ANALISIS CENTENARIOS
Gallego G
2024-11-05
Analisis de la funcion de densidad
#ColOpenData::climate_tags
hogares=fread("C:/Users/germa_30zwgix/OneDrive/Documentos/R/hogares.txt",header=TRUE)
glue::glue("Cantidad de Hogares {dim(hogares)[1]}")## Cantidad de Hogares 23573893names(hogares)## [1] "CNS" "U_DPRTMNT" "U_MNCP" "U_CLS" "P_SX"
## [6] "EDD_MYR_99" "MRC_LEA" "CNTDD_PRSNS" "LATI_MGN" "LONG_MGN"hogaresuniq=hogares %>% distinct(LATI_MGN,LONG_MGN, .keep_all = T)
glue::glue("Cantidad de coordenadas unicas {dim(hogaresuniq)[1]}")## Cantidad de coordenadas unicas 12904346#names(tumbas)
#hogares$paste=paste0(hogares$LATI_MGN,hogares$LONG_MGN)
#hogares$paste
cente=fread("C:/Users/germa_30zwgix/OneDrive/Documentos/R/puntoshogares.txt",header=TRUE)
centeuniq=cente %>% distinct(LATI_MGN,LONG_MGN, .keep_all = T)
glue::glue("Numero de coordenadas unicas con centenarios {dim(centeuniq)[1]}")## Numero de coordenadas unicas con centenarios 11879cente_base=merge(hogaresuniq,centeuniq,by=c("LATI_MGN","LONG_MGN"),all.x=T)
tumbaspp=ppp(cente_base$LONG_MGN,cente_base$LATI_MGN,c(min(cente_base$LONG_MGN)-1,max(cente_base$LONG_MGN)+1), c(min(cente_base$LATI_MGN)-1,max(cente_base$LATI_MGN)+1))
#plot(tumbaspp)
casos=subset(tumbaspp,!is.na(cente_base$CNTDD_PRSNS_MYR_99))
controles=subset(tumbaspp,is.na(cente_base$CNTDD_PRSNS_MYR_99))
glue::glue("distribucion de puntos segun reporte de centenarios")## distribucion de puntos segun reporte de centenarioskbl(table(!is.na(cente_base$CNTDD_PRSNS_MYR_99)), escape = F) %>%
kable_paper("hover", full_width = F)| Var1 | Freq |
|---|---|
| FALSE | 12892467 |
| TRUE | 11879 |
par(mfrow=c(1,2),mar=c(2,2,2,2)/2)
plot(casos)
plot(controles)
#par(mfrow=c(1,1),mar=c(2,2,2,2)/2)
#risk(casos,controles,doplot = T)
bw=seq(0.00001,0.5,length=10)
par(mfrow=c(4,4),mar=c(2,2,2,2)/2)
for (i in 1:length(bw))
{
plot(density.ppp(casos,sigma=bw[i]),cex.main=0.3,main=paste("casos, bw = ",round(bw[i],2)))
plot(density.ppp(controles,sigma=bw[i]),cex.main=0.3,main=paste("controles, bw = ",round(bw[i],2)))
}
#bw_fija=LSCV.risk(casos,controles)
bw_fija=0.06
f.tilde=bivariate.density(casos,h0=bw_fija)
g.tilde=bivariate.density(controles,h0=bw_fija)
rho.tilde=risk(f=f.tilde,g=g.tilde)
par(mfrow=c(1,1),mar=c(2,2,2,2)/2)
pval.tilde=tolerance(rs=rho.tilde,method="MC",ITER=200)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 30% | |===================== | 31% | |====================== | 31% | |====================== | 32% | 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|==================================================================== | 96% | |==================================================================== | 97% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%#plot(pval.tilde)
plot(rho.tilde,xlab="Este", ylab="Norte");tol.contour(pim=pval.tilde,levels=c(0.05,0.01),lty=1:2,add=TRUE)
#View(pval.tilde$v)