Heat Wave Analysis
Anna Alari
24 mars, 2021
Importing data
library(xlsx)
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## cat, commandArgs, getOption, inherits, isOpen, nullfile, parse,
## warningsBM<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_bm.xlsx",1)
bordeaux<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_bordeaux.xlsx",1)
clermont<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_clermont ferrant.xlsx",1)
dijon<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_Dijon.xlsx",1)
grenoble<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_Grenoble.xlsx",1)
lehavre<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_Le havre.xlsx",1)
lille<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_Lille.xlsx",1)
lyon<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_Lyon.xlsx",1)
marseille<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_Marseille.xlsx",1)
montpellier<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_Montpellier.xlsx",1)
nancy<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_Nancy.xlsx",1)
nantes<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_Nantes.xlsx",1)
nice<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_Nice.xlsx",1)
paris<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_Paris.xlsx",1)
rennes<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_Rennes.xlsx",1)
rouen<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_Rouen.xlsx",1)
strasbourg<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_StrasbourgVF.xlsx",1)
toulouse<-read.xlsx("C:/Users/Anna/Dropbox/CEPEM/Bases/base_Toulouse.xlsx",1)Data management
In order to handle all the database together, I create a list:
# Make variable names consistent between table
toulouse <- toulouse %>% rename(tempmaxmoy7j=tempmaxmoy)
villes<-list(BM,bordeaux,clermont,dijon,grenoble,lehavre,lille,lyon,marseille,montpellier,nancy,nantes,nice,paris,rennes,rouen,strasbourg,toulouse)
names<-c("BM","bordeaux","clermont","dijon","grenoble","lehavre","lille","lyon","marseille","montpellier","nancy","nantes","nice", "paris","rennes","rouen","strasbourg","toulouse")
names(villes)<-namesI may also have created a unique dataframe with all the table and stating a grouping by “city” variable.
We have mortality data only untill 2015, so I reduce database to 2000-2015 period:
villes<-lapply(villes,function(x){x<-x[x$Dates<"2016-01-01",]})Basic statistics and visualisation of data
For temperature
For Mean temperature
lapply(villes,function(x){summary(x$tempmoy)})## $BM
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -8.95 6.70 11.53 11.17 15.96 28.05 1
##
## $bordeaux
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -5.550 9.412 14.100 13.927 18.830 31.250
##
## $clermont
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -10.60 7.05 12.35 12.15 17.60 30.25
##
## $dijon
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -8.45 5.80 11.66 11.42 17.20 31.05
##
## $grenoble
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -13.00 5.60 11.70 11.36 17.20 29.15
##
## $lehavre
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -4.596 7.891 11.750 11.682 15.908 29.325 16
##
## $lille
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -8.95 6.70 11.53 11.17 15.96 28.05 1
##
## $lyon
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -8.300 7.188 13.338 13.045 19.021 31.925
##
## $marseille
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -3.50 10.23 15.75 15.74 21.72 31.57 2
##
## $montpellier
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.15 10.30 15.40 15.47 21.15 30.90
##
## $nancy
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -11.20 5.60 11.35 11.04 16.63 29.55 1
##
## $nantes
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.800 8.662 12.750 12.629 17.100 31.300
##
## $nice
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.342 11.344 16.050 16.350 21.450 31.350 1
##
## $paris
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -5.425 7.858 12.850 12.698 17.650 32.425
##
## $rennes
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -4.150 8.285 12.400 12.285 16.600 31.750 1
##
## $rouen
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -7.45 6.50 11.10 10.81 15.41 29.33 19
##
## $strasbourg
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -11.58 5.73 11.70 11.38 17.40 29.20
##
## $toulouse
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -7.30 8.85 14.01 14.03 19.51 31.83For Max temperature:
lapply(villes,function(x){summary(x$tempmax)})## $BM
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -5.10 9.50 15.10 14.89 20.20 36.50 1
##
## $bordeaux
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.20 13.10 18.60 18.61 24.10 40.40
##
## $clermont
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -6.90 11.30 17.50 17.23 23.30 39.80
##
## $dijon
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -5.00 9.40 16.30 16.03 22.60 39.30
##
## $grenoble
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -7.00 9.80 16.80 16.49 23.20 39.50
##
## $lehavre
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -2.70 9.80 14.10 14.13 18.30 36.30 16
##
## $lille
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -5.10 9.50 15.10 14.89 20.20 36.50 1
##
## $lyon
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -5.20 10.80 17.70 17.48 24.20 40.50
##
## $marseille
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -1.00 14.50 20.40 20.55 26.80 38.20 2
##
## $montpellier
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.60 14.50 19.80 20.12 25.90 37.20
##
## $nancy
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -7.10 9.10 15.70 15.56 22.10 39.30 1
##
## $nantes
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.50 12.20 16.90 17.04 21.90 39.20
##
## $nice
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 4.30 15.10 19.60 19.84 24.80 37.70 1
##
## $paris
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.9 10.5 16.3 16.3 21.9 39.7
##
## $rennes
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -1.10 11.90 16.60 16.72 21.60 39.50 1
##
## $rouen
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -4.70 9.70 14.90 14.79 19.90 37.90 19
##
## $strasbourg
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -8.40 9.10 16.40 15.98 22.80 38.70
##
## $toulouse
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -4.30 12.50 18.60 18.64 24.60 40.40For Min temperature:
lapply(villes,function(x){summary(x$tempmin)})## $BM
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -13.400 3.400 7.800 7.492 11.900 22.600 1
##
## $bordeaux
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -8.800 5.400 9.700 9.456 14.100 23.400
##
## $clermont
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -17.100 2.400 7.400 7.066 12.100 23.300
##
## $dijon
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -18.700 1.700 7.000 6.815 12.000 23.100
##
## $grenoble
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -20.200 1.200 6.500 6.227 11.500 21.300
##
## $lehavre
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -6.900 5.900 9.600 9.397 13.500 24.300 16
##
## $lille
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -13.400 3.400 7.800 7.492 11.900 22.600 1
##
## $lyon
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -12.70 3.30 9.00 8.63 14.10 24.60
##
## $marseille
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -9.50 6.10 11.50 11.13 16.80 27.00 2
##
## $montpellier
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -9.80 5.80 11.10 10.84 16.40 25.70
##
## $nancy
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -15.60 1.70 6.80 6.47 11.60 22.20 1
##
## $nantes
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -9.600 4.600 8.800 8.254 12.300 23.800
##
## $nice
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -2.1 7.8 12.8 12.9 18.0 28.1 1
##
## $paris
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -8.700 5.100 9.400 9.215 13.700 25.700
##
## $rennes
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -9.300 4.200 8.300 7.883 11.900 24.000 1
##
## $rouen
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -12.100 3.100 7.300 6.903 11.100 21.900 19
##
## $strasbourg
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -17.700 1.875 7.100 6.895 12.200 21.600
##
## $toulouse
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -12.50 4.90 9.80 9.54 14.70 24.10Cities of BM, Le Havre, Lille, Marseille, Nancy, Nice, Rennes and Rouen have missind data for temperature.
For Mortality
For Non accidental mortality:
lapply(villes,function(x){summary(x$nocc_tot)})## $BM
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 6.000 8.000 8.542 11.000 23.000 1
##
## $bordeaux
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.00 10.00 12.00 12.48 15.00 36.00
##
## $clermont
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 4.000 5.000 5.633 7.000 16.000 18
##
## $dijon
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 3.00 4.00 4.54 6.00 14.00 56
##
## $grenoble
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 6.000 8.000 7.716 10.000 21.000 3
##
## $lehavre
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 4.000 6.000 5.748 7.000 16.000 13
##
## $lille
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.00 17.00 21.00 20.98 24.00 45.00
##
## $lyon
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.00 15.00 18.00 18.23 21.00 75.00
##
## $marseille
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.00 18.00 22.00 21.91 25.00 45.00
##
## $montpellier
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 4.000 6.000 6.352 8.000 18.000 6
##
## $nancy
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 5.00 7.00 6.78 8.00 24.00 4
##
## $nantes
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 8.0 10.0 10.3 12.0 32.0
##
## $nice
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 9.00 12.00 12.12 15.00 33.00
##
## $paris
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 60.0 96.0 105.0 106.5 115.0 724.0
##
## $rennes
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 2.00 4.00 3.92 5.00 13.00 84
##
## $rouen
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 8.000 10.000 9.838 12.000 28.000
##
## $strasbourg
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 6.000 8.000 8.402 10.000 23.000
##
## $toulouse
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 9.00 12.00 11.87 14.00 34.00For Cardiovasculal Mortality:
lapply(villes,function(x){summary(x$cv_tot)})## $BM
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 2.000 2.327 3.000 9.000 1
##
## $bordeaux
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.000 3.000 3.658 5.000 14.000
##
## $clermont
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 1.000 1.633 2.000 8.000 18
##
## $dijon
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 1.000 1.258 2.000 7.000 56
##
## $grenoble
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 2.000 2.181 3.000 11.000 2
##
## $lehavre
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 1.000 1.553 2.000 8.000 13
##
## $lille
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 4.000 5.000 5.695 7.000 18.000
##
## $lyon
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 3.000 5.000 4.983 6.000 22.000
##
## $marseille
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 4.000 6.000 6.391 8.000 23.000
##
## $montpellier
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 2.000 1.847 3.000 8.000 6
##
## $nancy
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 2.000 1.841 3.000 10.000 4
##
## $nantes
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.000 3.000 2.899 4.000 12.000
##
## $nice
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.000 3.000 3.513 5.000 13.000
##
## $paris
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 8.0 22.0 26.0 26.5 31.0 179.0
##
## $rennes
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 1.000 1.191 2.000 7.000 82
##
## $rouen
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.000 3.000 2.814 4.000 14.000
##
## $strasbourg
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 2.000 2.439 3.000 11.000
##
## $toulouse
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.000 3.000 3.339 5.000 14.000For Respiratory mortality:
lapply(villes,function(x){summary(x$respi_tot)})## $BM
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 1.0000 0.7927 1.0000 7.0000 1
##
## $bordeaux
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 1.0000 0.8214 1.0000 6.0000
##
## $clermont
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.3417 1.0000 4.0000 18
##
## $dijon
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.2916 0.0000 4.0000 56
##
## $grenoble
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.4765 1.0000 8.0000 2
##
## $lehavre
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.3541 1.0000 4.0000 13
##
## $lille
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 1.000 1.574 2.000 12.000
##
## $lyon
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.000 1.126 2.000 8.000
##
## $marseille
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 1.000 1.565 2.000 11.000
##
## $montpellier
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.4322 1.0000 5.0000 6
##
## $nancy
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.5252 1.0000 5.0000 4
##
## $nantes
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.6475 1.0000 9.0000
##
## $nice
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 1.0000 0.8251 1.0000 6.0000
##
## $paris
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 5.000 6.000 6.827 9.000 52.000
##
## $rennes
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.3133 1.0000 4.0000 82
##
## $rouen
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.6244 1.0000 5.0000
##
## $strasbourg
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.5346 1.0000 7.0000
##
## $toulouse
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 1.0000 0.7414 1.0000 7.0000Imputing Missing data
To manage missing data for temperature I decide to imput missing data values according to these rules: - for mean and minumum temperature, when only one day value is missing, I impute by doing an average of value of previous and following days. When a longer period is missing (normally, a week), I impute by setting the value as an average between the value of the previous weekend and the following week - for maximum temperature, I impute by getting the value for the variable tempmax over 7 days
## imputation NA for Marseille
## imput NA temp max
villes[["marseille"]]$tempmax[which(is.na(villes[["marseille"]]$tempmax))] <- villes[["marseille"]]$tempmaxmoy7j[which(is.na(villes[["marseille"]]$tempmax))]
## imput NA temp min
for (r in 2:nrow(villes[["marseille"]])) {
mm<-c(villes[["marseille"]]$tempmin[r-2],villes[["marseille"]]$tempmin[r+2])
meantt<-mean(mm)
villes[["marseille"]]$tempmin[r] <- if (is.na(villes[["marseille"]]$tempmin[r])) meantt else villes[["marseille"]]$tempmin[r]
}
## imput NA temp moy
for (r in 2:nrow(villes[["marseille"]])) {
mm<-c(villes[["marseille"]]$tempmoy[r-2],villes[["marseille"]]$tempmoy[r+2])
meantt<-mean(mm)
villes[["marseille"]]$tempmoy[r] <- if (is.na(villes[["marseille"]]$tempmoy[r])) meantt else villes[["marseille"]]$tempmoy[r]
}
## Imputation NA for BM
## imput NA temp max
villes[["BM"]]$tempmax[which(is.na(villes[["BM"]]$tempmax))] <- villes[["BM"]]$tempmaxmoy7j[which(is.na(villes[["BM"]]$tempmax))]
## imput NA temp min
for (r in 2:nrow(villes[["BM"]])) {
mm<-c(villes[["BM"]]$tempmin[r-2],villes[["BM"]]$tempmin[r+2])
meantt<-mean(mm)
villes[["BM"]]$tempmin[r] <- if (is.na(villes[["BM"]]$tempmin[r])) meantt else villes[["BM"]]$tempmin[r]
}
## imput NA temp moy
for (r in 2:nrow(villes[["BM"]])) {
mm<-c(villes[["BM"]]$tempmoy[r-2],villes[["BM"]]$tempmoy[r+2])
meantt<-mean(mm)
villes[["BM"]]$tempmoy[r] <- if (is.na(villes[["BM"]]$tempmoy[r])) meantt else villes[["BM"]]$tempmoy[r]
}
## Imputation NA for Lille
# imput NA temp max
villes[["lille"]]$tempmax[which(is.na(villes[["lille"]]$tempmax))] <- villes[["lille"]]$tempmaxmoy7j[which(is.na(villes[["lille"]]$tempmax))]
## imput NA temp min
for (r in 2:nrow(villes[["lille"]])) {
mm<-c(villes[["lille"]]$tempmin[r-2],villes[["lille"]]$tempmin[r+2])
meantt<-mean(mm)
villes[["lille"]]$tempmin[r] <- if (is.na(villes[["lille"]]$tempmin[r])) meantt else villes[["lille"]]$tempmin[r]
}
## imput NA temp moy
for (r in 2:nrow(villes[["lille"]])) {
mm<-c(villes[["lille"]]$tempmoy[r-2],villes[["lille"]]$tempmoy[r+2])
meantt<-mean(mm)
villes[["lille"]]$tempmoy[r] <- if (is.na(villes[["lille"]]$tempmoy[r])) meantt else villes[["lille"]]$tempmoy[r]
}
## Imputation NA for Nancy
## imput NA temp max
villes[["nancy"]]$tempmax[which(is.na(villes[["nancy"]]$tempmax))] <- villes[["nancy"]]$tempmaxmoy7j[which(is.na(villes[["nancy"]]$tempmax))]
## imput NA temp min
for (r in 2:nrow(villes[["nancy"]])) {
mm<-c(villes[["nancy"]]$tempmin[r-2],villes[["nancy"]]$tempmin[r+2])
meantt<-mean(mm)
villes[["nancy"]]$tempmin[r] <- if (is.na(villes[["nancy"]]$tempmin[r])) meantt else villes[["nancy"]]$tempmin[r]
}
## imput NA temp moy
for (r in 2:nrow(villes[["nancy"]])) {
mm<-c(villes[["nancy"]]$tempmoy[r-2],villes[["nancy"]]$tempmoy[r+2])
meantt<-mean(mm)
villes[["nancy"]]$tempmoy[r] <- if (is.na(villes[["nancy"]]$tempmoy[r])) meantt else villes[["nancy"]]$tempmoy[r]
}
## Imputation NA for Nice
## imput NA temp max
villes[["nice"]]$tempmax[which(is.na(villes[["nice"]]$tempmax))] <- villes[["nice"]]$tempmaxmoy7j[which(is.na(villes[["nice"]]$tempmax))]
## imput NA temp min
for (r in 2:nrow(villes[["nice"]])) {
mm<-c(villes[["nice"]]$tempmin[r-2],villes[["nice"]]$tempmin[r+2])
meantt<-mean(mm)
villes[["nice"]]$tempmin[r] <- if (is.na(villes[["nice"]]$tempmin[r])) meantt else villes[["nice"]]$tempmin[r]
}
## imput NA temp moy
for (r in 2:nrow(villes[["nice"]])) {
mm<-c(villes[["nice"]]$tempmoy[r-2],villes[["nice"]]$tempmoy[r+2])
meantt<-mean(mm)
villes[["nice"]]$tempmoy[r] <- if (is.na(villes[["nice"]]$tempmoy[r])) meantt else villes[["nice"]]$tempmoy[r]
}
## Imputation NA for Rennes
## imput NA temp max
villes[["rennes"]]$tempmax[which(is.na(villes[["rennes"]]$tempmax))] <- villes[["rennes"]]$tempmaxmoy7j[which(is.na(villes[["rennes"]]$tempmax))]
## imput NA temp min
for (r in 2:nrow(villes[["rennes"]])) {
mm<-c(villes[["rennes"]]$tempmin[r-2],villes[["rennes"]]$tempmin[r+2])
meantt<-mean(mm)
villes[["rennes"]]$tempmin[r] <- if (is.na(villes[["rennes"]]$tempmin[r])) meantt else villes[["rennes"]]$tempmin[r]
}
## imput NA temp moy
for (r in 2:nrow(villes[["rennes"]])) {
mm<-c(villes[["rennes"]]$tempmoy[r-2],villes[["rennes"]]$tempmoy[r+2])
meantt<-mean(mm)
villes[["rennes"]]$tempmoy[r] <- if (is.na(villes[["rennes"]]$tempmoy[r])) meantt else villes[["rennes"]]$tempmoy[r]
}
## imputation NA for Le Havre
## imput NA temp moy
villes[["lehavre"]]$tempmax[which(is.na(villes[["lehavre"]]$tempmax))] <- villes[["lehavre"]]$tempmaxmoy7j[which(is.na(villes[["lehavre"]]$tempmax))]
## imput NA temp moy
# for only one specific day
villes[["lehavre"]]$tempmoy[which(villes[["lehavre"]]$Dates=="2000-05-07")]<-mean(villes[["lehavre"]]$tempmoy[which(villes[["lehavre"]]$Dates=="2000-05-06")],villes[["lehavre"]]$tempmoy[which(villes[["lehavre"]]$Dates=="2000-05-08")])
villes[["lehavre"]]$tempmoy[which(villes[["lehavre"]]$Dates=="2003-02-25")]<-mean(villes[["lehavre"]]$tempmoy[which(villes[["lehavre"]]$Dates=="2003-02-26")],villes[["lehavre"]]$tempmoy[which(villes[["lehavre"]]$Dates=="2003-02-24")])
