 0.82 0.38 0.08
## 188 0.45 0.63 0.45 0.48 0.72
## 189 0.62 0.75 0.61 0.59 0.41
## 190 0.37 0.64 0.41 0.48 0.72
## 191 0.39 0.68 0.45 0.43 0.82
## 192 0.68 0.74 0.65 0.60 0.34
## 193 0.46 0.68 0.51 0.40 0.78
## 194 0.56 0.76 0.58 0.46 0.62
## 195 0.40 0.63 0.47 0.67 0.43
## 196 0.56 0.73 0.64 0.50 0.59
## 197 0.51 0.62 0.52 0.62 0.55
## 198 0.61 0.67 0.60 0.57 0.43
## 199 0.50 0.65 0.45 0.61 0.59
## 200 0.42 0.68 0.51 0.60 0.62
## 201 0.70 0.75 0.63 0.69 0.27
## 202 0.71 0.77 0.74 0.43 0.10
## 203 0.59 0.62 0.62 0.38 0.16
## 204 0.66 0.69 0.63 0.62 0.15
## 205 0.75 0.76 0.73 0.38 0.10
## 206 0.79 0.80 0.77 0.39 0.12
## 207 0.66 0.70 0.67 0.42 0.09
## 208 0.54 0.52 0.55 0.45 0.26
## 209 0.64 0.58 0.67 0.32 0.26
## 210 0.55 0.57 0.52 0.54 0.31
## 211 0.16 0.39 0.00 0.93 0.42
## 212 0.86 0.77 0.76 0.98 0.02
## 213 0.91 0.86 0.81 0.98 0.02
## 214 0.65 0.58 0.54 0.83 0.22
## 215 0.30 0.46 0.17 0.91 0.32
## 216 0.67 0.63 0.56 0.93 0.17
## 217 0.86 0.77 0.72 0.99 0.03
## 218 0.11 0.48 0.35 0.78 0.45
## 219 0.23 0.62 0.34 0.58 0.61
## 220 0.17 0.58 0.30 0.67 0.53
## 221 0.16 0.41 0.28 0.67 0.52
## 222 0.22 0.56 0.36 0.61 0.59
## 223 0.18 0.56 0.33 0.66 0.51
## 224 0.16 0.51 0.27 0.71 0.44
## 225 0.27 0.46 0.36 0.70 0.57
## 226 0.08 0.47 0.25 0.66 0.52
## 227 0.05 0.43 0.40 0.68 0.49
## 228 0.71 0.68 0.56 0.96 0.11
## 229 0.63 0.67 0.56 0.95 0.12
## 230 0.07 0.39 0.07 0.91 0.45
## 231 0.71 0.66 0.67 0.97 0.12
## 232 0.80 0.69 0.69 0.99 0.05
## 233 0.86 0.80 0.77 0.97 0.05
## 234 0.73 0.71 0.66 0.70 0.11
## 235 0.34 0.56 0.24 0.94 0.24
## 236 0.38 0.48 0.31 0.94 0.21
## 237 0.20 0.57 0.27 0.54 0.63
## 238 0.77 0.71 0.69 0.97 0.14
## 239 0.54 0.55 0.44 0.95 0.23
## 240 0.59 0.68 0.41 0.94 0.11
## 241 0.62 0.63 0.47 0.92 0.13
## 242 0.59 0.61 0.48 0.88 0.21
## 243 0.71 0.68 0.68 0.95 0.09
## 244 0.77 0.74 0.69 0.98 0.05
## 245 0.72 0.65 0.52 0.85 0.14
## 246 0.32 0.50 0.11 0.97 0.19
## 247 0.32 0.57 0.20 0.94 0.25
## 248 0.71 0.66 0.46 0.94 0.14
## 249 0.40 0.51 0.27 0.95 0.19
## 250 0.39 0.58 0.18 0.95 0.18
## 251 0.14 0.36 0.23 0.81 0.50
## 252 0.18 0.42 0.03 0.85 0.40
## 253 0.83 0.97 0.95 0.77 0.29
## 254 0.80 0.95 0.93 0.65 0.28
## 255 0.72 0.96 0.92 0.72 0.44
## 256 0.81 0.97 0.97 0.71 0.11
## 257 0.78 0.96 0.93 0.69 0.08
## 258 0.83 0.99 0.98 0.79 0.41
## 259 0.91 0.90 0.89 0.20 0.67
## 260 0.80 0.70 0.88 0.41 0.10
## 261 0.99 0.87 0.91 0.15 0.19
## 262 0.89 0.78 0.93 0.26 0.10
## 263 0.86 0.75 0.92 0.47 0.07
## 264 0.94 0.83 0.85 0.21 0.19
## 265 0.97 0.85 0.86 0.24 0.24
## 266 0.89 0.80 0.83 0.14 0.18
## 267 0.96 0.86 0.84 0.21 0.20
## 268 0.87 0.76 0.82 0.16 0.14
## 269 0.90 0.83 0.95 0.25 0.05
## 270 0.88 0.89 0.87 0.26 0.55
## 271 0.90 0.93 0.88 0.29 0.59
## 272 0.85 0.95 0.95 0.36 0.67
## 273 0.90 0.92 0.90 0.24 0.44
## 274 0.76 0.82 0.85 0.64 0.01
## 275 0.73 0.71 0.77 0.54 0.03
## 276 0.79 0.94 0.84 0.64 0.02
## 277 0.72 0.68 0.79 0.59 0.06
## 278 0.75 0.75 0.77 0.60 0.05
## 279 0.85 0.70 0.86 0.66 0.07
## 280 0.77 0.63 0.79 0.50 0.07
## 281 0.70 0.48 0.80 0.46 0.19
## 282 0.87 0.86 0.98 0.58 0.00
## 283 0.78 0.70 0.80 0.64 0.05
## 284 0.95 0.89 0.85 0.16 0.61
## 285 0.99 0.87 0.82 0.19 0.50
## 286 0.99 0.83 0.84 0.13 0.55
## 287 0.99 0.85 0.83 0.14 0.66
## 288 0.99 0.80 0.80 0.08 0.64
## 289 0.89 0.97 0.97 0.44 0.37
## 290 0.80 0.94 0.99 0.66 0.02
## 291 0.82 0.99 0.99 0.74 0.41
## 292 0.82 0.89 0.91 0.61 0.00
## 293 0.81 0.84 0.96 0.68 0.02
## 294 0.91 0.82 0.79 0.12 0.44
## 295 0.97 0.78 0.81 0.16 0.48
## 296 0.99 0.88 0.86 0.15 0.30
## 297 0.94 0.83 0.84 0.13 0.31
## 298 0.95 0.81 0.84 0.18 0.40
## 299 0.99 0.86 0.85 0.18 0.50
## 300 0.92 0.90 0.90 0.22 0.53
## 301 0.92 0.83 0.80 0.30 0.38
## 302 0.98 0.84 0.81 0.13 0.45
## 303 0.90 0.83 0.81 0.11 0.37
## 304 0.89 0.84 0.81 0.12 0.23
## 305 0.89 0.95 0.94 0.27 0.52
## 306 0.87 0.93 0.93 0.29 0.68
## 307 0.89 0.92 0.92 0.33 0.35
## 308 0.89 0.93 0.93 0.29 0.46
## 309 0.79 0.95 0.96 0.67 0.43
## 310 0.94 0.95 0.89 0.24 0.60
## 311 0.91 0.86 0.87 0.24 0.69
## 312 0.85 0.89 0.91 0.37 0.74
## 313 0.83 0.92 0.87 0.38 0.68
## 314 0.88 0.92 0.91 0.29 0.76
## 315 0.90 0.90 0.95 0.46 0.03
## 316 0.87 0.94 0.96 0.41 0.16
## 317 0.89 0.94 0.88 0.25 0.50
## 318 0.94 0.95 0.95 0.29 0.23
## 319 0.78 0.78 0.95 0.45 0.04
## 320 0.95 0.92 0.94 0.24 0.14
## 321 0.75 0.98 0.90 0.70 0.43
## Unempl_LSKe GenEl2004_LCV LARent_LCV Unempl_LCV Cov_GenEl2004.LARent
## 0 0.97 0.00 0.93 0.82 0.95
## 1 0.98 0.01 0.94 0.90 0.90
## 2 0.94 0.16 0.99 0.96 0.89
## 3 0.93 0.15 0.95 0.97 0.88
## 4 0.98 0.09 0.96 0.95 0.88
## 5 0.97 0.20 0.61 0.84 0.68
## 6 0.99 0.10 0.83 0.95 0.77
## 7 0.24 0.64 0.19 0.09 0.35
## 8 0.73 0.01 0.82 0.44 0.99
## 9 0.84 0.02 0.94 0.50 0.99
## 10 0.79 0.10 0.61 0.46 0.89
## 11 0.75 0.17 0.54 0.41 0.87
## 12 0.66 0.32 0.52 0.31 0.88
## 13 0.80 0.15 0.58 0.46 0.89
## 14 0.65 0.13 0.52 0.33 0.76
## 15 0.54 0.19 0.34 0.18 0.71
## 16 0.57 0.34 0.42 0.22 0.74
## 17 0.57 0.17 0.43 0.29 0.72
## 18 0.73 0.17 0.50 0.32 0.79
## 19 0.65 0.22 0.41 0.34 0.77
## 20 0.54 0.25 0.33 0.07 0.65
## 21 0.41 0.41 0.29 0.01 0.68
## 22 0.29 0.52 0.19 0.07 0.56
## 23 0.36 0.51 0.28 0.11 0.75
## 24 0.43 0.46 0.33 0.10 0.67
## 25 0.39 0.48 0.30 0.03 0.71
## 26 0.82 0.10 0.70 0.47 0.95
## 27 0.80 0.07 0.72 0.43 0.97
## 28 0.83 0.00 0.87 0.51 0.98
## 29 0.84 0.05 0.78 0.57 0.95
## 30 0.93 0.04 0.83 0.77 0.94
## 31 0.96 0.01 0.93 0.88 0.92
## 32 0.91 0.10 0.72 0.66 0.85
## 33 0.90 0.05 0.80 0.67 0.93
## 34 0.97 0.04 0.86 0.90 0.91
## 35 0.98 0.10 0.84 0.99 0.84
## 36 0.30 0.64 0.26 0.23 0.47
## 37 0.29 0.55 0.21 0.15 0.55
## 38 0.28 0.57 0.27 0.15 0.69
## 39 0.33 0.60 0.33 0.18 0.70
## 40 0.35 0.53 0.23 0.10 0.63
## 41 0.33 0.54 0.26 0.19 0.70
## 42 0.35 0.57 0.29 0.15 0.71
## 43 0.39 0.45 0.32 0.14 0.85
## 44 0.54 0.27 0.50 0.23 0.96
## 45 0.53 0.22 0.55 0.18 0.88
## 46 0.63 0.16 0.63 0.28 0.95
## 47 0.79 0.05 0.79 0.46 0.99
## 48 0.64 0.16 0.59 0.30 0.98
## 49 0.24 0.59 0.19 0.12 0.40
## 50 0.72 0.21 0.43 0.46 0.69
## 51 0.71 0.30 0.37 0.50 0.68
## 52 0.67 0.18 0.49 0.49 0.77
## 53 0.85 0.20 0.63 0.62 0.77
## 54 0.79 0.10 0.66 0.56 0.89
## 55 0.81 0.19 0.53 0.56 0.82
## 56 0.90 0.16 0.60 0.65 0.73
## 57 0.46 0.43 0.30 0.11 0.83
## 58 0.22 0.58 0.14 0.17 0.43
## 59 0.22 0.56 0.11 0.07 0.41
## 60 0.19 0.62 0.17 0.22 0.39
## 61 0.15 0.74 0.14 0.23 0.30
## 62 0.16 0.69 0.14 0.20 0.38
## 63 0.30 0.57 0.21 0.13 0.53
## 64 0.28 0.58 0.26 0.17 0.54
## 65 0.86 0.00 0.90 0.69 0.97
## 66 0.95 0.00 0.94 0.71 0.94
## 67 0.91 0.17 0.98 0.84 0.82
## 68 0.47 0.27 0.30 0.07 0.69
## 69 0.33 0.45 0.23 0.05 0.58
## 70 0.37 0.45 0.27 0.09 0.54
## 71 0.28 0.52 0.25 0.11 0.57
## 72 0.72 0.02 0.83 0.50 0.99
## 73 0.85 0.02 0.89 0.62 0.96
## 74 0.89 0.06 0.98 0.63 0.94
## 75 0.69 0.09 0.81 0.36 0.97
## 76 0.44 0.29 0.64 0.16 0.89
## 77 0.79 0.06 0.92 0.52 0.92
## 78 0.98 0.12 0.90 0.99 0.78
## 79 0.37 0.38 0.24 0.08 0.54
## 80 0.34 0.49 0.16 0.12 0.45
## 81 0.27 0.52 0.21 0.06 0.41
## 82 0.32 0.61 0.21 0.14 0.46
## 83 0.22 0.51 0.10 0.07 0.48
## 84 0.23 0.61 0.22 0.17 0.42
## 85 0.21 0.66 0.15 0.25 0.38
## 86 0.24 0.63 0.15 0.27 0.40
## 87 0.93 0.01 0.96 0.73 0.93
## 88 0.91 0.05 0.94 0.67 0.94
## 89 0.52 0.36 0.41 0.20 0.90
## 90 0.63 0.20 0.44 0.20 0.85
## 91 0.55 0.32 0.36 0.09 0.74
## 92 0.51 0.30 0.38 0.12 0.84
## 93 0.54 0.15 0.86 0.90 0.73
## 94 0.44 0.15 0.80 0.55 0.51
## 95 0.48 0.15 0.82 0.58 0.62
## 96 0.45 0.20 0.72 0.60 0.52
## 97 0.91 0.29 0.84 0.99 0.74
## 98 0.99 0.30 0.84 0.96 0.72
## 99 0.90 0.38 0.78 0.85 0.71
## 100 0.93 0.51 0.62 0.79 0.49
## 101 0.97 0.36 0.75 0.94 0.74
## 102 0.79 0.48 0.58 0.76 0.50
## 103 0.59 0.24 0.86 0.85 0.58
## 104 0.51 0.04 0.86 0.69 0.62
## 105 0.57 0.32 0.78 0.85 0.60
## 106 0.59 0.27 0.69 0.73 0.62
## 107 0.71 0.25 0.88 0.97 0.66
## 108 0.94 0.30 0.91 0.99 0.74
## 109 0.97 0.33 0.89 0.99 0.74
## 110 0.88 0.51 0.43 0.69 0.50
## 111 0.80 0.52 0.44 0.64 0.60
## 112 0.79 0.36 0.78 0.64 0.71
## 113 0.62 0.57 0.29 0.51 0.47
## 114 0.78 0.57 0.33 0.57 0.41
## 115 0.49 0.26 0.81 0.74 0.56
## 116 0.78 0.21 0.91 0.56 0.89
## 117 0.82 0.27 0.92 0.58 0.86
## 118 0.88 0.28 0.99 0.87 0.81
## 119 0.86 0.43 0.98 0.87 0.76
## 120 0.88 0.29 0.95 0.87 0.83
## 121 0.84 0.41 0.93 0.76 0.75
## 122 0.77 0.41 0.87 0.68 0.73
## 123 0.88 0.28 0.91 0.85 0.85
## 124 0.23 0.45 0.49 0.01 0.85
## 125 0.22 0.37 0.59 0.01 0.87
## 126 0.04 0.67 0.27 0.00 0.60
## 127 0.07 0.73 0.07 0.16 0.33
## 128 0.11 0.53 0.46 0.00 0.75
## 129 0.52 0.30 0.82 0.18 0.87
## 130 0.23 0.48 0.68 0.01 0.82
## 131 0.61 0.30 0.87 0.35 0.90
## 132 0.12 0.67 0.05 0.34 0.35
## 133 0.37 0.46 0.70 0.22 0.77
## 134 0.07 0.59 0.21 0.07 0.57
## 135 0.12 0.64 0.20 0.27 0.47
## 136 0.29 0.46 0.54 0.13 0.77
## 137 0.62 0.44 0.72 0.50 0.79
## 138 0.55 0.47 0.70 0.38 0.78
## 139 0.16 0.56 0.42 0.34 0.65
## 140 0.22 0.56 0.28 0.23 0.65
## 141 0.31 0.58 0.30 0.32 0.53
## 142 0.50 0.44 0.56 0.39 0.70
## 143 0.86 0.31 0.97 0.69 0.85
## 144 0.85 0.31 0.93 0.65 0.84
## 145 0.73 0.31 0.87 0.57 0.84
## 146 0.68 0.25 0.93 0.40 0.94
## 147 0.53 0.46 0.42 0.42 0.64
## 148 0.48 0.39 0.25 0.39 0.61
## 149 0.36 0.67 0.09 0.33 0.31
## 150 0.25 0.68 0.04 0.30 0.31
## 151 0.96 0.33 0.92 0.98 0.74
## 152 0.90 0.23 0.98 0.97 0.86
## 153 0.88 0.28 0.97 0.96 0.72
## 154 0.88 0.36 0.89 0.92 0.69
## 155 0.93 0.34 0.85 0.98 0.70
## 156 0.88 0.39 0.81 0.97 0.63
## 157 0.52 0.53 0.51 0.68 0.40
## 158 0.82 0.48 0.60 0.71 0.49
## 159 0.75 0.49 0.48 0.59 0.53
## 160 0.90 0.25 0.98 0.79 0.88
## 161 0.69 0.50 0.89 0.57 0.79
## 162 0.38 0.55 0.45 0.35 0.29
## 163 0.22 0.86 0.19 0.38 0.12
## 164 0.33 0.65 0.44 0.47 0.23
## 165 0.50 0.35 0.58 0.53 0.56
## 166 0.36 0.19 0.81 0.36 0.52
## 167 0.46 0.23 0.82 0.43 0.55
## 168 0.46 0.23 0.82 0.58 0.60
## 169 0.46 0.09 0.90 0.54 0.59
## 170 0.37 0.14 0.82 0.36 0.54
## 171 0.43 0.26 0.78 0.47 0.52
## 172 0.15 0.79 0.41 0.61 0.16
## 173 0.21 0.73 0.20 0.33 0.22
## 174 0.26 0.75 0.10 0.40 0.25
## 175 0.38 0.72 0.25 0.30 0.26
## 176 0.46 0.13 0.86 0.33 0.59
## 177 0.46 0.12 0.82 0.32 0.49
## 178 0.41 0.24 0.76 0.21 0.53
## 179 0.34 0.32 0.72 0.26 0.45
## 180 0.58 0.06 0.86 0.62 0.70
## 181 0.52 0.06 0.87 0.44 0.63
## 182 0.21 0.58 0.51 0.30 0.32
## 183 0.27 0.75 0.08 0.48 0.24
## 184 0.21 0.77 0.09 0.36 0.20
## 185 0.08 0.76 0.12 0.53 0.21
## 186 0.16 0.75 0.12 0.50 0.23
## 187 0.14 0.80 0.06 0.32 0.19
## 188 0.28 0.46 0.49 0.38 0.43
## 189 0.49 0.62 0.35 0.39 0.35
## 190 0.42 0.42 0.68 0.53 0.47
## 191 0.55 0.34 0.76 0.58 0.45
## 192 0.49 0.68 0.33 0.34 0.31
## 193 0.44 0.49 0.62 0.52 0.40
## 194 0.46 0.62 0.45 0.42 0.30
## 195 0.54 0.44 0.55 0.44 0.43
## 196 0.37 0.58 0.37 0.37 0.35
## 197 0.50 0.51 0.51 0.37 0.42
## 198 0.41 0.60 0.40 0.28 0.35
## 199 0.48 0.53 0.48 0.34 0.42
## 200 0.47 0.51 0.48 0.40 0.40
## 201 0.34 0.70 0.37 0.30 0.28
## 202 0.49 0.71 0.09 0.43 0.27
## 203 0.52 0.59 0.08 0.35 0.37
## 204 0.46 0.66 0.31 0.46 0.33
## 205 0.62 0.75 0.12 0.50 0.24
## 206 0.46 0.79 0.13 0.33 0.20
## 207 0.29 0.68 0.06 0.29 0.31
## 208 0.68 0.53 0.40 0.60 0.40
## 209 0.77 0.62 0.38 0.56 0.36
## 210 0.62 0.56 0.47 0.59 0.41
## 211 0.52 0.23 0.31 0.04 0.63
## 212 0.01 0.86 0.27 0.44 0.19
## 213 0.01 0.90 0.34 0.46 0.14
## 214 0.24 0.67 0.15 0.17 0.40
## 215 0.23 0.43 0.17 0.06 0.50
## 216 0.08 0.66 0.13 0.06 0.33
## 217 0.00 0.82 0.38 0.49 0.19
## 218 0.94 0.15 0.67 0.72 0.59
## 219 0.60 0.16 0.82 0.54 0.47
## 220 0.72 0.17 0.67 0.48 0.51
## 221 0.80 0.24 0.72 0.57 0.60
## 222 0.73 0.17 0.78 0.55 0.48
## 223 0.82 0.17 0.75 0.59 0.45
## 224 0.80 0.14 0.77 0.67 0.50
## 225 0.80 0.33 0.68 0.50 0.47
## 226 0.62 0.05 0.83 0.55 0.60
## 227 0.97 0.10 0.75 0.67 0.67
## 228 0.04 0.68 0.24 0.13 0.29
## 229 0.06 0.62 0.06 0.05 0.33
## 230 0.60 0.10 0.36 0.38 0.59
## 231 0.04 0.71 0.28 0.26 0.30
## 232 0.00 0.78 0.31 0.30 0.25
## 233 0.01 0.85 0.26 0.34 0.18
## 234 0.07 0.72 0.28 0.37 0.28
## 235 0.27 0.42 0.32 0.33 0.36
## 236 0.17 0.46 0.07 0.00 0.45
## 237 0.54 0.19 0.73 0.34 0.49
## 238 0.03 0.73 0.17 0.18 0.27
## 239 0.16 0.54 0.09 0.03 0.41
## 240 0.10 0.68 0.05 0.04 0.26
## 241 0.15 0.65 0.06 0.08 0.34
## 242 0.17 0.61 0.10 0.08 0.38
## 243 0.04 0.69 0.11 0.20 0.30
## 244 0.01 0.77 0.26 0.41 0.24
## 245 0.16 0.72 0.11 0.22 0.31
## 246 0.20 0.38 0.13 0.02 0.40
## 247 0.20 0.40 0.10 0.05 0.37
## 248 0.08 0.70 0.03 0.02 0.27
## 249 0.16 0.51 0.09 0.01 0.43
## 250 0.15 0.50 0.08 0.03 0.32
## 251 0.81 0.18 0.62 0.56 0.63
## 252 0.46 0.22 0.24 0.12 0.63
## 253 0.09 0.83 0.39 0.77 0.11
## 254 0.10 0.81 0.47 0.93 0.06
## 255 0.11 0.72 0.45 0.70 0.21
## 256 0.14 0.83 0.34 0.89 0.16
## 257 0.05 0.78 0.31 0.73 0.17
## 258 0.15 0.85 0.40 0.96 0.01
## 259 0.80 0.90 0.72 0.88 0.11
## 260 0.11 0.79 0.05 0.55 0.24
## 261 0.52 0.98 0.54 0.75 0.04
## 262 0.41 0.89 0.31 0.68 0.15
## 263 0.05 0.86 0.00 0.65 0.19
## 264 0.50 0.94 0.38 0.66 0.04
## 265 0.54 0.97 0.50 0.65 0.05
## 266 0.60 0.87 0.49 0.69 0.09
## 267 0.54 0.97 0.47 0.76 0.04
## 268 0.52 0.90 0.42 0.70 0.09
## 269 0.28 0.90 0.13 0.71 0.12
## 270 0.60 0.87 0.61 0.84 0.13
## 271 0.60 0.91 0.57 0.78 0.10
## 272 0.58 0.84 0.58 0.85 0.12
## 273 0.58 0.92 0.60 0.80 0.04
## 274 0.08 0.77 0.02 0.57 0.18
## 275 0.03 0.74 0.02 0.41 0.26
## 276 0.09 0.80 0.19 0.65 0.14
## 277 0.04 0.73 0.00 0.25 0.28
## 278 0.02 0.73 0.00 0.54 0.27
## 279 0.03 0.85 0.01 0.52 0.23
## 280 0.02 0.77 0.01 0.27 0.25
## 281 0.06 0.70 0.03 0.25 0.33
## 282 0.04 0.87 0.01 0.79 0.13
## 283 0.06 0.78 0.00 0.55 0.23
## 284 0.71 0.98 0.71 0.85 0.07
## 285 0.76 0.97 0.68 0.85 0.10
## 286 0.80 0.96 0.61 0.81 0.14
## 287 0.75 0.94 0.72 0.92 0.15
## 288 0.77 0.98 0.71 0.87 0.10
## 289 0.16 0.91 0.51 0.91 0.01
## 290 0.12 0.80 0.27 0.92 0.06
## 291 0.11 0.82 0.48 0.96 0.01
## 292 0.06 0.82 0.11 0.64 0.19
## 293 0.06 0.82 0.06 0.80 0.18
## 294 0.70 0.88 0.62 0.78 0.07
## 295 0.72 0.94 0.64 0.80 0.12
## 296 0.61 0.98 0.49 0.73 0.05
## 297 0.55 0.95 0.55 0.68 0.05
## 298 0.65 0.94 0.59 0.77 0.07
## 299 0.70 0.97 0.66 0.83 0.09
## 300 0.66 0.93 0.69 0.82 0.07
## 301 0.61 0.91 0.56 0.72 0.14
## 302 0.77 0.96 0.70 0.79 0.12
## 303 0.67 0.88 0.54 0.75 0.06
## 304 0.65 0.87 0.48 0.74 0.13
## 305 0.56 0.91 0.64 0.84 0.05
## 306 0.62 0.87 0.65 0.81 0.08
## 307 0.48 0.99 0.50 0.72 0.03
## 308 0.52 0.91 0.58 0.79 0.03
## 309 0.31 0.79 0.45 0.93 0.03
## 310 0.64 0.93 0.66 0.86 0.08
## 311 0.73 0.90 0.68 0.84 0.12
## 312 0.66 0.85 0.66 0.86 0.19
## 313 0.67 0.82 0.65 0.90 0.19
## 314 0.75 0.88 0.72 0.89 0.15
## 315 0.15 0.91 0.12 0.80 0.13
## 316 0.18 0.93 0.33 0.87 0.01
## 317 0.54 0.97 0.66 0.79 0.03
## 318 0.31 0.99 0.45 0.79 0.01
## 319 0.17 0.80 0.15 0.69 0.17
## 320 0.30 0.99 0.37 0.72 0.02
## 321 0.08 0.76 0.45 0.68 0.18
## Cov_GenEl2004.Unempl Cov_LARent.Unempl Corr_GenEl2004.LARent
## 0 0.50 0.26 0.93
## 1 0.42 0.36 0.75
## 2 0.26 0.32 0.72
## 3 0.24 0.44 0.75
## 4 0.25 0.46 0.78
## 5 0.56 0.65 0.69
## 6 0.44 0.62 0.67
## 7 0.84 0.28 0.49
## 8 0.71 0.07 0.98
## 9 0.61 0.13 0.95
## 10 0.90 0.15 0.90
## 11 0.91 0.07 0.87
## 12 0.92 0.07 0.91
## 13 0.86 0.13 0.96
## 14 0.96 0.05 0.61
## 15 0.99 0.01 0.58
## 16 0.98 0.00 0.65
## 17 0.98 0.03 0.73
## 18 0.96 0.01 0.83
## 19 0.95 0.04 0.75
## 20 0.95 0.06 0.78
## 21 0.99 0.09 0.72
## 22 0.86 0.18 0.63
## 23 0.83 0.16 0.83
## 24 0.91 0.13 0.85
## 25 0.91 0.11 0.87
## 26 0.84 0.06 0.99
## 27 0.78 0.03 0.99
## 28 0.68 0.15 0.98
## 29 0.60 0.21 0.95
## 30 0.54 0.31 0.93
## 31 0.34 0.35 0.88
## 32 0.60 0.32 0.82
## 33 0.55 0.31 0.95
## 34 0.51 0.39 0.92
## 35 0.25 0.57 0.79
## 36 0.67 0.39 0.61
## 37 0.68 0.34 0.67
## 38 0.66 0.30 0.86
## 39 0.64 0.31 0.87
## 40 0.82 0.22 0.88
## 41 0.82 0.26 0.75
## 42 0.78 0.26 0.86
## 43 0.86 0.14 0.98
## 44 0.76 0.04 0.98
## 45 0.85 0.09 0.97
## 46 0.81 0.09 0.99
## 47 0.68 0.09 0.97
## 48 0.90 0.05 0.99
## 49 0.76 0.32 0.61
## 50 0.90 0.22 0.57
## 51 0.89 0.22 0.59
## 52 0.93 0.13 0.68
## 53 0.74 0.40 0.71
## 54 0.80 0.20 0.90
## 55 0.79 0.32 0.82
## 56 0.68 0.46 0.73
## 57 0.92 0.05 0.97
## 58 0.72 0.36 0.44
## 59 0.73 0.28 0.44
## 60 0.63 0.40 0.55
## 61 0.59 0.47 0.41
## 62 0.67 0.39 0.55
## 63 0.78 0.24 0.58
## 64 0.74 0.32 0.68
## 65 0.56 0.16 0.95
## 66 0.41 0.25 0.88
## 67 0.31 0.29 0.64
## 68 0.96 0.04 0.67
## 69 0.89 0.11 0.65
## 70 0.88 0.14 0.68
## 71 0.91 0.18 0.66
## 72 0.60 0.16 0.95
## 73 0.51 0.14 0.94
## 74 0.41 0.17 0.77
## 75 0.67 0.06 0.95
## 76 0.63 0.18 0.89
## 77 0.54 0.11 0.92
## 78 0.42 0.64 0.65
## 79 0.88 0.13 0.49
## 80 0.85 0.23 0.42
## 81 0.80 0.30 0.45
## 82 0.85 0.23 0.60
## 83 0.91 0.15 0.50
## 84 0.63 0.41 0.54
## 85 0.57 0.46 0.54
## 86 0.66 0.55 0.48
## 87 0.45 0.19 0.87
## 88 0.45 0.19 0.87
## 89 0.95 0.04 0.94
## 90 0.95 0.02 0.87
## 91 0.99 0.00 0.64
## 92 0.96 0.01 0.94
## 93 0.59 0.58 0.56
## 94 0.94 0.53 0.75
## 95 0.83 0.45 0.69
## 96 0.84 0.55 0.71
## 97 0.47 0.47 0.51
## 98 0.34 0.35 0.52
## 99 0.39 0.31 0.51
## 100 0.50 0.50 0.31
## 101 0.44 0.44 0.49
## 102 0.52 0.57 0.32
## 103 0.53 0.63 0.52
## 104 0.75 0.63 0.63
## 105 0.49 0.62 0.45
## 106 0.61 0.48 0.55
## 107 0.40 0.65 0.50
## 108 0.35 0.52 0.54
## 109 0.31 0.58 0.47
## 110 0.52 0.44 0.30
## 111 0.47 0.38 0.31
## 112 0.42 0.26 0.53
## 113 0.49 0.56 0.26
## 114 0.61 0.54 0.34
## 115 0.76 0.50 0.60
## 116 0.41 0.12 0.78
## 117 0.38 0.16 0.67
## 118 0.27 0.28 0.61
## 119 0.21 0.29 0.59
## 120 0.30 0.30 0.62
## 121 0.35 0.21 0.58
## 122 0.35 0.21 0.63
## 123 0.41 0.27 0.68
## 124 0.53 0.18 0.81
## 125 0.41 0.10 0.77
## 126 0.24 0.40 0.44
## 127 0.16 0.71 0.29
## 128 0.37 0.19 0.67
## 129 0.55 0.06 0.78
## 130 0.36 0.17 0.70
## 131 0.45 0.10 0.67
## 132 0.22 0.72 0.27
## 133 0.49 0.13 0.65
## 134 0.36 0.40 0.43
## 135 0.31 0.62 0.36
## 136 0.48 0.20 0.62
## 137 0.42 0.18 0.63
## 138 0.45 0.15 0.59
## 139 0.30 0.46 0.38
## 140 0.40 0.37 0.36
## 141 0.40 0.48 0.32
## 142 0.49 0.32 0.49
## 143 0.37 0.24 0.64
## 144 0.30 0.18 0.70
## 145 0.37 0.10 0.74
## 146 0.48 0.06 0.78
## 147 0.51 0.30 0.45
## 148 0.54 0.45 0.49
## 149 0.44 0.67 0.30
## 150 0.32 0.69 0.34
## 151 0.22 0.54 0.53
## 152 0.29 0.33 0.65
## 153 0.15 0.47 0.49
## 154 0.20 0.39 0.49
## 155 0.23 0.39 0.48
## 156 0.25 0.51 0.49
## 157 0.67 0.58 0.37
## 158 0.55 0.56 0.43
## 159 0.63 0.55 0.37
## 160 0.29 0.18 0.64
## 161 0.30 0.14 0.55
## 162 0.57 0.59 0.73
## 163 0.18 0.87 0.19
## 164 0.29 0.81 0.38
## 165 0.80 0.53 0.76
## 166 0.90 0.44 0.81
## 167 0.94 0.35 0.90
## 168 0.81 0.46 0.81
## 169 0.91 0.46 0.81
## 170 0.94 0.37 0.80
## 171 0.89 0.39 0.91
## 172 0.19 0.95 0.28
## 173 0.26 0.76 0.34
## 174 0.29 0.75 0.30
## 175 0.37 0.75 0.55
## 176 0.97 0.38 0.81
## 177 0.88 0.55 0.68
## 178 0.90 0.44 0.79
## 179 0.83 0.30 0.91
## 180 0.92 0.49 0.74
## 181 0.92 0.52 0.76
## 182 0.77 0.59 0.76
## 183 0.29 0.76 0.27
## 184 0.28 0.77 0.22
## 185 0.23 0.92 0.25
## 186 0.25 0.81 0.27
## 187 0.24 0.84 0.18
## 188 0.73 0.47 0.83
## 189 0.58 0.64 0.76
## 190 0.77 0.46 0.82
## 191 0.78 0.52 0.83
## 192 0.50 0.67 0.64
## 193 0.66 0.52 0.88
## 194 0.59 0.57 0.81
## 195 0.72 0.56 0.74
## 196 0.53 0.62 0.74
## 197 0.72 0.57 0.77
## 198 0.55 0.63 0.80
## 199 0.71 0.57 0.86
## 200 0.77 0.60 0.86
## 201 0.50 0.71 0.63
## 202 0.36 0.76 0.29
## 203 0.46 0.62 0.37
## 204 0.59 0.67 0.40
## 205 0.37 0.75 0.27
## 206 0.34 0.79 0.25
## 207 0.38 0.68 0.28
## 208 0.62 0.56 0.43
## 209 0.49 0.65 0.36
## 210 0.59 0.56 0.41
## 211 0.98 0.02 0.37
## 212 0.24 0.75 0.15
## 213 0.20 0.81 0.16
## 214 0.60 0.50 0.45
## 215 0.79 0.26 0.40
## 216 0.45 0.57 0.23
## 217 0.26 0.74 0.22
## 218 0.71 0.67 0.42
## 219 0.76 0.71 0.49
## 220 0.79 0.68 0.56
## 221 0.78 0.56 0.46
## 222 0.74 0.61 0.55
## 223 0.70 0.67 0.42
## 224 0.80 0.71 0.49
## 225 0.68 0.61 0.40
## 226 0.80 0.63 0.58
## 227 0.67 0.67 0.54
## 228 0.53 0.65 0.22
## 229 0.50 0.53 0.38
## 230 0.88 0.09 0.33
## 231 0.43 0.69 0.25
## 232 0.31 0.70 0.21
## 233 0.29 0.78 0.23
## 234 0.39 0.68 0.38
## 235 0.61 0.39 0.19
## 236 0.73 0.30 0.43
## 237 0.83 0.64 0.63
## 238 0.39 0.67 0.29
## 239 0.61 0.38 0.41
## 240 0.59 0.43 0.20
## 241 0.63 0.46 0.39
## 242 0.64 0.49 0.42
## 243 0.40 0.67 0.32
## 244 0.30 0.69 0.31
## 245 0.56 0.55 0.42
## 246 0.82 0.17 0.29
## 247 0.75 0.24 0.26
## 248 0.56 0.42 0.20
## 249 0.74 0.27 0.37
## 250 0.78 0.28 0.25
## 251 0.75 0.47 0.45
## 252 0.98 0.03 0.51
## 253 0.16 0.97 0.18
## 254 0.15 0.95 0.20
## 255 0.23 0.95 0.24
## 256 0.20 0.97 0.23
## 257 0.21 0.96 0.22
## 258 0.08 0.99 0.14
## 259 0.12 0.88 0.08
## 260 0.11 0.75 0.17
## 261 0.04 0.88 0.01
## 262 0.02 0.88 0.03
## 263 0.09 0.87 0.10
## 264 0.06 0.86 0.03
## 265 0.06 0.86 0.04
## 266 0.08 0.83 0.00
## 267 0.07 0.89 0.04
## 268 0.03 0.87 0.00
## 269 0.01 0.90 0.01
## 270 0.16 0.88 0.12
## 271 0.14 0.88 0.12
## 272 0.14 0.94 0.16
## 273 0.11 0.91 0.11
## 274 0.14 0.83 0.14
## 275 0.23 0.74 0.23
## 276 0.17 0.91 0.15
## 277 0.20 0.77 0.21
## 278 0.22 0.78 0.21
## 279 0.13 0.83 0.13
## 280 0.21 0.77 0.23
## 281 0.20 0.73 0.28
## 282 0.03 0.98 0.12
## 283 0.19 0.79 0.18
## 284 0.12 0.85 0.09
## 285 0.14 0.82 0.04
## 286 0.13 0.81 0.08
## 287 0.17 0.80 0.11
## 288 0.11 0.80 0.03
## 289 0.02 0.97 0.08
## 290 0.01 0.98 0.22
## 291 0.04 0.99 0.19
## 292 0.16 0.91 0.15
## 293 0.20 0.94 0.19
## 294 0.08 0.83 0.02
## 295 0.15 0.77 0.07
## 296 0.05 0.87 0.03
## 297 0.07 0.89 0.00
## 298 0.09 0.84 0.02
## 299 0.11 0.84 0.07
## 300 0.10 0.86 0.10
## 301 0.11 0.81 0.08
## 302 0.11 0.82 0.05
## 303 0.08 0.83 0.00
## 304 0.09 0.82 0.02
## 305 0.11 0.94 0.10
## 306 0.10 0.91 0.12
## 307 0.05 0.93 0.10
## 308 0.08 0.92 0.10
## 309 0.07 0.95 0.13
## 310 0.12 0.89 0.11
## 311 0.15 0.86 0.13
## 312 0.18 0.88 0.15
## 313 0.21 0.85 0.20
## 314 0.16 0.89 0.13
## 315 0.01 0.94 0.10
## 316 0.00 0.95 0.12
## 317 0.06 0.88 0.06
## 318 0.01 0.95 0.01
## 319 0.01 0.92 0.12
## 320 0.02 0.93 0.01
## 321 0.19 0.96 0.18
## Corr_GenEl2004.Unempl Corr_LARent.Unempl Spearman_rho_GenEl2004.LARent
## 0 0.12 0.94 0.94
## 1 0.04 0.97 0.86
## 2 0.00 0.97 0.68
## 3 0.00 0.95 0.62
## 4 0.01 0.98 0.84
## 5 0.37 0.95 0.65
## 6 0.13 0.99 0.79
## 7 0.94 0.13 0.21
## 8 0.38 0.81 0.99
## 9 0.28 0.83 0.99
## 10 0.54 0.66 0.62
## 11 0.59 0.56 0.57
## 12 0.56 0.56 0.66
## 13 0.51 0.66 0.86
## 14 0.67 0.44 0.31
## 15 0.75 0.29 0.29
## 16 0.69 0.33 0.41
## 17 0.72 0.37 0.38
## 18 0.67 0.47 0.44
## 19 0.71 0.43 0.43
## 20 0.82 0.27 0.39
## 21 0.85 0.22 0.42
## 22 0.84 0.28 0.35
## 23 0.88 0.33 0.52
## 24 0.84 0.39 0.69
## 25 0.87 0.30 0.64
## 26 0.45 0.73 0.95
## 27 0.42 0.73 0.99
## 28 0.36 0.83 0.97
## 29 0.37 0.78 0.93
## 30 0.21 0.87 0.94
## 31 0.06 0.94 0.92
## 32 0.38 0.90 0.85
## 33 0.37 0.85 0.94
## 34 0.14 0.93 0.92
## 35 0.04 0.98 0.86
## 36 0.82 0.46 0.38
## 37 0.78 0.67 0.40
## 38 0.65 0.68 0.58
## 39 0.70 0.64 0.72
## 40 0.78 0.44 0.48
## 41 0.84 0.45 0.52
## 42 0.79 0.50 0.73
## 43 0.66 0.66 0.93
## 44 0.49 0.65 0.96
## 45 0.54 0.72 0.91
## 46 0.45 0.73 0.96
## 47 0.37 0.84 0.99
## 48 0.55 0.65 0.98
## 49 0.95 0.21 0.30
## 50 0.65 0.59 0.32
## 51 0.57 0.54 0.29
## 52 0.64 0.53 0.34
## 53 0.49 0.78 0.61
## 54 0.40 0.72 0.82
## 55 0.46 0.79 0.68
## 56 0.43 0.85 0.65
## 57 0.82 0.42 0.91
## 58 0.89 0.20 0.31
## 59 0.87 0.16 0.27
## 60 0.87 0.27 0.32
## 61 0.92 0.27 0.23
## 62 0.92 0.30 0.32
## 63 0.86 0.36 0.35
## 64 0.85 0.37 0.42
## 65 0.24 0.87 0.97
## 66 0.10 0.93 0.93
## 67 0.05 0.92 0.81
## 68 0.88 0.20 0.35
## 69 0.94 0.14 0.34
## 70 0.96 0.16 0.35
## 71 0.95 0.24 0.38
## 72 0.29 0.80 0.98
## 73 0.17 0.83 0.98
## 74 0.03 0.90 0.95
## 75 0.36 0.84 0.97
## 76 0.36 0.70 0.87
## 77 0.17 0.83 0.93
## 78 0.12 0.99 0.77
## 79 0.85 0.10 0.26
## 80 0.83 0.09 0.25
## 81 0.91 0.08 0.24
## 82 0.90 0.18 0.32
## 83 0.88 0.09 0.26
## 84 0.89 0.32 0.34
## 85 0.86 0.37 0.35
## 86 0.92 0.41 0.33
## 87 0.07 0.92 0.93
## 88 0.11 0.89 0.94
## 89 0.71 0.42 0.84
## 90 0.71 0.44 0.59
## 91 0.74 0.28 0.39
## 92 0.83 0.33 0.81
## 93 0.43 0.95 0.71
## 94 0.76 0.56 0.61
## 95 0.74 0.72 0.72
## 96 0.79 0.51 0.68
## 97 0.30 0.87 0.78
## 98 0.29 0.84 0.71
## 99 0.37 0.72 0.80
## 100 0.38 0.68 0.84
## 101 0.27 0.79 0.84
## 102 0.58 0.67 0.78
## 103 0.46 0.90 0.78
## 104 0.61 0.83 0.75
## 105 0.37 0.76 0.73
## 106 0.66 0.75 0.81
## 107 0.29 0.97 0.65
## 108 0.06 0.96 0.56
## 109 0.18 0.90 0.60
## 110 0.59 0.53 0.90
## 111 0.47 0.55 0.90
## 112 0.37 0.77 0.90
## 113 0.61 0.48 0.88
## 114 0.71 0.42 0.90
## 115 