+predi <- function(ij)
+{
#setwd("/Users/bp/Desktop/CONTRATS_AirNormand/2016/RapportFinalBruno")
-rm(list=ls())
+#rm(list=ls())
# Lecture des données: pm = dataframe 2 colonnes, date-time puis PM10 horaire
pm = read.table("DATA/mesures_horaires_hloc_pm10_a_filer.csv",sep=",",dec=".",header=T)
nbvois=10
j=1 # numéro de semaine
-ij=6 # numéro du jour (0 = lundi)
+#ij=6 # numéro du jour (0 = lundi)
Err24 = NULL
ErrPrev = NULL
large = 1
bornes = mean(dataj[25:48]) + c(-large,large)
indcond = varexp[,"PMjour"]>=bornes[1] & varexp[,"PMjour"]<=bornes[2]
+ if (sum(indcond) < 10)
+ {
+ large = 2
+ bornes = mean(dataj[25:48]) + c(-large,large)
+ indcond = varexp[,"PMjour"]>=bornes[1] & varexp[,"PMjour"]<=bornes[2]
+ }
+ while (sum(indcond) < 10)
+ {
+ large = large + 3
+ bornes = mean(dataj[25:48]) + c(-large,large)
+ indcond = varexp[,"PMjour"]>=bornes[1] & varexp[,"PMjour"]<=bornes[2]
+ }
data = data[indcond,] #pollution du 2eme jour == pollution du jour courant +/- 1
varexp = varexp[indcond,]
# w = 1/(D[ind]^2)
# w = w/sum(w)
# W = w %o% rep(1,48)
+
+#print("Voisins + ij")
+#print(sort(D)[1:nbvois])
+#print(dateJPrev)
+#print(rownames(data)[sort(ind)])
+#print(data[sort(ind), 1:48])
+
JourMoy = apply(data[ind, 1:48], 2, mean)
#JourMoy = apply(W*data[ind, 1:48], 2, sum)
NomFile = paste("Voisins_Epandage_PMjour_Hc_",Hc,".png",sep="")
} else {
erreurPrev = mean(abs(dataj[(H+1):48] - JourMoy[(H+1):48]))
}
+
+ list(serie=dataj, prev=JourMoy, err=erreurPrev, line=(nl+ij), neighbs=ind, dates=data[ind,1])
# erreur24 = mean(abs(dataj[25:48] - JourMoy[25:48]))
# #png(NomFile)
# matplot(t(data[ind, 1:48]), type = "l", lwd=1.4, lty=1, col=1:length(ind),
## xx = dev.off()
#
##length(D)
+}