# List of longer Period with NA (need of package?)
period1<-as.Date(c(as.Date("2007-10-21"):as.Date("2007-10-27")))
period2<-as.Date(c(as.Date("2007-11-03"):as.Date("2007-11-09")))
## Previous and following weeks to compute mean value
period1bef<-as.Date(c(as.Date("2007-10-15"):as.Date("2007-10-20")))
period1aft<-as.Date(c(as.Date("2007-10-28"):as.Date("2007-11-02")))
period2aft<-as.Date(c(as.Date("2007-11-10"):as.Date("2007-11-15")))
## imput NA tempmoy La Havre
villes[["lehavre"]]$tempmoy[which(villes[["lehavre"]]$Dates %in% period1)]<-mean(villes[["lehavre"]]$tempmoy[which(villes[["lehavre"]]$Dates %in% c(period1bef,period1aft))])
villes[["lehavre"]]$tempmoy[which(villes[["lehavre"]]$Dates %in% period2)]<-mean(villes[["lehavre"]]$tempmoy[which(villes[["lehavre"]]$Dates %in% c(period1aft,period2aft))])
## Imput NA tempmin
# for only one specific day
villes[["lehavre"]]$tempmin[which(villes[["lehavre"]]$Dates=="2000-05-07")]<-mean(villes[["lehavre"]]$tempmin[which(villes[["lehavre"]]$Dates=="2000-05-06")],villes[["lehavre"]]$tempmin[which(villes[["lehavre"]]$Dates=="2000-05-08")])
villes[["lehavre"]]$tempmin[which(villes[["lehavre"]]$Dates=="2003-02-25")]<-mean(villes[["lehavre"]]$tempmin[which(villes[["lehavre"]]$Dates=="2003-02-26")],villes[["lehavre"]]$tempmin[which(villes[["lehavre"]]$Dates=="2003-02-24")])
## imput NA tempmin La Havre
villes[["lehavre"]]$tempmin[which(villes[["lehavre"]]$Dates %in% period1)]<-mean(villes[["lehavre"]]$tempmin[which(villes[["lehavre"]]$Dates %in% c(period1bef,period1aft))])
villes[["lehavre"]]$tempmin[which(villes[["lehavre"]]$Dates %in% period2)]<-mean(villes[["lehavre"]]$tempmin[which(villes[["lehavre"]]$Dates %in% c(period1aft,period2aft))])
## imputation NA pour rouen
villes[["rouen"]]$tempmax[which(is.na(villes[["rouen"]]$tempmax))] <- villes[["rouen"]]$tempmaxmoy7j[which(is.na(villes[["rouen"]]$tempmax))]
## imput NA temp moy
# for only one specific day
villes[["rouen"]]$tempmoy[which(villes[["rouen"]]$Dates=="2004-08-17")]<-mean(villes[["rouen"]]$tempmoy[which(villes[["rouen"]]$Dates=="2004-08-16")],villes[["rouen"]]$tempmoy[which(villes[["rouen"]]$Dates=="2004-08-18")])
# List of longer Period with NA (need of package?)
period1<-as.Date(c(as.Date("2003-06-14"):as.Date("2003-06-19")))
period2<-as.Date(c(as.Date("2004-08-03"):as.Date("2004-08-05")))
period3<-as.Date(c(as.Date("2009-03-17"):as.Date("2009-03-25")))
## Previous and following weeks to compute mean value
period1bef<-as.Date(c(as.Date("2003-06-08"):as.Date("2003-06-13")))
period1aft<-as.Date(c(as.Date("2003-06-20"):as.Date("2003-06-25")))
period2bef<-as.Date(c(as.Date("2004-07-29"):as.Date("2004-08-02")))
period2aft<-as.Date(c(as.Date("2004-08-06"):as.Date("2004-08-11")))
period3bef<-as.Date(c(as.Date("2009-03-11"):as.Date("2009-03-16")))
period3aft<-as.Date(c(as.Date("2009-03-26"):as.Date("2009-03-30")))
## imput tempmoy NA Rouen
villes[["rouen"]]$tempmoy[which(villes[["rouen"]]$Dates %in% period1)]<-mean(villes[["rouen"]]$tempmoy[which(villes[["rouen"]]$Dates %in% c(period1bef,period1aft))])
villes[["rouen"]]$tempmoy[which(villes[["rouen"]]$Dates %in% period2)]<-mean(villes[["rouen"]]$tempmoy[which(villes[["rouen"]]$Dates %in% c(period2bef,period2aft))])
villes[["rouen"]]$tempmoy[which(villes[["rouen"]]$Dates %in% period3)]<-mean(villes[["rouen"]]$tempmoy[which(villes[["rouen"]]$Dates %in% c(period3bef,period3aft))])Graphical Representation of the Data
I create a unique database for graphic representation
lapply(villes,function(x){summary(x$Zone)})## $BM
## bm NA's
## 5843 1
##
## $bordeaux
## bordeaux
## 5844
##
## $clermont
## clermont NA's
## 5826 18
##
## $dijon
## dijon NA's
## 5788 56
##
## $grenoble
## grenoble NA's
## 5842 2
##
## $lehavre
## lehavre NA's
## 5831 13
##
## $lille
## lille
## 5844
##
## $lyon
## lyon
## 5844
##
## $marseille
## marseille
## 5844
##
## $montpellier
## montpellier NA's
## 5838 6
##
## $nancy
## ncy NA's
## 5840 4
##
## $nantes
## ntes
## 5844
##
## $nice
## nice
## 5844
##
## $paris
## paris
## 5844
##
## $rennes
## rennes NA's
## 5762 82
##
## $rouen
## rouen
## 5844
##
## $strasbourg
## strasbourg
## 5844
##
## $toulouse
## toulouse
## 5844#bb<-function(x){x$Zone<-if (is.na(x$Zone)) {x$Zone=levels(x$Zone)} else {x$Zone=x$Zone}}
#lapply(villes,bb)
#test<-lapply(villes,function(x){x$Zone[which(is.na(x$Zone))]<-levels(x$Zone)})
## Impute missing data for Zone
for (i in 1:18){
villes[[i]]$Zone[which(is.na(villes[[i]]$Zone))]<-levels(villes[[i]]$Zone)
}
data<-rbind(villes[["BM"]],villes[["bordeaux"]],villes[["clermont"]],villes[["dijon"]],villes[["grenoble"]],villes[["lehavre"]],villes[["lille"]],villes[["lyon"]],villes[["marseille"]],villes[["montpellier"]],villes[["nancy"]],villes[["nantes"]],villes[["nice"]],villes[["paris"]],villes[["rennes"]],villes[["rouen"]],villes[["strasbourg"]],villes[["toulouse"]])
data <- data %>% group_by(Zone)Average Temprature DIstribution:
ggplot(data, aes(x = tempmoy, y = Zone, fill=..x..)) +
geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
scale_fill_viridis(option = "B") + xlab("Temperature [°C]") +
ylab("Urban Agglomeration") +
theme_ipsum() +
theme(
legend.position="none",
panel.spacing = unit(0.1, "lines"),
strip.text.x = element_text(size = 8)
)## Picking joint bandwidth of 1.05## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): famille de
## police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): famille de
## police introuvable dans la base de données des polices Windows## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
Descriptive Statistics:
tabdesc<-data %>% summarise(
'Temp 25 Perc'=quantile(tempmoy)[2],
'Mean Temp'=mean(tempmoy),
'Temp 75 Perc'=quantile(tempmoy)[4],
)
tabdesc[,2:4]<-round(tabdesc[,2:4],digits = 2)
tabdesc %>% kable(caption = "Average Temperature statistics per City") %>% kable_styling(bootstrap_options = c("hover", "condensed","bordered"),full_width = F)%>% add_header_above(c(" ", "Average Temperature" = 3))%>%
column_spec(1, bold = T, border_right = T)%>%
column_spec(4, border_right = T)| Zone | Temp 25 Perc | Mean Temp | Temp 75 Perc |
|---|---|---|---|
| bm | 6.70 | 11.18 | 15.96 |
| bordeaux | 9.41 | 13.93 | 18.83 |
| clermont | 7.05 | 12.15 | 17.60 |
| dijon | 5.80 | 11.42 | 17.20 |
| grenoble | 5.60 | 11.36 | 17.20 |
| lehavre | 7.90 | 11.68 | 15.90 |
| lille | 6.70 | 11.18 | 15.96 |
| lyon | 7.19 | 13.04 | 19.02 |
| marseille | 10.23 | 15.74 | 21.72 |
| montpellier | 10.30 | 15.47 | 21.15 |
| ncy | 5.60 | 11.03 | 16.63 |
| ntes | 8.66 | 12.63 | 17.10 |
| nice | 11.35 | 16.35 | 21.45 |
| paris | 7.86 | 12.70 | 17.65 |
| rennes | 8.28 | 12.28 | 16.60 |
| rouen | 6.51 | 10.82 | 15.42 |
| strasbourg | 5.73 | 11.38 | 17.40 |
| toulouse | 8.85 | 14.03 | 19.51 |
Maximum Temprature DIstribution:
ggplot(data, aes(x = tempmax, y = Zone, fill=..x..)) +
geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
scale_fill_viridis(option = "B") + xlab("Temperature [°C]") +
ylab("Urban Agglomeration") +
theme_ipsum() +
theme(
legend.position="none",
panel.spacing = unit(0.1, "lines"),
strip.text.x = element_text(size = 8)
)## Picking joint bandwidth of 1.2## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
Descriptive Statistics:
tabdesc<-data %>% summarise(
'Temp 25 Perc'=quantile(tempmax)[2],
'Mean Temp'=mean(tempmax),
'Temp 75 Perc'=quantile(tempmax)[4],
)
tabdesc[,2:4]<-round(tabdesc[,2:4],digits = 2)
tabdesc %>% kable(caption = "Maximum Temperature statistics per City") %>% kable_styling(bootstrap_options = c("hover", "condensed","bordered"),full_width = F)%>% add_header_above(c(" ", "MAx Temperature" = 3))%>%
column_spec(1, bold = T, border_right = T)%>%
column_spec(4, border_right = T)| Zone | Temp 25 Perc | Mean Temp | Temp 75 Perc |
|---|---|---|---|
| bm | 9.5 | 14.89 | 20.2 |
| bordeaux | 13.1 | 18.61 | 24.1 |
| clermont | 11.3 | 17.23 | 23.3 |
| dijon | 9.4 | 16.03 | 22.6 |
| grenoble | 9.8 | 16.49 | 23.2 |
| lehavre | 9.8 | 14.13 | 18.3 |
| lille | 9.5 | 14.89 | 20.2 |
| lyon | 10.8 | 17.48 | 24.2 |
| marseille | 14.5 | 20.56 | 26.8 |
| montpellier | 14.5 | 20.12 | 25.9 |
| ncy | 9.1 | 15.56 | 22.1 |
| ntes | 12.2 | 17.04 | 21.9 |
| nice | 15.1 | 19.84 | 24.8 |
| paris | 10.5 | 16.30 | 21.9 |
| rennes | 11.9 | 16.72 | 21.6 |
| rouen | 9.7 | 14.80 | 20.0 |
| strasbourg | 9.1 | 15.98 | 22.8 |
| toulouse | 12.5 | 18.64 | 24.6 |
Minimum Temprature DIstribution:
ggplot(data, aes(x = tempmin, y = Zone, fill=..x..)) +
geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
scale_fill_viridis(option = "B") + xlab("Temperature [°C]") +
ylab("Urban Agglomeration") +
theme_ipsum() +
theme(
legend.position="none",
panel.spacing = unit(0.1, "lines"),
strip.text.x = element_text(size = 8)
)## Picking joint bandwidth of 0.963## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
Descriptive Statistics:
tabdesc<-data %>% summarise(
'Temp 25 Perc'=quantile(tempmin,na.rm = TRUE)[2],
'Mean Temp'=mean(tempmin,na.rm = TRUE),
'Temp 75 Perc'=quantile(tempmin, na.rm = TRUE)[4],
)
tabdesc[,2:4]<-round(tabdesc[,2:4],digits = 2)
tabdesc %>% kable(caption = "Minimum Temperature statistics per City") %>% kable_styling(bootstrap_options = c("hover", "condensed","bordered"),full_width = F)%>% add_header_above(c(" ", "Min Temperature" = 3))%>%
column_spec(1, bold = T, border_right = T)%>%
column_spec(4, border_right = T)| Zone | Temp 25 Perc | Mean Temp | Temp 75 Perc |
|---|---|---|---|
| bm | 3.40 | 7.49 | 11.9 |
| bordeaux | 5.40 | 9.46 | 14.1 |
| clermont | 2.40 | 7.07 | 12.1 |
| dijon | 1.70 | 6.82 | 12.0 |
| grenoble | 1.20 | 6.23 | 11.5 |
| lehavre | 5.90 | 9.40 | 13.5 |
| lille | 3.40 | 7.49 | 11.9 |
| lyon | 3.30 | 8.63 | 14.1 |
| marseille | 6.10 | 11.13 | 16.8 |
| montpellier | 5.80 | 10.84 | 16.4 |
| ncy | 1.70 | 6.47 | 11.6 |
| ntes | 4.60 | 8.25 | 12.3 |
| nice | 7.80 | 12.90 | 18.0 |
| paris | 5.10 | 9.22 | 13.7 |
| rennes | 4.20 | 7.88 | 11.9 |
| rouen | 3.10 | 6.90 | 11.1 |
| strasbourg | 1.87 | 6.89 | 12.2 |
| toulouse | 4.90 | 9.54 | 14.7 |
Non accidental Mortality:
Paris:
plotmort<-villes[["paris"]]
plotmort$Dates<-as.Date(plotmort$Dates,format = "%y/%m/%d")
plotmort %>%
ggplot( aes(x=Dates, y=nocc_tot)) + scale_x_date(date_breaks = "1 year",date_labels = "%Y",limit=c(as.Date("2000-01-01"),as.Date("2015-12-31"))) + geom_line(color="#69b3a2") +
annotate(geom="text", x=as.Date("2003-08-12"), y=724,label="\n Canicule \n été 2003")+annotate(geom="point", x=as.Date("2003-08-12"), y=724, size=7, shape=21, fill="transparent") +
annotate(geom="text", x=as.Date("2003-08-11"), y=497,label="\n Canicule \n été 2003")+annotate(geom="point", x=as.Date("2003-08-11"), y=497, size=7, shape=21, fill="transparent") +
labs(y="Nombre") +
theme_ipsum(plot_title_size = 16, axis_title_just=1)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
Lyon:
plotmort<-villes[["lyon"]]
plotmort$Dates<-as.Date(plotmort$Dates,format = "%y/%m/%d")
plotmort %>%
ggplot( aes(x=Dates, y=nocc_tot)) + scale_x_date(date_breaks = "1 year",date_labels = "%Y",limit=c(as.Date("2000-01-01"),as.Date("2015-12-31"))) + geom_line(color="#69b3a2") +
labs(y="Nombre") +
theme_ipsum(plot_title_size = 16, axis_title_just=1)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
Marseille:
plotmort<-villes[["marseille"]]
plotmort$Dates<-as.Date(plotmort$Dates,format = "%y/%m/%d")
plotmort %>%