0.74 0.74 0.73
## 116 0.11 0.79 0.89
## 117 0.13 0.85 0.76
## 118 0.01 0.93 0.61
## 119 0.04 0.86 0.51
## 120 0.03 0.87 0.55
## 121 0.11 0.83 0.62
## 122 0.24 0.70 0.71
## 123 0.14 0.83 0.73
## 124 0.37 0.68 0.50
## 125 0.26 0.74 0.60
## 126 0.26 0.59 0.36
## 127 0.25 0.49 0.29
## 128 0.27 0.61 0.49
## 129 0.28 0.74 0.75
## 130 0.33 0.62 0.67
## 131 0.31 0.85 0.81
## 132 0.49 0.55 0.74
## 133 0.32 0.61 0.72
## 134 0.52 0.52 0.58
## 135 0.44 0.62 0.74
## 136 0.49 0.56 0.80
## 137 0.43 0.67 0.85
## 138 0.40 0.64 0.77
## 139 0.36 0.59 0.80
## 140 0.53 0.58 0.84
## 141 0.50 0.52 0.87
## 142 0.40 0.63 0.92
## 143 0.03 0.83 0.75
## 144 0.08 0.84 0.71
## 145 0.20 0.80 0.74
## 146 0.12 0.83 0.90
## 147 0.48 0.59 0.89
## 148 0.58 0.61 0.88
## 149 0.67 0.39 0.80
## 150 0.60 0.51 0.75
## 151 0.02 0.98 0.50
## 152 0.00 0.95 0.64
## 153 0.01 0.90 0.46
## 154 0.10 0.87 0.62
## 155 0.15 0.85 0.56
## 156 0.05 0.86 0.44
## 157 0.75 0.54 0.82
## 158 0.70 0.70 0.90
## 159 0.69 0.56 0.92
## 160 0.04 0.90 0.72
## 161 0.25 0.64 0.71
## 162 0.76 0.00 0.41
## 163 0.54 0.29 0.41
## 164 0.48 0.16 0.44
## 165 0.94 0.43 0.78
## 166 0.89 0.35 0.53
## 167 0.95 0.24 0.49
## 168 0.86 0.48 0.80
## 169 0.82 0.54 0.60
## 170 0.88 0.44 0.45
## 171 0.98 0.22 0.56
## 172 0.37 0.47 0.43
## 173 0.57 0.18 0.46
## 174 0.71 0.29 0.55
## 175 0.71 0.09 0.59
## 176 0.83 0.45 0.59
## 177 0.77 0.69 0.41
## 178 0.86 0.47 0.40
## 179 0.96 0.10 0.39
## 180 0.74 0.82 0.72
## 181 0.75 0.75 0.56
## 182 0.98 0.01 0.30
## 183 0.63 0.47 0.59
## 184 0.60 0.39 0.54
## 185 0.46 0.48 0.52
## 186 0.48 0.57 0.57
## 187 0.59 0.44 0.59
## 188 0.99 0.00 0.42
## 189 0.97 0.06 0.66
## 190 0.97 0.18 0.62
## 191 0.98 0.10 0.57
## 192 0.95 0.12 0.71
## 193 0.98 0.02 0.59
## 194 0.96 0.00 0.53
## 195 0.96 0.33 0.77
## 196 0.93 0.03 0.55
## 197 0.99 0.11 0.73
## 198 0.95 0.06 0.62
## 199 0.98 0.03 0.57
## 200 0.99 0.06 0.64
## 201 0.96 0.14 0.64
## 202 0.74 0.40 0.59
## 203 0.80 0.41 0.81
## 204 0.91 0.38 0.73
## 205 0.73 0.40 0.70
## 206 0.73 0.25 0.59
## 207 0.68 0.44 0.70
## 208 0.78 0.46 0.82
## 209 0.73 0.47 0.85
## 210 0.80 0.43 0.78
## 211 0.80 0.05 0.20
## 212 0.57 0.16 0.03
## 213 0.51 0.13 0.04
## 214 0.88 0.28 0.34
## 215 0.72 0.07 0.24
## 216 0.57 0.23 0.08
## 217 0.57 0.31 0.00
## 218 0.52 0.95 0.49
## 219 0.59 0.91 0.33
## 220 0.63 0.89 0.44
## 221 0.53 0.84 0.45
## 222 0.59 0.88 0.43
## 223 0.50 0.94 0.39
## 224 0.47 0.93 0.43
## 225 0.47 0.87 0.31
## 226 0.57 0.89 0.53
## 227 0.39 0.97 0.64
## 228 0.56 0.43 0.13
## 229 0.70 0.02 0.23
## 230 0.64 0.23 0.19
## 231 0.56 0.53 0.13
## 232 0.55 0.25 0.01
## 233 0.67 0.08 0.09
## 234 0.89 0.18 0.28
## 235 0.45 0.34 0.13
## 236 0.80 0.00 0.29
## 237 0.68 0.70 0.35
## 238 0.68 0.06 0.15
## 239 0.75 0.07 0.23
## 240 0.65 0.06 0.10
## 241 0.83 0.10 0.30
## 242 0.88 0.11 0.31
## 243 0.77 0.04 0.23
## 244 0.65 0.08 0.19
## 245 0.89 0.34 0.29
## 246 0.68 0.03 0.18
## 247 0.56 0.12 0.18
## 248 0.68 0.06 0.11
## 249 0.74 0.07 0.24
## 250 0.70 0.05 0.15
## 251 0.57 0.87 0.33
## 252 0.84 0.10 0.33
## 253 0.43 0.52 0.23
## 254 0.35 0.70 0.31
## 255 0.39 0.57 0.40
## 256 0.40 0.61 0.37
## 257 0.39 0.62 0.41
## 258 0.36 0.51 0.21
## 259 0.20 0.25 0.05
## 260 0.08 0.64 0.23
## 261 0.09 0.37 0.06
## 262 0.05 0.54 0.16
## 263 0.07 0.64 0.21
## 264 0.15 0.27 0.05
## 265 0.15 0.24 0.08
## 266 0.12 0.42 0.04
## 267 0.14 0.23 0.07
## 268 0.04 0.45 0.09
## 269 0.06 0.56 0.10
## 270 0.28 0.16 0.08
## 271 0.34 0.10 0.08
## 272 0.31 0.09 0.13
## 273 0.21 0.24 0.12
## 274 0.44 0.51 0.50
## 275 0.45 0.63 0.58
## 276 0.45 0.51 0.41
## 277 0.43 0.63 0.46
## 278 0.41 0.61 0.40
## 279 0.33 0.59 0.28
## 280 0.44 0.59 0.51
## 281 0.31 0.57 0.45
## 282 0.27 0.72 0.21
## 283 0.34 0.73 0.33
## 284 0.22 0.21 0.07
## 285 0.22 0.25 0.02
## 286 0.24 0.16 0.01
## 287 0.24 0.17 0.04
## 288 0.16 0.25 0.02
## 289 0.15 0.39 0.16
## 290 0.40 0.72 0.30
## 291 0.32 0.56 0.21
## 292 0.33 0.71 0.39
## 293 0.33 0.79 0.32
## 294 0.18 0.34 0.02
## 295 0.21 0.23 0.06
## 296 0.18 0.21 0.02
## 297 0.16 0.30 0.07
## 298 0.18 0.31 0.06
## 299 0.22 0.23 0.05
## 300 0.22 0.19 0.08
## 301 0.30 0.26 0.11
## 302 0.20 0.29 0.04
## 303 0.14 0.37 0.00
## 304 0.17 0.31 0.01
## 305 0.18 0.24 0.10
## 306 0.26 0.13 0.13
## 307 0.22 0.21 0.19
## 308 0.23 0.17 0.13
## 309 0.17 0.30 0.18
## 310 0.25 0.17 0.04
## 311 0.25 0.20 0.07
## 312 0.37 0.11 0.14
## 313 0.41 0.10 0.11
## 314 0.26 0.11 0.11
## 315 0.19 0.58 0.15
## 316 0.11 0.35 0.15
## 317 0.10 0.34 0.21
## 318 0.13 0.33 0.10
## 319 0.04 0.61 0.25
## 320 0.03 0.51 0.18
## 321 0.37 0.33 0.24
## Spearman_rho_GenEl2004.Unempl Spearman_rho_LARent.Unempl
## 0 0.27 0.87
## 1 0.22 0.93
## 2 0.00 0.92
## 3 0.00 0.97
## 4 0.01 0.93
## 5 0.35 0.89
## 6 0.31 0.93
## 7 0.98 0.54
## 8 0.45 0.68
## 9 0.41 0.76
## 10 0.49 0.53
## 11 0.57 0.40
## 12 0.57 0.28
## 13 0.53 0.33
## 14 0.61 0.51
## 15 0.74 0.47
## 16 0.70 0.31
## 17 0.67 0.35
## 18 0.59 0.39
## 19 0.67 0.41
## 20 0.88 0.40
## 21 0.94 0.39
## 22 0.97 0.52
## 23 0.98 0.45
## 24 0.91 0.33
## 25 0.94 0.30
## 26 0.53 0.38
## 27 0.57 0.59
## 28 0.47 0.79
## 29 0.39 0.72
## 30 0.32 0.83
## 31 0.20 0.88
## 32 0.40 0.86
## 33 0.36 0.86
## 34 0.33 0.84
## 35 0.16 0.94
## 36 0.85 0.56
## 37 0.71 0.74
## 38 0.73 0.65
## 39 0.79 0.52
## 40 0.80 0.52
## 41 0.84 0.61
## 42 0.88 0.48
## 43 0.75 0.58
## 44 0.68 0.39
## 45 0.69 0.45
## 46 0.69 0.46
## 47 0.51 0.58
## 48 0.63 0.37
## 49 0.97 0.68
## 50 0.59 0.66
## 51 0.48 0.65
## 52 0.57 0.63
## 53 0.52 0.65
## 54 0.42 0.58
## 55 0.41 0.74
## 56 0.42 0.74
## 57 0.88 0.33
## 58 0.91 0.68
## 59 0.89 0.67
## 60 0.82 0.73
## 61 0.90 0.70
## 62 0.91 0.73
## 63 0.93 0.54
## 64 0.83 0.62
## 65 0.32 0.80
## 66 0.29 0.88
## 67 0.07 0.86
## 68 0.89 0.39
## 69 0.98 0.52
## 70 0.99 0.59
## 71 0.97 0.57
## 72 0.39 0.73
## 73 0.28 0.83
## 74 0.23 0.87
## 75 0.42 0.67
## 76 0.38 0.61
## 77 0.29 0.68
## 78 0.21 0.94
## 79 0.91 0.60
## 80 0.88 0.60
## 81 0.96 0.61
## 82 0.98 0.58
## 83 0.98 0.56
## 84 0.86 0.68
## 85 0.85 0.67
## 86 0.77 0.68
## 87 0.18 0.89
## 88 0.30 0.84
## 89 0.73 0.25
## 90 0.72 0.30
## 91 0.80 0.41
## 92 0.87 0.33
## 93 0.36 0.86
## 94 0.65 0.31
## 95 0.69 0.38
## 96 0.65 0.25
## 97 0.40 0.47
## 98 0.34 0.48
## 99 0.38 0.25
## 100 0.62 0.19
## 101 0.48 0.25
## 102 0.62 0.15
## 103 0.39 0.82
## 104 0.43 0.72
## 105 0.36 0.68
## 106 0.67 0.25
## 107 0.26 0.88
## 108 0.15 0.94
## 109 0.23 0.92
## 110 0.72 0.04
## 111 0.60 0.03
## 112 0.47 0.12
## 113 0.76 0.00
## 114 0.78 0.01
## 115 0.66 0.44
## 116 0.20 0.61
## 117 0.20 0.41
## 118 0.01 0.92
## 119 0.04 0.91
## 120 0.06 0.85
## 121 0.26 0.55
## 122 0.32 0.20
## 123 0.28 0.46
## 124 0.30 0.42
## 125 0.24 0.27
## 126 0.21 0.22
## 127 0.20 0.29
## 128 0.25 0.20
## 129 0.28 0.20
## 130 0.30 0.18
## 131 0.33 0.20
## 132 0.55 0.12
## 133 0.38 0.09
## 134 0.46 0.14
## 135 0.48 0.11
## 136 0.52 0.03
## 137 0.54 0.09
## 138 0.44 0.07
## 139 0.44 0.06
## 140 0.59 0.02
## 141 0.58 0.03
## 142 0.56 0.02
## 143 0.06 0.73
## 144 0.21 0.67
## 145 0.26 0.25
## 146 0.22 0.42
## 147 0.60 0.00
## 148 0.67 0.02
## 149 0.73 0.02
## 150 0.69 0.03
## 151 0.02 0.95
## 152 0.00 0.94
## 153 0.01 0.90
## 154 0.15 0.79
## 155 0.27 0.78
## 156 0.08 0.91
## 157 0.76 0.21
## 158 0.74 0.11
## 159 0.72 0.03
## 160 0.09 0.86
## 161 0.32 0.21
## 162 0.92 0.14
## 163 0.65 0.22
## 164 0.68 0.20
## 165 0.76 0.14
## 166 0.66 0.18
## 167 0.70 0.21
## 168 0.75 0.19
## 169 0.63 0.30
## 170 0.73 0.22
## 171 0.78 0.16
## 172 0.50 0.27
## 173 0.71 0.22
## 174 0.76 0.16
## 175 0.75 0.13
## 176 0.67 0.14
## 177 0.60 0.20
## 178 0.67 0.21
## 179 0.84 0.24
## 180 0.56 0.48
## 181 0.61 0.36
## 182 0.93 0.23
## 183 0.67 0.16
## 184 0.67 0.16
## 185 0.57 0.30
## 186 0.55 0.23
## 187 0.63 0.10
## 188 0.98 0.14
## 189 0.92 0.10
## 190 0.83 0.13
## 191 0.90 0.17
## 192 0.85 0.07
## 193 0.99 0.04
## 194 0.99 0.08
## 195 0.81 0.10
## 196 0.96 0.06
## 197 0.96 0.02
## 198 0.89 0.10
## 199 0.98 0.11
## 200 0.99 0.08
## 201 0.90 0.11
## 202 0.79 0.10
## 203 0.84 0.01
## 204 0.83 0.04
## 205 0.76 0.07
## 206 0.75 0.14
## 207 0.72 0.09
## 208 0.81 0.06
## 209 0.75 0.03
## 210 0.78 0.08
## 211 0.80 0.61
## 212 0.35 0.98
## 213 0.30 0.98
## 214 0.86 0.65
## 215 0.84 0.76
## 216 0.54 0.96
## 217 0.39 0.94
## 218 0.51 0.78
## 219 0.44 0.49
## 220 0.48 0.41
## 221 0.56 0.62
## 222 0.44 0.41
## 223 0.42 0.82
## 224 0.38 0.59
## 225 0.37 0.78
## 226 0.42 0.34
## 227 0.40 0.63
## 228 0.51 0.97
## 229 0.69 0.91
## 230 0.60 0.80
## 231 0.47 0.99
## 232 0.37 0.97
## 233 0.49 0.96
## 234 0.72 0.81
## 235 0.45 0.96
## 236 0.88 0.87
## 237 0.51 0.29
## 238 0.59 0.96
## 239 0.78 0.90
## 240 0.70 0.85
## 241 0.85 0.80
## 242 0.90 0.74
## 243 0.67 0.94
## 244 0.50 0.97
## 245 0.82 0.66
## 246 0.73 0.92
## 247 0.57 0.91
## 248 0.71 0.95
## 249 0.79 0.89
## 250 0.74 0.93
## 251 0.59 0.85
## 252 0.85 0.62
## 253 0.40 0.72
## 254 0.31 0.83
## 255 0.40 0.56
## 256 0.34 0.80
## 257 0.37 0.66
## 258 0.29 0.82
## 259 0.13 0.30
## 260 0.05 0.60
## 261 0.06 0.61
## 262 0.01 0.63
## 263 0.03 0.58
## 264 0.08 0.52
## 265 0.13 0.52
## 266 0.09 0.47
## 267 0.09 0.54
## 268 0.04 0.64
## 269 0.04 0.69
## 270 0.21 0.37
## 271 0.24 0.25
## 272 0.23 0.26
## 273 0.15 0.44
## 274 0.50 0.28
## 275 0.48 0.24
## 276 0.45 0.37
## 277 0.44 0.29
## 278 0.35 0.57
## 279 0.18 0.62
## 280 0.40 0.26
## 281 0.30 0.26
## 282 0.10 0.84
## 283 0.27 0.66
## 284 0.17 0.45
## 285 0.15 0.34
## 286 0.14 0.40
## 287 0.20 0.34
## 288 0.11 0.40
## 289 0.07 0.77
## 290 0.32 0.86
## 291 0.25 0.89
## 292 0.27 0.69
## 293 0.28 0.79
## 294 0.15 0.40
## 295 0.17 0.36
## 296 0.10 0.34
## 297 0.09 0.50
## 298 0.07 0.48
## 299 0.13 0.42
## 300 0.13 0.42
## 301 0.17 0.41
## 302 0.13 0.38
## 303 0.10 0.51
## 304 0.13 0.53
## 305 0.14 0.37
## 306 0.20 0.32
## 307 0.13 0.48
## 308 0.15 0.50
## 309 0.25 0.25
## 310 0.19 0.34
## 311 0.17 0.39
## 312 0.29 0.20
## 313 0.27 0.27
## 314 0.21 0.35
## 315 0.12 0.80
## 316 0.05 0.87
## 317 0.08 0.53
## 318 0.05 0.58
## 319 0.03 0.88
## 320 0.01 0.70
## 321 0.42 0.42Ahora visualizamos la información anterior más ordenada:
Encontrar las correlaciones locales inusuales:
gwss.mc.data <- data.frame(gwss.mc)
gwss.mc.dataAhora correlacionamos las variables Gene1204 - `LARent
gwss.mc.out.1 <- ifelse(gwss.mc.data$Corr_GenEl2004.LARent < 0.975 & gwss.mc.data$Corr_GenEl2004.LARent > 0.025 , 0, 1)las correlaciones proporcionan una evaluación preliminar de la no-estacionariedad de la relación entre el dependiente y un variable independiente de una regresión GW
gwss.mc.out.2 <-ifelse(gwss.mc.data$Corr_LARent.Unempl < 0.975 & gwss.mc.data$Corr_LARent.Unempl > 0.025 , 0, 1)
gwss.mc.out.2## [1] 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [75] 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [149] 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## [186] 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [223] 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [260] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [297] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Las correlaciones proporcionan una evaluación de la colinealidad local entre dos variables independientes de tal regresión GW
En ambos casos, especificamos un núcleo bisquare. Además, como La disposición espacial de los DE en el Gran Dublín no es una teselación de zonas de igual tamaño, tiene sentido especificar un ancho de banda adaptativo, N = 109. Esto implica que el kernel bi-cuadrado cambiará de radio, pero siempre incluirá los 109 ED para cada correlación local. Los anchos de banda para las correlaciones de GW no se pueden encontrar de manera óptima usando validación cruzada (aunque ver 53, para una alternativa). También realizamos el pruebas de Monte Carlo correspondientes para las dos especificaciones de correlación GW. Comandos para realizar nuestro análisis de correlación GW son los siguientes, donde usamos la función gwss para encontrar Resumen de estadísticas de GW y montecarlo.gwss
gwss.mc.out <- data.frame(Dub.voter$X, Dub.voter$Y, gwss.mc.out.1,
gwss.mc.out.2)
gwss.mc.outgwss.mc.out.1.sig <- subset(gwss.mc.out, gwss.mc.out.1==1, select =
c(Dub.voter.X, Dub.voter.Y, gwss.mc.out.1))
gwss.mc.out.1.sig gwss.mc.out.2.sig <- subset(gwss.mc.out, gwss.mc.out.2==1, select =
c(Dub.voter.X, Dub.voter.Y, gwss.mc.out.2))
gwss.mc.out.2.sigEl resto del código, verá que los datos se convierten en una especie de mapa con “+” para demostrar el nivel de significancia de la prueba de monte carlo
Entonces se pueden mapearlas
pts.1 <- list("sp.points", cbind(gwss.mc.out.1.sig[,1],
gwss.mc.out.1.sig[,2]), cex=2, pch="+", col="black")
pts.1## [[1]]
## [1] "sp.points"
##
## [[2]]
## [,1] [,2]
## [1,] 316809.4 235404.7
## [2,] 314664.3 236885.6
## [3,] 315548.6 236459.2
## [4,] 315460.8 235985.1
## [5,] 318638.9 236523.5
## [6,] 317881.8 235949.1
## [7,] 316680.4 236572.4
## [8,] 315875.1 237020.6
## [9,] 321050.7 227660.7
## [10,] 322555.8 228798.8
## [11,] 321728.0 228717.3
## [12,] 320557.6 229591.2
## [13,] 324457.6 228248.5
## [14,] 323130.8 227502.8
## [15,] 323244.9 227794.2
## [16,] 323459.3 228413.9
## [17,] 324040.7 228426.7
## [18,] 319874.3 227267.1
## [19,] 320287.9 228243.3
##
## $cex
## [1] 2
##
## $pch
## [1] "+"
##
## $col
## [1] "black" pts.2 <- list("sp.points", cbind(gwss.mc.out.2.sig[,1],
gwss.mc.out.2.sig[,2]), cex=2, pch="+", col="black")
pts.2## [[1]]
## [1] "sp.points"
##
## [[2]]
## [,1] [,2]
## [1,] 313956.8 234737.3
## [2,] 311839.2 236191.5
## [3,] 312999.9 235231.0
## [4,] 311511.5 235137.4
## [5,] 313168.8 233670.3
## [6,] 305780.8 222360.7
## [7,] 300184.6 224296.4
## [8,] 303046.8 224011.5
## [9,] 305820.2 226529.5
## [10,] 307447.7 226305.8
## [11,] 322505.4 249308.6
## [12,] 320312.0 243938.1
##
## $cex
## [1] 2
##
## $pch
## [1] "+"
##
## $col
## [1] "black"Los resultados de esta prueba de Monte Carlo se presentan mediante un gráfico.
Las pruebas confirman si la estadística de resumen de GW o aspectos de Los GW PCA son significativamente diferentes a los encontrados por casualidad o artefactos de variación aleatoria en los datos. Aquí los datos de la muestra se aleatorizan sucesivamente y el modelo GW es aplicado después de cada asignación al azar.
Entonces es posible una base de una prueba de significancia comparando el verdadero resultado con resultados de una gran cantidad de distribuciones aleatorias
Paleta de colores para mapas
library(RColorBrewer)
mypalette.gwss.1 <-brewer.pal(5,"Blues")
mypalette.gwss.2 <-brewer.pal(6,"Greens")Flecha norte y barra de escala del mapa
map.na <- list("SpatialPolygonsRescale", layout.north.arrow(),
offset = c(329000,261500), scale = 4000, col=1)
map.na## [[1]]
## [1] "SpatialPolygonsRescale"
##
## [[2]]
## class : SpatialPolygons
## features : 1
## extent : 0, 0.3908, 0, 1 (xmin, xmax, ymin, ymax)
## crs : NA
##
## $offset
## [1] 329000 261500
##
## $scale
## [1] 4000
##
## $col
## [1] 1 map.scale.1 <- list("SpatialPolygonsRescale", layout.scale.bar(),
offset = c(326500,217000), scale = 5000, col=1, fill =
c("transparent", "green"))
map.scale.1## [[1]]
## [1] "SpatialPolygonsRescale"
##
## [[2]]
## class : SpatialPolygons
## features : 2
## extent : 0, 1, 0, 0.05 (xmin, xmax, ymin, ymax)
## crs : NA
##
## $offset
## [1] 326500 217000
##
## $scale
## [1] 5000
##
## $col
## [1] 1
##
## $fill
## [1] "transparent" "green"map.scale.2 <- list("sp.text", c(326500,217900), "0", cex=0.9, col=1)
map.scale.3 <- list("sp.text", c(331500,217900),"5km", cex=0.9,col=1)
map.layout.1 <-list(map.na,map.scale.1,map.scale.2,map.scale.3,pts.1)
map.layout.2 <- list(map.na,map.scale.1,map.scale.2,map.scale.3,pts.2)Definir pts.1
pts.1 <- list("sp.points",
cbind(gwss.mc.out.1.sig[,1],
gwss.mc.out.1.sig[,2]), cex=2, pch="+", col="black")Aquí se construye una matriz a partir de las dos columnas del marco de datos.
La ayuda para sp.pointsdice que tiene que convertir en un SpatialPointsobjeto:
pts.1 <- list("sp.points",
SpatialPoints(
cbind(
gwss.mc.out.1.sig[,1],
gwss.mc.out.1.sig[,2])
), cex=2, pch="+", col="black")
map.layout.1 <-list(pts.1)Mapas de correlación GW
X11(width=10,height=12)
spplot(gwss.1$SDF,"Corr_GenEl2004.LARent",key.space = "right",col.regions =mypalette.gwss.1,at=c(-1,-0.8,-0.6,-0.4,-0.2,0),par.settings =list(fontsize=list(text=15)), main = list(label="Fig 2. GWcorrelations: GenEl2004 and LARent", cex=1.25), sub=list(label="+ Results of Monte Carlo test", cex=1.15), sp.layout=map.layout.1)
pts.2 <- list("sp.points",
SpatialPoints(
cbind(
gwss.mc.out.1.sig[,1],
gwss.mc.out.1.sig[,2])
), cex=2, pch="+", col="black")
map.layout.2 <-list(pts.2)X11(width=10,height=12)
spplot(gwss.1$SDF,"Corr_LARent.Unempl",key.space = "right",col.regions=mypalette.gwss.2,at=c(-0.2,0,0.2,0.4,0.6,0.8,1),par.settings=list(fontsize=list(text=15)),main=list(label="Fig 3. GWcorrelations: LARent and Unempl", cex=1.25), sub=list(label="+ Results of Monte Carlo test", cex=1.15), sp.layout=map.layout.2)
# Figura 2 y 3. Muestran las Correlaciones de GW y pruebas de Monte Carlo asociadas para: (a) GenEl2004 y LARent; y (b) LARent y Unempl.
#Las correlaciones globales son -0,68 y 0,67, respectivamente.En la Fig 2. se observa la relación entre participación y LARent parece no estacionaria, donde esta relación es más fuerte en áreas del centro y suroeste de Dublín.
Aquí la participación tiende a ser baja, mientras que el alquiler de las autoridades locales tiende a ser alto.
El asociado de La prueba de Monte Carlo sugiere muchos casos de relaciones inusuales, como las que se encuentran en el norte que son inusualmente débiles.
Por otro lado, la Fig 3. muestra correlaciones positivas consistentemente fuertes entre LARent y Unempl se encuentran en tres áreas distintas del Gran Dublín; áreas donde Los efectos de colinealidad local en la regresión GW probablemente sean una causa de preocupación.
Ejemplos: PCA a GW PCA
Para aplicaciones de PCA (Análisis de Componentes Principales) y GW PCA (Análisis de componentes principales geográficamente ponderados), utilizan las ocho variables: DiffAdd, LARent, SC1, Unempl, LowEduc, Age18_24, Age25_44 y Age45_64.
Estandarizamos los datos y especifique el PCA con la matriz de covarianza. Los mismos datos estandarizados (globalmente)
También se utiliza en la calibración GW PCA, que se especifica de manera similar con covarianza (local) matrices. El efecto de esta estandarización es hacer que cada variable tenga la misma importancia en el análisis posterior (al menos para el caso de PCA) 2. Los resultados de PCA (datos PTV y cargas) se encuentran utilizando las funciones de escala y princomp, de la siguiente manera:
#PTV Planificación de la reducción del volumen objetivo
Primero se realiza el PCA con datos escalados
Data.scaled <-scale(as.matrix(Dub.voter@data[,4:11]))
pca <- princomp(Data.scaled, cor=F)
(pca$sdev^2/sum(pca$sdev^2))*100## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## 36.084435 25.586984 11.919681 10.530373 6.890565 3.679812 3.111449 2.196701Data.scaled## DiffAdd LARent SC1 Unempl LowEduc Age18_24
## 0 1.992823795 0.042053392 0.08300173 0.08457623 0.153934876 1.771140106
## 1 2.239591527 0.292337172 -0.02478889 0.86401437 0.134079700 1.393727651
## 2 3.856283914 2.441084645 0.41661456 0.26076238 0.047383080 2.409260974
## 3 0.667772834 0.896271968 -0.42029894 1.44331222 0.289973276 0.203479389
## 4 0.655999870 -0.062969721 -0.17752907 1.03360347 0.125807857 -0.243334824
## 5 -0.548624447 -0.546893343 -0.32680579 -0.88627481 -0.405833978 -0.255738085
## 6 0.211761215 -0.591185933 -0.37400996 -0.58790966 1.510686426 -1.715506549
## 7 -0.838906933 -0.516965178 -0.88162743 -0.26877159 -0.595713048 1.055052895
## 8 -0.480214120 1.772460485 -0.95781219 2.36534497 0.521433333 -1.113538370
## 9 0.895173683 0.373433201 -0.68308484 2.30784739 0.545892089 0.678542348
## 10 -0.874080881 -0.264862900 -0.93846342 0.21088621 0.077299461 0.441461163
## 11 -0.954853724 -0.119191566 -0.92860451 -0.24861993 -0.261369457 -1.677175361
## 12 -0.277809440 -0.417642527 -0.64006740 -0.54828438 0.376412305 0.188557250
## 13 -0.783470197 -0.523444244 -0.79096907 -0.30348047 -0.439351124 -0.587992949
## 14 -0.904214933 0.978759667 -1.20729021 2.17228627 -0.279739696 1.156309054
## 15 -1.129077899 2.453312090 -1.17800569 4.08763423 0.985641402 0.541448813
## 16 -0.908521637 3.405096344 -1.11461767 2.49335644 0.292126296 -0.050341770
## 17 -1.050319090 3.435914458 -1.23170949 3.44318064 0.585085882 -1.131224778
## 18 -0.753242071 -0.599998341 -0.37578744 -0.53812628 -0.100986633 -1.546120439
## 19 -0.778178730 -0.591239357 -0.59860524 -0.51399302 -0.313277505 0.122888458
## 20 -0.605562783 -0.606138431 -0.75520430 -0.78919474 -0.542240168 -0.830147678
## 21 0.352255488 -0.546308590 -0.18721477 -0.61036757 -0.158954270 0.679419520
## 22 -0.539018613 -0.452910428 -0.88900090 -0.49809788 -0.383656349 -0.955416467
## 23 -0.744428285 -0.603118388 -0.52326795 -0.77138170 -0.510597789 0.962732538
## 24 -0.418632239 -0.598327033 -0.42879238 -0.46721363 -0.354799260 -0.334846943
## 25 -0.150865267 -0.464323584 -0.38117717 -0.67949525 -0.375045644 0.671172393
## 26 0.092764758 -0.584790871 0.28147563 -0.69400947 -0.609487972 -0.183543362
## 27 0.708179554 -0.487492883 0.38849822 -0.53808538 -0.341432073 0.328498830
## 28 1.132722475 -0.534617821 0.40993213 -0.33038350 -0.498854666 0.737411288
## 29 0.899710437 -0.352877057 0.10725790 -0.29860345 -0.179718591 0.895974827
## 30 -0.606112065 -0.438223755 -0.88378030 0.58957874 0.431440373 -0.372867271
## 31 0.771588299 -0.231192185 -0.46493953 1.26598866 -0.103349032 0.219012695
## 32 -0.879248926 -0.256375535 -1.05210251 0.61244565 0.021973854 -0.265390599
## 33 -0.954740900 -0.051782252 -1.16293856 1.34008177 0.485604754 -0.388419542
## 34 -0.719934606 -0.351126686 -0.88940624 0.44425506 -0.475869827 0.008301569
## 35 0.360866941 0.078087787 -0.56063129 0.35744860 -0.045794643 0.328962730
## 36 -0.633572911 -0.235437751 -0.31773738 -0.67940493 -0.103349032 -0.791932423
## 37 -0.143853075 -0.485783258 0.83699937 -0.82294325 -0.550144220 -0.042110970
## 38 -0.391657905 -0.605033528 0.99492792 -0.83464851 -0.216868185 -1.333239060
## 39 0.115197610 -0.579155580 0.93267371 -0.60820428 -0.465522103 -0.805273213
## 40 -0.238716171 -0.574201764 1.05735224 -0.90673909 -0.443741193 -0.789945718
## 41 -0.280247868 -0.386033535 -0.19956498 -0.66879637 -0.299256009 -0.110676455
## 42 -0.624379405 -0.196990749 -0.79137880 0.66685649 -0.104814684 -0.459636826
## 43 1.196233120 -0.503711449 1.02592415 -0.54706782 -0.564649215 -0.581764025
## 44 0.860323696 -0.578191954 -0.19243358 -0.29287797 -0.672560597 -0.072916690
## 45 -0.647872833 -0.600702783 -0.29067508 -0.73505656 -0.049965690 -0.369100331
## 46 0.256026931 -0.544566441 0.16655393 -0.56923964 -0.264716763 0.542569066
## 47 0.665192072 -0.084309747 -0.51858000 -0.55856348 0.017565878 -0.346948743
## 48 0.229789538 -0.600412371 -0.00054184 -0.68543564 -0.222766608 0.853341393
## 49 -0.784471263 -0.318438633 -0.88818826 0.91524001 -0.157129093 -0.697359522
## 50 -1.129146213 0.244829432 -1.23707122 1.63130133 0.636368548 0.542622168
## 51 -1.092043827 0.109838987 -1.13888376 0.78611973 0.938131688 -1.244176392
## 52 -0.821331372 -0.543925752 -0.98808971 -0.34427383 -0.089125045 -0.823839761
## 53 -1.057787795 -0.271373114 -1.21698372 1.08792361 0.300668127 -0.728686202
## 54 -0.519229130 0.183408927 -1.01055553 0.97742090 0.452433358 -0.022384919
## 55 -1.195821630 0.140426594 -1.25183687 1.35113663 0.849529996 -0.415976479
## 56 -0.891306721 -0.108166679 -1.12791123 1.17148485 0.097526560 -0.549144429
## 57 -0.420605025 -0.441501889 -0.38330757 -0.31569337 -0.376467216 0.429534336
## 58 -0.489340780 -0.567627028 -0.69080146 -0.47531883 -0.108500854 -0.069410313
## 59 -0.998068158 -0.339125212 -0.99085889 -0.37057171 -0.575895064 -0.356309031
## 60 -1.026640612 -0.434794340 -1.09347921 0.50823305 -0.197562629 0.429289770
## 61 -0.406984001 -0.449078729 -0.54370366 -0.15773796 0.092962927 -0.329980035
## 62 -0.578699968 -0.203282040 -0.83839674 0.22911184 -0.035127341 -0.340976919
## 63 -0.832764234 -0.431281042 -0.81220992 -0.28333354 -0.313838144 -0.249180490
## 64 -0.870196287 -0.406396611 -0.71239524 0.31083451 0.348884495 -0.875524932
## 65 2.534331856 -0.272397455 -0.59154417 2.70705740 0.740252142 1.823240033
## 66 1.400902917 0.961791659 -0.35145199 0.69707007 0.241849199 0.549127980
## 67 0.727921755 2.701953279 -0.55847077 1.66094612 0.123678254 0.748854591
## 68 -0.511228100 -0.435841809 -0.64688560 -0.08861256 0.055500643 -0.618227081
## 69 -1.103718640 0.617105872 -1.16443100 1.28163253 0.909248426 -0.377391325
## 70 -1.024458320 1.062965455 -1.23294455 1.86356117 0.062755895 -1.339511850
## 71 -1.008157569 -0.486065813 -1.04595223 -0.40767640 -0.196468900 0.233399362
## 72 0.287798069 3.435914458 -0.93405676 3.78998613 1.421714189 0.587563887
## 73 1.580870669 2.296197586 -0.67144471 2.43792899 0.067231368 1.191947710
## 74 2.019115481 3.435914458 0.03559196 -0.21604878 -0.435061613 2.056092358
## 75 0.241427341 -0.546103155 -0.64027121 0.41715587 -0.236184762 0.129672148
## 76 0.036647656 -0.322836736 -0.69593139 0.80039386 0.056823035 0.257541119
## 77 1.992639723 2.530301602 -0.18828901 1.32398327 0.180375844 1.879846863
## 78 4.056065932 3.222736641 0.75192022 -0.68171326 -0.112604404 -1.529977945
## 79 -1.191151672 -0.473054256 -1.09207077 -0.10553964 -0.265820135 1.285465004
## 80 -1.019845100 1.230135104 -1.15763299 2.90190884 0.479831683 1.500046965
## 81 -1.154870845 2.011231510 -1.27480099 3.02678301 0.634941466 -0.522082725
## 82 -0.777465919 0.263028836 -1.19624995 1.63962542 0.552196872 -0.830345408
## 83 -0.957783392 -0.419265027 -1.07914234 0.06150729 -0.107436052 0.484683501
## 84 -1.133201673 -0.526412281 -0.13415888 -0.18176499 -0.232701084 -0.439632073
## 85 -0.726480504 0.022874972 -0.88364209 0.31302072 0.918076775 -0.531507494
## 86 -0.631202308 -0.519943502 0.01480203 -0.64112610 -0.456228162 -0.513160280
## 87 1.249630224 3.435914458 -0.58180572 2.00907344 0.051890235 1.327300755
## 88 1.993265634 3.435914458 -0.65094894 1.23900288 0.148772034 0.787451618
## 89 0.408002816 -0.268808050 -0.08353600 -0.97075362 -0.218504646 1.286012845
## 90 -0.479973473 -0.549986172 -0.69737792 -0.25365581 -0.431487020 0.375520595
## 91 -0.362889966 -0.387288181 -0.47936088 -0.21680201 0.262773310 -0.033003163
## 92 0.103336768 -0.479968657 -0.23831103 -0.46060654 0.054988215 0.125636896
## 93 0.867634577 0.244920647 0.43424723 -0.46770101 -0.288430296 -1.085894524
## 94 2.179285435 2.071576437 -1.22778468 4.46354600 3.239600623 1.707256661
## 95 -1.049394816 0.079271064 -1.21580888 0.83404353 -0.205614072 0.096250251
## 96 -1.297623224 1.663310077 -1.24078148 2.06958770 0.138922963 1.252881853
## 97 -0.599159871 -0.175926756 -1.09567684 0.53328295 -0.028409310 -1.177598106
## 98 -0.766669565 -0.111835512 -1.01022622 0.97807624 0.644468204 -0.507824525
## 99 -0.408694455 -0.001614426 -0.62830421 0.93888854 -0.102001845 -0.585863924
## 100 -0.491908987 -0.214811523 -0.78501625 0.55216769 0.174258400 -0.113294282
## 101 -0.504731277 0.070402923 -1.11332921 0.78341149 0.130251648 -0.466166715
## 102 -0.410757076 -0.552531592 -0.90122185 -0.09879850 -0.605595179 -0.699946675
## 103 -1.013340615 0.066065170 -1.22184407 1.08784465 0.722873697 0.180014104
## 104 -0.852802190 0.216469128 -1.16067207 1.13620187 0.334010332 -0.470178880
## 105 -0.154273294 -0.269750937 -0.63269817 0.50342856 0.193243001 -1.326442177
## 106 -0.585275701 0.945362595 -1.05532896 1.32079637 -0.155484367 -0.484950783
## 107 -0.749663830 0.043032409 -1.07562499 0.98778975 -0.198923591 0.039616870
## 108 0.375356479 0.030190846 -0.13082368 0.34222413 0.901716297 -1.233448976
## 109 0.983372351 0.488946268 -0.51449582 0.93235814 0.593051920 0.165783280
## 110 -0.788641014 -0.111862285 -1.06128406 1.03858728 1.131185878 -0.652162352
## 111 -0.808201099 -0.218133400 -1.01865178 0.73130425 0.374014090 -0.153288121
## 112 1.454617460 -0.386565472 0.52091567 -0.31594937 0.295499775 0.444818789
## 113 -0.343488356 -0.262868615 -0.77214854 0.53134081 0.136431079 -0.208698344
## 114 -0.211445592 0.013612323 -0.37166712 0.21631916 0.183356737 -0.105563497
## 115 -1.154760467 0.144178338 -1.25841609 1.80451757 2.948682482 -0.492623353
## 116 1.729815312 3.435914458 -0.12854026 0.19168450 0.483608767 2.439711472
## 117 0.977451550 -0.344438741 0.89274029 -0.62452999 -0.672560597 0.166831961
## 118 1.189648582 3.435914458 -0.46279545 1.24671007 0.611542002 1.690588260
## 119 2.679409676 0.660821485 0.38849822 0.46821905 -0.104756830 1.978105401
## 120 0.708654164 3.435914458 -0.57659196 1.17647169 0.277938124 0.609335352
## 121 0.167782612 -0.409518075 -0.20925345 -0.32503633 0.001442329 -0.403100249
## 122 1.603559657 -0.344438741 0.43490636 -0.16341364 -0.442978062 0.904193424
## 123 0.501881476 2.113770202 -0.46219623 2.82272853 0.416757681 0.297784636
## 124 -0.104255686 1.125149579 -0.56932854 0.65191945 -0.058391811 0.635481460
## 125 0.459912994 -0.549201087 1.60549194 -0.81410243 -0.312499222 -0.660153204
## 126 0.854189638 -0.600062621 2.24980949 -0.93421792 -0.476755554 -0.303944749
## 127 0.798631111 -0.127237400 1.80237866 -0.63727099 -0.549056001 -0.042715705
## 128 1.758202063 -0.520560294 2.25048359 -0.79051487 -0.497380749 -0.475676249
## 129 0.831682439 -0.417843669 0.44731793 -0.48735772 -0.219996471 -0.027259578
## 130 0.816698075 -0.446365259 1.40593535 -0.80586734 -0.504505140 -0.014653805
## 131 1.584150873 -0.477285403 1.81061425 -0.61151237 -0.379566574 0.025335582
## 132 0.658145478 -0.567197445 2.63638429 -0.88297373 -0.537158598 0.522177262
## 133 2.276679892 -0.195093228 1.75797574 -0.67437340 -0.504368768 1.078656541
## 134 1.850784554 -0.514264345 2.07481575 -0.79266870 -0.322943369 0.879337265
## 135 0.708403408 -0.567421269 2.86147314 -0.81511961 -0.616451197 0.377839105
## 136 1.700005873 -0.534144858 1.47026152 -0.61449629 -0.205614072 0.601972323
## 137 2.521940808 -0.056268329 0.65349852 -0.26491232 -0.122976922 1.795588106
## 138 2.497924347 -0.044990739 0.84077116 0.17123897 -0.074832183 1.778959778
## 139 1.658763083 -0.490472827 1.87816378 -0.59024071 -0.368980545 1.138499291
## 140 1.050291024 0.366074554 1.28401281 0.12661095 -0.005347332 0.765777617
## 141 1.498261994 -0.490086543 1.42604515 -0.59199047 -0.511639173 0.755868829
## 142 2.143625465 -0.461467581 1.50159309 -0.47523249 -0.672560597 1.072879974
## 143 1.416033855 3.435914458 -0.06089111 -0.31915502 -0.203138718 1.610698704
## 144 1.356231556 3.435914458 -0.29322499 0.30686314 -0.073650295 0.587253851
## 145 1.768897657 1.555381627 0.14704628 0.16826698 -0.329793639 1.688041511
## 146 2.242627334 2.881652126 0.81285699 -0.37485153 0.031781600 1.228474257
## 147 0.757674130 -0.540692983 0.73933951 -0.50989459 -0.516087096 0.106941881
## 148 0.076692590 -0.520356681 0.56058294 -0.13330572 -0.397392704 -0.242990157
## 149 -0.219580479 -0.564459308 1.05817367 -0.83603207 -0.672560597 -0.541000373
## 150 -0.299325843 -0.587003795 1.50826624 -1.03756709 -0.672560597 -0.626632359
## 151 3.004376501 0.562335156 0.20185198 0.75058359 0.456531398 1.251954384
## 152 1.905136910 3.435914458 -0.11100006 1.49660837 0.072030552 1.107628430
## 153 -0.391955942 2.998035700 -1.01609096 3.09763980 1.050329826 0.476466554
## 154 1.304625937 0.536581918 -0.75315562 1.30126160 0.746315863 -0.210308883
## 155 0.591011592 2.800099255 -0.57873425 1.61295566 0.609821514 0.017686099
## 156 1.185471005 -0.148829927 -0.10992957 0.03622317 0.058353429 -0.390651802
## 157 -0.565986589 -0.363988095 -0.88067496 0.53717138 -0.376537468 -0.255105644
## 158 -0.607780291 -0.557255264 -0.98647404 -0.13604387 -0.103740240 -1.096888439
## 159 -0.534648649 -0.351065160 -0.88623432 0.10428633 -0.280112996 -0.670323215
## 160 0.075741414 3.435914458 -0.61769257 2.01299236 0.484455925 -0.022925833
## 161 1.838841702 -0.125454869 0.44697999 -0.08878543 -0.280112996 0.889109011
## 162 -0.541052701 -0.574161706 -0.46821547 -0.11864891 1.484587077 -1.437114860
## 163 -0.710410944 -0.614463984 0.39577319 -0.91290651 -0.504879818 0.643038921
## 164 0.115054135 -0.060889325 -0.47933891 0.03719168 -0.073413366 -0.459691577
## 165 -0.659566559 -0.600351170 -0.84114574 -0.56585903 -0.118015194 -1.263979951
## 166 -0.959621506 0.757615444 -0.84525092 0.65077976 1.327946900 0.326608764
## 167 -0.364401348 0.508772830 -1.06897398 0.60844682 0.346571686 1.206198654
## 168 -0.406620911 -0.578162751 -0.62389006 -0.37328563 -0.321384048 -0.575449018
## 169 -0.898129134 0.351770886 -1.11517733 0.42139308 0.032703142 0.200627881
## 170 -1.144787466 0.838969968 -1.17691876 1.86213312 0.134170614 -0.022018648
## 171 -0.485158638 -0.574478527 -0.75949929 -0.24054199 -0.339602763 -0.093152816
## 172 -0.562290143 0.105182429 0.15962032 -0.26461731 0.592901774 -0.443684641
## 173 0.112762443 -0.597674841 0.46930191 -0.88699944 -0.567727911 -0.987274496
## 174 -0.870293785 -0.604552789 0.59499347 -0.80924075 -0.519506033 0.990867495
## 175 -0.283794479 -0.586784872 -0.24451861 -0.58646437 -0.276087964 0.017952434
## 176 0.737129772 -0.555555645 0.34328136 -0.66459626 -0.280308600 -0.947108818
## 177 -0.387970264 -0.292781754 -0.15784138 -0.56911353 -0.345882751 -0.156355005
## 178 -0.448686238 -0.490536054 -0.38868510 -0.70521524 -0.378977007 0.453325983
## 179 -0.611200873 -0.573619846 -0.64705474 -0.88784715 12.048757161 -2.012164975
## 180 -0.803100226 -0.611562577 -0.85489185 -0.56159076 -0.005724765 -0.527918331
## 181 -0.707703500 0.079191231 -0.82809131 0.08212607 3.131393081 1.358883545
## 182 -0.529341856 -0.517290221 -0.65014851 -0.62188746 -0.250202125 -1.597591389
## 183 -0.713513108 -0.610200434 0.37860040 -0.85058038 -0.531206454 -0.817884593
## 184 -0.383562148 -0.588113923 1.16501736 -0.69116907 -0.613339441 -0.280510633
## 185 -0.535309776 -0.608303723 1.24338347 -0.85307162 -0.424810314 0.331531577
## 186 -0.479868801 -0.503325569 0.44641283 -0.63408881 -0.435878466 -0.306709183
## 187 0.177244156 -0.584075291 1.68476983 -0.88320776 -0.548405824 -0.780066449
## 188 -0.027014617 -0.515674282 -0.62382316 -0.21124883 0.354566652 -0.719811087
## 189 -1.013458982 0.179899031 -1.17210481 1.17148485 0.038097403 -0.170122590
## 190 -0.598444125 -0.614463984 -0.84790093 -0.58086102 -0.672560597 1.350893188
## 191 0.341914180 1.698128345 -1.21781231 1.82976268 0.226959883 1.076405810
## 192 -0.104154438 -0.546199189 -1.04949091 0.19232166 0.159761669 -1.223119660
## 193 -0.163013386 0.677024781 -1.03597026 0.92511523 -0.152752799 0.852357852
## 194 -1.080855362 1.553550532 -1.25523034 2.38688664 0.592160683 -0.131024722
## 195 -1.065994256 -0.603524398 -0.97118812 -0.55279539 -0.048201122 0.793030620
## 196 -0.739891205 0.301149742 -1.02982128 0.70669346 -0.042129135 0.496712739
## 197 -0.794851701 -0.405681597 -0.94676933 -0.63633391 -0.475776157 0.543652543
## 198 -0.854744813 -0.530570480 -1.10700719 -0.11809564 -0.101972917 -0.303819580
## 199 -0.828531875 -0.573455482 -0.95166175 -0.35793543 -0.325257557 1.011000220
## 200 -0.469135545 -0.445056501 -1.05142483 0.36706101 0.121383352 1.139347932
## 201 -0.802243451 0.289526250 -1.04049673 0.45834364 0.167587204 0.350853261
## 202 -0.437888418 -0.614463984 0.44993295 -0.95922218 1.539010426 -0.367404368
## 203 -0.172817314 -0.591993092 1.41111965 -0.91442450 -0.453911219 -0.407053374
## 204 -0.842173768 -0.515674282 -0.63411670 -0.49208269 -0.281227388 -0.193208245
## 205 -0.857740349 -0.608938215 0.29307978 -0.98372524 -0.672560597 0.732990304
## 206 -1.060393828 -0.579330596 -0.38558579 -1.00006951 -0.076888780 0.772992554
## 207 -0.644300135 -0.592003461 0.58364506 -0.88282888 -0.556119782 -1.363483417
## 208 -0.649505028 -0.480197301 -0.79188979 -0.70449912 -0.180598809 -0.646053815
## 209 -0.656154945 -0.592106786 -0.45847084 -0.37366735 -0.548405824 -0.682327716
## 210 -0.805523492 -0.595589909 -0.81306634 -0.34130203 -0.347935215 -0.543828463
## 211 -0.905693706 -0.614463984 -0.53546840 0.26079949 -0.672560597 -1.055991207
## 212 -0.775003035 0.027085300 -0.40647290 -0.27434655 -0.314902946 0.576661334
## 213 -0.130421667 -0.302022449 -0.76073908 -0.19859702 -0.007835083 -0.223156913
## 214 -0.845881952 -0.389802980 -0.70281177 -0.26742647 -0.579465524 -0.023821310
## 215 -0.972554011 -0.614463984 0.01372617 -0.09959659 0.323217679 4.688736175
## 216 -0.981786810 -0.515271026 -0.04094214 -0.62439802 -0.183220177 -0.045841793
## 217 -1.240799343 -0.492464641 -0.43605206 -0.41404434 -0.672560597 -1.129161224
## 218 1.572979691 -0.457167740 -0.51499655 1.70687193 0.996115855 0.105367952
## 219 0.305268958 -0.582131109 -0.40545662 -0.41959581 1.111115813 0.341163477
## 220 0.005966604 0.381089631 -0.69325044 1.09232348 -0.087430729 -0.501955277
## 221 -0.775301561 0.532732681 -1.06717778 1.20092097 -0.058522673 -0.598747410
## 222 -0.169231409 -0.580659607 0.22889963 -0.65277739 -0.148300744 0.785459801
## 223 0.259755822 0.499589042 -0.62681519 1.10315338 -0.547787330 -0.802731906
## 224 -0.818071874 -0.594544101 0.09970032 -0.85576622 -0.384783138 0.642278343
## 225 -0.917704056 2.249947570 -1.26140748 0.01277819 0.024903780 -0.180020329
## 226 0.234145644 -0.582311027 0.97685850 -0.76718463 -0.450884869 0.237529604
## 227 -0.743067064 -0.581614200 1.55892558 -0.94942213 -0.524655099 -0.971435772
## 228 -0.476285343 -0.406158813 -0.28817987 -0.54546462 4.089141545 4.026913372
## 229 -0.031097304 -0.490523376 -0.22115550 -0.67269181 6.904147932 -0.633518460
## 230 3.216919341 -0.550172246 -0.92213289 0.47094735 -0.444121381 -1.939731788
## 231 -0.541778719 -0.259167622 -0.10585109 -0.86018635 -0.494819990 0.268384798
## 232 -0.923091554 -0.483806633 -0.04528173 -0.63257423 -0.218691985 -0.690838043
## 233 -0.569537276 -0.529550392 0.20081193 -0.59617259 -0.279925658 0.738124701
## 234 -0.448912538 -0.531226277 0.92754183 -0.54370009 -0.423055388 0.046484500
## 235 -0.990314786 -0.239995035 -0.42140427 -0.12686885 -0.361613715 -1.914958250
## 236 -0.198631944 -0.594372811 -0.40165781 -0.65345658 0.193008827 -0.710085517
## 237 0.080151160 -0.569954335 0.42318500 -1.19386780 -0.672560597 2.761417521
## 238 -0.834571696 -0.371926351 -0.52252060 -0.31657876 -0.047137698 0.475419852
## 239 0.068267818 -0.429839431 0.83170015 -0.72259088 -0.597080898 0.170544644
## 240 -0.603817275 -0.530305869 0.29273955 -0.85880941 -0.535905080 -0.246211729
## 241 -0.971535010 -0.605382428 0.14355839 -0.80774567 0.229163871 1.147405739
## 242 -0.948744774 -0.455452405 -0.36454826 -0.60788428 -0.176836877 -0.041436146
## 243 -0.416510924 -0.354557761 -0.66758942 -0.44614192 -0.332133999 -0.153526915
## 244 0.051825060 -0.304925720 0.04734148 -0.35449366 -0.257486305 -0.458223028
## 245 -0.275531606 -0.600194906 0.23508670 -0.70834837 -0.229229803 -0.261172788
## 246 0.071027598 -0.594190382 -0.51307631 -0.71648386 -0.559410612 0.242374744
## 247 -0.578384648 -0.284824380 -0.67043576 -0.27725587 -0.250448696 -0.817166398
## 248 1.813000334 -0.214621844 -0.12741914 -0.63269863 -0.200586225 -0.295191505
## 249 -0.232617818 -0.588500005 -0.13251407 -0.71498575 -0.487413213 -0.958029181
## 250 0.521783456 -0.448515240 -0.60810373 -0.21877217 0.305452158 -0.817458624
## 251 2.941532689 -0.570318180 -0.09098500 0.52379229 1.218733788 -0.601416360
## 252 -0.202779684 -0.523614360 -0.38151610 -0.72249507 -0.553193988 -0.486394925
## 253 -0.461322173 -0.597319542 -0.14027920 -0.83093806 -0.473788436 1.151792421
## 254 -0.687355605 -0.603575878 -0.17009749 -0.61070442 -0.355348879 -0.688660699
## 255 -0.799826055 -0.604248646 0.74140219 -0.95998355 -0.672560597 0.617810718
## 256 -0.269111739 -0.614463984 1.07138047 -0.95323123 -0.672560597 -1.022986539
## 257 -0.428913223 -0.075312115 -0.36361272 -0.47922222 -0.253232609 -0.045791495
## 258 -0.103264728 -0.591487767 0.55431252 -0.94534624 -0.576702274 0.377774954
## 259 -0.691538073 -0.345542024 -0.17734610 -0.56123422 -0.495883415 0.056932884
## 260 1.016992364 -0.520993725 2.08827633 -0.90962304 -0.135787602 -0.450766329
## 261 0.537362265 -0.580750983 1.76048495 -0.91301084 -0.598601649 0.150833930
## 262 0.550538515 -0.503142816 2.54931067 -1.00268155 -0.612302189 -0.375343107
## 263 0.457572553 -0.614463984 1.92917099 -0.90724199 -0.672560597 -0.332061400
## 264 0.319740073 -0.566998612 1.55081289 -0.83466309 -0.672560597 -0.254740032
## 265 -0.531724850 -0.591351998 1.18275404 -0.91185563 -0.311488142 -0.291743177
## 266 0.890342315 -0.578830172 2.13539081 -0.86062204 -0.523157765 -0.287537075
## 267 -0.356084509 -0.382712792 0.58159605 -0.57650716 -0.431880983 -0.505906589
## 268 0.318154671 0.136014841 0.98043722 -0.27820544 2.553592851 -0.034728151
## 269 1.062298929 -0.340378237 2.13282137 -0.98259446 -0.373431223 0.286006396
## 270 -0.714021304 -0.614463984 0.15018010 -0.68182725 -0.588154747 -0.413328143
## 271 -0.802441707 -0.070789017 -0.11147540 -0.03942164 0.920284895 0.270092306
## 272 1.717452306 -0.602039529 1.36295015 -0.79018522 -0.672560597 -1.920365248
## 273 -0.635695041 -0.500288393 0.21322351 -0.61527905 -0.341233714 0.142084314
## 274 -0.497684683 0.016998116 0.28070564 -0.58674006 -0.212373428 -1.329892978
## 275 -0.702355621 -0.614463984 1.57537445 -0.97194424 -0.352956175 -0.894861788
## 276 -0.472891826 -0.280298568 -0.56303029 0.34736775 0.751645382 0.242569836
## 277 -0.525087813 -0.607888681 1.38445634 -0.87324033 -0.672560597 -1.212450953
## 278 -0.063809999 -0.614463984 1.74793939 -1.01784335 -0.672560597 -0.473926689
## 279 3.684187576 -0.366481630 0.31875138 -1.04785755 -0.479364525 7.110278353
## 280 0.126568843 -0.008799919 -0.11577560 0.09257407 0.065381396 -0.005299288
## 281 0.715960806 -0.600176922 2.84506838 -0.93740294 -0.554320777 0.251746576
## 282 -0.015056440 -0.607045716 2.09780299 -0.99169809 -0.430044785 0.666819024
## 283 0.966183521 -0.508141550 0.98769039 -0.67923376 -0.098263330 -0.052225075
## 284 -0.649737849 -0.614463984 1.06474557 -0.75479242 -0.672560597 -0.515932960
## 285 -0.192405605 -0.518597035 0.69417149 -0.28303039 -0.456652429 -0.680298793
## 286 -0.039789788 -0.356785834 1.32549777 -0.32986961 -0.411671791 -0.902787170
## 287 -0.043607698 -0.128747867 1.28943217 -0.66589537 0.375405358 -0.486989106
## 288 -0.562812361 -0.608568253 1.62523580 -1.01253992 -0.672560597 -0.260325300
## 289 -0.460506157 -0.303875578 -0.06557516 -0.45383738 -0.317434777 0.566810760
## 290 0.278559890 -0.582017699 2.26109197 -0.98651754 -0.672560597 -1.428640319
## 291 -0.541767306 -0.343816563 0.69824932 -0.75606843 -0.627219055 1.512425653
## 292 -0.372857749 -0.614463984 0.97638348 -0.57812135 -0.577166489 -1.569823363
## 293 -0.009931274 0.034572546 1.01572469 -0.50732117 -0.229637540 0.105972523
## 294 1.319591878 -0.546006877 1.06586929 -0.31020703 -0.672560597 -0.653800761
## 295 -0.011381027 -0.477563096 1.01052656 -0.37308662 0.416652992 -0.098912397
## 296 0.414198340 -0.590449898 1.28427523 -0.35842186 -0.165009727 -1.006501356
## 297 -0.027839926 -0.257077668 -0.77956742 0.30142073 -0.324049496 -0.710363890
## 298 0.123978299 0.729263443 -0.34538441 0.51669518 -0.131836954 -0.768817249
## 299 -0.739434367 -0.088093206 0.33415255 -0.32840104 -0.201047685 0.388644690
## 300 -0.410000733 0.422719016 -0.52485725 0.13800341 -0.407912614 -2.191479828
## 301 -0.926886638 0.132579877 -0.78330730 0.29453058 1.116390231 -0.449978210
## 302 -0.077871882 -0.191942479 1.74513668 -0.64776177 -0.618835636 -0.443777652
## 303 1.165492396 -0.118499275 2.30685466 -0.13180154 -0.212629641 -1.749386904
## 304 0.532632308 0.621737682 -0.18096812 0.69914115 -0.113739458 -0.239252242
## 305 -0.513292839 -0.558884165 1.49509281 -0.99649596 -0.672560597 -0.323405125
## 306 -0.371185121 -0.596591365 1.74330174 -0.89143926 -0.672560597 0.257087444
## 307 -0.277066792 -0.594535920 2.18969138 -0.92627140 -0.510216224 -1.557866522
## 308 0.146725166 -0.593798791 2.25576784 -0.98576754 -0.507393741 -0.603880323
## 309 -0.167676496 -0.272976983 0.61597370 -0.58494088 -0.321215994 -0.547801049
## 310 -0.522116407 -0.518301155 1.45738225 -0.88161232 -0.557578547 -1.158465412
## 311 -0.429670381 0.426000187 -0.39753895 0.38764647 -0.326219046 0.562187758
## 312 -0.163381856 -0.282818147 0.49891932 -0.37908477 -0.373322401 -1.315379823
## 313 -0.532116147 0.690327398 -0.30636243 0.33535802 -0.230448884 -0.024291312
## 314 -0.315331980 -0.589781585 0.01073999 -0.54724013 -0.389790323 -0.396305179
## 315 -0.349677032 -0.610236037 2.18523718 -0.94763053 -0.602316747 -0.806552937
## 316 -0.215185896 -0.611400156 1.02360393 -0.95796113 -0.573673168 0.095926857
## 317 -0.317618781 -0.595566417 1.59600631 -0.81268025 -0.577232609 0.190527134
## 318 -0.227991229 -0.475127118 1.54828464 -1.03326985 -0.455175758 -0.367430754
## 319 -0.175034170 -0.532222792 2.37636512 -1.05540039 -0.672560597 -0.098209209
## 320 -0.046647744 -0.515302174 1.70296997 -0.53732250 1.822111298 -0.759350592
## 321 -0.278130303 -0.520269153 -0.60088443 -0.55812987 -0.247408571 1.774210453
## Age25_44 Age45_64
## 0 1.174970276 -1.672806406
## 1 2.052393893 -1.122701786
## 2 2.675385233 -2.241797604
## 3 1.134058657 -0.838806691
## 4 0.948686430 -0.272703148
## 5 0.329953590 -0.754770291
## 6 -0.034295506 -0.037155868
## 7 -0.489708329 0.877914842
## 8 -0.105071949 -0.474125811
## 9 1.088157751 -0.933815089
## 10 -0.254147328 -0.090705058
## 11 -0.754492805 -0.576061885
## 12 -0.898704379 0.984385954
## 13 -0.383210680 0.378459450
## 14 -1.021164394 1.232517449
## 15 -0.742306889 -0.231742081
## 16 -0.439952658 -0.723013309
## 17 -0.170712561 -1.807825494
## 18 -0.962702908 -0.027585699
## 19 -0.711798789 0.549895777
## 20 -0.711949782 0.414007969
## 21 0.217679504 0.821871235
## 22 -0.604100318 0.023109826
## 23 -0.696890109 1.023306791
## 24 -0.514442959 1.061604463
## 25 0.312174119 0.020683195
## 26 -0.377593097 0.750727508
## 27 0.612847406 -0.388798346
## 28 1.103848389 -0.845418098
## 29 1.050979568 -0.430420280
## 30 -0.281913085 -0.239471864
## 31 0.859013942 -0.483701254
## 32 -0.645579124 0.646834032
## 33 -0.577891210 0.458739866
## 34 -0.275040691 0.253987082
## 35 0.255352047 -0.264495932
## 36 -0.867109665 -0.251252021
## 37 -0.438078895 0.316160168
## 38 -0.541468288 0.270805302
## 39 -0.236743708 0.039560984
## 40 -0.505131393 -0.247509907
## 41 0.116895700 -0.322538159
## 42 0.008860535 0.083220217
## 43 1.323448952 -0.136594791
## 44 0.892445635 -0.680231976
## 45 -0.270256386 -0.485643614
## 46 0.105325917 -0.196387153
## 47 0.250174983 -0.940788087
## 48 -0.215450616 -0.173497277
## 49 -0.624023648 -0.087923282
## 50 -0.646466392 -0.560839866
## 51 -0.498675786 -0.565321377
## 52 -0.313916726 -0.055411274
## 53 -0.517895472 -0.659096924
## 54 0.173974074 0.174551161
## 55 -0.535736496 0.363092286
## 56 -0.101323087 0.428523800
## 57 -0.459310433 0.368777541
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## 222 -0.550318421 1.636742527
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## 224 -1.543058672 2.110517672
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## 271 -0.673997947 1.074531713
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## 275 -1.325150325 0.568311174
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## 283 0.250174983 -0.575403362
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## 285 -1.065819239 0.868687316