ggplot( aes(x=Dates, y=nocc_tot)) + scale_x_date(date_breaks = "1 year",date_labels = "%Y",limit=c(as.Date("2000-01-01"),as.Date("2015-12-31"))) + geom_line(color="#69b3a2") +
labs(y="Nombre") +
theme_ipsum(plot_title_size = 16, axis_title_just=1)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
Toulouse:
plotmort<-villes[["toulouse"]]
plotmort$Dates<-as.Date(plotmort$Dates,format = "%y/%m/%d")
plotmort %>%
ggplot( aes(x=Dates, y=nocc_tot)) + scale_x_date(date_breaks = "1 year",date_labels = "%Y",limit=c(as.Date("2000-01-01"),as.Date("2015-12-31"))) + geom_line(color="#69b3a2") +
labs(y="Nombre") +
theme_ipsum(plot_title_size = 16, axis_title_just=1)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows## Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
## famille de police introuvable dans la base de données des polices Windows
Descriptive Statistics for Mortality:
tabmort<-data %>% summarise(
'Minimum Nocc'=min(nocc_tot, na.rm=TRUE),
'M Nocc'=mean(nocc_tot,na.rm=TRUE),
'Max Nocc'=max(nocc_tot,na.rm=TRUE),
'Minimum Cv'=min(cv_tot,na.rm=TRUE),
'M Cv'=mean(cv_tot,na.rm=TRUE),
'Max Cv'=max(cv_tot,na.rm=TRUE),
'Minimum Respi'=min(respi_tot,na.rm=TRUE),
'M Respi'=mean(respi_tot,na.rm=TRUE),
'Max Respi'=max(respi_tot,na.rm=TRUE),
)
tabmort %>% kable(caption = "Mortality per City") %>% kable_styling(bootstrap_options = c("hover", "condensed","bordered"),full_width = F)%>% add_header_above(c(" ", "Non Accidental Causes" = 3, "Cardiovascual Causes" = 3, "Respiratory causes"=3))%>%
column_spec(1, bold = T, border_right = T)%>%
column_spec(4, border_right = T)%>%
column_spec(7, border_right = T)| Zone | Minimum Nocc | M Nocc | Max Nocc | Minimum Cv | M Cv | Max Cv | Minimum Respi | M Respi | Max Respi |
|---|---|---|---|---|---|---|---|---|---|
| bm | 1 | 8.542187 | 23 | 0 | 2.327058 | 9 | 0 | 0.7927435 | 7 |
| bordeaux | 2 | 12.483402 | 36 | 0 | 3.658453 | 14 | 0 | 0.8213552 | 6 |
| clermont | 0 | 5.633196 | 16 | 0 | 1.633024 | 8 | 0 | 0.3417439 | 4 |
| dijon | 0 | 4.539910 | 14 | 0 | 1.258120 | 7 | 0 | 0.2916379 | 4 |
| grenoble | 0 | 7.716145 | 21 | 0 | 2.180760 | 11 | 0 | 0.4765491 | 8 |
| lehavre | 0 | 5.747556 | 16 | 0 | 1.552564 | 8 | 0 | 0.3541417 | 4 |
| lille | 7 | 20.984086 | 45 | 0 | 5.694559 | 18 | 0 | 1.5735797 | 12 |
| lyon | 4 | 18.229637 | 75 | 0 | 4.982546 | 22 | 0 | 1.1264545 | 8 |
| marseille | 7 | 21.914613 | 45 | 0 | 6.390657 | 23 | 0 | 1.5645106 | 11 |
| montpellier | 0 | 6.351833 | 18 | 0 | 1.846694 | 8 | 0 | 0.4321686 | 5 |
| ncy | 0 | 6.780137 | 24 | 0 | 1.841438 | 10 | 0 | 0.5251712 | 5 |
| ntes | 1 | 10.303388 | 32 | 0 | 2.898700 | 12 | 0 | 0.6475017 | 9 |
| nice | 1 | 12.123546 | 33 | 0 | 3.512663 | 13 | 0 | 0.8251198 | 6 |
| paris | 60 | 106.479808 | 724 | 8 | 26.504791 | 179 | 0 | 6.8273443 | 52 |
| rennes | 0 | 3.919965 | 13 | 0 | 1.191253 | 7 | 0 | 0.3132593 | 4 |
| rouen | 1 | 9.837611 | 28 | 0 | 2.814168 | 14 | 0 | 0.6244011 | 5 |
| strasbourg | 1 | 8.401780 | 23 | 0 | 2.438912 | 11 | 0 | 0.5345654 | 7 |
| toulouse | 1 | 11.871834 | 34 | 0 | 3.338809 | 14 | 0 | 0.7414442 | 7 |
Creation of Heat Wave Variable and set up data set for analysis
Still some data management:
for(i in 1:length(villes)) {
villes[[i]]$Vacances<-as.factor(villes[[i]]$Vacances) #convert variable Vacances into factor
villes[[i]]$annee<-as.factor(year(villes[[i]]$Dates)) #creation variable year and month
villes[[i]]$mois<-as.factor(month(villes[[i]]$Dates))
villes[[i]]$saison[villes[[i]]$mois %in% c("10","11","12","1","2","3","4")]<-"cold" #create variable factor saison
villes[[i]]$saison[villes[[i]]$mois %in% c("5","6","7","8","9")]<-"warm"
}
# creation of no2 variable of today and previous day
#function filter {stats}
filter<-stats::filter
for (i in 1:length(villes)){
villes[[i]]<-transform(villes[[i]], no2moy = as.vector(filter(no2,sides = 1, filter = rep(1, 2))/2), no2Lag1=Lag(no2,1),no2Lag2=Lag(no2,2))
} Definition of Heat Wave threshold:
trshld975<-c()
trshld995<-c()
for (i in 1:length(villes)){
trshld975[i]<-quantile(villes[[i]]$tempmoy, probs=c(0.975),na.rm=TRUE)
trshld995[i]<-quantile(villes[[i]]$tempmoy, probs=c(0.995),na.rm=TRUE)
}Creation of Heat Wave Variable
## First condition for heat wave
for (i in 1:length(villes)){
villes[[i]]$heat_wave1<-NA
for (r in 3:nrow(villes[[i]])){
villes[[i]]$heat_wave1[r]<- if (villes[[i]]$tempmoy[r] >= trshld975[i] & villes[[i]]$tempmoy[r-1] >= trshld975[i] & villes[[i]]$tempmoy[r-2] >= trshld975[i] ) 1 else 0
}}
## Second condition for heat wave
for (i in 1:length(villes)){
for (r in 3:nrow(villes[[i]])){
villes[[i]]$heat_wave[r]<- if (villes[[i]]$heat_wave1[r] == 1 & (villes[[i]]$tempmoy[r] >= trshld995[i] | villes[[i]]$tempmoy[r-1] >= trshld995[i] | villes[[i]]$tempmoy[r-2] >= trshld995[i])) 1 else 0
}}See if heat wave variable is OK:
lapply(villes,function(x){summary(x$heat_wave)})## $BM
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.005135 0.000000 1.000000 2
##
## $bordeaux
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00000 0.00000 0.00000 0.00445 0.00000 1.00000 2
##
## $clermont
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.004108 0.000000 1.000000 2
##
## $dijon
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.005306 0.000000 1.000000 2
##
## $grenoble
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.006162 0.000000 1.000000 2
##
## $lehavre
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.003937 0.000000 1.000000 2
##
## $lille
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.005135 0.000000 1.000000 2
##
## $lyon
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.005478 0.000000 1.000000 2
##
## $marseille
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.005991 0.000000 1.000000 2
##
## $montpellier
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.004793 0.000000 1.000000 2
##
## $nancy
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.005478 0.000000 1.000000 2
##
## $nantes
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.004108 0.000000 1.000000 2
##
## $nice
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00000 0.00000 0.00000 0.00445 0.00000 1.00000 2
##
## $paris
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.004622 0.000000 1.000000 2
##
## $rennes
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.005478 0.000000 1.000000 2
##
## $rouen
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.004279 0.000000 1.000000 2
##
## $strasbourg
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.006505 0.000000 1.000000 2
##
## $toulouse
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000000 0.000000 0.000000 0.004964 0.000000 1.000000 2villes[[2]][which(is.na(villes[[2]]$heat_wave)),]## Dates Jours jours_fÃ.riÃ.s Vacances tempmin tempmax tempmoy
## 1 2000-01-01 samedi 1 1 7.8 11.8 9.608333
## 2 2000-01-02 dimanche 0 1 7.2 10.5 8.904167
## tempmaxmoy7j Zone total_tot nocc_tot cv_tot respi_tot total_75 nocc_75
## 1 NA bordeaux 21 19 6 0 10 9
## 2 NA bordeaux 16 14 8 1 12 11
## cv_75 respi_75 o3 moyO3 no2 pm10full pm10moy pm25 annee mois saison
## 1 3 0 40.66667 NA NA NA NA NA 2000 1 cold
## 2 7 1 13.66667 27.16667 NA NA NA NA 2000 1 cold
## no2moy no2Lag1 no2Lag2 heat_wave1 heat_wave
## 1 NA NA NA NA NA
## 2 NA NA NA NA NAThe two first days of our study period have NAs (in Januray), but it’s fine because we will work only with summer data.
Number of Heat Wave for each city:
| Heat wave=0 | Heat wave=1 | |
|---|---|---|
| BM | 5812 | 30 |
| bordeaux | 5816 | 26 |
| clermont | 5818 | 24 |
| dijon | 5811 | 31 |
| grenoble | 5806 | 36 |
| lehavre | 5819 | 23 |
| lille | 5812 | 30 |
| lyon | 5810 | 32 |
| marseille | 5807 | 35 |
| montpellier | 5814 | 28 |
| nancy | 5810 | 32 |
| nantes | 5818 | 24 |
| nice | 5816 | 26 |
| paris | 5815 | 27 |
| rennes | 5810 | 32 |
| rouen | 5817 | 25 |
| strasbourg | 5804 | 38 |
| toulouse | 5813 | 29 |
table of N obs of heat wave per month:
## $BM
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 480 0
## 7 478 18
## 8 484 12
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $bordeaux
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 479 1
## 7 484 12
## 8 483 13
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $clermont
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 480 0
## 7 487 9
## 8 481 15
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $dijon
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 477 3
## 7 479 17
## 8 485 11
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $grenoble
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 475 5
## 7 482 14
## 8 479 17
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $lehavre
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 479 1
## 7 480 16
## 8 490 6
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $lille
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 480 0
## 7 478 18
## 8 484 12
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $lyon
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 476 4
## 7 482 14
## 8 482 14
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $marseille
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 480 0
## 7 477 19
## 8 480 16
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $montpellier
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 474 6
## 7 479 17
## 8 491 5
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $nancy
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 478 2
## 7 481 15
## 8 481 15
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $nantes
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 479 1
## 7 483 13
## 8 486 10
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $nice
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 480 0
## 7 490 6
## 8 476 20
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $paris
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 480 0
## 7 482 14
## 8 483 13
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $rennes
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 479 1
## 7 483 13
## 8 478 18
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $rouen
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 480 0
## 7 484 12
## 8 483 13
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $strasbourg
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 474 6
## 7 481 15
## 8 479 17
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0
##
## $toulouse
##
## 0 1
## 1 494 0
## 2 452 0
## 3 496 0
## 4 480 0
## 5 496 0
## 6 479 1
## 7 489 7
## 8 475 21
## 9 480 0
## 10 496 0
## 11 480 0
## 12 496 0Heat wave events occured only in the period going from June until August.
Creating Database only with summer months:
villes_s<-list()
for(i in 1:length(villes)) {
villes_s[[i]]<-villes[[i]][villes[[i]]$saison=="warm",]
#villes_s[[i]]$temps <- ave(villes_s[[i]]$time,villes_s[[i]]$annee, FUN = seq_along)
}
names(villes_s)<-namesNon accidental Mortality
Cardiovascular Mortality
Respiratory Mortality
MODEL
IDENTIFICATION STAGE: Testing of different types of model, in order to identify the better specification for temporal (saison and trend) components
Model with natural cubic spline with 2 or more degrees of freedom per summer (selected bu the AIC criteria), with a unique time indicator going from 1 to 2448 (tot number of days observed)
see Chen et al. 2019, Wu et al. 2013, 2014, Ma et al. 2015, Chen et al. 2015, Anderson and Bell 2009.