## 286 -0.752330702 1.034153178
## 287 -1.210581239 1.288683931
## 288 -1.122678688 0.789688782
## 289 -0.555695424 1.381928322
## 290 0.073819752 -0.510959534
## 291 -1.170414201 1.380258280
## 292 -0.407218824 0.511206234
## 293 -0.209617223 0.020349929
## 294 0.958121845 -0.094323320
## 295 -1.009503244 0.454028428
## 296 -0.459382815 0.790187509
## 297 0.278226707 -0.584674451
## 298 -0.415552793 -0.769926868
## 299 -0.940070756 0.461818378
## 300 -1.103396410 0.385484997
## 301 -0.327796934 0.124445396
## 302 -1.053878501 0.858352353
## 303 1.330241550 0.056601707
## 304 -0.199064046 -0.087113363
## 305 -1.174676854 0.624604046
## 306 -0.825268358 0.680167679
## 307 -1.226042718 0.895688320
## 308 -1.240602543 1.694984206
## 309 1.388675596 -1.657468349
## 310 -1.333937273 1.820693917
## 311 -0.144397996 -0.206209714
## 312 0.108596586 -0.248322170
## 313 -0.637918244 0.172894402
## 314 0.501003012 -0.251258663
## 315 -1.315314586 0.648548809
## 316 -0.566224869 0.364843007
## 317 -1.274236252 1.407782431
## 318 -0.203077368 -0.336460127
## 319 -1.019200895 0.444324836
## 320 0.123199870 -0.051973905
## 321 -1.919386891 -0.421810057
## attr(,"scaled:center")
## DiffAdd LARent SC1 Unempl LowEduc Age18_24 Age25_44
## 9.8634261 15.1705327 8.0512735 7.5630365 0.4882499 13.4328069 31.6466419
## Age45_64
## 20.8653071
## attr(,"scaled:scale")
## DiffAdd LARent SC1 Unempl LowEduc Age18_24 Age25_44
## 6.1334599 24.6890510 6.1429741 5.2813004 0.7259568 6.0638089 6.7420456
## Age45_64
## 5.1190317pca$loadings##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## DiffAdd 0.389 0.444 0.149 0.123 0.293 0.445 0.575
## LARent 0.441 -0.226 -0.144 -0.172 0.612 0.149 -0.539 0.132
## SC1 -0.130 0.576 0.135 0.590 -0.343 -0.401
## Unempl 0.361 -0.462 -0.189 0.197 0.670 -0.355
## LowEduc 0.131 -0.308 0.362 0.861
## Age18_24 0.237 -0.845 0.359 -0.224 -0.200
## Age25_44 0.436 0.302 0.317 -0.291 0.448 -0.177 -0.546
## Age45_64 -0.493 -0.118 -0.179 0.144 0.289 0.748 0.142 -0.164
##
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## SS loadings 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## Proportion Var 0.125 0.125 0.125 0.125 0.125 0.125 0.125 0.125
## Cumulative Var 0.125 0.250 0.375 0.500 0.625 0.750 0.875 1.000A partir de los datos de PTV, los dos primeros componentes representan colectivamente el 61,6% del variación en los datos. De las cargas, los componentes uno y dos representan principalmente más (Edad 45_64) y residentes acomodados (SC1), respectivamente. Sin embargo, estos resultados pueden no representar de manera confiable la estructura social local, y una aplicación de GW PCA puede ser útil. Aquí Se encuentra un ancho de banda para GW PCA mediante validación cruzada, donde es necesario decidir un priori en el número de componentes, k retener, siempre que m ≠ k. Por lo tanto, elegimos encontrar un ancho de banda adaptativo óptimo usando un kernel bi-cuadrado, con k = 3. Aquí el bw.gwpca La función se utiliza dentro del siguiente conjunto de comandos:
GWPCA aplica un enfoque de ponderación para realizar un PCA individual para cada observación en el espacio geográfico
Coords <- as.matrix(cbind(Dub.voter$X,Dub.voter$Y))
Coords## [,1] [,2]
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## [180,] 300873.7 228208.3
## [181,] 308458.7 234052.7
## [182,] 307505.8 234527.9
## [183,] 300184.6 224296.4
## [184,] 313019.8 228098.2
## [185,] 313772.7 228178.2
## [186,] 314814.2 227618.7
## [187,] 314126.7 227821.2
## [188,] 314215.9 228927.2
## [189,] 303046.8 224011.5
## [190,] 310204.6 227147.5
## [191,] 307021.0 229099.9
## [192,] 305843.7 228024.7
## [193,] 310163.4 227518.9
## [194,] 305820.2 226529.5
## [195,] 307447.7 226305.8
## [196,] 309820.3 229569.6
## [197,] 308208.1 225778.1
## [198,] 308777.2 228341.1
## [199,] 309640.2 226786.7
## [200,] 308499.1 226747.0
## [201,] 307899.7 227723.9
## [202,] 310239.4 228368.7
## [203,] 312089.3 228536.4
## [204,] 312648.3 229550.0
## [205,] 310872.2 229462.1
## [206,] 312134.1 229121.8
## [207,] 311299.5 228457.7
## [208,] 313198.3 228792.5
## [209,] 311563.9 230276.9
## [210,] 312206.6 230071.1
## [211,] 310856.1 230122.1
## [212,] 316288.1 242083.0
## [213,] 319947.7 261598.1
## [214,] 320463.7 263122.8
## [215,] 324091.1 240302.2
## [216,] 320093.0 242311.4
## [217,] 314259.5 253450.0
## [218,] 316519.8 262358.1
## [219,] 309639.8 238670.6
## [220,] 304306.8 239113.1
## [221,] 306663.2 238711.5
## [222,] 307996.1 239612.4
## [223,] 306840.4 237558.7
## [224,] 307416.4 240239.8
## [225,] 307846.7 237874.4
## [226,] 305614.9 241002.2
## [227,] 307741.8 236089.3
## [228,] 309651.1 237152.4
## [229,] 310500.7 252424.8
## [230,] 322505.4 249308.6
## [231,] 312746.8 242685.9
## [232,] 307652.4 256086.3
## [233,] 314223.4 259239.1
## [234,] 322789.1 258223.6
## [235,] 327806.7 238018.1
## [236,] 312428.9 247712.4
## [237,] 320312.0 243938.1
## [238,] 303593.8 236509.6
## [239,] 320333.4 254870.4
## [240,] 323553.5 245010.9
## [241,] 321510.5 245462.9
## [242,] 324294.4 243964.4
## [243,] 324042.5 242399.4
## [244,] 325924.0 254371.5
## [245,] 324643.3 260042.1
## [246,] 324256.5 239079.5
## [247,] 316440.0 245154.9
## [248,] 316281.0 247966.0
## [249,] 316973.1 249753.8
## [250,] 319366.3 246514.7
## [251,] 318234.0 246366.8
## [252,] 308527.3 241743.3
## [253,] 317396.0 240570.1
## [254,] 315968.6 226661.3
## [255,] 316691.4 226731.6
## [256,] 315339.1 226466.2
## [257,] 316677.3 227462.0
## [258,] 315942.8 227399.5
## [259,] 316536.3 226097.7
## [260,] 324608.0 224598.1
## [261,] 319810.0 229884.3
## [262,] 321050.7 227660.7
## [263,] 320796.7 228867.5
## [264,] 319148.0 230026.9
## [265,] 322976.1 228259.0
## [266,] 321877.6 227829.9
## [267,] 322555.8 228798.8
## [268,] 322484.3 227580.4
## [269,] 321728.0 228717.3
## [270,] 320557.6 229591.2
## [271,] 324044.6 225497.2
## [272,] 324152.4 224406.6
## [273,] 322717.0 223454.2
## [274,] 323192.8 226108.3
## [275,] 315305.7 228576.8
## [276,] 315517.6 228929.2
## [277,] 315538.9 227893.4
## [278,] 315923.9 229224.1
## [279,] 316611.6 228773.8
## [280,] 318331.5 229866.3
## [281,] 316593.9 229699.7
## [282,] 317110.8 230086.2
## [283,] 318180.8 228833.7
## [284,] 317291.8 229132.5
## [285,] 324947.7 226105.6
## [286,] 326069.4 227287.4
## [287,] 326766.4 226547.9
## [288,] 326385.0 226067.5
## [289,] 325742.6 226480.5
## [290,] 318815.9 226449.6
## [291,] 317692.0 227863.0
## [292,] 317524.5 226380.4
## [293,] 316627.5 228109.3
## [294,] 317435.2 228528.0
## [295,] 324457.6 228248.5
## [296,] 324762.7 227545.1
## [297,] 324119.3 227564.6
## [298,] 323130.8 227502.8
## [299,] 323244.9 227794.2
## [300,] 324614.0 226679.7
## [301,] 323988.8 226170.5
## [302,] 323596.1 227070.5
## [303,] 325521.0 227276.0
## [304,] 323459.3 228413.9
## [305,] 324040.7 228426.7
## [306,] 322348.5 226208.6
## [307,] 322300.9 225227.3
## [308,] 321762.0 226903.2
## [309,] 321224.3 226169.3
## [310,] 318734.2 222065.1
## [311,] 325386.7 225077.1
## [312,] 325207.2 223465.6
## [313,] 323320.8 220738.9
## [314,] 325538.8 222601.8
## [315,] 325965.4 220750.5
## [316,] 318710.7 228607.4
## [317,] 318862.3 227442.4
## [318,] 320514.6 226694.4
## [319,] 319874.3 227267.1
## [320,] 319645.1 228710.9
## [321,] 320287.9 228243.3
## [322,] 314569.0 221883.7Muestra las coordenadas para las 322 obs.
Ahora una línea para ver la información mas ordenada
Data.scaled <- scale(as.matrix(Dub.voter@data[,4:11]))
pca <- princomp(Data.scaled, cor=F)
(pca$sdev^2/sum(pca$sdev^2))*100## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## 36.084435 25.586984 11.919681 10.530373 6.890565 3.679812 3.111449 2.196701pca$loadings##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## DiffAdd 0.389 0.444 0.149 0.123 0.293 0.445 0.575
## LARent 0.441 -0.226 -0.144 -0.172 0.612 0.149 -0.539 0.132
## SC1 -0.130 0.576 0.135 0.590 -0.343 -0.401
## Unempl 0.361 -0.462 -0.189 0.197 0.670 -0.355
## LowEduc 0.131 -0.308 0.362 0.861
## Age18_24 0.237 -0.845 0.359 -0.224 -0.200
## Age25_44 0.436 0.302 0.317 -0.291 0.448 -0.177 -0.546
## Age45_64 -0.493 -0.118 -0.179 0.144 0.289 0.748 0.142 -0.164
##
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## SS loadings 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## Proportion Var 0.125 0.125 0.125 0.125 0.125 0.125 0.125 0.125
## Cumulative Var 0.125 0.250 0.375 0.500 0.625 0.750 0.875 1.000A partir de los datos de PTV, los dos primeros componentes representan colectivamente el 61,6% de la variación en los datos. De las cargas, los componentes uno y dos representan principalmente más (Edad 45_64) y residentes acomodados (SC1), respectivamente. Sin embargo, estos resultados pueden no representar de manera confiable la estructura social local, y una aplicación de GW PCA puede ser útil. Aquí Se encuentra un ancho de banda para GW PCA mediante validación cruzada, donde es necesario decidir a priori en el número de componentes, k retener, siempre que m ≠ k. Por lo tanto, elegimos encontrar un ancho de banda adaptativo óptimo usando un kernel bi-cuadrado, con k = 3. Aquí el bw.gwpca
La función se utiliza dentro del siguiente conjunto de comandos:
Crea un marco de datos con datos escalados
Coords <- as.matrix(cbind(Dub.voter$X,Dub.voter$Y))Data.scaled.spdf <- SpatialPointsDataFrame(Coords,
as.data.frame(Data.scaled))GWPCA Monte Carlo test
bw.gwpca.1 <-bw.gwpca(Data.scaled.spdf,vars=colnames(Data.scaled.spdf@data), k=3, adaptive=TRUE)## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 700.8372
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 614.7564
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 648.8595
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 630.3535
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 619.4017
## Adaptive bandwidth(number of nearest neighbours): 134 CV score: 614.0499
## Adaptive bandwidth(number of nearest neighbours): 142 CV score: 620.5101
## Adaptive bandwidth(number of nearest neighbours): 130 CV score: 611.0627
## Adaptive bandwidth(number of nearest neighbours): 127 CV score: 613.0468
## Adaptive bandwidth(number of nearest neighbours): 131 CV score: 610.6125
## Adaptive bandwidth(number of nearest neighbours): 133 CV score: 614.2431
## Adaptive bandwidth(number of nearest neighbours): 131 CV score: 610.6125La inspección del objeto bw.gwpca.1 indica que se utilizará un ancho de banda de N = 131 para calibre el ajuste GW PCA. Observe que ahora especificamos todos los componentes k = 8, pero Enfocaremos nuestra investigación solo en los dos primeros componentes. Esta especificación asegura que el Los datos de PTV se estiman correctamente. El ajuste de GW PCA se realiza mediante la función gwpca:
gwpca.1 <- gwpca(Data.scaled.spdf, vars =
colnames(Data.scaled.spdf@data), bw=bw.gwpca.1, k=8, adaptive=TRUE)Las salidas de GW PCA se visualizan e interpretan, centrándose en:
cómo los datos la dimensionalidad varía espacialmente y
cómo las variables originales influyen en los componentes.
Para el primero, la distribución espacial de PTV local para los dos primeros componentes puede ser mapeado. Para este último, observamos el cambio de tamaño y signo de las ocho cargas locales. juntos, para un componente dado, en cada uno de los 322 obs.
En este sentido, mapeamos multivariante glifos que tienen radios alrededor de un eje central en el que la longitud del radio corresponde a el tamaño de la carga local, y su color corresponde al letrero (en este caso, azul significa positivo y rojo significa negativo).
Los glifos se escalan en relación con el radio con el carga absoluta más grande. La variable correspondiente a cada carga local está siempre en el mismo lugar en el glifo, como sigue: DiffAdd está en 0o (norte); LARent es 45o (noreste); SC1 es 90o (este); Unempl es 135o (sureste), LowEduc es 180o (sur), Age18_24 es 225o (suroeste), Age25_44 es 270o (oeste) y Age45_64 es 315o (noreste). Comandos para conducir estas visualizaciones son las siguientes:
Para mapas PTV
prop.var <- function(gwpca.obj, n.components){return((rowSums(gwpca.obj$var[,1:n.components])/rowSums(gwpca.obj$var))*100)}
var.gwpca <- prop.var(gwpca.1, 2)
Dub.voter$var.gwpca <- var.gwpcaPaleta de colores
library(RColorBrewer)
mypalette.gwpca.1 <-brewer.pal(8,"YlGnBu")
map.layout.3 <- list(map.na,map.scale.1,map.scale.2,map.scale.3)Ahora visualizamos el grafico de GW PCA: PTV para componentes locales 1 a 2
X11(width=10,height=12)
spplot(Dub.voter,"var.gwpca",key.space = "right", col.regions =
mypalette.gwpca.1, cuts=7, par.settings =list(fontsize=list(text=15)),
main=list(label="Fig 4. GW PCA: PTV for local components 1 to 2", cex=1.25),
sp.layout=map.layout.3)
loadings.1 <- gwpca.1$loadings[,,1]ahora visualizamos GW PCA: glifos multivariados de cargas
X11(width=10,height=12)
plot(Dub.voter)
glyph.plot(loadings.1,Coords,r1=20,add=T,alpha=0.85)
title(main=list("Fig 5. GW PCA: Multivariate glyphs of loadings", cex=1.75,
col="black", font=1), sub=list("For component 1", cex=1.5,
col="black", font=3))
La Figura 4 presenta el mapa PTV local, donde hay una clara variación espacial en los Datos PTV. Los porcentajes más altos se encuentran en el sur y en el centro de Dublín, mientras que los los porcentajes se encuentran en el norte. Los datos de PTV también son generalmente más altos en el caso local, 17 que en el caso global (al 61,6%).
La Figura 5 presenta un mapa de glifos multivariante para el cargas en el primer componente, donde una preponderancia espacial de glifos de un color o otro, o radios más grandes en las mismas variables proporcionan una indicación general de las estructuras estando representados en cada uno de los 322 obs.
Este mapa no está destinado a ser examinado en detalle, pero indica claramente fuertes tendencias locales en la estructura social en el Gran Dublín.
___________________ el Monte Carlo asociado, La prueba evalúa si los valores propios locales para el primer componente varían significativamente en el espacio. Los resultados se dan en la Figura 6, donde el valor p para las observaciones de los valores propios se calculan en 0,02. Por lo tanto, una aplicación de GW PCA se considera valiosa como la hipótesis nula de la estacionariedad del valor propio local, se rechaza firmemente al nivel del 95%, ya que primer componente dominante. Los comandos para realizar esta prueba son los siguientes:
PRIMERO SE ENCUENTRA EL ANCHO DE BANDA
GWPCA Monte Carlo test
gwpca.mc <-montecarlo.gwpca.2(Data.scaled.spdf, vars =colnames(Data.scaled.spdf@data), k=3, adaptive=TRUE)## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 700.8372
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 614.7564
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 648.8595
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 630.3535
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 619.4017
## Adaptive bandwidth(number of nearest neighbours): 134 CV score: 614.0499
## Adaptive bandwidth(number of nearest neighbours): 142 CV score: 620.5101
## Adaptive bandwidth(number of nearest neighbours): 130 CV score: 611.0627
## Adaptive bandwidth(number of nearest neighbours): 127 CV score: 613.0468
## Adaptive bandwidth(number of nearest neighbours): 131 CV score: 610.6125
## Adaptive bandwidth(number of nearest neighbours): 133 CV score: 614.2431
## Adaptive bandwidth(number of nearest neighbours): 131 CV score: 610.6125
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 824.6067
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 774.5463
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 743.5687
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 764.9024
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 762.9666
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 730.0285
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 756.5334
## Adaptive bandwidth(number of nearest neighbours): 69 CV score: 728.9005
## Adaptive bandwidth(number of nearest neighbours): 73 CV score: 731.6964
## Adaptive bandwidth(number of nearest neighbours): 68 CV score: 729.8581
## Adaptive bandwidth(number of nearest neighbours): 71 CV score: 730.0065
## Adaptive bandwidth(number of nearest neighbours): 69 CV score: 728.9005
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 863.0721
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 883.5594
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 865.7253
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 879.5832
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 843.9375
## Adaptive bandwidth(number of nearest neighbours): 228 CV score: 846.9201
## Adaptive bandwidth(number of nearest neighbours): 210 CV score: 850.4404
## Adaptive bandwidth(number of nearest neighbours): 221 CV score: 843.6042
## Adaptive bandwidth(number of nearest neighbours): 224 CV score: 842.7615
## Adaptive bandwidth(number of nearest neighbours): 225 CV score: 843.2602
## Adaptive bandwidth(number of nearest neighbours): 222 CV score: 844.6902
## Adaptive bandwidth(number of nearest neighbours): 224 CV score: 842.7615
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 890.2109
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 898.4209
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 847.5357
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 854.7408
## Adaptive bandwidth(number of nearest neighbours): 228 CV score: 855.0772
## Adaptive bandwidth(number of nearest neighbours): 257 CV score: 848.6496
## Adaptive bandwidth(number of nearest neighbours): 239 CV score: 848.2449
## Adaptive bandwidth(number of nearest neighbours): 250 CV score: 848.2035
## Adaptive bandwidth(number of nearest neighbours): 243 CV score: 846.8806
## Adaptive bandwidth(number of nearest neighbours): 241 CV score: 847.7788
## Adaptive bandwidth(number of nearest neighbours): 244 CV score: 847.1445
## Adaptive bandwidth(number of nearest neighbours): 242 CV score: 847.6305
## Adaptive bandwidth(number of nearest neighbours): 243 CV score: 846.8806
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 711.4084
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 670.1936
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 700.0636
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 669.0415
## Adaptive bandwidth(number of nearest neighbours): 171 CV score: 680.2359
## Adaptive bandwidth(number of nearest neighbours): 142 CV score: 671.7481
## Adaptive bandwidth(number of nearest neighbours): 160 CV score: 673.2906
## Adaptive bandwidth(number of nearest neighbours): 148 CV score: 668.0842
## Adaptive bandwidth(number of nearest neighbours): 145 CV score: 668.9105
## Adaptive bandwidth(number of nearest neighbours): 149 CV score: 667.2146
## Adaptive bandwidth(number of nearest neighbours): 151 CV score: 668.7287
## Adaptive bandwidth(number of nearest neighbours): 149 CV score: 667.2146
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1009.78
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 796.8163
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 729.3403
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 743.3696
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 745.6008
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 725.2635
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 732.4889
## Adaptive bandwidth(number of nearest neighbours): 69 CV score: 721.1107
## Adaptive bandwidth(number of nearest neighbours): 73 CV score: 723.697
## Adaptive bandwidth(number of nearest neighbours): 68 CV score: 722.4056
## Adaptive bandwidth(number of nearest neighbours): 71 CV score: 719.9973
## Adaptive bandwidth(number of nearest neighbours): 71 CV score: 719.9973
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1123.186
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 1070.818
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 1026.766
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 1018.667
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 991.4678
## Adaptive bandwidth(number of nearest neighbours): 19 CV score: 979.1629
## Adaptive bandwidth(number of nearest neighbours): 11 CV score: 1133.63
## Adaptive bandwidth(number of nearest neighbours): 22 CV score: 964.6408
## Adaptive bandwidth(number of nearest neighbours): 26 CV score: 967.7647
## Adaptive bandwidth(number of nearest neighbours): 21 CV score: 977.7957
## Adaptive bandwidth(number of nearest neighbours): 24 CV score: 953.8572
## Adaptive bandwidth(number of nearest neighbours): 24 CV score: 953.8572
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 849.5373
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 859.981
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 878.364
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 830.096
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 837.1494
## Adaptive bandwidth(number of nearest neighbours): 181 CV score: 832.5622
## Adaptive bandwidth(number of nearest neighbours): 163 CV score: 831.7206
## Adaptive bandwidth(number of nearest neighbours): 174 CV score: 828.7547
## Adaptive bandwidth(number of nearest neighbours): 177 CV score: 829.6434
## Adaptive bandwidth(number of nearest neighbours): 172 CV score: 829.5953
## Adaptive bandwidth(number of nearest neighbours): 175 CV score: 828.4986
## Adaptive bandwidth(number of nearest neighbours): 176 CV score: 828.9103
## Adaptive bandwidth(number of nearest neighbours): 175 CV score: 828.4986
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 827.0232
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 843.0724
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 860.9296
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 824.2626
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 828.8041
## Adaptive bandwidth(number of nearest neighbours): 181 CV score: 825.781
## Adaptive bandwidth(number of nearest neighbours): 163 CV score: 824.6532
## Adaptive bandwidth(number of nearest neighbours): 174 CV score: 824.349
## Adaptive bandwidth(number of nearest neighbours): 167 CV score: 823.7102
## Adaptive bandwidth(number of nearest neighbours): 165 CV score: 823.967
## Adaptive bandwidth(number of nearest neighbours): 168 CV score: 823.7141
## Adaptive bandwidth(number of nearest neighbours): 166 CV score: 823.6784
## Adaptive bandwidth(number of nearest neighbours): 166 CV score: 823.6784
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 824.956
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 818.6043
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 844.4466
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 810.568
## Adaptive bandwidth(number of nearest neighbours): 171 CV score: 815.7266
## Adaptive bandwidth(number of nearest neighbours): 142 CV score: 811.1991
## Adaptive bandwidth(number of nearest neighbours): 160 CV score: 809.1692
## Adaptive bandwidth(number of nearest neighbours): 163 CV score: 810.6136
## Adaptive bandwidth(number of nearest neighbours): 156 CV score: 808.9408
## Adaptive bandwidth(number of nearest neighbours): 155 CV score: 809.5454
## Adaptive bandwidth(number of nearest neighbours): 158 CV score: 808.6879
## Adaptive bandwidth(number of nearest neighbours): 158 CV score: 808.6879
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 875.2568
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 917.0682
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 831.416
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 823.1849
## Adaptive bandwidth(number of nearest neighbours): 293 CV score: 823.0818
## Adaptive bandwidth(number of nearest neighbours): 304 CV score: 827.7212
## Adaptive bandwidth(number of nearest neighbours): 286 CV score: 822.4514
## Adaptive bandwidth(number of nearest neighbours): 281 CV score: 822.9232
## Adaptive bandwidth(number of nearest neighbours): 288 CV score: 822.6512
## Adaptive bandwidth(number of nearest neighbours): 283 CV score: 822.4436
## Adaptive bandwidth(number of nearest neighbours): 283 CV score: 822.4436
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 835.4925
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 815.4711
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 736.9722
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 765.4409
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 774.8236
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 722.7471
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 731.0735
## Adaptive bandwidth(number of nearest neighbours): 69 CV score: 726.4601
## Adaptive bandwidth(number of nearest neighbours): 62 CV score: 718.8442
## Adaptive bandwidth(number of nearest neighbours): 61 CV score: 720.4251
## Adaptive bandwidth(number of nearest neighbours): 64 CV score: 720.303
## Adaptive bandwidth(number of nearest neighbours): 62 CV score: 718.8442
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 748.0177
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 725.679
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 759.0018
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 728.9548
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 734.3958