Two equivalent function are studied in order to check wheter they show different results: glm function and gam function.
# Creation of variable TIME
for (i in 1:length(villes_s)){
villes_s[[i]]$time<-1:nrow(villes_s[[i]]) # Create a new variable for time
}
m.outcome<-list() # Model with glm function
m.outcome_g<-list() # Model with gam function
for (i in 1:18){
m.outcome[[i]]<-glm(nocc_tot~heat_wave+no2moy+ns(time,df=round(2*length(time)/153))+Jours+Vacances,data=villes_s[[i]],family=poisson)
}
for (i in 1:18){
m.outcome_g[[i]]<-gam(nocc_tot~heat_wave+no2moy+ns(time,df=round(2*length(time)/153))+Jours+Vacances,data=villes_s[[i]],family=poisson)}
names(m.outcome)<-names
names(m.outcome_g)<-namesREsults for glm function
lapply(m.outcome,function(x){summary(x)})## $BM
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.0491 -0.7529 -0.0432 0.6513 3.0355
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 1.9226319 0.0821882 23.393
## heat_wave -0.0532369 0.1671437 -0.319
## no2moy 0.0012357 0.0018644 0.663
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 NA NA NA
## ns(time, df = round(2 * length(time)/153))7 NA NA NA
## ns(time, df = round(2 * length(time)/153))8 NA NA NA
## ns(time, df = round(2 * length(time)/153))9 NA NA NA
## ns(time, df = round(2 * length(time)/153))10 NA NA NA
## ns(time, df = round(2 * length(time)/153))11 NA NA NA
## ns(time, df = round(2 * length(time)/153))12 -0.0703808 1.6084895 -0.044
## ns(time, df = round(2 * length(time)/153))13 0.0880275 0.3026162 0.291
## ns(time, df = round(2 * length(time)/153))14 0.1423609 0.1851439 0.769
## ns(time, df = round(2 * length(time)/153))15 -0.0618071 0.1561037 -0.396
## ns(time, df = round(2 * length(time)/153))16 0.0547162 0.1426173 0.384
## ns(time, df = round(2 * length(time)/153))17 0.1373622 0.1328332 1.034
## ns(time, df = round(2 * length(time)/153))18 0.0028773 0.1349284 0.021
## ns(time, df = round(2 * length(time)/153))19 -0.0635845 0.1377571 -0.462
## ns(time, df = round(2 * length(time)/153))20 0.1727768 0.1295614 1.334
## ns(time, df = round(2 * length(time)/153))21 -0.0001943 0.1221726 -0.002
## ns(time, df = round(2 * length(time)/153))22 0.0881091 0.1268144 0.695
## ns(time, df = round(2 * length(time)/153))23 -0.0217584 0.1194112 -0.182
## ns(time, df = round(2 * length(time)/153))24 0.2740305 0.1245848 2.200
## ns(time, df = round(2 * length(time)/153))25 -0.1466061 0.1219275 -1.202
## ns(time, df = round(2 * length(time)/153))26 0.1791592 0.1269286 1.411
## ns(time, df = round(2 * length(time)/153))27 -0.2458586 0.1223631 -2.009
## ns(time, df = round(2 * length(time)/153))28 0.2705559 0.1314522 2.058
## ns(time, df = round(2 * length(time)/153))29 -0.0644477 0.1474187 -0.437
## ns(time, df = round(2 * length(time)/153))30 0.4368027 0.2988670 1.462
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi 0.0552982 0.0395788 1.397
## Jourslundi 0.0899628 0.0388818 2.314
## Joursmardi 0.0976611 0.0392444 2.489
## Joursmercredi 0.0913028 0.0395431 2.309
## Jourssamedi 0.0234589 0.0395554 0.593
## Joursvendredi 0.1171899 0.0392498 2.986
## Vacances1 -0.0106470 0.0278878 -0.382
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 0.75010
## no2moy 0.50745
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.96510
## ns(time, df = round(2 * length(time)/153))13 0.77114
## ns(time, df = round(2 * length(time)/153))14 0.44194
## ns(time, df = round(2 * length(time)/153))15 0.69215
## ns(time, df = round(2 * length(time)/153))16 0.70123
## ns(time, df = round(2 * length(time)/153))17 0.30109
## ns(time, df = round(2 * length(time)/153))18 0.98299
## ns(time, df = round(2 * length(time)/153))19 0.64439
## ns(time, df = round(2 * length(time)/153))20 0.18235
## ns(time, df = round(2 * length(time)/153))21 0.99873
## ns(time, df = round(2 * length(time)/153))22 0.48719
## ns(time, df = round(2 * length(time)/153))23 0.85541
## ns(time, df = round(2 * length(time)/153))24 0.02784 *
## ns(time, df = round(2 * length(time)/153))25 0.22921
## ns(time, df = round(2 * length(time)/153))26 0.15810
## ns(time, df = round(2 * length(time)/153))27 0.04451 *
## ns(time, df = round(2 * length(time)/153))28 0.03957 *
## ns(time, df = round(2 * length(time)/153))29 0.66198
## ns(time, df = round(2 * length(time)/153))30 0.14387
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.16236
## Jourslundi 0.02068 *
## Joursmardi 0.01283 *
## Joursmercredi 0.02095 *
## Jourssamedi 0.55314
## Joursvendredi 0.00283 **
## Vacances1 0.70262
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1345.0 on 1210 degrees of freedom
## Residual deviance: 1302.8 on 1182 degrees of freedom
## (1237 observations deleted due to missingness)
## AIC: 6015.1
##
## Number of Fisher Scoring iterations: 4
##
##
## $bordeaux
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.6343 -0.7294 -0.0227 0.6264 3.0736
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 2.406333 0.060046 40.075
## heat_wave 0.214455 0.109573 1.957
## no2moy 0.002257 0.001737 1.300
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 NA NA NA
## ns(time, df = round(2 * length(time)/153))7 NA NA NA
## ns(time, df = round(2 * length(time)/153))8 NA NA NA
## ns(time, df = round(2 * length(time)/153))9 NA NA NA
## ns(time, df = round(2 * length(time)/153))10 NA NA NA
## ns(time, df = round(2 * length(time)/153))11 NA NA NA
## ns(time, df = round(2 * length(time)/153))12 -0.355271 1.296334 -0.274
## ns(time, df = round(2 * length(time)/153))13 0.120255 0.237558 0.506
## ns(time, df = round(2 * length(time)/153))14 -0.171739 0.134904 -1.273
## ns(time, df = round(2 * length(time)/153))15 -0.011240 0.106168 -0.106
## ns(time, df = round(2 * length(time)/153))16 0.010237 0.103572 0.099
## ns(time, df = round(2 * length(time)/153))17 -0.152227 0.098259 -1.549
## ns(time, df = round(2 * length(time)/153))18 -0.044714 0.100300 -0.446
## ns(time, df = round(2 * length(time)/153))19 -0.015040 0.096022 -0.157
## ns(time, df = round(2 * length(time)/153))20 0.035776 0.098922 0.362
## ns(time, df = round(2 * length(time)/153))21 -0.029963 0.095243 -0.315
## ns(time, df = round(2 * length(time)/153))22 0.001445 0.099723 0.014
## ns(time, df = round(2 * length(time)/153))23 -0.078848 0.095473 -0.826
## ns(time, df = round(2 * length(time)/153))24 -0.074488 0.098820 -0.754
## ns(time, df = round(2 * length(time)/153))25 0.104838 0.091127 1.150
## ns(time, df = round(2 * length(time)/153))26 -0.054977 0.096850 -0.568
## ns(time, df = round(2 * length(time)/153))27 0.056275 0.090110 0.625
## ns(time, df = round(2 * length(time)/153))28 -0.037238 0.100418 -0.371
## ns(time, df = round(2 * length(time)/153))29 0.128647 0.081225 1.584
## ns(time, df = round(2 * length(time)/153))30 0.010018 0.116864 0.086
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi 0.049104 0.031439 1.562
## Jourslundi 0.064325 0.029856 2.155
## Joursmardi 0.042604 0.030940 1.377
## Joursmercredi 0.029669 0.031525 0.941
## Jourssamedi 0.044057 0.030434 1.448
## Joursvendredi 0.045112 0.031414 1.436
## Vacances1 -0.013433 0.021144 -0.635
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.0503 .
## no2moy 0.1937
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.7840
## ns(time, df = round(2 * length(time)/153))13 0.6127
## ns(time, df = round(2 * length(time)/153))14 0.2030
## ns(time, df = round(2 * length(time)/153))15 0.9157
## ns(time, df = round(2 * length(time)/153))16 0.9213
## ns(time, df = round(2 * length(time)/153))17 0.1213
## ns(time, df = round(2 * length(time)/153))18 0.6557
## ns(time, df = round(2 * length(time)/153))19 0.8755
## ns(time, df = round(2 * length(time)/153))20 0.7176
## ns(time, df = round(2 * length(time)/153))21 0.7531
## ns(time, df = round(2 * length(time)/153))22 0.9884
## ns(time, df = round(2 * length(time)/153))23 0.4089
## ns(time, df = round(2 * length(time)/153))24 0.4510
## ns(time, df = round(2 * length(time)/153))25 0.2500
## ns(time, df = round(2 * length(time)/153))26 0.5703
## ns(time, df = round(2 * length(time)/153))27 0.5323
## ns(time, df = round(2 * length(time)/153))28 0.7108
## ns(time, df = round(2 * length(time)/153))29 0.1132
## ns(time, df = round(2 * length(time)/153))30 0.9317
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.1183
## Jourslundi 0.0312 *
## Joursmardi 0.1685
## Joursmercredi 0.3466
## Jourssamedi 0.1477
## Joursvendredi 0.1510
## Vacances1 0.5252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1490.4 on 1376 degrees of freedom
## Residual deviance: 1446.0 on 1348 degrees of freedom
## (1071 observations deleted due to missingness)
## AIC: 7370
##
## Number of Fisher Scoring iterations: 4
##
##
## $clermont
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2512 -0.7605 -0.0554 0.6036 3.2973
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 1.802e+00 1.035e-01 17.410
## heat_wave 3.754e-01 7.836e-02 4.791
## no2moy 1.006e-03 1.578e-03 0.637
## ns(time, df = round(2 * length(time)/153))1 -4.221e-03 1.252e-01 -0.034
## ns(time, df = round(2 * length(time)/153))2 -4.299e-01 1.713e-01 -2.510
## ns(time, df = round(2 * length(time)/153))3 -1.136e-01 1.424e-01 -0.797
## ns(time, df = round(2 * length(time)/153))4 -1.559e-01 1.615e-01 -0.965
## ns(time, df = round(2 * length(time)/153))5 -2.770e-01 1.462e-01 -1.895
## ns(time, df = round(2 * length(time)/153))6 -7.394e-02 1.574e-01 -0.470
## ns(time, df = round(2 * length(time)/153))7 -1.905e-02 1.448e-01 -0.132
## ns(time, df = round(2 * length(time)/153))8 -3.251e-01 1.595e-01 -2.039
## ns(time, df = round(2 * length(time)/153))9 -1.708e-01 1.481e-01 -1.153
## ns(time, df = round(2 * length(time)/153))10 -2.449e-01 1.590e-01 -1.540
## ns(time, df = round(2 * length(time)/153))11 -1.690e-01 1.485e-01 -1.138
## ns(time, df = round(2 * length(time)/153))12 -3.312e-01 1.599e-01 -2.072
## ns(time, df = round(2 * length(time)/153))13 -1.596e-01 1.483e-01 -1.076
## ns(time, df = round(2 * length(time)/153))14 -2.124e-01 1.603e-01 -1.325
## ns(time, df = round(2 * length(time)/153))15 -3.318e-01 1.492e-01 -2.224
## ns(time, df = round(2 * length(time)/153))16 -4.655e-02 1.577e-01 -0.295
## ns(time, df = round(2 * length(time)/153))17 -2.499e-01 1.465e-01 -1.707
## ns(time, df = round(2 * length(time)/153))18 -5.361e-02 1.584e-01 -0.339
## ns(time, df = round(2 * length(time)/153))19 -1.522e-01 1.448e-01 -1.051
## ns(time, df = round(2 * length(time)/153))20 -6.131e-02 1.561e-01 -0.393
## ns(time, df = round(2 * length(time)/153))21 -2.005e-01 1.454e-01 -1.379
## ns(time, df = round(2 * length(time)/153))22 -9.564e-02 1.571e-01 -0.609
## ns(time, df = round(2 * length(time)/153))23 -2.914e-01 1.467e-01 -1.986
## ns(time, df = round(2 * length(time)/153))24 -2.184e-02 1.563e-01 -0.140
## ns(time, df = round(2 * length(time)/153))25 -2.543e-01 1.462e-01 -1.739
## ns(time, df = round(2 * length(time)/153))26 -9.181e-05 1.539e-01 -0.001
## ns(time, df = round(2 * length(time)/153))27 -3.029e-01 1.467e-01 -2.064
## ns(time, df = round(2 * length(time)/153))28 -3.936e-02 1.542e-01 -0.255
## ns(time, df = round(2 * length(time)/153))29 -2.189e-01 1.452e-01 -1.508
## ns(time, df = round(2 * length(time)/153))30 6.225e-02 1.264e-01 0.493
## ns(time, df = round(2 * length(time)/153))31 -4.470e-01 2.554e-01 -1.750
## ns(time, df = round(2 * length(time)/153))32 -7.470e-02 1.153e-01 -0.648
## Joursjeudi -9.711e-03 3.488e-02 -0.278
## Jourslundi 2.025e-02 3.321e-02 0.610
## Joursmardi 8.673e-03 3.422e-02 0.253
## Joursmercredi 1.824e-02 3.465e-02 0.526
## Jourssamedi -8.247e-03 3.414e-02 -0.242
## Joursvendredi 1.632e-02 3.489e-02 0.468
## Vacances1 -1.720e-02 2.294e-02 -0.750
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 1.66e-06 ***
## no2moy 0.5240
## ns(time, df = round(2 * length(time)/153))1 0.9731
## ns(time, df = round(2 * length(time)/153))2 0.0121 *
## ns(time, df = round(2 * length(time)/153))3 0.4253
## ns(time, df = round(2 * length(time)/153))4 0.3344
## ns(time, df = round(2 * length(time)/153))5 0.0581 .
## ns(time, df = round(2 * length(time)/153))6 0.6385
## ns(time, df = round(2 * length(time)/153))7 0.8953
## ns(time, df = round(2 * length(time)/153))8 0.0415 *
## ns(time, df = round(2 * length(time)/153))9 0.2489
## ns(time, df = round(2 * length(time)/153))10 0.1236
## ns(time, df = round(2 * length(time)/153))11 0.2553
## ns(time, df = round(2 * length(time)/153))12 0.0383 *
## ns(time, df = round(2 * length(time)/153))13 0.2818
## ns(time, df = round(2 * length(time)/153))14 0.1851
## ns(time, df = round(2 * length(time)/153))15 0.0261 *
## ns(time, df = round(2 * length(time)/153))16 0.7678
## ns(time, df = round(2 * length(time)/153))17 0.0879 .
## ns(time, df = round(2 * length(time)/153))18 0.7349
## ns(time, df = round(2 * length(time)/153))19 0.2932
## ns(time, df = round(2 * length(time)/153))20 0.6944
## ns(time, df = round(2 * length(time)/153))21 0.1678
## ns(time, df = round(2 * length(time)/153))22 0.5427
## ns(time, df = round(2 * length(time)/153))23 0.0470 *
## ns(time, df = round(2 * length(time)/153))24 0.8888
## ns(time, df = round(2 * length(time)/153))25 0.0821 .
## ns(time, df = round(2 * length(time)/153))26 0.9995
## ns(time, df = round(2 * length(time)/153))27 0.0390 *
## ns(time, df = round(2 * length(time)/153))28 0.7985
## ns(time, df = round(2 * length(time)/153))29 0.1316
## ns(time, df = round(2 * length(time)/153))30 0.6224
## ns(time, df = round(2 * length(time)/153))31 0.0801 .
## ns(time, df = round(2 * length(time)/153))32 0.5172
## Joursjeudi 0.7807
## Jourslundi 0.5420
## Joursmardi 0.7999
## Joursmercredi 0.5986
## Jourssamedi 0.8091
## Joursvendredi 0.6400
## Vacances1 0.4534
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 2463.1 on 2430 degrees of freedom
## Residual deviance: 2390.8 on 2389 degrees of freedom
## (17 observations deleted due to missingness)
## AIC: 10794
##
## Number of Fisher Scoring iterations: 4
##
##
## $dijon
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.04307 -0.72221 -0.08563 0.60042 3.12209
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 1.2020639 0.1255461 9.575
## heat_wave 0.3188149 0.0789414 4.039
## no2moy 0.0069614 0.0020802 3.346
## ns(time, df = round(2 * length(time)/153))1 0.0172788 0.1442790 0.120
## ns(time, df = round(2 * length(time)/153))2 0.0911124 0.1961534 0.464
## ns(time, df = round(2 * length(time)/153))3 -0.1446592 0.1650644 -0.876
## ns(time, df = round(2 * length(time)/153))4 0.2717373 0.1873788 1.450
## ns(time, df = round(2 * length(time)/153))5 -0.3411138 0.1712113 -1.992
## ns(time, df = round(2 * length(time)/153))6 0.4630186 0.1827002 2.534
## ns(time, df = round(2 * length(time)/153))7 -0.0608143 0.1688923 -0.360
## ns(time, df = round(2 * length(time)/153))8 -0.1208891 0.1857408 -0.651
## ns(time, df = round(2 * length(time)/153))9 -0.0724796 0.1744117 -0.416
## ns(time, df = round(2 * length(time)/153))10 0.1790974 0.1859095 0.963
## ns(time, df = round(2 * length(time)/153))11 -0.3468612 0.1771479 -1.958
## ns(time, df = round(2 * length(time)/153))12 0.2202232 0.1838563 1.198
## ns(time, df = round(2 * length(time)/153))13 -0.1380913 0.1726888 -0.800
## ns(time, df = round(2 * length(time)/153))14 0.1718846 0.1850739 0.929
## ns(time, df = round(2 * length(time)/153))15 -0.0064035 0.1699375 -0.038
## ns(time, df = round(2 * length(time)/153))16 0.1706140 0.1830455 0.932
## ns(time, df = round(2 * length(time)/153))17 0.0318045 0.1689801 0.188
## ns(time, df = round(2 * length(time)/153))18 0.2147801 0.1855678 1.157
## ns(time, df = round(2 * length(time)/153))19 -0.0860551 0.1720281 -0.500
## ns(time, df = round(2 * length(time)/153))20 0.2069020 0.1841954 1.123
## ns(time, df = round(2 * length(time)/153))21 0.1344377 0.1691244 0.795
## ns(time, df = round(2 * length(time)/153))22 0.1270269 0.1813214 0.701
## ns(time, df = round(2 * length(time)/153))23 0.2045805 0.1647611 1.242
## ns(time, df = round(2 * length(time)/153))24 0.3118946 0.1799017 1.734
## ns(time, df = round(2 * length(time)/153))25 -0.0444794 0.1671422 -0.266
## ns(time, df = round(2 * length(time)/153))26 0.3862037 0.1786276 2.162
## ns(time, df = round(2 * length(time)/153))27 0.0049116 0.1677394 0.029
## ns(time, df = round(2 * length(time)/153))28 0.3180926 0.1778748 1.788
## ns(time, df = round(2 * length(time)/153))29 0.1399787 0.1666145 0.840
## ns(time, df = round(2 * length(time)/153))30 0.2276601 0.1429915 1.592
## ns(time, df = round(2 * length(time)/153))31 0.1947785 0.3040767 0.641
## ns(time, df = round(2 * length(time)/153))32 0.3019432 0.1222136 2.471
## Joursjeudi 0.0120091 0.0395837 0.303
## Jourslundi 0.0249575 0.0377689 0.661
## Joursmardi 0.0377725 0.0388582 0.972
## Joursmercredi 0.0250714 0.0394439 0.636
## Jourssamedi 0.0485215 0.0383909 1.264
## Joursvendredi 0.0359819 0.0397763 0.905
## Vacances1 0.0003654 0.0257663 0.014
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 5.38e-05 ***
## no2moy 0.000818 ***
## ns(time, df = round(2 * length(time)/153))1 0.904674
## ns(time, df = round(2 * length(time)/153))2 0.642293
## ns(time, df = round(2 * length(time)/153))3 0.380823
## ns(time, df = round(2 * length(time)/153))4 0.147002
## ns(time, df = round(2 * length(time)/153))5 0.046332 *
## ns(time, df = round(2 * length(time)/153))6 0.011267 *
## ns(time, df = round(2 * length(time)/153))7 0.718789
## ns(time, df = round(2 * length(time)/153))8 0.515144
## ns(time, df = round(2 * length(time)/153))9 0.677728
## ns(time, df = round(2 * length(time)/153))10 0.335368
## ns(time, df = round(2 * length(time)/153))11 0.050226 .
## ns(time, df = round(2 * length(time)/153))12 0.230995
## ns(time, df = round(2 * length(time)/153))13 0.423911
## ns(time, df = round(2 * length(time)/153))14 0.353026
## ns(time, df = round(2 * length(time)/153))15 0.969942
## ns(time, df = round(2 * length(time)/153))16 0.351292
## ns(time, df = round(2 * length(time)/153))17 0.850709
## ns(time, df = round(2 * length(time)/153))18 0.247101
## ns(time, df = round(2 * length(time)/153))19 0.616907
## ns(time, df = round(2 * length(time)/153))20 0.261321
## ns(time, df = round(2 * length(time)/153))21 0.426669
## ns(time, df = round(2 * length(time)/153))22 0.483576
## ns(time, df = round(2 * length(time)/153))23 0.214355
## ns(time, df = round(2 * length(time)/153))24 0.082972 .
## ns(time, df = round(2 * length(time)/153))25 0.790149
## ns(time, df = round(2 * length(time)/153))26 0.030613 *
## ns(time, df = round(2 * length(time)/153))27 0.976641
## ns(time, df = round(2 * length(time)/153))28 0.073728 .
## ns(time, df = round(2 * length(time)/153))29 0.400833
## ns(time, df = round(2 * length(time)/153))30 0.111357
## ns(time, df = round(2 * length(time)/153))31 0.521810
## ns(time, df = round(2 * length(time)/153))32 0.013488 *
## Joursjeudi 0.761597
## Jourslundi 0.508744
## Joursmardi 0.331022
## Joursmercredi 0.525023
## Jourssamedi 0.206273
## Joursvendredi 0.365674
## Vacances1 0.988686
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 2467.5 on 2416 degrees of freedom
## Residual deviance: 2361.6 on 2375 degrees of freedom
## (31 observations deleted due to missingness)
## AIC: 10169
##
## Number of Fisher Scoring iterations: 4
##
##
## $grenoble
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.8394 -0.7359 -0.0359 0.6109 3.1502