## Adaptive bandwidth(number of nearest neighbours): 134 CV score: 723.5708
## Adaptive bandwidth(number of nearest neighbours): 142 CV score: 723.9661
## Adaptive bandwidth(number of nearest neighbours): 130 CV score: 724.4416
## Adaptive bandwidth(number of nearest neighbours): 137 CV score: 723.436
## Adaptive bandwidth(number of nearest neighbours): 138 CV score: 723.2382
## Adaptive bandwidth(number of nearest neighbours): 140 CV score: 723.5665
## Adaptive bandwidth(number of nearest neighbours): 138 CV score: 723.2382
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 933.28
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 938.5746
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 939.7897
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 954.3819
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 934.4322
## Adaptive bandwidth(number of nearest neighbours): 188 CV score: 940.9914
## Adaptive bandwidth(number of nearest neighbours): 206 CV score: 929.3814
## Adaptive bandwidth(number of nearest neighbours): 210 CV score: 930.3745
## Adaptive bandwidth(number of nearest neighbours): 203 CV score: 931.9841
## Adaptive bandwidth(number of nearest neighbours): 207 CV score: 929.7118
## Adaptive bandwidth(number of nearest neighbours): 204 CV score: 931.1112
## Adaptive bandwidth(number of nearest neighbours): 206 CV score: 929.3814
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 862.8681
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 862.2813
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 861.0516
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 851.2482
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 818.8947
## Adaptive bandwidth(number of nearest neighbours): 19 CV score: 782.2869
## Adaptive bandwidth(number of nearest neighbours): 11 CV score: 1000.16
## Adaptive bandwidth(number of nearest neighbours): 22 CV score: 798.1032
## Adaptive bandwidth(number of nearest neighbours): 15 CV score: 783.4149
## Adaptive bandwidth(number of nearest neighbours): 19 CV score: 782.2869
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 912.4089
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 954.1728
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 916.2859
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 911.2254
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 923.5874
## Adaptive bandwidth(number of nearest neighbours): 181 CV score: 906.5669
## Adaptive bandwidth(number of nearest neighbours): 188 CV score: 907.4069
## Adaptive bandwidth(number of nearest neighbours): 176 CV score: 908.2054
## Adaptive bandwidth(number of nearest neighbours): 183 CV score: 905.993
## Adaptive bandwidth(number of nearest neighbours): 185 CV score: 906.6056
## Adaptive bandwidth(number of nearest neighbours): 182 CV score: 906.1361
## Adaptive bandwidth(number of nearest neighbours): 184 CV score: 906.3257
## Adaptive bandwidth(number of nearest neighbours): 183 CV score: 905.993
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 999.1266
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 922.433
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 868.0209
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 850.1118
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 966.3237
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 836.1932
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 837.3975
## Adaptive bandwidth(number of nearest neighbours): 54 CV score: 842.5067
## Adaptive bandwidth(number of nearest neighbours): 61 CV score: 832.3132
## Adaptive bandwidth(number of nearest neighbours): 62 CV score: 831.4416
## Adaptive bandwidth(number of nearest neighbours): 64 CV score: 831.8489
## Adaptive bandwidth(number of nearest neighbours): 62 CV score: 831.4416
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 813.3571
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 718.8545
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 658.0008
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 690.4367
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 701.5841
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 654.971
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 662.7451
## Adaptive bandwidth(number of nearest neighbours): 69 CV score: 658.4482
## Adaptive bandwidth(number of nearest neighbours): 62 CV score: 654.8303
## Adaptive bandwidth(number of nearest neighbours): 61 CV score: 655.6589
## Adaptive bandwidth(number of nearest neighbours): 64 CV score: 653.0355
## Adaptive bandwidth(number of nearest neighbours): 64 CV score: 653.0355
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 820.6756
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 719.5682
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 788.2381
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 767.4293
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 712.1084
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 718.249
## Adaptive bandwidth(number of nearest neighbours): 113 CV score: 714.8678
## Adaptive bandwidth(number of nearest neighbours): 101 CV score: 709.9768
## Adaptive bandwidth(number of nearest neighbours): 98 CV score: 711.6486
## Adaptive bandwidth(number of nearest neighbours): 102 CV score: 710.6188
## Adaptive bandwidth(number of nearest neighbours): 99 CV score: 710.6418
## Adaptive bandwidth(number of nearest neighbours): 101 CV score: 709.9768
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 969.5768
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 807.8
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 772.9911
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 733.6917
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 820.5152
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 754.0991
## Adaptive bandwidth(number of nearest neighbours): 40 CV score: 747.311
## Adaptive bandwidth(number of nearest neighbours): 51 CV score: 734.8113
## Adaptive bandwidth(number of nearest neighbours): 44 CV score: 736.7783
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 733.6917
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1039.284
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 1020.099
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 935.3609
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 902.8997
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 1062.764
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 905.9942
## Adaptive bandwidth(number of nearest neighbours): 40 CV score: 964.6958
## Adaptive bandwidth(number of nearest neighbours): 51 CV score: 894.3485
## Adaptive bandwidth(number of nearest neighbours): 55 CV score: 911.5762
## Adaptive bandwidth(number of nearest neighbours): 50 CV score: 896.979
## Adaptive bandwidth(number of nearest neighbours): 53 CV score: 903.3964
## Adaptive bandwidth(number of nearest neighbours): 51 CV score: 894.3485
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 901.8429
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 795.5518
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 792.4763
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 988.4406
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 770.9273
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 779.1231
## Adaptive bandwidth(number of nearest neighbours): 87 CV score: 773.2232
## Adaptive bandwidth(number of nearest neighbours): 98 CV score: 771.9012
## Adaptive bandwidth(number of nearest neighbours): 91 CV score: 772.5345
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 770.9273
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1085.38
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 1001.417
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 1069.758
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 1023.42
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 1005.056
## Adaptive bandwidth(number of nearest neighbours): 134 CV score: 1010.513
## Adaptive bandwidth(number of nearest neighbours): 116 CV score: 998.2221
## Adaptive bandwidth(number of nearest neighbours): 112 CV score: 998.0073
## Adaptive bandwidth(number of nearest neighbours): 109 CV score: 999.3055
## Adaptive bandwidth(number of nearest neighbours): 113 CV score: 997.6741
## Adaptive bandwidth(number of nearest neighbours): 115 CV score: 998.5131
## Adaptive bandwidth(number of nearest neighbours): 113 CV score: 997.6741
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 777.5773
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 776.2402
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 831.3487
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 757.7475
## Adaptive bandwidth(number of nearest neighbours): 171 CV score: 762.0172
## Adaptive bandwidth(number of nearest neighbours): 142 CV score: 758.1529
## Adaptive bandwidth(number of nearest neighbours): 160 CV score: 759.9161
## Adaptive bandwidth(number of nearest neighbours): 148 CV score: 756.8379
## Adaptive bandwidth(number of nearest neighbours): 145 CV score: 757.4146
## Adaptive bandwidth(number of nearest neighbours): 149 CV score: 757.3347
## Adaptive bandwidth(number of nearest neighbours): 146 CV score: 756.8896
## Adaptive bandwidth(number of nearest neighbours): 148 CV score: 756.8379
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 828.9525
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 722.7954
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 780.4745
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 732.4852
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 747.077
## Adaptive bandwidth(number of nearest neighbours): 134 CV score: 720.4902
## Adaptive bandwidth(number of nearest neighbours): 142 CV score: 721.4759
## Adaptive bandwidth(number of nearest neighbours): 130 CV score: 719.2198
## Adaptive bandwidth(number of nearest neighbours): 127 CV score: 719.9012
## Adaptive bandwidth(number of nearest neighbours): 131 CV score: 719.4183
## Adaptive bandwidth(number of nearest neighbours): 128 CV score: 718.5348
## Adaptive bandwidth(number of nearest neighbours): 128 CV score: 718.5348
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1199.104
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 953.9491
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 759.7224
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 860.8851
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 789.9316
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 760.6839
## Adaptive bandwidth(number of nearest neighbours): 84 CV score: 760.6466
## Adaptive bandwidth(number of nearest neighbours): 72 CV score: 762.4074
## Adaptive bandwidth(number of nearest neighbours): 79 CV score: 754.6921
## Adaptive bandwidth(number of nearest neighbours): 80 CV score: 755.5868
## Adaptive bandwidth(number of nearest neighbours): 77 CV score: 758.4655
## Adaptive bandwidth(number of nearest neighbours): 79 CV score: 754.6921
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1011.68
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 963.6219
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 914.753
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 939.2854
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 951.2459
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 891.3922
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 887.0132
## Adaptive bandwidth(number of nearest neighbours): 54 CV score: 932.5807
## Adaptive bandwidth(number of nearest neighbours): 61 CV score: 884.1656
## Adaptive bandwidth(number of nearest neighbours): 62 CV score: 885.3068
## Adaptive bandwidth(number of nearest neighbours): 59 CV score: 883.6915
## Adaptive bandwidth(number of nearest neighbours): 59 CV score: 883.6915
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 567.1625
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 531.201
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 536.512
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 570.4861
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 515.6724
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 518.5267
## Adaptive bandwidth(number of nearest neighbours): 113 CV score: 521.1446
## Adaptive bandwidth(number of nearest neighbours): 101 CV score: 515.1738
## Adaptive bandwidth(number of nearest neighbours): 98 CV score: 515.8419
## Adaptive bandwidth(number of nearest neighbours): 102 CV score: 514.838
## Adaptive bandwidth(number of nearest neighbours): 104 CV score: 515.3551
## Adaptive bandwidth(number of nearest neighbours): 102 CV score: 514.838
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1073.851
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 1000.396
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 913.2474
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 842.9546
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 883.5499
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 884.1487
## Adaptive bandwidth(number of nearest neighbours): 40 CV score: 830.7193
## Adaptive bandwidth(number of nearest neighbours): 36 CV score: 842.2911
## Adaptive bandwidth(number of nearest neighbours): 43 CV score: 830.7801
## Adaptive bandwidth(number of nearest neighbours): 38 CV score: 834.8259
## Adaptive bandwidth(number of nearest neighbours): 41 CV score: 831.8716
## Adaptive bandwidth(number of nearest neighbours): 39 CV score: 837.1848
## Adaptive bandwidth(number of nearest neighbours): 40 CV score: 830.7193
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 825.1538
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 877.7147
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 854.8323
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 823.1475
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 839.1721
## Adaptive bandwidth(number of nearest neighbours): 181 CV score: 822.177
## Adaptive bandwidth(number of nearest neighbours): 188 CV score: 822.9468
## Adaptive bandwidth(number of nearest neighbours): 176 CV score: 821.5314
## Adaptive bandwidth(number of nearest neighbours): 174 CV score: 821.422
## Adaptive bandwidth(number of nearest neighbours): 171 CV score: 822.4849
## Adaptive bandwidth(number of nearest neighbours): 174 CV score: 821.422
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 825.8756
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 874.7882
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 821.7827
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 829.8484
## Adaptive bandwidth(number of nearest neighbours): 228 CV score: 819.7727
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 819.7676
## Adaptive bandwidth(number of nearest neighbours): 210 CV score: 821.3171
## Adaptive bandwidth(number of nearest neighbours): 221 CV score: 819.3405
## Adaptive bandwidth(number of nearest neighbours): 224 CV score: 818.6383
## Adaptive bandwidth(number of nearest neighbours): 225 CV score: 818.9102
## Adaptive bandwidth(number of nearest neighbours): 222 CV score: 819.5426
## Adaptive bandwidth(number of nearest neighbours): 224 CV score: 818.6383
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 802.3133
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 851.4608
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 849.4663
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 792.7236
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 815.9056
## Adaptive bandwidth(number of nearest neighbours): 181 CV score: 793.6409
## Adaptive bandwidth(number of nearest neighbours): 163 CV score: 800.2292
## Adaptive bandwidth(number of nearest neighbours): 174 CV score: 790.1167
## Adaptive bandwidth(number of nearest neighbours): 177 CV score: 790.1193
## Adaptive bandwidth(number of nearest neighbours): 172 CV score: 789.4576
## Adaptive bandwidth(number of nearest neighbours): 171 CV score: 789.2959
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 792.7236
## Adaptive bandwidth(number of nearest neighbours): 171 CV score: 789.2959
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 945.6216
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 822.4288
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 782.9983
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 766.7725
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 909.6095
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 771.1325
## Adaptive bandwidth(number of nearest neighbours): 40 CV score: 775.1587
## Adaptive bandwidth(number of nearest neighbours): 51 CV score: 764.2876
## Adaptive bandwidth(number of nearest neighbours): 55 CV score: 778.3562
## Adaptive bandwidth(number of nearest neighbours): 50 CV score: 766.5191
## Adaptive bandwidth(number of nearest neighbours): 53 CV score: 775.5586
## Adaptive bandwidth(number of nearest neighbours): 51 CV score: 764.2876
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 724.4975
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 661.2503
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 637.6034
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 745.9958
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 638.4129
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 660.7126
## Adaptive bandwidth(number of nearest neighbours): 84 CV score: 633.9383
## Adaptive bandwidth(number of nearest neighbours): 87 CV score: 631.6724
## Adaptive bandwidth(number of nearest neighbours): 91 CV score: 636.765
## Adaptive bandwidth(number of nearest neighbours): 86 CV score: 632.2857
## Adaptive bandwidth(number of nearest neighbours): 89 CV score: 635.7732
## Adaptive bandwidth(number of nearest neighbours): 87 CV score: 631.6724
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 840.8153
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 852.2758
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 850.2024
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 836.2487
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 839.5495
## Adaptive bandwidth(number of nearest neighbours): 181 CV score: 837.0023
## Adaptive bandwidth(number of nearest neighbours): 163 CV score: 835.3954
## Adaptive bandwidth(number of nearest neighbours): 158 CV score: 836.261
## Adaptive bandwidth(number of nearest neighbours): 165 CV score: 835.3424
## Adaptive bandwidth(number of nearest neighbours): 167 CV score: 836.0254
## Adaptive bandwidth(number of nearest neighbours): 164 CV score: 835.2849
## Adaptive bandwidth(number of nearest neighbours): 163 CV score: 835.3954
## Adaptive bandwidth(number of nearest neighbours): 164 CV score: 835.2849
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 888.3553
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 920.0374
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 891.0012
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 903.8619
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 881.6885
## Adaptive bandwidth(number of nearest neighbours): 228 CV score: 885.1856
## Adaptive bandwidth(number of nearest neighbours): 210 CV score: 884.7206
## Adaptive bandwidth(number of nearest neighbours): 221 CV score: 881.2638
## Adaptive bandwidth(number of nearest neighbours): 224 CV score: 883.337
## Adaptive bandwidth(number of nearest neighbours): 219 CV score: 881.4087
## Adaptive bandwidth(number of nearest neighbours): 222 CV score: 881.5027
## Adaptive bandwidth(number of nearest neighbours): 220 CV score: 881.3673
## Adaptive bandwidth(number of nearest neighbours): 221 CV score: 881.2638
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 886.9874
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 862.772
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 802.8604
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 800.1571
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 841.6366
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 769.1526
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 782.7222
## Adaptive bandwidth(number of nearest neighbours): 54 CV score: 780.2497
## Adaptive bandwidth(number of nearest neighbours): 61 CV score: 771.6331
## Adaptive bandwidth(number of nearest neighbours): 56 CV score: 774.4514
## Adaptive bandwidth(number of nearest neighbours): 59 CV score: 769.5171
## Adaptive bandwidth(number of nearest neighbours): 57 CV score: 772.8996
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 769.1526
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 812.507
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 889.8471
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 810.8893
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 818.2402
## Adaptive bandwidth(number of nearest neighbours): 228 CV score: 812.3921
## Adaptive bandwidth(number of nearest neighbours): 257 CV score: 813.6807
## Adaptive bandwidth(number of nearest neighbours): 239 CV score: 810.1943
## Adaptive bandwidth(number of nearest neighbours): 234 CV score: 810.2785
## Adaptive bandwidth(number of nearest neighbours): 241 CV score: 810.7175
## Adaptive bandwidth(number of nearest neighbours): 236 CV score: 810.1226
## Adaptive bandwidth(number of nearest neighbours): 236 CV score: 810.1226
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 843.8863
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 865.4071
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 848.043
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 844.0365
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 844.8187
## Adaptive bandwidth(number of nearest neighbours): 188 CV score: 842.9373
## Adaptive bandwidth(number of nearest neighbours): 181 CV score: 842.8873
## Adaptive bandwidth(number of nearest neighbours): 176 CV score: 843.8839
## Adaptive bandwidth(number of nearest neighbours): 183 CV score: 842.8523
## Adaptive bandwidth(number of nearest neighbours): 185 CV score: 842.7833
## Adaptive bandwidth(number of nearest neighbours): 186 CV score: 842.4252
## Adaptive bandwidth(number of nearest neighbours): 187 CV score: 842.7583
## Adaptive bandwidth(number of nearest neighbours): 186 CV score: 842.4252
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1067.244
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 959.631
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 911.3684
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 974.0315
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 920.6361
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 920.8911
## Adaptive bandwidth(number of nearest neighbours): 84 CV score: 914.8922
## Adaptive bandwidth(number of nearest neighbours): 72 CV score: 910.6938
## Adaptive bandwidth(number of nearest neighbours): 69 CV score: 914.6638
## Adaptive bandwidth(number of nearest neighbours): 73 CV score: 909.2458
## Adaptive bandwidth(number of nearest neighbours): 75 CV score: 909.7607
## Adaptive bandwidth(number of nearest neighbours): 73 CV score: 909.2458
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 888.7655
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 900.983
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 986.1594
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 861.9459
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 855.7959
## Adaptive bandwidth(number of nearest neighbours): 141 CV score: 871.7342
## Adaptive bandwidth(number of nearest neighbours): 159 CV score: 857.3984
## Adaptive bandwidth(number of nearest neighbours): 147 CV score: 858.4771
## Adaptive bandwidth(number of nearest neighbours): 154 CV score: 855.4009
## Adaptive bandwidth(number of nearest neighbours): 156 CV score: 856.4104
## Adaptive bandwidth(number of nearest neighbours): 153 CV score: 855.6725
## Adaptive bandwidth(number of nearest neighbours): 155 CV score: 856.1028
## Adaptive bandwidth(number of nearest neighbours): 154 CV score: 855.4009
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 922.5374
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 862.3055
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 822.3307
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 827.7027
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 849.8475
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 814.7913
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 825.6214
## Adaptive bandwidth(number of nearest neighbours): 69 CV score: 817.7299
## Adaptive bandwidth(number of nearest neighbours): 62 CV score: 816.3753
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 814.7913
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1004.167
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 918.7176
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 854.3973
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 809.3528
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 831.6765
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 813.5056
## Adaptive bandwidth(number of nearest neighbours): 40 CV score: 819.2775
## Adaptive bandwidth(number of nearest neighbours): 51 CV score: 811.9668
## Adaptive bandwidth(number of nearest neighbours): 44 CV score: 810.4792
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 809.3528
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 859.2962
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 869.6367
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 852.5596
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 851.6837
## Adaptive bandwidth(number of nearest neighbours): 293 CV score: 855.287
## Adaptive bandwidth(number of nearest neighbours): 264 CV score: 852.988
## Adaptive bandwidth(number of nearest neighbours): 282 CV score: 852.4645
## Adaptive bandwidth(number of nearest neighbours): 270 CV score: 852.4523
## Adaptive bandwidth(number of nearest neighbours): 277 CV score: 851.9212
## Adaptive bandwidth(number of nearest neighbours): 272 CV score: 852.2643
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 851.6837
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 801.3491
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 749.6724
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 712.848
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 765.6336
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 742.7118
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 732.9939
## Adaptive bandwidth(number of nearest neighbours): 84 CV score: 720.6271
## Adaptive bandwidth(number of nearest neighbours): 72 CV score: 711.5285
## Adaptive bandwidth(number of nearest neighbours): 69 CV score: 715.9056
## Adaptive bandwidth(number of nearest neighbours): 73 CV score: 712.5465
## Adaptive bandwidth(number of nearest neighbours): 70 CV score: 707.6933
## Adaptive bandwidth(number of nearest neighbours): 70 CV score: 707.6933
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 944.5466
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 941.5056
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 883.9744
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 906.8752
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 920.4276
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 873.171
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 880.1284
## Adaptive bandwidth(number of nearest neighbours): 69 CV score: 873.144
## Adaptive bandwidth(number of nearest neighbours): 73 CV score: 877.2481
## Adaptive bandwidth(number of nearest neighbours): 68 CV score: 873.0432
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 873.171
## Adaptive bandwidth(number of nearest neighbours): 68 CV score: 873.0432
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1021.887
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 929.5767
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 901.6032
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 831.7872
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 909.2143
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 825.4518
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 829.5243
## Adaptive bandwidth(number of nearest neighbours): 54 CV score: 833.7421
## Adaptive bandwidth(number of nearest neighbours): 61 CV score: 828.6495
## Adaptive bandwidth(number of nearest neighbours): 56 CV score: 830.971
## Adaptive bandwidth(number of nearest neighbours): 59 CV score: 827.7236
## Adaptive bandwidth(number of nearest neighbours): 57 CV score: 828.2998
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 825.4518
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 869.7284
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 864.0259
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 810.4774
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 752.5875
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 845.5341
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 773.6198
## Adaptive bandwidth(number of nearest neighbours): 40 CV score: 769.2347
## Adaptive bandwidth(number of nearest neighbours): 51 CV score: 757.7584
## Adaptive bandwidth(number of nearest neighbours): 44 CV score: 753.4916
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 752.5875
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 806.9416
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 821.0168
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 790.3535
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 788.3861
## Adaptive bandwidth(number of nearest neighbours): 293 CV score: 791.124
## Adaptive bandwidth(number of nearest neighbours): 264 CV score: 787.763
## Adaptive bandwidth(number of nearest neighbours): 257 CV score: 788.6227
## Adaptive bandwidth(number of nearest neighbours): 268 CV score: 787.2919
## Adaptive bandwidth(number of nearest neighbours): 271 CV score: 786.7714
## Adaptive bandwidth(number of nearest neighbours): 272 CV score: 787.4757
## Adaptive bandwidth(number of nearest neighbours): 269 CV score: 787.0934
## Adaptive bandwidth(number of nearest neighbours): 271 CV score: 786.7714
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 909.9965
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 982.9726
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 854.1156
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 845.8609
## Adaptive bandwidth(number of nearest neighbours): 293 CV score: 857.018
## Adaptive bandwidth(number of nearest neighbours): 264 CV score: 844.263
## Adaptive bandwidth(number of nearest neighbours): 257 CV score: 848.5967
## Adaptive bandwidth(number of nearest neighbours): 268 CV score: 844.9271
## Adaptive bandwidth(number of nearest neighbours): 261 CV score: 846.6572
## Adaptive bandwidth(number of nearest neighbours): 265 CV score: 844.7837
## Adaptive bandwidth(number of nearest neighbours): 262 CV score: 845.1795
## Adaptive bandwidth(number of nearest neighbours): 264 CV score: 844.263
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 799.6173
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 789.1875
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 799.2842
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 795.118
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 792.5041
## Adaptive bandwidth(number of nearest neighbours): 134 CV score: 792.1407
## Adaptive bandwidth(number of nearest neighbours): 116 CV score: 789.9531
## Adaptive bandwidth(number of nearest neighbours): 127 CV score: 790.0234
## Adaptive bandwidth(number of nearest neighbours): 120 CV score: 788.4337
## Adaptive bandwidth(number of nearest neighbours): 119 CV score: 788.9669
## Adaptive bandwidth(number of nearest neighbours): 122 CV score: 788.7168
## Adaptive bandwidth(number of nearest neighbours): 120 CV score: 788.4337
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 763.8978
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 744.3984
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 777.8827
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 746.0377
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 747.0534
## Adaptive bandwidth(number of nearest neighbours): 134 CV score: 747.6678
## Adaptive bandwidth(number of nearest neighbours): 116 CV score: 747.8131
## Adaptive bandwidth(number of nearest neighbours): 127 CV score: 745.4588
## Adaptive bandwidth(number of nearest neighbours): 120 CV score: 746.5388
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 744.3984
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 808.8404
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 926.6247
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 730.4553
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 725.8685
## Adaptive bandwidth(number of nearest neighbours): 293 CV score: 742.4367
## Adaptive bandwidth(number of nearest neighbours): 264 CV score: 724.1394
## Adaptive bandwidth(number of nearest neighbours): 257 CV score: 726.0386
## Adaptive bandwidth(number of nearest neighbours): 268 CV score: 724.406
## Adaptive bandwidth(number of nearest neighbours): 261 CV score: 725.1865
## Adaptive bandwidth(number of nearest neighbours): 265 CV score: 724.0182
## Adaptive bandwidth(number of nearest neighbours): 267 CV score: 724.3803
## Adaptive bandwidth(number of nearest neighbours): 265 CV score: 724.0182
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 821.9374
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 831.6103
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 857.4086
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 827.0855
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 836.2162
## Adaptive bandwidth(number of nearest neighbours): 188 CV score: 809.943
## Adaptive bandwidth(number of nearest neighbours): 181 CV score: 806.4401
## Adaptive bandwidth(number of nearest neighbours): 176 CV score: 806.3438
## Adaptive bandwidth(number of nearest neighbours): 174 CV score: 815.0239
## Adaptive bandwidth(number of nearest neighbours): 178 CV score: 807.2473
## Adaptive bandwidth(number of nearest neighbours): 175 CV score: 807.9471
## Adaptive bandwidth(number of nearest neighbours): 177 CV score: 807.1462
## Adaptive bandwidth(number of nearest neighbours): 176 CV score: 806.3438
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 766.4097
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 716.6791
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 707.173
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 789.4633
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 674.5612
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 687.4142
## Adaptive bandwidth(number of nearest neighbours): 87 CV score: 696.5635
## Adaptive bandwidth(number of nearest neighbours): 98 CV score: 674.9465
## Adaptive bandwidth(number of nearest neighbours): 91 CV score: 677.9848
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 674.5612
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1000.525
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 949.864
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 911.6403
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 958.14
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 931.8316
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 952.2743
## Adaptive bandwidth(number of nearest neighbours): 84 CV score: 910.9226
## Adaptive bandwidth(number of nearest neighbours): 87 CV score: 916.5974
## Adaptive bandwidth(number of nearest neighbours): 80 CV score: 909.8552
## Adaptive bandwidth(number of nearest neighbours): 79 CV score: 909.2742
## Adaptive bandwidth(number of nearest neighbours): 77 CV score: 912.406
## Adaptive bandwidth(number of nearest neighbours): 79 CV score: 909.2742
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 778.2122
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 798.2341
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 825.5813
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 778.4261
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 792.046
## Adaptive bandwidth(number of nearest neighbours): 188 CV score: 775.5706
## Adaptive bandwidth(number of nearest neighbours): 181 CV score: 775.9433
## Adaptive bandwidth(number of nearest neighbours): 192 CV score: 777.0056
## Adaptive bandwidth(number of nearest neighbours): 185 CV score: 775.9185
## Adaptive bandwidth(number of nearest neighbours): 189 CV score: 776.2865
## Adaptive bandwidth(number of nearest neighbours): 186 CV score: 775.6464
## Adaptive bandwidth(number of nearest neighbours): 188 CV score: 775.5706
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 918.8162
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 884.9058
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 797.2243
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 832.0022
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 848.3269
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 789.0435
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 801.9097
## Adaptive bandwidth(number of nearest neighbours): 69 CV score: 793.532
## Adaptive bandwidth(number of nearest neighbours): 62 CV score: 792.7539
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 789.0435
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 840.6417
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 982.2597
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 849.2537
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 886.8755
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 841.8416
## Adaptive bandwidth(number of nearest neighbours): 188 CV score: 848.3965
## Adaptive bandwidth(number of nearest neighbours): 206 CV score: 841.8376
## Adaptive bandwidth(number of nearest neighbours): 194 CV score: 840.4728
## Adaptive bandwidth(number of nearest neighbours): 192 CV score: 839.9204
## Adaptive bandwidth(number of nearest neighbours): 189 CV score: 846.1101
## Adaptive bandwidth(number of nearest neighbours): 192 CV score: 839.9204
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 881.3714
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 883.3238
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 895.0202
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 884.3332
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 906.3687
## Adaptive bandwidth(number of nearest neighbours): 188 CV score: 878.0574
## Adaptive bandwidth(number of nearest neighbours): 181 CV score: 879.6028
## Adaptive bandwidth(number of nearest neighbours): 192 CV score: 877.2655
## Adaptive bandwidth(number of nearest neighbours): 195 CV score: 878.3918
## Adaptive bandwidth(number of nearest neighbours): 190 CV score: 877.5529
## Adaptive bandwidth(number of nearest neighbours): 193 CV score: 877.9298
## Adaptive bandwidth(number of nearest neighbours): 191 CV score: 877.66
## Adaptive bandwidth(number of nearest neighbours): 192 CV score: 877.2655
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 923.453
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 948.7237
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 954.323
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 915.6536
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 925.3575
## Adaptive bandwidth(number of nearest neighbours): 181 CV score: 914.5857
## Adaptive bandwidth(number of nearest neighbours): 188 CV score: 919.0021
## Adaptive bandwidth(number of nearest neighbours): 176 CV score: 914.451
## Adaptive bandwidth(number of nearest neighbours): 174 CV score: 914.3128
## Adaptive bandwidth(number of nearest neighbours): 171 CV score: 916.055
## Adaptive bandwidth(number of nearest neighbours): 174 CV score: 914.3128
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1004.958
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 993.9691
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 953.3552
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 876.6965
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 938.0676
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 942.415
## Adaptive bandwidth(number of nearest neighbours): 40 CV score: 856.152
## Adaptive bandwidth(number of nearest neighbours): 36 CV score: 871.3569
## Adaptive bandwidth(number of nearest neighbours): 43 CV score: 850.8264
## Adaptive bandwidth(number of nearest neighbours): 44 CV score: 851.6542
## Adaptive bandwidth(number of nearest neighbours): 41 CV score: 854.1198
## Adaptive bandwidth(number of nearest neighbours): 43 CV score: 850.8264
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 936.6131
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 804.9317
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 806.4536
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 873.0153
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 809.8953
## Adaptive bandwidth(number of nearest neighbours): 134 CV score: 806.2093
## Adaptive bandwidth(number of nearest neighbours): 116 CV score: 804.8706
## Adaptive bandwidth(number of nearest neighbours): 112 CV score: 807.6966
## Adaptive bandwidth(number of nearest neighbours): 119 CV score: 803.7403
## Adaptive bandwidth(number of nearest neighbours): 120 CV score: 803.7526
## Adaptive bandwidth(number of nearest neighbours): 117 CV score: 804.0924
## Adaptive bandwidth(number of nearest neighbours): 119 CV score: 803.7403
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 902.947
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 871.5047
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 843.5416
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 878.2001
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 861.0933
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 864.6713
## Adaptive bandwidth(number of nearest neighbours): 84 CV score: 837.3072
## Adaptive bandwidth(number of nearest neighbours): 87 CV score: 836.1023
## Adaptive bandwidth(number of nearest neighbours): 91 CV score: 861.1337
## Adaptive bandwidth(number of nearest neighbours): 86 CV score: 837.0716
## Adaptive bandwidth(number of nearest neighbours): 89 CV score: 861.1748
## Adaptive bandwidth(number of nearest neighbours): 87 CV score: 836.1023
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 755.4379
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 713.861
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 754.6451
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 711.7917
## Adaptive bandwidth(number of nearest neighbours): 171 CV score: 718.7748
## Adaptive bandwidth(number of nearest neighbours): 142 CV score: 708.9757
## Adaptive bandwidth(number of nearest neighbours): 134 CV score: 710.2953
## Adaptive bandwidth(number of nearest neighbours): 145 CV score: 708.9761
## Adaptive bandwidth(number of nearest neighbours): 138 CV score: 709.58
## Adaptive bandwidth(number of nearest neighbours): 142 CV score: 708.9757
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 769.216
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 752.5942
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 740.7663
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 829.0663
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 736.8218
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 726.2293
## Adaptive bandwidth(number of nearest neighbours): 113 CV score: 756.951
## Adaptive bandwidth(number of nearest neighbours): 101 CV score: 728.809
## Adaptive bandwidth(number of nearest neighbours): 108 CV score: 740.6372
## Adaptive bandwidth(number of nearest neighbours): 103 CV score: 727.0998
## Adaptive bandwidth(number of nearest neighbours): 106 CV score: 727.4253
## Adaptive bandwidth(number of nearest neighbours): 104 CV score: 726.5424
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 726.2293
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1087.747
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 1051.899
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 1008.138
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 1011.121
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 1070.242
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 1028.058
## Adaptive bandwidth(number of nearest neighbours): 84 CV score: 1053.41
## Adaptive bandwidth(number of nearest neighbours): 72 CV score: 1020.64
## Adaptive bandwidth(number of nearest neighbours): 79 CV score: 989.4617
## Adaptive bandwidth(number of nearest neighbours): 80 CV score: 987.1185
## Adaptive bandwidth(number of nearest neighbours): 82 CV score: 1044.762
## Adaptive bandwidth(number of nearest neighbours): 80 CV score: 987.1185
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1021.22
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 956.7202
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 974.5753
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 970.9874
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 952.6835
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 953.1054
## Adaptive bandwidth(number of nearest neighbours): 113 CV score: 954.706
## Adaptive bandwidth(number of nearest neighbours): 101 CV score: 950.2466
## Adaptive bandwidth(number of nearest neighbours): 98 CV score: 951.0542
## Adaptive bandwidth(number of nearest neighbours): 102 CV score: 951.6865
## Adaptive bandwidth(number of nearest neighbours): 99 CV score: 950.2971
## Adaptive bandwidth(number of nearest neighbours): 101 CV score: 950.2466
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 945.3077
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 871.6153
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 912.7078
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 891.6167
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 891.8402
## Adaptive bandwidth(number of nearest neighbours): 134 CV score: 864.2536
## Adaptive bandwidth(number of nearest neighbours): 142 CV score: 876.9785
## Adaptive bandwidth(number of nearest neighbours): 130 CV score: 866.33
## Adaptive bandwidth(number of nearest neighbours): 137 CV score: 869.4008
## Adaptive bandwidth(number of nearest neighbours): 132 CV score: 863.1918
## Adaptive bandwidth(number of nearest neighbours): 131 CV score: 863.8141
## Adaptive bandwidth(number of nearest neighbours): 133 CV score: 864.4527
## Adaptive bandwidth(number of nearest neighbours): 132 CV score: 863.1918
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 939.7902
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 944.4038
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 942.2415
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 942.9525
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 938.7251
## Adaptive bandwidth(number of nearest neighbours): 228 CV score: 938.5262
## Adaptive bandwidth(number of nearest neighbours): 235 CV score: 938.99
## Adaptive bandwidth(number of nearest neighbours): 223 CV score: 938.189
## Adaptive bandwidth(number of nearest neighbours): 221 CV score: 938.8553
## Adaptive bandwidth(number of nearest neighbours): 225 CV score: 938.9563
## Adaptive bandwidth(number of nearest neighbours): 222 CV score: 938.5673
## Adaptive bandwidth(number of nearest neighbours): 224 CV score: 938.8342
## Adaptive bandwidth(number of nearest neighbours): 223 CV score: 938.189
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1056.334
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 1068.208
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 1035.043
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 960.2186
## Adaptive bandwidth(number of nearest neighbours): 293 CV score: 962.3983
## Adaptive bandwidth(number of nearest neighbours): 264 CV score: 957.5954
## Adaptive bandwidth(number of nearest neighbours): 257 CV score: 1033.298
## Adaptive bandwidth(number of nearest neighbours): 268 CV score: 960.8716
## Adaptive bandwidth(number of nearest neighbours): 261 CV score: 959.2979
## Adaptive bandwidth(number of nearest neighbours): 265 CV score: 959.1268
## Adaptive bandwidth(number of nearest neighbours): 262 CV score: 956.3726
## Adaptive bandwidth(number of nearest neighbours): 262 CV score: 956.3726
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 888.1204
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 863.7653
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 858.7539
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 895.3117