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 2.056765 0.075448 27.261
## heat_wave 0.114980 0.092664 1.241
## no2moy 0.008719 0.002585 3.373
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 NA NA NA
## ns(time, df = round(2 * length(time)/153))7 NA NA NA
## ns(time, df = round(2 * length(time)/153))8 NA NA NA
## ns(time, df = round(2 * length(time)/153))9 NA NA NA
## ns(time, df = round(2 * length(time)/153))10 NA NA NA
## ns(time, df = round(2 * length(time)/153))11 NA NA NA
## ns(time, df = round(2 * length(time)/153))12 -0.847711 1.781629 -0.476
## ns(time, df = round(2 * length(time)/153))13 -0.373198 0.317202 -1.177
## ns(time, df = round(2 * length(time)/153))14 -0.241921 0.173950 -1.391
## ns(time, df = round(2 * length(time)/153))15 -0.321894 0.135164 -2.382
## ns(time, df = round(2 * length(time)/153))16 -0.114496 0.129657 -0.883
## ns(time, df = round(2 * length(time)/153))17 -0.186402 0.119784 -1.556
## ns(time, df = round(2 * length(time)/153))18 -0.150188 0.125399 -1.198
## ns(time, df = round(2 * length(time)/153))19 -0.288523 0.119669 -2.411
## ns(time, df = round(2 * length(time)/153))20 -0.095634 0.122265 -0.782
## ns(time, df = round(2 * length(time)/153))21 -0.185639 0.115776 -1.603
## ns(time, df = round(2 * length(time)/153))22 -0.071266 0.122342 -0.583
## ns(time, df = round(2 * length(time)/153))23 -0.233828 0.116005 -2.016
## ns(time, df = round(2 * length(time)/153))24 -0.070739 0.119855 -0.590
## ns(time, df = round(2 * length(time)/153))25 -0.143490 0.112180 -1.279
## ns(time, df = round(2 * length(time)/153))26 0.035204 0.119060 0.296
## ns(time, df = round(2 * length(time)/153))27 -0.072588 0.107899 -0.673
## ns(time, df = round(2 * length(time)/153))28 -0.092633 0.121559 -0.762
## ns(time, df = round(2 * length(time)/153))29 -0.164095 0.100746 -1.629
## ns(time, df = round(2 * length(time)/153))30 0.074303 0.141862 0.524
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi -0.041093 0.039522 -1.040
## Jourslundi -0.029132 0.037547 -0.776
## Joursmardi -0.071709 0.039735 -1.805
## Joursmercredi -0.051252 0.039783 -1.288
## Jourssamedi 0.009798 0.037745 0.260
## Joursvendredi -0.047649 0.039708 -1.200
## Vacances1 -0.044894 0.026709 -1.681
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 0.214672
## no2moy 0.000744 ***
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.634212
## ns(time, df = round(2 * length(time)/153))13 0.239382
## ns(time, df = round(2 * length(time)/153))14 0.164301
## ns(time, df = round(2 * length(time)/153))15 0.017242 *
## ns(time, df = round(2 * length(time)/153))16 0.377200
## ns(time, df = round(2 * length(time)/153))17 0.119673
## ns(time, df = round(2 * length(time)/153))18 0.231041
## ns(time, df = round(2 * length(time)/153))19 0.015908 *
## ns(time, df = round(2 * length(time)/153))20 0.434104
## ns(time, df = round(2 * length(time)/153))21 0.108839
## ns(time, df = round(2 * length(time)/153))22 0.560220
## ns(time, df = round(2 * length(time)/153))23 0.043835 *
## ns(time, df = round(2 * length(time)/153))24 0.555051
## ns(time, df = round(2 * length(time)/153))25 0.200861
## ns(time, df = round(2 * length(time)/153))26 0.767471
## ns(time, df = round(2 * length(time)/153))27 0.501113
## ns(time, df = round(2 * length(time)/153))28 0.446036
## ns(time, df = round(2 * length(time)/153))29 0.103355
## ns(time, df = round(2 * length(time)/153))30 0.600438
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.298465
## Jourslundi 0.437824
## Joursmardi 0.071123 .
## Joursmercredi 0.197644
## Jourssamedi 0.795174
## Joursvendredi 0.230146
## Vacances1 0.092796 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1501.6 on 1376 degrees of freedom
## Residual deviance: 1414.2 on 1348 degrees of freedom
## (1071 observations deleted due to missingness)
## AIC: 6678
##
## Number of Fisher Scoring iterations: 4
##
##
## $lehavre
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3975 -0.7575 -0.0925 0.6415 3.1556
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 1.689863 0.086531 19.529
## heat_wave 0.188521 0.152470 1.236
## no2moy 0.002378 0.001645 1.446
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 NA NA NA
## ns(time, df = round(2 * length(time)/153))7 NA NA NA
## ns(time, df = round(2 * length(time)/153))8 NA NA NA
## ns(time, df = round(2 * length(time)/153))9 NA NA NA
## ns(time, df = round(2 * length(time)/153))10 NA NA NA
## ns(time, df = round(2 * length(time)/153))11 NA NA NA
## ns(time, df = round(2 * length(time)/153))12 1.081215 2.025416 0.534
## ns(time, df = round(2 * length(time)/153))13 -0.481348 0.361765 -1.331
## ns(time, df = round(2 * length(time)/153))14 0.193238 0.199014 0.971
## ns(time, df = round(2 * length(time)/153))15 -0.185487 0.159779 -1.161
## ns(time, df = round(2 * length(time)/153))16 0.010392 0.151858 0.068
## ns(time, df = round(2 * length(time)/153))17 -0.012538 0.146274 -0.086
## ns(time, df = round(2 * length(time)/153))18 -0.032894 0.148836 -0.221
## ns(time, df = round(2 * length(time)/153))19 -0.037785 0.141317 -0.267
## ns(time, df = round(2 * length(time)/153))20 0.016982 0.144286 0.118
## ns(time, df = round(2 * length(time)/153))21 -0.023956 0.139254 -0.172
## ns(time, df = round(2 * length(time)/153))22 0.039813 0.144087 0.276
## ns(time, df = round(2 * length(time)/153))23 -0.058861 0.139278 -0.423
## ns(time, df = round(2 * length(time)/153))24 -0.013532 0.145108 -0.093
## ns(time, df = round(2 * length(time)/153))25 -0.101316 0.147798 -0.686
## ns(time, df = round(2 * length(time)/153))26 0.030102 0.145997 0.206
## ns(time, df = round(2 * length(time)/153))27 -0.004336 0.134259 -0.032
## ns(time, df = round(2 * length(time)/153))28 -0.075751 0.147616 -0.513
## ns(time, df = round(2 * length(time)/153))29 0.143777 0.119021 1.208
## ns(time, df = round(2 * length(time)/153))30 0.017265 0.169846 0.102
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi -0.029075 0.044270 -0.657
## Jourslundi -0.043060 0.044069 -0.977
## Joursmardi 0.026681 0.043600 0.612
## Joursmercredi -0.025979 0.044304 -0.586
## Jourssamedi -0.069627 0.044622 -1.560
## Joursvendredi -0.031317 0.044343 -0.706
## Vacances1 -0.030141 0.031395 -0.960
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.216
## no2moy 0.148
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.593
## ns(time, df = round(2 * length(time)/153))13 0.183
## ns(time, df = round(2 * length(time)/153))14 0.332
## ns(time, df = round(2 * length(time)/153))15 0.246
## ns(time, df = round(2 * length(time)/153))16 0.945
## ns(time, df = round(2 * length(time)/153))17 0.932
## ns(time, df = round(2 * length(time)/153))18 0.825
## ns(time, df = round(2 * length(time)/153))19 0.789
## ns(time, df = round(2 * length(time)/153))20 0.906
## ns(time, df = round(2 * length(time)/153))21 0.863
## ns(time, df = round(2 * length(time)/153))22 0.782
## ns(time, df = round(2 * length(time)/153))23 0.673
## ns(time, df = round(2 * length(time)/153))24 0.926
## ns(time, df = round(2 * length(time)/153))25 0.493
## ns(time, df = round(2 * length(time)/153))26 0.837
## ns(time, df = round(2 * length(time)/153))27 0.974
## ns(time, df = round(2 * length(time)/153))28 0.608
## ns(time, df = round(2 * length(time)/153))29 0.227
## ns(time, df = round(2 * length(time)/153))30 0.919
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.511
## Jourslundi 0.329
## Joursmardi 0.541
## Joursmercredi 0.558
## Jourssamedi 0.119
## Joursvendredi 0.480
## Vacances1 0.337
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1412.4 on 1350 degrees of freedom
## Residual deviance: 1390.6 on 1322 degrees of freedom
## (1097 observations deleted due to missingness)
## AIC: 6099.4
##
## Number of Fisher Scoring iterations: 4
##
##
## $lille
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1561 -0.7169 -0.0459 0.6457 3.6924
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 2.9301643 0.0461883 63.440
## heat_wave 0.0568800 0.0566277 1.004
## no2moy 0.0030755 0.0010057 3.058
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 -0.4173841 1.0011442 -0.417
## ns(time, df = round(2 * length(time)/153))7 0.0522596 0.1826347 0.286
## ns(time, df = round(2 * length(time)/153))8 -0.0895261 0.1012971 -0.884
## ns(time, df = round(2 * length(time)/153))9 -0.0023044 0.0809648 -0.028
## ns(time, df = round(2 * length(time)/153))10 -0.0347031 0.0773453 -0.449
## ns(time, df = round(2 * length(time)/153))11 -0.0239223 0.0751314 -0.318
## ns(time, df = round(2 * length(time)/153))12 -0.0135597 0.0766903 -0.177
## ns(time, df = round(2 * length(time)/153))13 0.0129686 0.0732767 0.177
## ns(time, df = round(2 * length(time)/153))14 -0.0939691 0.0753948 -1.246
## ns(time, df = round(2 * length(time)/153))15 -0.0243957 0.0744723 -0.328
## ns(time, df = round(2 * length(time)/153))16 -0.0837097 0.0757357 -1.105
## ns(time, df = round(2 * length(time)/153))17 -0.0353626 0.0737123 -0.480
## ns(time, df = round(2 * length(time)/153))18 -0.0300079 0.0754776 -0.398
## ns(time, df = round(2 * length(time)/153))19 -0.0921934 0.0735197 -1.254
## ns(time, df = round(2 * length(time)/153))20 0.0686105 0.0746496 0.919
## ns(time, df = round(2 * length(time)/153))21 -0.1028769 0.0728181 -1.413
## ns(time, df = round(2 * length(time)/153))22 0.0090584 0.0746651 0.121
## ns(time, df = round(2 * length(time)/153))23 -0.1052407 0.0725068 -1.451
## ns(time, df = round(2 * length(time)/153))24 0.0014422 0.0748668 0.019
## ns(time, df = round(2 * length(time)/153))25 -0.0449458 0.0715981 -0.628
## ns(time, df = round(2 * length(time)/153))26 0.0504185 0.0742851 0.679
## ns(time, df = round(2 * length(time)/153))27 -0.1461649 0.0703003 -2.079
## ns(time, df = round(2 * length(time)/153))28 0.0980994 0.0771729 1.271
## ns(time, df = round(2 * length(time)/153))29 -0.0809418 0.0636213 -1.272
## ns(time, df = round(2 * length(time)/153))30 0.0562432 0.0902751 0.623
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi -0.0005368 0.0210316 -0.026
## Jourslundi 0.0471171 0.0198702 2.371
## Joursmardi 0.0228186 0.0204975 1.113
## Joursmercredi -0.0007559 0.0209003 -0.036
## Jourssamedi -0.0291911 0.0205450 -1.421
## Joursvendredi -0.0070078 0.0211021 -0.332
## Vacances1 -0.0162439 0.0142787 -1.138
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 0.31516
## no2moy 0.00223 **
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 0.67675
## ns(time, df = round(2 * length(time)/153))7 0.77477
## ns(time, df = round(2 * length(time)/153))8 0.37681
## ns(time, df = round(2 * length(time)/153))9 0.97729
## ns(time, df = round(2 * length(time)/153))10 0.65366
## ns(time, df = round(2 * length(time)/153))11 0.75018
## ns(time, df = round(2 * length(time)/153))12 0.85966
## ns(time, df = round(2 * length(time)/153))13 0.85952
## ns(time, df = round(2 * length(time)/153))14 0.21263
## ns(time, df = round(2 * length(time)/153))15 0.74323
## ns(time, df = round(2 * length(time)/153))16 0.26904
## ns(time, df = round(2 * length(time)/153))17 0.63141
## ns(time, df = round(2 * length(time)/153))18 0.69094
## ns(time, df = round(2 * length(time)/153))19 0.20984
## ns(time, df = round(2 * length(time)/153))20 0.35804
## ns(time, df = round(2 * length(time)/153))21 0.15772
## ns(time, df = round(2 * length(time)/153))22 0.90344
## ns(time, df = round(2 * length(time)/153))23 0.14665
## ns(time, df = round(2 * length(time)/153))24 0.98463
## ns(time, df = round(2 * length(time)/153))25 0.53017
## ns(time, df = round(2 * length(time)/153))26 0.49732
## ns(time, df = round(2 * length(time)/153))27 0.03760 *
## ns(time, df = round(2 * length(time)/153))28 0.20367
## ns(time, df = round(2 * length(time)/153))29 0.20329
## ns(time, df = round(2 * length(time)/153))30 0.53327
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.97964
## Jourslundi 0.01773 *
## Joursmardi 0.26561
## Joursmercredi 0.97115
## Jourssamedi 0.15536
## Joursvendredi 0.73982
## Vacances1 0.25527
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1924.7 on 1833 degrees of freedom
## Residual deviance: 1873.9 on 1799 degrees of freedom
## (614 observations deleted due to missingness)
## AIC: 10710
##
## Number of Fisher Scoring iterations: 4
##
##
## $lyon
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.14857 -0.76117 -0.07346 0.67240 2.95468
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 2.9125595 0.0473853 61.465
## heat_wave 0.1563655 0.0739074 2.116
## no2moy 0.0013012 0.0009089 1.432
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 NA NA NA
## ns(time, df = round(2 * length(time)/153))7 NA NA NA
## ns(time, df = round(2 * length(time)/153))8 NA NA NA
## ns(time, df = round(2 * length(time)/153))9 NA NA NA
## ns(time, df = round(2 * length(time)/153))10 NA NA NA
## ns(time, df = round(2 * length(time)/153))11 NA NA NA
## ns(time, df = round(2 * length(time)/153))12 -0.7298697 1.1247436 -0.649
## ns(time, df = round(2 * length(time)/153))13 -0.2277132 0.2019194 -1.128
## ns(time, df = round(2 * length(time)/153))14 -0.1367086 0.1109097 -1.233
## ns(time, df = round(2 * length(time)/153))15 -0.1799387 0.0870755 -2.066
## ns(time, df = round(2 * length(time)/153))16 -0.0237138 0.0824077 -0.288
## ns(time, df = round(2 * length(time)/153))17 -0.2322444 0.0792241 -2.931
## ns(time, df = round(2 * length(time)/153))18 -0.0972782 0.0807360 -1.205
## ns(time, df = round(2 * length(time)/153))19 -0.1711809 0.0779121 -2.197
## ns(time, df = round(2 * length(time)/153))20 -0.0598599 0.0790243 -0.757
## ns(time, df = round(2 * length(time)/153))21 -0.1250276 0.0767600 -1.629
## ns(time, df = round(2 * length(time)/153))22 -0.1781182 0.0798667 -2.230
## ns(time, df = round(2 * length(time)/153))23 -0.0527890 0.0769501 -0.686
## ns(time, df = round(2 * length(time)/153))24 -0.1190518 0.0790031 -1.507
## ns(time, df = round(2 * length(time)/153))25 -0.1286481 0.0757797 -1.698
## ns(time, df = round(2 * length(time)/153))26 -0.1928600 0.0796651 -2.421
## ns(time, df = round(2 * length(time)/153))27 -0.1322194 0.0741284 -1.784
## ns(time, df = round(2 * length(time)/153))28 -0.1853229 0.0816273 -2.270
## ns(time, df = round(2 * length(time)/153))29 -0.0589414 0.0677450 -0.870
## ns(time, df = round(2 * length(time)/153))30 -0.1172928 0.0948979 -1.236
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi 0.0312913 0.0259773 1.205
## Jourslundi 0.0200727 0.0247807 0.810
## Joursmardi 0.0393040 0.0253448 1.551
## Joursmercredi 0.0095416 0.0258789 0.369
## Jourssamedi -0.0200380 0.0253538 -0.790
## Joursvendredi 0.0722111 0.0256371 2.817
## Vacances1 -0.0215830 0.0175241 -1.232
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 0.03437 *
## no2moy 0.15225
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.51639
## ns(time, df = round(2 * length(time)/153))13 0.25943
## ns(time, df = round(2 * length(time)/153))14 0.21772
## ns(time, df = round(2 * length(time)/153))15 0.03878 *
## ns(time, df = round(2 * length(time)/153))16 0.77353
## ns(time, df = round(2 * length(time)/153))17 0.00337 **
## ns(time, df = round(2 * length(time)/153))18 0.22824
## ns(time, df = round(2 * length(time)/153))19 0.02801 *
## ns(time, df = round(2 * length(time)/153))20 0.44876
## ns(time, df = round(2 * length(time)/153))21 0.10335
## ns(time, df = round(2 * length(time)/153))22 0.02573 *
## ns(time, df = round(2 * length(time)/153))23 0.49270
## ns(time, df = round(2 * length(time)/153))24 0.13183
## ns(time, df = round(2 * length(time)/153))25 0.08957 .
## ns(time, df = round(2 * length(time)/153))26 0.01548 *
## ns(time, df = round(2 * length(time)/153))27 0.07448 .
## ns(time, df = round(2 * length(time)/153))28 0.02319 *
## ns(time, df = round(2 * length(time)/153))29 0.38427
## ns(time, df = round(2 * length(time)/153))30 0.21646
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.22837
## Jourslundi 0.41793
## Joursmardi 0.12096
## Joursmercredi 0.71235
## Jourssamedi 0.42933
## Joursvendredi 0.00485 **
## Vacances1 0.21809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1494.7 on 1376 degrees of freedom
## Residual deviance: 1425.7 on 1348 degrees of freedom
## (1071 observations deleted due to missingness)
## AIC: 7879.6
##
## Number of Fisher Scoring iterations: 4
##
##
## $marseille
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4263 -0.6918 -0.0618 0.6503 2.9680