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 828.5964
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 838.3297
## Adaptive bandwidth(number of nearest neighbours): 87 CV score: 837.7796
## Adaptive bandwidth(number of nearest neighbours): 98 CV score: 830.0774
## Adaptive bandwidth(number of nearest neighbours): 91 CV score: 828.2837
## Adaptive bandwidth(number of nearest neighbours): 90 CV score: 832.2278
## Adaptive bandwidth(number of nearest neighbours): 93 CV score: 828.4765
## Adaptive bandwidth(number of nearest neighbours): 91 CV score: 828.2837
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 980.7385
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 803.977
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 808.2628
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 860.2114
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 789.6126
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 787.6058
## Adaptive bandwidth(number of nearest neighbours): 87 CV score: 792.5564
## Adaptive bandwidth(number of nearest neighbours): 98 CV score: 785.3567
## Adaptive bandwidth(number of nearest neighbours): 102 CV score: 783.115
## Adaptive bandwidth(number of nearest neighbours): 102 CV score: 783.115
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 704.9443
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 615.4862
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 637.9688
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 637.7678
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 611.248
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 614.6933
## Adaptive bandwidth(number of nearest neighbours): 113 CV score: 611.2359
## Adaptive bandwidth(number of nearest neighbours): 116 CV score: 612.7709
## Adaptive bandwidth(number of nearest neighbours): 109 CV score: 611.7089
## Adaptive bandwidth(number of nearest neighbours): 113 CV score: 611.2359
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 746.0139
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 690.4868
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 702.8555
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 757.15
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 662.3049
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 662.8695
## Adaptive bandwidth(number of nearest neighbours): 113 CV score: 666.847
## Adaptive bandwidth(number of nearest neighbours): 101 CV score: 662.3888
## Adaptive bandwidth(number of nearest neighbours): 108 CV score: 661.9573
## Adaptive bandwidth(number of nearest neighbours): 109 CV score: 662.8344
## Adaptive bandwidth(number of nearest neighbours): 106 CV score: 662.2442
## Adaptive bandwidth(number of nearest neighbours): 108 CV score: 661.9573
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 761.2966
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 742.5687
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 807.6035
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 753.202
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 736.5257
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 737.3696
## Adaptive bandwidth(number of nearest neighbours): 113 CV score: 737.7484
## Adaptive bandwidth(number of nearest neighbours): 101 CV score: 735.6722
## Adaptive bandwidth(number of nearest neighbours): 98 CV score: 736.2896
## Adaptive bandwidth(number of nearest neighbours): 102 CV score: 736.141
## Adaptive bandwidth(number of nearest neighbours): 99 CV score: 735.9451
## Adaptive bandwidth(number of nearest neighbours): 101 CV score: 735.6722
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 718.1376
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 770.6249
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 715.3679
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 735.6646
## Adaptive bandwidth(number of nearest neighbours): 228 CV score: 711.3193
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 711.631
## Adaptive bandwidth(number of nearest neighbours): 235 CV score: 712.7527
## Adaptive bandwidth(number of nearest neighbours): 223 CV score: 710.8852
## Adaptive bandwidth(number of nearest neighbours): 221 CV score: 710.5487
## Adaptive bandwidth(number of nearest neighbours): 218 CV score: 711.1275
## Adaptive bandwidth(number of nearest neighbours): 221 CV score: 710.5487
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 886.9836
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 940.6357
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 858.0733
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 845.6253
## Adaptive bandwidth(number of nearest neighbours): 293 CV score: 839.61
## Adaptive bandwidth(number of nearest neighbours): 304 CV score: 834.8453
## Adaptive bandwidth(number of nearest neighbours): 311 CV score: 830.7446
## Adaptive bandwidth(number of nearest neighbours): 315 CV score: 828.7187
## Adaptive bandwidth(number of nearest neighbours): 318 CV score: 828.1351
## Adaptive bandwidth(number of nearest neighbours): 319 CV score: 827.9303
## Adaptive bandwidth(number of nearest neighbours): 321 CV score: 829.5226
## Adaptive bandwidth(number of nearest neighbours): 319 CV score: 827.9303
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1029.303
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 1088.86
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 1051.791
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 1027.934
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 1065.309
## Adaptive bandwidth(number of nearest neighbours): 181 CV score: 1030.745
## Adaptive bandwidth(number of nearest neighbours): 163 CV score: 1035.801
## Adaptive bandwidth(number of nearest neighbours): 174 CV score: 1029.4
## Adaptive bandwidth(number of nearest neighbours): 167 CV score: 1030.704
## Adaptive bandwidth(number of nearest neighbours): 171 CV score: 1028.331
## Adaptive bandwidth(number of nearest neighbours): 168 CV score: 1027.833
## Adaptive bandwidth(number of nearest neighbours): 168 CV score: 1027.833
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 950.4982
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 854.3734
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 845.5705
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 886.3457
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 846.4532
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 845.1762
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 857.8044
## Adaptive bandwidth(number of nearest neighbours): 69 CV score: 842.8508
## Adaptive bandwidth(number of nearest neighbours): 73 CV score: 843.0043
## Adaptive bandwidth(number of nearest neighbours): 68 CV score: 843.149
## Adaptive bandwidth(number of nearest neighbours): 71 CV score: 841.6662
## Adaptive bandwidth(number of nearest neighbours): 71 CV score: 841.6662
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 933.5892
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 984.2604
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 970.696
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 920.2356
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 951.9094
## Adaptive bandwidth(number of nearest neighbours): 181 CV score: 926.6486
## Adaptive bandwidth(number of nearest neighbours): 163 CV score: 927.7822
## Adaptive bandwidth(number of nearest neighbours): 174 CV score: 922.8496
## Adaptive bandwidth(number of nearest neighbours): 167 CV score: 926.682
## Adaptive bandwidth(number of nearest neighbours): 171 CV score: 921.1285
## Adaptive bandwidth(number of nearest neighbours): 168 CV score: 921.3358
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 920.2356
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 670.2691
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 733.3613
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 681.9961
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 681.8826
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 671.4124
## Adaptive bandwidth(number of nearest neighbours): 188 CV score: 675.2488
## Adaptive bandwidth(number of nearest neighbours): 206 CV score: 670.8372
## Adaptive bandwidth(number of nearest neighbours): 194 CV score: 672.1931
## Adaptive bandwidth(number of nearest neighbours): 201 CV score: 669.5468
## Adaptive bandwidth(number of nearest neighbours): 203 CV score: 670.3293
## Adaptive bandwidth(number of nearest neighbours): 200 CV score: 670.2497
## Adaptive bandwidth(number of nearest neighbours): 202 CV score: 670.2156
## Adaptive bandwidth(number of nearest neighbours): 201 CV score: 669.5468
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1113.909
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 1044.393
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 861.4472
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 922.1668
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 947.6683
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 884.8821
## Adaptive bandwidth(number of nearest neighbours): 84 CV score: 909.198
## Adaptive bandwidth(number of nearest neighbours): 72 CV score: 865.5298
## Adaptive bandwidth(number of nearest neighbours): 79 CV score: 881.2705
## Adaptive bandwidth(number of nearest neighbours): 74 CV score: 862.2811
## Adaptive bandwidth(number of nearest neighbours): 77 CV score: 868.284
## Adaptive bandwidth(number of nearest neighbours): 75 CV score: 862.8495
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 861.4472
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1110.729
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 1141.874
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 1067.536
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 1080.528
## Adaptive bandwidth(number of nearest neighbours): 228 CV score: 1092.465
## Adaptive bandwidth(number of nearest neighbours): 257 CV score: 1059.218
## Adaptive bandwidth(number of nearest neighbours): 264 CV score: 1081.162
## Adaptive bandwidth(number of nearest neighbours): 252 CV score: 1061.797
## Adaptive bandwidth(number of nearest neighbours): 259 CV score: 1067.902
## Adaptive bandwidth(number of nearest neighbours): 254 CV score: 1058.525
## Adaptive bandwidth(number of nearest neighbours): 254 CV score: 1058.525
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 996.5019
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 956.669
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 885.3842
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 895.6139
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 898.3015
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 877.0152
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 874.7301
## Adaptive bandwidth(number of nearest neighbours): 54 CV score: 884.7856
## Adaptive bandwidth(number of nearest neighbours): 61 CV score: 874.0968
## Adaptive bandwidth(number of nearest neighbours): 62 CV score: 872.4133
## Adaptive bandwidth(number of nearest neighbours): 64 CV score: 872.7497
## Adaptive bandwidth(number of nearest neighbours): 62 CV score: 872.4133
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 940.2305
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 969.9727
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 895.5371
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 870.4817
## Adaptive bandwidth(number of nearest neighbours): 293 CV score: 857.6655
## Adaptive bandwidth(number of nearest neighbours): 304 CV score: 852.8982
## Adaptive bandwidth(number of nearest neighbours): 311 CV score: 847.5902
## Adaptive bandwidth(number of nearest neighbours): 315 CV score: 844.6802
## Adaptive bandwidth(number of nearest neighbours): 318 CV score: 844.9219
## Adaptive bandwidth(number of nearest neighbours): 313 CV score: 851.2754
## Adaptive bandwidth(number of nearest neighbours): 316 CV score: 851.2281
## Adaptive bandwidth(number of nearest neighbours): 314 CV score: 847.9096
## Adaptive bandwidth(number of nearest neighbours): 315 CV score: 844.6802
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 929.3481
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 903.3932
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 854.2841
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 820.4187
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 913.3409
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 815.2591
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 830.9872
## Adaptive bandwidth(number of nearest neighbours): 54 CV score: 822.8806
## Adaptive bandwidth(number of nearest neighbours): 61 CV score: 814.6537
## Adaptive bandwidth(number of nearest neighbours): 62 CV score: 814.6943
## Adaptive bandwidth(number of nearest neighbours): 59 CV score: 814.2418
## Adaptive bandwidth(number of nearest neighbours): 59 CV score: 814.2418
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 892.01
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 928.3217
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 882.8832
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 898.9485
## Adaptive bandwidth(number of nearest neighbours): 228 CV score: 881.1415
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 883.8639
## Adaptive bandwidth(number of nearest neighbours): 235 CV score: 880.9539
## Adaptive bandwidth(number of nearest neighbours): 239 CV score: 881.605
## Adaptive bandwidth(number of nearest neighbours): 232 CV score: 881.1844
## Adaptive bandwidth(number of nearest neighbours): 236 CV score: 881.1353
## Adaptive bandwidth(number of nearest neighbours): 233 CV score: 880.8755
## Adaptive bandwidth(number of nearest neighbours): 233 CV score: 880.8755
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 883.1111
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 886.3563
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 901.476
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 889.9102
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 886.9326
## Adaptive bandwidth(number of nearest neighbours): 188 CV score: 883.9072
## Adaptive bandwidth(number of nearest neighbours): 206 CV score: 883.4792
## Adaptive bandwidth(number of nearest neighbours): 194 CV score: 883.3237
## Adaptive bandwidth(number of nearest neighbours): 201 CV score: 883.4694
## Adaptive bandwidth(number of nearest neighbours): 196 CV score: 883.1531
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 883.1111
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 833.1257
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 746.1929
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 811.3596
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 760.6192
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 753.8668
## Adaptive bandwidth(number of nearest neighbours): 134 CV score: 746.6815
## Adaptive bandwidth(number of nearest neighbours): 116 CV score: 746.3475
## Adaptive bandwidth(number of nearest neighbours): 127 CV score: 745.4057
## Adaptive bandwidth(number of nearest neighbours): 131 CV score: 746.7751
## Adaptive bandwidth(number of nearest neighbours): 126 CV score: 745.1951
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 746.1929
## Adaptive bandwidth(number of nearest neighbours): 126 CV score: 745.1951
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 670.2458
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 716.2022
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 656.5098
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 678.6026
## Adaptive bandwidth(number of nearest neighbours): 228 CV score: 655.617
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 658.6993
## Adaptive bandwidth(number of nearest neighbours): 235 CV score: 654.8416
## Adaptive bandwidth(number of nearest neighbours): 239 CV score: 654.724
## Adaptive bandwidth(number of nearest neighbours): 242 CV score: 654.375
## Adaptive bandwidth(number of nearest neighbours): 243 CV score: 654.8715
## Adaptive bandwidth(number of nearest neighbours): 240 CV score: 654.1327
## Adaptive bandwidth(number of nearest neighbours): 240 CV score: 654.1327
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 994.2753
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 930.421
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 854.7108
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 814.9036
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 949.1361
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 827.0093
## Adaptive bandwidth(number of nearest neighbours): 40 CV score: 853.956
## Adaptive bandwidth(number of nearest neighbours): 51 CV score: 823.6799
## Adaptive bandwidth(number of nearest neighbours): 44 CV score: 826.4657
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 814.9036
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1047.935
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 1075.704
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 998.2638
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 1003.829
## Adaptive bandwidth(number of nearest neighbours): 228 CV score: 1012.92
## Adaptive bandwidth(number of nearest neighbours): 257 CV score: 999.2644
## Adaptive bandwidth(number of nearest neighbours): 239 CV score: 1001.891
## Adaptive bandwidth(number of nearest neighbours): 250 CV score: 998.4817
## Adaptive bandwidth(number of nearest neighbours): 243 CV score: 1001.55
## Adaptive bandwidth(number of nearest neighbours): 247 CV score: 998.2629
## Adaptive bandwidth(number of nearest neighbours): 249 CV score: 998.3231
## Adaptive bandwidth(number of nearest neighbours): 247 CV score: 998.2629
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 915.7762
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 954.5056
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 863.3435
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 838.8573
## Adaptive bandwidth(number of nearest neighbours): 293 CV score: 830.0075
## Adaptive bandwidth(number of nearest neighbours): 304 CV score: 823.9574
## Adaptive bandwidth(number of nearest neighbours): 311 CV score: 825.9471
## Adaptive bandwidth(number of nearest neighbours): 299 CV score: 825.9641
## Adaptive bandwidth(number of nearest neighbours): 306 CV score: 824.6454
## Adaptive bandwidth(number of nearest neighbours): 301 CV score: 824.8528
## Adaptive bandwidth(number of nearest neighbours): 304 CV score: 823.9574
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 920.0449
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 932.0695
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 914.9249
## Adaptive bandwidth(number of nearest neighbours): 275 CV score: 935.2698
## Adaptive bandwidth(number of nearest neighbours): 228 CV score: 919.1302
## Adaptive bandwidth(number of nearest neighbours): 257 CV score: 918.8172
## Adaptive bandwidth(number of nearest neighbours): 239 CV score: 915.2529
## Adaptive bandwidth(number of nearest neighbours): 250 CV score: 915.8591
## Adaptive bandwidth(number of nearest neighbours): 243 CV score: 914.1203
## Adaptive bandwidth(number of nearest neighbours): 241 CV score: 914.1397
## Adaptive bandwidth(number of nearest neighbours): 244 CV score: 913.8456
## Adaptive bandwidth(number of nearest neighbours): 245 CV score: 914.4355
## Adaptive bandwidth(number of nearest neighbours): 244 CV score: 913.8456
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 914.3961
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 987.0343
## Adaptive bandwidth(number of nearest neighbours): 246 CV score: 917.1309
## Adaptive bandwidth(number of nearest neighbours): 170 CV score: 963.9559
## Adaptive bandwidth(number of nearest neighbours): 217 CV score: 910.904
## Adaptive bandwidth(number of nearest neighbours): 228 CV score: 913.2501
## Adaptive bandwidth(number of nearest neighbours): 210 CV score: 910.6086
## Adaptive bandwidth(number of nearest neighbours): 205 CV score: 911.5211
## Adaptive bandwidth(number of nearest neighbours): 212 CV score: 910.5739
## Adaptive bandwidth(number of nearest neighbours): 214 CV score: 910.3785
## Adaptive bandwidth(number of nearest neighbours): 215 CV score: 910.53
## Adaptive bandwidth(number of nearest neighbours): 213 CV score: 910.3453
## Adaptive bandwidth(number of nearest neighbours): 213 CV score: 910.3453
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 739.2304
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 710.3248
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 714.0228
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 708.2749
## Adaptive bandwidth(number of nearest neighbours): 171 CV score: 711.0172
## Adaptive bandwidth(number of nearest neighbours): 142 CV score: 707.5065
## Adaptive bandwidth(number of nearest neighbours): 134 CV score: 707.0892
## Adaptive bandwidth(number of nearest neighbours): 130 CV score: 706.8397
## Adaptive bandwidth(number of nearest neighbours): 127 CV score: 708.4365
## Adaptive bandwidth(number of nearest neighbours): 131 CV score: 706.5881
## Adaptive bandwidth(number of nearest neighbours): 133 CV score: 706.3392
## Adaptive bandwidth(number of nearest neighbours): 133 CV score: 706.3392
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1038.036
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 1010.183
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 1008.611
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 1009.28
## Adaptive bandwidth(number of nearest neighbours): 95 CV score: 1011.481
## Adaptive bandwidth(number of nearest neighbours): 66 CV score: 1004.45
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 994.1547
## Adaptive bandwidth(number of nearest neighbours): 54 CV score: 994.7272
## Adaptive bandwidth(number of nearest neighbours): 61 CV score: 997.2953
## Adaptive bandwidth(number of nearest neighbours): 56 CV score: 993.6388
## Adaptive bandwidth(number of nearest neighbours): 55 CV score: 995.0147
## Adaptive bandwidth(number of nearest neighbours): 57 CV score: 992.2068
## Adaptive bandwidth(number of nearest neighbours): 57 CV score: 992.2068
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 1015.983
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 941.6621
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 920.6782
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 880.6278
## Adaptive bandwidth(number of nearest neighbours): 29 CV score: 932.4171
## Adaptive bandwidth(number of nearest neighbours): 58 CV score: 890.9268
## Adaptive bandwidth(number of nearest neighbours): 40 CV score: 885.4067
## Adaptive bandwidth(number of nearest neighbours): 51 CV score: 884.5523
## Adaptive bandwidth(number of nearest neighbours): 44 CV score: 887.114
## Adaptive bandwidth(number of nearest neighbours): 48 CV score: 880.6278
## Adaptive bandwidth(number of nearest neighbours): 199 CV score: 828.9745
## Adaptive bandwidth(number of nearest neighbours): 124 CV score: 813.815
## Adaptive bandwidth(number of nearest neighbours): 76 CV score: 823.9002
## Adaptive bandwidth(number of nearest neighbours): 152 CV score: 825.8182
## Adaptive bandwidth(number of nearest neighbours): 105 CV score: 814.0861
## Adaptive bandwidth(number of nearest neighbours): 134 CV score: 820.5411
## Adaptive bandwidth(number of nearest neighbours): 116 CV score: 810.8888
## Adaptive bandwidth(number of nearest neighbours): 112 CV score: 812.2683
## Adaptive bandwidth(number of nearest neighbours): 119 CV score: 811.53
## Adaptive bandwidth(number of nearest neighbours): 114 CV score: 811.3823
## Adaptive bandwidth(number of nearest neighbours): 117 CV score: 811.6637
## Adaptive bandwidth(number of nearest neighbours): 115 CV score: 810.4302
## Adaptive bandwidth(number of nearest neighbours): 115 CV score: 810.4302X11(width=8,height=5)
plot.mcsims(gwpca.mc)
El ancho de banda óptimo para la regresión básica de GW se encuentra en N = 109 (“201”) de acuerdo con un enfoque automático AICc a través de la función bw.gwr. Este ancho de banda es entonces utilizado calibrar la regresión básica GW a través de la función gwr.basic.
Luego llevamos a cabo el Prueba de Monte Carlo donde los resultados se presentan en la Tabla 1. Los resultados sugieren (aprox, al 95%) que el término Intercept junto con DiffAdd, LARent, LowEduc, Age25_44 y Age45_64 variables, todas deben fijarse como globales en el modelo mixto. En consecuencia, El modelo mixto se calibra utilizando la función gwr.mixed con el mismo ancho de banda adaptativo que el encontrado para el modelo básico. Observe que los coeficientes que varían geográficamente en el modelo mixto son menos variables que los coeficientes correspondientes del modelo básico, aunque se utiliza el mismo ancho de banda. Los comandos para realizar estas operaciones son los siguientes, donde la función de impresión imita la salida del software ejecutable GW regression
##Construcción de modelos mixtos para la regresión de GW
Pruebas de Monte Carlo para la no estacionariedad del coeficiente de regresión para una regresión GW mixta
Surgen dificultades al decidir si una relación debe fijarse globalmente o permitir que varíe localmente. Aquí Fotheringham et al. adopta un procedimiento paso a paso, donde todas las combinaciones posibles de variables globales y locales se prueban las relaciones y se elige un modelo mixto óptimo de acuerdo con un AIC minimizado valor. Este enfoque es completo, pero computacionalmente costoso, y se utiliza en el Software ejecutable GW regression 4.0.
Alternativamente, un enfoque de Monte Carlo puede ser utilizado para probar la variación (espacial) significativa en cada coeficiente de regresión (o relación) del modelo de regresión básico de GW, donde la hipótesis nula es que la relación entre variable dependiente e independiente es constante (1, 3, 4).
El procedimiento es análogo al presentado para los valores propios locales de un GW PCA, donde para la regresión básica GW se compara la verdadera variabilidad en cada coeficiente de regresión local al encontrado a partir de una serie de conjuntos de datos aleatorios. Si la verdadera varianza del coeficiente no se encuentra en la cola del \(5%\) superior de los resultados clasificados, entonces se puede aceptar la hipótesis nula al nivel del \(95%\); y la relación correspondiente debe fijarse globalmente cuando especificando la regresión mixta de GW.
Observe que si todas las relaciones se consideran no estacionarias, entonces se debe preferir la regresión básica de GW. Por el contrario, si todas las relaciones se consideran estacionarios, entonces se debe preferir la regresión global estándar. Avances en el modelo de regresión mixto GW, donde se puede permitir que las relaciones varíen
Ejemplo: especificación del modelo de regresión GW mixto
Ahora se demuestra la construcción de modelos para regresión GW mixta usando Datos de DubVoter. Primero se calibra una regresión básica de GW. Luego llevar a cabo el Monte Carlo prueba en las salidas de este modelo, para medir la variación significativa (o no estacionariedad) en cada coeficiente, incluido el término de intersección. Finalmente, se ajusta una regresión GW mixta según los resultados de la prueba de Monte Carlo. Las regresiones investigan lo local / global relaciones entre la respuesta: GenEl2004 y estos ocho predictores: DiffAdd, LARent, SC1, Unempl, LowEduc, Age18_24, Age25_44 y Age45_64. Para ambas regresiones de GW, especificar un kernel bi-cuadrado con un ancho de banda adaptativo El ancho de banda óptimo para la regresión básica de GW se encuentra en N = 109 en de acuerdo con un enfoque automático AICc a través de la función bw.gwr. Este ancho de banda es entonces utilizado calibrar la regresión básica GW a través de la función gwr.basic. Luego llevamos a cabo la Prueba de Monte Carlo donde los resultados se presentan en la Tabla.
Los resultados sugieren (digamos, al 95%) que el término Intercept junto con DiffAdd, LARent, LowEduc, Age25_44 y Age45_64 variables, todas deben fijarse como globales en el modelo mixto. En consecuencia, el modelo mixto se calibra utilizando la función gwr.mixed con el mismo ancho de banda adaptativo que el encontrado para el modelo básico. Observe que los coeficientes que varían geográficamente en el modelo mixto son menos variables que los coeficientes correspondientes del modelo básico, aunque se utiliza el mismo ancho de banda. Los comandos para realizar estas operaciones son los siguientes, donde la función de impresión imita la salida del software ejecutable GW regression
modelo Basico GWR
bw.gwr.1 <- bw.gwr(GenEl2004 ~ DiffAdd + LARent + SC1 + Unempl + LowEduc + Age18_24 + Age25_44 + Age45_64, data = Dub.voter,
approach = "AICc",kernel = "bisquare", adaptive = TRUE)## Adaptive bandwidth (number of nearest neighbours): 206 AICc value: 1950.248
## Adaptive bandwidth (number of nearest neighbours): 135 AICc value: 1925.648
## Adaptive bandwidth (number of nearest neighbours): 90 AICc value: 1927.43
## Adaptive bandwidth (number of nearest neighbours): 161 AICc value: 1933.323
## Adaptive bandwidth (number of nearest neighbours): 116 AICc value: 1921.995
## Adaptive bandwidth (number of nearest neighbours): 107 AICc value: 1921.608
## Adaptive bandwidth (number of nearest neighbours): 99 AICc value: 1922.882
## Adaptive bandwidth (number of nearest neighbours): 109 AICc value: 1921.287
## Adaptive bandwidth (number of nearest neighbours): 113 AICc value: 1921.645
## Adaptive bandwidth (number of nearest neighbours): 109 AICc value: 1921.287bgwr.res <- gwr.basic(GenEl2004 ~ DiffAdd + LARent + SC1 + Unempl + LowEduc + Age18_24 + Age25_44 + Age45_64, data = Dub.voter, bw = bw.gwr.1, kernel = "bisquare", adaptive = TRUE)
print(bgwr.res)## ***********************************************************************
## * Package GWmodel *
## ***********************************************************************
## Program starts at: 2021-05-24 23:50:06
## Call:
## gwr.basic(formula = GenEl2004 ~ DiffAdd + LARent + SC1 + Unempl +
## LowEduc + Age18_24 + Age25_44 + Age45_64, data = Dub.voter,
## bw = bw.gwr.1, kernel = "bisquare", adaptive = TRUE)
##
## Dependent (y) variable: GenEl2004
## Independent variables: DiffAdd LARent SC1 Unempl LowEduc Age18_24 Age25_44 Age45_64
## Number of data points: 322
## ***********************************************************************
## * Results of Global Regression *
## ***********************************************************************
##
## Call:
## lm(formula = formula, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -23.9343 -3.3500 0.4952 3.4707 13.4373
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 77.70467 3.93928 19.726 < 2e-16 ***
## DiffAdd -0.08583 0.08594 -0.999 0.3187
## LARent -0.09402 0.01765 -5.326 1.92e-07 ***
## SC1 0.08637 0.07085 1.219 0.2238
## Unempl -0.72162 0.09387 -7.687 1.96e-13 ***
## LowEduc -0.13073 0.43022 -0.304 0.7614
## Age18_24 -0.13992 0.05480 -2.554 0.0111 *
## Age25_44 -0.35365 0.07450 -4.747 3.15e-06 ***
## Age45_64 -0.09202 0.09023 -1.020 0.3086
##
## ---Significance stars
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Residual standard error: 5.304 on 313 degrees of freedom
## Multiple R-squared: 0.6383
## Adjusted R-squared: 0.629
## F-statistic: 69.03 on 8 and 313 DF, p-value: < 2.2e-16
## ***Extra Diagnostic information
## Residual sum of squares: 8805.251
## Sigma(hat): 5.245609
## AIC: 1999.15
## AICc: 1999.858
## BIC: 1772.641
## ***********************************************************************
## * Results of Geographically Weighted Regression *
## ***********************************************************************
##
## *********************Model calibration information*********************
## Kernel function: bisquare
## Adaptive bandwidth: 109 (number of nearest neighbours)
## Regression points: the same locations as observations are used.
## Distance metric: Euclidean distance metric is used.
##
## ****************Summary of GWR coefficient estimates:******************
## Min. 1st Qu. Median 3rd Qu. Max.
## Intercept 53.22830962 73.31782964 81.66277747 95.06908086 116.7660
## DiffAdd -0.72807886 -0.33380997 -0.15837712 0.15858002 0.5465
## LARent -0.19491170 -0.12060836 -0.08443575 -0.03691619 0.0940
## SC1 -0.15781787 0.03528402 0.30881760 0.42006638 0.8796
## Unempl -2.31794903 -1.14350301 -0.76487866 -0.47532711 -0.0925
## LowEduc -7.67491216 -0.73694598 0.53323357 1.80977357 3.4140
## Age18_24 -0.39700178 -0.25290301 -0.14571296 0.00076421 0.3669
## Age25_44 -1.09503913 -0.72092749 -0.45360107 -0.30484438 0.2184
## Age45_64 -0.92361942 -0.40984558 -0.11024847 0.04679070 0.4931
## ************************Diagnostic information*************************
## Number of data points: 322
## Effective number of parameters (2trace(S) - trace(S'S)): 79.90559
## Effective degrees of freedom (n-2trace(S) + trace(S'S)): 242.0944
## AICc (GWR book, Fotheringham, et al. 2002, p. 61, eq 2.33): 1921.287
## AIC (GWR book, Fotheringham, et al. 2002,GWR p. 96, eq. 4.22): 1826.147
## BIC (GWR book, Fotheringham, et al. 2002,GWR p. 61, eq. 2.34): 1799.906
## Residual sum of squares: 4516.821
## R-square value: 0.8144397
## Adjusted R-square value: 0.7529397
##
## ***********************************************************************
## Program stops at: 2021-05-24 23:50:06Monte Carlo test
bgwr.mc <- montecarlo.gwr(GenEl2004 ~ DiffAdd + LARent + SC1 + Unempl + LowEduc + Age18_24 + Age25_44 + Age45_64, data = Dub.voter, bw = bw.gwr.1, kernel = "bisquare", adaptive = TRUE) ##
## Tests based on the Monte Carlo significance test
##
## p-value
## (Intercept) 0.34
## DiffAdd 0.99
## LARent 0.99
## SC1 0.99
## Unempl 0.99
## LowEduc 0.99
## Age18_24 0.99
## Age25_44 0.99
## Age45_64 0.99Modelo GWR mixto (“NO ME FUNCIONA”)
#mgwr.res <- gwr.mixed(GenEl2004 ~ DiffAdd + LARent + SC1 + Unempl + LowEduc + Age18_24 + Age25_44 + Age45_64, data = Dub.voter, bw = bw.gwr.1, fixed.vars = c("DiffAdd", "LARent", "LowEduc", "Age25_44", "Age45_64"),intercept.fixed = TRUE, kernel = "bisquare", adaptive = TRUE)
#print(mgwr.res)Como ejemplo, las Figuras 7 presentan las superficies de coeficientes correspondientes a Unempl encontrado en las regresiones de GW básica y mixta, respectivamente. La variación espacial en este coeficiente es más claro mayor cuando se usa la regresión básica de GW. Diferencias en el Las superficies de coeficientes ocurren principalmente en las áreas noroeste y suroeste de Dublín. Los comandos para estos mapas son los siguientes:
Para mapas de coeficientes
mypalette.gwr <- brewer.pal(6, "Spectral")X11(width=10,height=12)
spplot(bgwr.res$SDF, "Unempl", key.space = "right", col.regions = mypalette.gwr, at = c(-3, -2.5, -2, -1.5, -1, -0.5, 0), main = "Basic GW regression coefficient estimates for Unempl", sp.layout=map.layout.3)
Modelo mixto no me corre
#X11(width = 10, height = 12)
#spplot(mgwr.res$SDF, "Unempl_L", key.space = "right", col.regions = mypalette.gwr, at = c(-3, -2.5, -2, -1.5, -1, -0.5, 0), main = "Mixed GW regression coefficient estimates for Unempl",sp.layout=map.layout.3)Ahora se presenta el uso de las funciones de regresión de GW para realizar: (i) múltiples pruebas de hipótesis, (ii) diagnósticos de colinealidad y (iii) ajustes heterocedásticos.
Para pruebas de hipótesis múltiples
gwr.t.adj <- gwr.t.adjust(bgwr.res)
mypalette.gwr.mht <- brewer.pal(4, "Spectral")Figura8
X11(width=10,height=12)
spplot(gwr.t.adj$SDF, "Unempl_p", key.space = "right", col.regions = mypalette.gwr.mht, at = c(0, 0.025, 0.05, 0.1, 1.00), main = "Original p-values for Unempl", sp.layout=map.layout.3)
Figura 9
X11(width=10,height=12)
spplot(gwr.t.adj$SDF, "Unempl_p_bh", key.space = "right", col.regions = mypalette.gwr.mht, at = c(0, 0.025, 0.05, 0.1, 1.0000001), main = "p-values adjusted by Benjamini-Hochberg for Unempl", sp.layout = map.layout.3)
Figura 10
X11(width=10,height=12)
spplot(gwr.t.adj$SDF, "Unempl_p_bo", key.space = "right",col.regions = mypalette.gwr.mht, at = c(0, 0.025, 0.05, 0.1, 1.0000001), main = "p-values adjusted by Bonferroni for Unempl", sp.layout=map.layout.3)
Figura 11
X11(width=10,height=12)
spplot(gwr.t.adj$SDF, "Unempl_p_fb",key.space = "right",
col.regions = mypalette.gwr.mht, at = c(0, 0.025, 0.05, 0.1, 1.0000001),main ="p-values adjusted by the Fotheringham-Byrne approach for Unempl", sp.layout = map.layout.3)
Las figuras anteriores muestran Los valores p asociados con Unempl de la regresión básica de GW: (a) ajustado por Bonferroni; y (b) ajustado por Fotheringham-Byrne
Ahora visualizamos los Niveles de complejidad para diferentes diagnósticos de colinealidad localizados; fig11 correlaciones locales; fig 12 local VIF; y fig 13 CN locales
Fig 12
#mypalette.coll.2 <-brewer.pal(6,"PuBuGn")
#X11(width=10,height=12)
#spplot(gwr.coll.data$SDF,"DiffAdd_VIF",key.space = "right",col.regions=mypalette.coll.2 at=c(7,8,9,10,11,12,13),par.settings=list(fontsize=list(text=15)),main=list(label="Local VIFs for DiffAdd",cex=1.25),sp.layout=map.layout.3)Para una especificación de regresión de GW dada, correlaciones locales, VIF locales, VDP locales y los CN locales se pueden encontrar usando la función gwr.collin.diagno. Los mismos CN locales también se puede encontrar usando la función gwr.lcr. Mapas de ejemplo que presentan correlaciones locales, Los VIF locales y los CN locales se dan en las Figuras 11-13, lo que refleja los diagnósticos para los mismos regresión básica de GW de la sección 4. Las escalas de cada mapa se eligen para resaltar nuestras umbrales. Los comandos para construir estos mapas se dan a continuación. Claramente, significativo La colinealidad está presente en nuestro modelo de regresión GW de estudio, donde DiffAdd parece ser un causa principal con respecto a su relación con Age25_44 en áreas centrales de Dublín. Como el Los CN locales son grandes en todas partes, la simple eliminación de una variable del análisis puede ir de alguna manera para aliviar este problema; antes de pasar a un análisis más centrado en lo local 30 con algún modelo compensado localmente. Esto, por supuesto, pone en duda la validez de la resultados de Kavanagh et al., donde se aplicó la regresión básica de GW a estos datos.
Regresión heterocedástica de GW
La regresión básica de GW supone que el término de error se distribuye normalmente con cero varianza media y constante (estacionaria) sobre la región de estudio.
Los comandos para ajustarse a nuestro modelo heterocedástico son los siguientes, señalando que el la función gwr.hetero se especifica con el mismo ancho de banda que el encontrado para el modelo básico:
hgwr.res <- gwr.hetero(GenEl2004 ~ DiffAdd + LARent + SC1 + Unempl + LowEduc + Age18_24 + Age25_44 + Age45_64, data = Dub.voter, bw = bw.gwr.1, kernel = "bisquare", adaptive = TRUE)## Iteration Delta
## ================
## 1 0.6581X11(width=10,height=12)
spplot(hgwr.res, "Unempl", key.space = "right", col.regions =mypalette.gwr, at = c(-3, -2.5, -2, -1.5, -1, -0.5, 0), main = "Heteroskedastic GW regression coefficient estimates for Unempl", sp.layout=map.layout.3)
Análisis discriminante de GW
El análisis discriminante (DA) permite modelar y predecir un análisis categórico variable dependiente explicada por un conjunto de variables independientes
##Ejemplo: análisis discriminante de GW Para demostrar un GW DA, utilizamos los datos de USelect. Aquí calibramos un GW DA usando la función gwda en GWmodel, junto con un DA estándar (LDA) utilizando la función lda del paquete MASS R. El GW DA se lleva a cabo con un ancho de banda adaptativo (kernel bisquare) con su óptimo encontrado usando la función bw.gwda. Las matrices de confusión resultantes se presentan en la Tabla 2. Aquí, el DA
La precisión de clasificación es del 72,5%, mientras que GWDA proporciona una clasificación ligeramente mejorada. precisión del 74,0%. Una característica interesante es que el modelo global predice solo un condado en la categoría “Límite”.
Los resultados de la elección presidencial real se mapean en el nivel de condado donde Bush fue un claro ganador en la mayoría de los condados, mientras que el La elección fue más competitiva en áreas como Wisconsin y Maine. Los resultados de la clasificación usando DA y GW DA se mapean, donde el patrón espacial en el GW DA Las clasificaciones parecen marginalmente más cercanas a los resultados verdaderos que las encontradas con el DA.
Los comandos para realizar todas estas operaciones y visualizaciones se dan a continuación.
library(MASS)
data(USelect)
lda.res <-lda(winner~unemploy+pctcoled+PEROVER65+pcturban+WHITE,USelect2004)
lda.pred <- predict(lda.res, USelect2004)
lda.SDF <- SpatialPolygonsDataFrame(Sr = polygons(USelect2004), data = data.frame(lda.pred), match.ID = F)
CM.lda <- confusion.matrix(USelect2004$winner, lda.pred$class)
CM.lda## Borderline Bush Kerry Total
## Borderline 1 1 4 6
## Bush 543 2099 166 2808
## Kerry 92 49 156 297
## Total 636 2149 326 3111lda.cr <- length(which(USelect2004$winner == lda.pred$class))/nrow(USelect2004@data)
lda.cr## [1] 0.7251688Dmat <- gw.dist (dp.locat = coordinates (USelect2004))
bw.gwda.ab <- bw.gwda(winner~unemploy+pctcoled+PEROVER65+pcturban+WHITE, USelect2004, kernel= "bisquare", adaptive=T, dMat=Dmat) ## Adaptive bandwidth: 1201 Correctly predicted proportion: 0.7383478
## Adaptive bandwidth: 1930 Correctly predicted proportion: 0.7386692
## Adaptive bandwidth: 2381 Correctly predicted proportion: 0.7338476
## Adaptive bandwidth: 1589 Correctly predicted proportion: 0.7380264
## Adaptive bandwidth: 2103 Correctly predicted proportion: 0.7364192
## Adaptive bandwidth: 1800 Correctly predicted proportion: 0.7393121
## Adaptive bandwidth: 1719 Correctly predicted proportion: 0.737062
## Adaptive bandwidth: 1850 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1849 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336
## Adaptive bandwidth: 1848 Correctly predicted proportion: 0.7396336gwda.ab <- gwda(winner~unemploy+pctcoled+PEROVER65+pcturban+WHITE, USelect2004, bw=bw.gwda.ab, kernel= "bisquare", adaptive=T, dMat=Dmat)
print(gwda.ab)## ***********************************************************************
## * Package GWmodel *
## ***********************************************************************
## Call:
## gwda(formula = winner ~ unemploy + pctcoled + PEROVER65 + pcturban +
## WHITE, data = USelect2004, kernel = "bisquare", adaptive = T,
## bw = bw.gwda.ab, dMat = Dmat)
##
## Grouping factor: winner with the following groups:
##
## Borderline Bush Kerry
## Discriminators: unemploy pctcoled PEROVER65 pcturban WHITE
## Prediction: No prediction data is given and leave-one-out cross-validation will be applied
## Meams: Localised mean is used for GW discriminant analysis
## Variance-covariance: Localised variance-covariance matrix is used for GW discriminant analysis
## Localised prior probability is used for GW discriminant analysis
## Adaptive bandwidth: 1848 (number of nearest neighbours)
## Distance metric: Euclidean distance metric is used.
## The correct ratio is validated as 0.7396336
## The number of points for prediction is 3111
## ***********************************************************************CM.gwda.ab <- confusion.matrix(USelect2004$winner, gwda.ab$SDF$group.predicted)
CM.gwda.ab## Borderline Bush Kerry Total
## Borderline 29 15 12 56
## Bush 522 2103 145 2770
## Kerry 85 31 169 285
## Total 636 2149 326 3111gwda.cr <- length(which(USelect2004$winner ==
gwda.ab$SDF$group.predicted))/nrow(USelect2004@data)
gwda.cr## [1] 0.7396336Paleta de colores
mypalette.gwda <- brewer.pal(3, "Spectral")
xy <- coordinates(USelect2004)Mapeamos
X11(width=16,height=8)
USelect2004$winner <- factor(USelect2004$winner, levels = c("Borderline","Bush", "Kerry"))
spplot(USelect2004, "winner", key.space = "right",col.regions = mypalette.gwda, par.settings=list(fontsize=list(text=20)),main = list(label="Results of the 2004 US presidential election", cex=1.25))
El grafico anterior muestra Resultados de las elecciones presidenciales estadounidenses de 2004. Resultados de clasificación utilizando: (a) DA; y (b) GW DA.
X11(width=16,height=8)
pts.lda.correct <- list("sp.points", xy[which(USelect2004$winner == lda.pred$class),], cex=1, pch="+", col="black")
lda.SDF$class <- factor(lda.SDF$class,levels = c("Borderline","Bush","Kerry"))
spplot(lda.SDF, "class", key.space = "right", col.regions = mypalette.gwda,
par.settings=list(fontsize=list(text=20)),main = list(label="Classification results using DA ", cex=1.25),sub=list(label="+ the correct classification", cex=1),sp.layout=pts.lda.correct)
Ahora
X11(width=16,height=8)
pts.gwda.ab.correct <- list("sp.points", xy[which(USelect2004$winner == gwda.ab$SDF$group.predicted),], cex=1, pch="+", col="black")
gwda.ab$SDF$group.predicted <- factor(gwda.ab$SDF$group.predicted, levels = c("Borderline","Bush", "Kerry"))
spplot(gwda.ab$SDF, "group.predicted", key.space = "right", col.regions = mypalette.gwda,par.settings=list(fontsize=list(text=20)),main = list(label="Classification results using GW DA", cex=1.25),sub=list(label="+ the correct classification", cex=1),sp.layout=pts.gwda.ab.correct)
Selección mejorada del ancho de banda del kernel
En GWmodel, se proporcionan varias funciones para ayudar en la selección del ancho de banda. Estas incluir: bw.ggwr, bw.gwda, bw.gwpca, bw.gwr y bw.gwr.lcr para ancho de banda automático selección al calibrar una regresión GW generalizada, un GW DA, un GW PCA, un GW básico regresión y regresión GW con un término de cresta compensado local, respectivamente. Sin emabargo, no siempre se recomienda simplemente conectar el ancho de banda resultante (óptimo) en el dado Modelo GW, sin antes comprobar el comportamiento de la función de ancho de banda completo.
Observe que los datos de la puntuación CV se suman para proporcionar la Puntuación CV para un ancho de banda determinado. Como ejemplo del uso de estas funciones, además investigar el GW PCA realizado en la sección 3 del articulo. Aquí usamos bw.gwpca, gwpca.cv y Funciones gwpca.cv.contrib. Por lo tanto, la función de ancho de banda CV, y un histograma y mapa de los datos de la puntuación CV para un ancho de banda óptimo de N = 131, se encuentran de la siguiente manera
library(classInt)
sample.n <- 322
bwd.range.adapt <- c(seq(40,sample.n,by=20))
cv.score <- matrix(nrow=length(bwd.range.adapt),ncol=1)
for(i in 1:length(bwd.range.adapt)) cv.score[i] <-
gwpca.cv(bwd.range.adapt[i],Data.scaled,Coords,k=3,robust=F,
kernel="bisquare",adaptive=TRUE,p=2,theta=0,longlat=F)## Adaptive bandwidth(number of nearest neighbours): 40 CV score: 741.6429
## Adaptive bandwidth(number of nearest neighbours): 60 CV score: 688.7911
## Adaptive bandwidth(number of nearest neighbours): 80 CV score: 643.618
## Adaptive bandwidth(number of nearest neighbours): 100 CV score: 620.3628
## Adaptive bandwidth(number of nearest neighbours): 120 CV score: 609.17
## Adaptive bandwidth(number of nearest neighbours): 140 CV score: 619.8276
## Adaptive bandwidth(number of nearest neighbours): 160 CV score: 642.197
## Adaptive bandwidth(number of nearest neighbours): 180 CV score: 668.7868
## Adaptive bandwidth(number of nearest neighbours): 200 CV score: 701.7453
## Adaptive bandwidth(number of nearest neighbours): 220 CV score: 722.198
## Adaptive bandwidth(number of nearest neighbours): 240 CV score: 737.1957
## Adaptive bandwidth(number of nearest neighbours): 260 CV score: 747.3381
## Adaptive bandwidth(number of nearest neighbours): 280 CV score: 766.7746
## Adaptive bandwidth(number of nearest neighbours): 300 CV score: 801.564
## Adaptive bandwidth(number of nearest neighbours): 320 CV score: 808.4945Mapeamos
X11(width=6,height=6)
plot(bwd.range.adapt,cv.score,ylab="",xlab="",cex=1,pch=19)
title(ylab = list("CV score", cex=1.25, col="black", font=1))
title(xlab = list("No. of nearest neighbours", cex=1.25, col="black",font=1))
title(main = list("GW PCA: Bandwidth function", cex=1.5, col="black",font=1))
cv.score.data.opt <-gwpca.cv.contrib(Data.scaled,Coords,bw=131,k=3,robust=F,kernel="bisquare",adaptive=TRUE,p=2,theta=0,longlat=F)X11(width=6,height=6)
hist(cv.score.data.opt,ylab="",xlab="",main="")
title(ylab = list("Frequency", cex=1.25, col="black", font=1))
title(xlab = list("CV score data", cex=1.25, col="black", font=1))
title(main = list("GW PCA: CV score data for a bandwidth of 131", cex=1.5, col="black", font=1))
Dub.voter$cv.score.data.opt <- cv.score.data.opt
mypalette.cv.score <- brewer.pal(9,"Spectral")
#b1 <- classIntervals(cv.score.data.opt,n=9,style="quantile")b1$brks[length(b$brks)] <- b1$brks[length(b1$brks)]*1.001X11(width=10,height=12)
spplot(Dub.voter,"cv.score.data.opt",key.space = "right",col.regions=mypalette.cv.score, cuts=8, par.settings=list(fontsize=list(text=15)), main=list(label="GW PCA: CV score data for a bandwidth of 131", cex=1.25), sub=list(label="Quantile intervals",cex=1.05),sp.layout=map.layout.3)
De las Figurasse desprende claramente que la función de ancho de banda CV se comporta bien, alcanzando un mínimo claro en N = 131; y por lo tanto proporciona seguridad en el uso de este ancho de banda.
En este ancho de banda específico, los datos de la puntuación CV están muy sesgados positivamente, con un extremo valor de 98,4 que corresponde a un DE en el suroeste del Gran Dublín. Los datos del censo en este ED justifica un escrutinio adicional y tal vez en error.