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 2.9438244 0.0463758 63.478
## heat_wave -0.0061396 0.0692970 -0.089
## no2moy 0.0010167 0.0005496 1.850
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 NA NA NA
## ns(time, df = round(2 * length(time)/153))7 NA NA NA
## ns(time, df = round(2 * length(time)/153))8 NA NA NA
## ns(time, df = round(2 * length(time)/153))9 NA NA NA
## ns(time, df = round(2 * length(time)/153))10 NA NA NA
## ns(time, df = round(2 * length(time)/153))11 NA NA NA
## ns(time, df = round(2 * length(time)/153))12 1.1729170 1.0095439 1.162
## ns(time, df = round(2 * length(time)/153))13 -0.2493245 0.1854388 -1.345
## ns(time, df = round(2 * length(time)/153))14 0.1044123 0.1018025 1.026
## ns(time, df = round(2 * length(time)/153))15 -0.0477790 0.0806113 -0.593
## ns(time, df = round(2 * length(time)/153))16 0.0739550 0.0765966 0.966
## ns(time, df = round(2 * length(time)/153))17 0.0116577 0.0726962 0.160
## ns(time, df = round(2 * length(time)/153))18 0.0420978 0.0744723 0.565
## ns(time, df = round(2 * length(time)/153))19 -0.0000861 0.0727438 -0.001
## ns(time, df = round(2 * length(time)/153))20 0.0085915 0.0757071 0.113
## ns(time, df = round(2 * length(time)/153))21 -0.0414456 0.0731189 -0.567
## ns(time, df = round(2 * length(time)/153))22 -0.0178394 0.0748848 -0.238
## ns(time, df = round(2 * length(time)/153))23 -0.0166517 0.0715315 -0.233
## ns(time, df = round(2 * length(time)/153))24 0.0716058 0.0732643 0.977
## ns(time, df = round(2 * length(time)/153))25 -0.0105791 0.0709071 -0.149
## ns(time, df = round(2 * length(time)/153))26 0.0812486 0.0736330 1.103
## ns(time, df = round(2 * length(time)/153))27 -0.0568571 0.0695597 -0.817
## ns(time, df = round(2 * length(time)/153))28 0.0203177 0.0765947 0.265
## ns(time, df = round(2 * length(time)/153))29 -0.1028092 0.0635562 -1.618
## ns(time, df = round(2 * length(time)/153))30 0.1350026 0.0892668 1.512
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi 0.0348800 0.0230216 1.515
## Jourslundi 0.0106272 0.0228441 0.465
## Joursmardi 0.0178174 0.0229298 0.777
## Joursmercredi 0.0505939 0.0228933 2.210
## Jourssamedi 0.0236652 0.0228738 1.035
## Joursvendredi 0.0374145 0.0229958 1.627
## Vacances1 -0.0334064 0.0162029 -2.062
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.9294
## no2moy 0.0643 .
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.2453
## ns(time, df = round(2 * length(time)/153))13 0.1788
## ns(time, df = round(2 * length(time)/153))14 0.3051
## ns(time, df = round(2 * length(time)/153))15 0.5534
## ns(time, df = round(2 * length(time)/153))16 0.3343
## ns(time, df = round(2 * length(time)/153))17 0.8726
## ns(time, df = round(2 * length(time)/153))18 0.5719
## ns(time, df = round(2 * length(time)/153))19 0.9991
## ns(time, df = round(2 * length(time)/153))20 0.9096
## ns(time, df = round(2 * length(time)/153))21 0.5708
## ns(time, df = round(2 * length(time)/153))22 0.8117
## ns(time, df = round(2 * length(time)/153))23 0.8159
## ns(time, df = round(2 * length(time)/153))24 0.3284
## ns(time, df = round(2 * length(time)/153))25 0.8814
## ns(time, df = round(2 * length(time)/153))26 0.2698
## ns(time, df = round(2 * length(time)/153))27 0.4137
## ns(time, df = round(2 * length(time)/153))28 0.7908
## ns(time, df = round(2 * length(time)/153))29 0.1057
## ns(time, df = round(2 * length(time)/153))30 0.1304
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.1297
## Jourslundi 0.6418
## Joursmardi 0.4371
## Joursmercredi 0.0271 *
## Jourssamedi 0.3009
## Joursvendredi 0.1037
## Vacances1 0.0392 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1401.9 on 1369 degrees of freedom
## Residual deviance: 1364.7 on 1341 degrees of freedom
## (1078 observations deleted due to missingness)
## AIC: 8019.3
##
## Number of Fisher Scoring iterations: 4
##
##
## $montpellier
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4697 -0.7451 -0.0615 0.5804 3.7192
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 1.867209 0.080556 23.179
## heat_wave 0.094337 0.125066 0.754
## no2moy 0.002230 0.001518 1.469
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 NA NA NA
## ns(time, df = round(2 * length(time)/153))7 NA NA NA
## ns(time, df = round(2 * length(time)/153))8 NA NA NA
## ns(time, df = round(2 * length(time)/153))9 NA NA NA
## ns(time, df = round(2 * length(time)/153))10 NA NA NA
## ns(time, df = round(2 * length(time)/153))11 NA NA NA
## ns(time, df = round(2 * length(time)/153))12 1.703083 1.851477 0.920
## ns(time, df = round(2 * length(time)/153))13 -0.490328 0.342764 -1.431
## ns(time, df = round(2 * length(time)/153))14 -0.140765 0.189339 -0.743
## ns(time, df = round(2 * length(time)/153))15 -0.284779 0.152543 -1.867
## ns(time, df = round(2 * length(time)/153))16 -0.163013 0.142297 -1.146
## ns(time, df = round(2 * length(time)/153))17 -0.113334 0.135098 -0.839
## ns(time, df = round(2 * length(time)/153))18 -0.146302 0.135702 -1.078
## ns(time, df = round(2 * length(time)/153))19 -0.128420 0.132371 -0.970
## ns(time, df = round(2 * length(time)/153))20 -0.175653 0.134572 -1.305
## ns(time, df = round(2 * length(time)/153))21 -0.006325 0.128852 -0.049
## ns(time, df = round(2 * length(time)/153))22 -0.232699 0.137708 -1.690
## ns(time, df = round(2 * length(time)/153))23 -0.143804 0.131708 -1.092
## ns(time, df = round(2 * length(time)/153))24 -0.106862 0.134345 -0.795
## ns(time, df = round(2 * length(time)/153))25 -0.087953 0.128022 -0.687
## ns(time, df = round(2 * length(time)/153))26 -0.253850 0.134702 -1.885
## ns(time, df = round(2 * length(time)/153))27 -0.064197 0.124108 -0.517
## ns(time, df = round(2 * length(time)/153))28 -0.142462 0.136128 -1.047
## ns(time, df = round(2 * length(time)/153))29 0.030188 0.111554 0.271
## ns(time, df = round(2 * length(time)/153))30 0.041435 0.158406 0.262
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi 0.001458 0.042230 0.035
## Jourslundi -0.045309 0.041759 -1.085
## Joursmardi -0.009648 0.041863 -0.230
## Joursmercredi -0.007036 0.042235 -0.167
## Jourssamedi -0.076159 0.042457 -1.794
## Joursvendredi 0.029509 0.041887 0.704
## Vacances1 0.004159 0.029590 0.141
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.4507
## no2moy 0.1419
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.3577
## ns(time, df = round(2 * length(time)/153))13 0.1526
## ns(time, df = round(2 * length(time)/153))14 0.4572
## ns(time, df = round(2 * length(time)/153))15 0.0619 .
## ns(time, df = round(2 * length(time)/153))16 0.2520
## ns(time, df = round(2 * length(time)/153))17 0.4015
## ns(time, df = round(2 * length(time)/153))18 0.2810
## ns(time, df = round(2 * length(time)/153))19 0.3320
## ns(time, df = round(2 * length(time)/153))20 0.1918
## ns(time, df = round(2 * length(time)/153))21 0.9608
## ns(time, df = round(2 * length(time)/153))22 0.0911 .
## ns(time, df = round(2 * length(time)/153))23 0.2749
## ns(time, df = round(2 * length(time)/153))24 0.4264
## ns(time, df = round(2 * length(time)/153))25 0.4921
## ns(time, df = round(2 * length(time)/153))26 0.0595 .
## ns(time, df = round(2 * length(time)/153))27 0.6050
## ns(time, df = round(2 * length(time)/153))28 0.2953
## ns(time, df = round(2 * length(time)/153))29 0.7867
## ns(time, df = round(2 * length(time)/153))30 0.7936
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.9725
## Jourslundi 0.2779
## Joursmardi 0.8177
## Joursmercredi 0.8677
## Jourssamedi 0.0728 .
## Joursvendredi 0.4811
## Vacances1 0.8882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1409.4 on 1373 degrees of freedom
## Residual deviance: 1356.1 on 1345 degrees of freedom
## (1074 observations deleted due to missingness)
## AIC: 6293.9
##
## Number of Fisher Scoring iterations: 4
##
##
## $nancy
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.94989 -0.77694 -0.05847 0.63718 2.63307
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 1.834708 0.083264 22.035
## heat_wave 0.098727 0.106288 0.929
## no2moy 0.001206 0.001922 0.628
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 NA NA NA
## ns(time, df = round(2 * length(time)/153))7 NA NA NA
## ns(time, df = round(2 * length(time)/153))8 NA NA NA
## ns(time, df = round(2 * length(time)/153))9 NA NA NA
## ns(time, df = round(2 * length(time)/153))10 NA NA NA
## ns(time, df = round(2 * length(time)/153))11 NA NA NA
## ns(time, df = round(2 * length(time)/153))12 -2.809303 1.848424 -1.520
## ns(time, df = round(2 * length(time)/153))13 0.322093 0.326818 0.986
## ns(time, df = round(2 * length(time)/153))14 -0.193081 0.183362 -1.053
## ns(time, df = round(2 * length(time)/153))15 -0.008448 0.146195 -0.058
## ns(time, df = round(2 * length(time)/153))16 -0.137363 0.140392 -0.978
## ns(time, df = round(2 * length(time)/153))17 -0.097259 0.133486 -0.729
## ns(time, df = round(2 * length(time)/153))18 0.002130 0.135340 0.016
## ns(time, df = round(2 * length(time)/153))19 -0.188359 0.131636 -1.431
## ns(time, df = round(2 * length(time)/153))20 0.238769 0.128679 1.856
## ns(time, df = round(2 * length(time)/153))21 -0.099640 0.126437 -0.788
## ns(time, df = round(2 * length(time)/153))22 0.097217 0.130992 0.742
## ns(time, df = round(2 * length(time)/153))23 -0.081745 0.127118 -0.643
## ns(time, df = round(2 * length(time)/153))24 0.040640 0.129536 0.314
## ns(time, df = round(2 * length(time)/153))25 -0.012969 0.124106 -0.105
## ns(time, df = round(2 * length(time)/153))26 0.040635 0.129386 0.314
## ns(time, df = round(2 * length(time)/153))27 0.075839 0.120104 0.631
## ns(time, df = round(2 * length(time)/153))28 -0.082754 0.134255 -0.616
## ns(time, df = round(2 * length(time)/153))29 0.019538 0.110540 0.177
## ns(time, df = round(2 * length(time)/153))30 0.206773 0.156193 1.324
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi 0.007604 0.041888 0.182
## Jourslundi 0.052132 0.039782 1.310
## Joursmardi 0.008518 0.041168 0.207
## Joursmercredi 0.028408 0.041490 0.685
## Jourssamedi -0.044056 0.041516 -1.061
## Joursvendredi 0.037797 0.041781 0.905
## Vacances1 -0.028393 0.028427 -0.999
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.3530
## no2moy 0.5303
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.1286
## ns(time, df = round(2 * length(time)/153))13 0.3244
## ns(time, df = round(2 * length(time)/153))14 0.2923
## ns(time, df = round(2 * length(time)/153))15 0.9539
## ns(time, df = round(2 * length(time)/153))16 0.3279
## ns(time, df = round(2 * length(time)/153))17 0.4662
## ns(time, df = round(2 * length(time)/153))18 0.9874
## ns(time, df = round(2 * length(time)/153))19 0.1525
## ns(time, df = round(2 * length(time)/153))20 0.0635 .
## ns(time, df = round(2 * length(time)/153))21 0.4307
## ns(time, df = round(2 * length(time)/153))22 0.4580
## ns(time, df = round(2 * length(time)/153))23 0.5202
## ns(time, df = round(2 * length(time)/153))24 0.7537
## ns(time, df = round(2 * length(time)/153))25 0.9168
## ns(time, df = round(2 * length(time)/153))26 0.7535
## ns(time, df = round(2 * length(time)/153))27 0.5278
## ns(time, df = round(2 * length(time)/153))28 0.5376
## ns(time, df = round(2 * length(time)/153))29 0.8597
## ns(time, df = round(2 * length(time)/153))30 0.1856
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.8560
## Jourslundi 0.1900
## Joursmardi 0.8361
## Joursmercredi 0.4935
## Jourssamedi 0.2886
## Joursvendredi 0.3656
## Vacances1 0.3179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1437.2 on 1374 degrees of freedom
## Residual deviance: 1391.9 on 1346 degrees of freedom
## (1073 observations deleted due to missingness)
## AIC: 6446
##
## Number of Fisher Scoring iterations: 4
##
##
## $nantes
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2018 -0.7205 -0.0167 0.5929 3.8579
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 2.313e+00 6.365e-02 36.341
## heat_wave 1.451e-03 1.894e-01 0.008
## no2moy 7.072e-04 2.361e-03 0.300
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 NA NA NA
## ns(time, df = round(2 * length(time)/153))7 NA NA NA
## ns(time, df = round(2 * length(time)/153))8 NA NA NA
## ns(time, df = round(2 * length(time)/153))9 NA NA NA
## ns(time, df = round(2 * length(time)/153))10 NA NA NA
## ns(time, df = round(2 * length(time)/153))11 NA NA NA
## ns(time, df = round(2 * length(time)/153))12 -1.688e+00 1.418e+00 -1.191
## ns(time, df = round(2 * length(time)/153))13 2.804e-01 2.597e-01 1.080
## ns(time, df = round(2 * length(time)/153))14 -2.140e-01 1.470e-01 -1.456
## ns(time, df = round(2 * length(time)/153))15 2.829e-02 1.211e-01 0.234
## ns(time, df = round(2 * length(time)/153))16 -2.121e-01 1.126e-01 -1.883
## ns(time, df = round(2 * length(time)/153))17 1.397e-02 1.081e-01 0.129
## ns(time, df = round(2 * length(time)/153))18 -1.450e-01 1.142e-01 -1.270
## ns(time, df = round(2 * length(time)/153))19 -4.267e-02 1.073e-01 -0.398
## ns(time, df = round(2 * length(time)/153))20 -9.187e-06 1.085e-01 0.000
## ns(time, df = round(2 * length(time)/153))21 -1.114e-01 1.060e-01 -1.051
## ns(time, df = round(2 * length(time)/153))22 -8.017e-02 1.154e-01 -0.695
## ns(time, df = round(2 * length(time)/153))23 9.803e-03 1.050e-01 0.093
## ns(time, df = round(2 * length(time)/153))24 -8.299e-02 1.077e-01 -0.771
## ns(time, df = round(2 * length(time)/153))25 -7.010e-03 1.013e-01 -0.069
## ns(time, df = round(2 * length(time)/153))26 -3.424e-02 1.128e-01 -0.304
## ns(time, df = round(2 * length(time)/153))27 -2.956e-02 9.890e-02 -0.299
## ns(time, df = round(2 * length(time)/153))28 1.994e-02 1.096e-01 0.182
## ns(time, df = round(2 * length(time)/153))29 -1.462e-01 9.055e-02 -1.614
## ns(time, df = round(2 * length(time)/153))30 1.210e-01 1.272e-01 0.951
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi 4.213e-02 3.431e-02 1.228
## Jourslundi 4.235e-02 3.366e-02 1.258
## Joursmardi 6.232e-02 3.412e-02 1.826
## Joursmercredi 2.132e-02 3.433e-02 0.621
## Jourssamedi 4.105e-03 3.410e-02 0.120
## Joursvendredi 3.819e-02 3.462e-02 1.103
## Vacances1 -4.280e-02 2.329e-02 -1.838
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.9939
## no2moy 0.7645
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.2338
## ns(time, df = round(2 * length(time)/153))13 0.2803
## ns(time, df = round(2 * length(time)/153))14 0.1453
## ns(time, df = round(2 * length(time)/153))15 0.8152
## ns(time, df = round(2 * length(time)/153))16 0.0597 .
## ns(time, df = round(2 * length(time)/153))17 0.8972
## ns(time, df = round(2 * length(time)/153))18 0.2042
## ns(time, df = round(2 * length(time)/153))19 0.6908
## ns(time, df = round(2 * length(time)/153))20 0.9999
## ns(time, df = round(2 * length(time)/153))21 0.2931
## ns(time, df = round(2 * length(time)/153))22 0.4873
## ns(time, df = round(2 * length(time)/153))23 0.9256
## ns(time, df = round(2 * length(time)/153))24 0.4408
## ns(time, df = round(2 * length(time)/153))25 0.9449
## ns(time, df = round(2 * length(time)/153))26 0.7614
## ns(time, df = round(2 * length(time)/153))27 0.7651
## ns(time, df = round(2 * length(time)/153))28 0.8556
## ns(time, df = round(2 * length(time)/153))29 0.1064
## ns(time, df = round(2 * length(time)/153))30 0.3415
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.2195
## Jourslundi 0.2084
## Joursmardi 0.0678 .
## Joursmercredi 0.5346
## Jourssamedi 0.9042
## Joursvendredi 0.2700
## Vacances1 0.0660 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1381.2 on 1312 degrees of freedom
## Residual deviance: 1348.4 on 1284 degrees of freedom
## (1135 observations deleted due to missingness)
## AIC: 6772.2
##
## Number of Fisher Scoring iterations: 4
##
##
## $nice
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3116 -0.7364 -0.0479 0.6655 3.3366
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 2.3755509 0.0709423 33.486
## heat_wave 0.0056155 0.1025784 0.055
## no2moy 0.0004202 0.0017629 0.238
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 NA NA NA
## ns(time, df = round(2 * length(time)/153))7 NA NA NA
## ns(time, df = round(2 * length(time)/153))8 NA NA NA
## ns(time, df = round(2 * length(time)/153))9 NA NA NA
## ns(time, df = round(2 * length(time)/153))10 NA NA NA
## ns(time, df = round(2 * length(time)/153))11 NA NA NA
## ns(time, df = round(2 * length(time)/153))12 0.0084291 1.3740326 0.006
## ns(time, df = round(2 * length(time)/153))13 -0.0310653 0.2503218 -0.124
## ns(time, df = round(2 * length(time)/153))14 -0.1534734 0.1399368 -1.097
## ns(time, df = round(2 * length(time)/153))15 0.0410992 0.1096022 0.375
## ns(time, df = round(2 * length(time)/153))16 -0.0521964 0.1097822 -0.475
## ns(time, df = round(2 * length(time)/153))17 0.0076135 0.0985664 0.077
## ns(time, df = round(2 * length(time)/153))18 0.0110507 0.1033049 0.107
## ns(time, df = round(2 * length(time)/153))19 -0.0123503 0.0988009 -0.125
## ns(time, df = round(2 * length(time)/153))20 -0.1340540 0.1033003 -1.298
## ns(time, df = round(2 * length(time)/153))21 0.0522221 0.0985708 0.530
## ns(time, df = round(2 * length(time)/153))22 -0.1879697 0.1040421 -1.807
## ns(time, df = round(2 * length(time)/153))23 0.1249519 0.0974231 1.283
## ns(time, df = round(2 * length(time)/153))24 -0.2242161 0.1020966 -2.196
## ns(time, df = round(2 * length(time)/153))25 0.1831806 0.0962511 1.903
## ns(time, df = round(2 * length(time)/153))26 -0.1401847 0.1006560 -1.393
## ns(time, df = round(2 * length(time)/153))27 0.0155690 0.0936435 0.166
## ns(time, df = round(2 * length(time)/153))28 0.0016791 0.1022707 0.016
## ns(time, df = round(2 * length(time)/153))29 -0.0484968 0.0849791 -0.571
## ns(time, df = round(2 * length(time)/153))30 0.0181329 0.1193208 0.152
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi -0.0109434 0.0328211 -0.333
## Jourslundi 0.0455709 0.0308760 1.476
## Joursmardi 0.0123528 0.0321719 0.384
## Joursmercredi 0.0075825 0.0326404 0.232
## Jourssamedi 0.0442128 0.0316191 1.398
## Joursvendredi 0.0661642 0.0323894 2.043
## Vacances1 0.0112118 0.0222758 0.503
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.9563
## no2moy 0.8116
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.9951
## ns(time, df = round(2 * length(time)/153))13 0.9012
## ns(time, df = round(2 * length(time)/153))14 0.2728
## ns(time, df = round(2 * length(time)/153))15 0.7077
## ns(time, df = round(2 * length(time)/153))16 0.6345
## ns(time, df = round(2 * length(time)/153))17 0.9384
## ns(time, df = round(2 * length(time)/153))18 0.9148
## ns(time, df = round(2 * length(time)/153))19 0.9005
## ns(time, df = round(2 * length(time)/153))20 0.1944
## ns(time, df = round(2 * length(time)/153))21 0.5963
## ns(time, df = round(2 * length(time)/153))22 0.0708 .
## ns(time, df = round(2 * length(time)/153))23 0.1996
## ns(time, df = round(2 * length(time)/153))24 0.0281 *
## ns(time, df = round(2 * length(time)/153))25 0.0570 .
## ns(time, df = round(2 * length(time)/153))26 0.1637
## ns(time, df = round(2 * length(time)/153))27 0.8680
## ns(time, df = round(2 * length(time)/153))28 0.9869
## ns(time, df = round(2 * length(time)/153))29 0.5682
## ns(time, df = round(2 * length(time)/153))30 0.8792
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.7388
## Jourslundi 0.1400
## Joursmardi 0.7010
## Joursmercredi 0.8163
## Jourssamedi 0.1620
## Joursvendredi 0.0411 *
## Vacances1 0.6147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1437.6 on 1358 degrees of freedom
## Residual deviance: 1408.2 on 1330 degrees of freedom
## (1089 observations deleted due to missingness)
## AIC: 7158.5
##
## Number of Fisher Scoring iterations: 4
##
##
## $paris
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4542 -0.7920 -0.0590 0.7474 3.8062
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 4.4622057 0.0218318 204.390
## heat_wave 0.2105394 0.0296090 7.111
## no2moy 0.0030195 0.0003559 8.484
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 NA NA NA
## ns(time, df = round(2 * length(time)/153))7 NA NA NA
## ns(time, df = round(2 * length(time)/153))8 NA NA NA
## ns(time, df = round(2 * length(time)/153))9 NA NA NA
## ns(time, df = round(2 * length(time)/153))10 NA NA NA
## ns(time, df = round(2 * length(time)/153))11 NA NA NA
## ns(time, df = round(2 * length(time)/153))12 0.5134040 0.4527567 1.134
## ns(time, df = round(2 * length(time)/153))13 -0.1508802 0.0823362 -1.832
## ns(time, df = round(2 * length(time)/153))14 0.1110419 0.0465671 2.385
## ns(time, df = round(2 * length(time)/153))15 -0.0931674 0.0369533 -2.521
## ns(time, df = round(2 * length(time)/153))16 0.1164288 0.0358935 3.244
## ns(time, df = round(2 * length(time)/153))17 -0.0866836 0.0337168 -2.571
## ns(time, df = round(2 * length(time)/153))18 0.0867078 0.0348089 2.491
## ns(time, df = round(2 * length(time)/153))19 -0.0497632 0.0332638 -1.496
## ns(time, df = round(2 * length(time)/153))20 0.0724571 0.0342344 2.116
## ns(time, df = round(2 * length(time)/153))21 -0.0266357 0.0329402 -0.809
## ns(time, df = round(2 * length(time)/153))22 0.0902264 0.0343832 2.624
## ns(time, df = round(2 * length(time)/153))23 -0.0085582 0.0326267 -0.262
## ns(time, df = round(2 * length(time)/153))24 0.0666488 0.0338982 1.966
## ns(time, df = round(2 * length(time)/153))25 -0.0401769 0.0321449 -1.250
## ns(time, df = round(2 * length(time)/153))26 0.1048259 0.0338546 3.096
## ns(time, df = round(2 * length(time)/153))27 -0.0969348 0.0319516 -3.034
## ns(time, df = round(2 * length(time)/153))28 0.0581616 0.0349793 1.663
## ns(time, df = round(2 * length(time)/153))29 -0.0014165 0.0288877 -0.049
## ns(time, df = round(2 * length(time)/153))30 0.0743031 0.0412569 1.801
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi 0.0359921 0.0107097 3.361
## Jourslundi 0.0455830 0.0103614 4.399
## Joursmardi 0.0288158 0.0106208 2.713
## Joursmercredi 0.0180862 0.0107540 1.682
## Jourssamedi 0.0185546 0.0105251 1.763
## Joursvendredi 0.0138401 0.0107701 1.285
## Vacances1 -0.0376135 0.0073780 -5.098
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 1.15e-12 ***
## no2moy < 2e-16 ***
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.256815
## ns(time, df = round(2 * length(time)/153))13 0.066878 .
## ns(time, df = round(2 * length(time)/153))14 0.017100 *
## ns(time, df = round(2 * length(time)/153))15 0.011695 *
## ns(time, df = round(2 * length(time)/153))16 0.001180 **
## ns(time, df = round(2 * length(time)/153))17 0.010142 *
## ns(time, df = round(2 * length(time)/153))18 0.012740 *
## ns(time, df = round(2 * length(time)/153))19 0.134649
## ns(time, df = round(2 * length(time)/153))20 0.034302 *
## ns(time, df = round(2 * length(time)/153))21 0.418742
## ns(time, df = round(2 * length(time)/153))22 0.008687 **
## ns(time, df = round(2 * length(time)/153))23 0.793085
## ns(time, df = round(2 * length(time)/153))24 0.049282 *
## ns(time, df = round(2 * length(time)/153))25 0.211348
## ns(time, df = round(2 * length(time)/153))26 0.001959 **
## ns(time, df = round(2 * length(time)/153))27 0.002415 **
## ns(time, df = round(2 * length(time)/153))28 0.096364 .
## ns(time, df = round(2 * length(time)/153))29 0.960893
## ns(time, df = round(2 * length(time)/153))30 0.071705 .
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.000777 ***
## Jourslundi 1.09e-05 ***
## Joursmardi 0.006665 **
## Joursmercredi 0.092605 .
## Jourssamedi 0.077918 .
## Joursvendredi 0.198776
## Vacances1 3.43e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1995.9 on 1376 degrees of freedom
## Residual deviance: 1757.8 on 1348 degrees of freedom
## (1071 observations deleted due to missingness)
## AIC: 10632
##
## Number of Fisher Scoring iterations: 4
##
##
## $rennes
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8204 -0.7675 -0.0942 0.5922 3.6103
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 1.3847088 0.1030159 13.442
## heat_wave -0.2123030 0.3376929 -0.629
## no2moy -0.0038778 0.0031209 -1.243
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 NA NA NA
## ns(time, df = round(2 * length(time)/153))7 NA NA NA
## ns(time, df = round(2 * length(time)/153))8 NA NA NA
## ns(time, df = round(2 * length(time)/153))9 NA NA NA
## ns(time, df = round(2 * length(time)/153))10 NA NA NA
## ns(time, df = round(2 * length(time)/153))11 NA NA NA
## ns(time, df = round(2 * length(time)/153))12 1.0974902 2.4202446 0.453
## ns(time, df = round(2 * length(time)/153))13 -0.3829355 0.4462680 -0.858
## ns(time, df = round(2 * length(time)/153))14 -0.0852518 0.2447391 -0.348
## ns(time, df = round(2 * length(time)/153))15 -0.1116258 0.1924692 -0.580
## ns(time, df = round(2 * length(time)/153))16 -0.0306236 0.1824174 -0.168
## ns(time, df = round(2 * length(time)/153))17 -0.2703200 0.1749598 -1.545
## ns(time, df = round(2 * length(time)/153))18 -0.0889802 0.1786972 -0.498
## ns(time, df = round(2 * length(time)/153))19 0.0106399 0.1704341 0.062
## ns(time, df = round(2 * length(time)/153))20 -0.4235258 0.1783575 -2.375
## ns(time, df = round(2 * length(time)/153))21 0.1390606 0.1669839 0.833
## ns(time, df = round(2 * length(time)/153))22 -0.0984123 0.1744111 -0.564
## ns(time, df = round(2 * length(time)/153))23 -0.0007603 0.1662277 -0.005
## ns(time, df = round(2 * length(time)/153))24 -0.0871773 0.1775229 -0.491
## ns(time, df = round(2 * length(time)/153))25 0.0279932 0.1651031 0.170
## ns(time, df = round(2 * length(time)/153))26 -0.0213547 0.1712863 -0.125
## ns(time, df = round(2 * length(time)/153))27 -0.0628965 0.1592337 -0.395
## ns(time, df = round(2 * length(time)/153))28 0.1182774 0.1737917 0.681
## ns(time, df = round(2 * length(time)/153))29 -0.1725963 0.1492209 -1.157
## ns(time, df = round(2 * length(time)/153))30 -0.0214462 0.2047781 -0.105
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi 0.1175346 0.0562062 2.091
## Jourslundi 0.1160030 0.0532403 2.179
## Joursmardi 0.0978408 0.0553755 1.767
## Joursmercredi 0.0141606 0.0568249 0.249
## Jourssamedi 0.0969454 0.0547858 1.770
## Joursvendredi -0.0179802 0.0582975 -0.308
## Vacances1 -0.0465582 0.0379961 -1.225
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.5296
## no2moy 0.2140
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.6502
## ns(time, df = round(2 * length(time)/153))13 0.3908
## ns(time, df = round(2 * length(time)/153))14 0.7276
## ns(time, df = round(2 * length(time)/153))15 0.5619
## ns(time, df = round(2 * length(time)/153))16 0.8667
## ns(time, df = round(2 * length(time)/153))17 0.1223
## ns(time, df = round(2 * length(time)/153))18 0.6185
## ns(time, df = round(2 * length(time)/153))19 0.9502
## ns(time, df = round(2 * length(time)/153))20 0.0176 *
## ns(time, df = round(2 * length(time)/153))21 0.4050
## ns(time, df = round(2 * length(time)/153))22 0.5726
## ns(time, df = round(2 * length(time)/153))23 0.9964
## ns(time, df = round(2 * length(time)/153))24 0.6234
## ns(time, df = round(2 * length(time)/153))25 0.8654
## ns(time, df = round(2 * length(time)/153))26 0.9008
## ns(time, df = round(2 * length(time)/153))27 0.6928
## ns(time, df = round(2 * length(time)/153))28 0.4961
## ns(time, df = round(2 * length(time)/153))29 0.2474
## ns(time, df = round(2 * length(time)/153))30 0.9166
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.0365 *
## Jourslundi 0.0293 *
## Joursmardi 0.0773 .
## Joursmercredi 0.8032
## Jourssamedi 0.0768 .
## Joursvendredi 0.7578
## Vacances1 0.2204
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1280.2 on 1333 degrees of freedom
## Residual deviance: 1237.6 on 1305 degrees of freedom
## (1114 observations deleted due to missingness)
## AIC: 5369.5
##
## Number of Fisher Scoring iterations: 4
##
##
## $rouen
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3258 -0.6979 -0.0772 0.6079 3.6075
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 2.204044 0.070154 31.417
## heat_wave 0.155263 0.132243 1.174
## no2moy 0.002787 0.002122 1.314
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 NA NA NA
## ns(time, df = round(2 * length(time)/153))7 NA NA NA
## ns(time, df = round(2 * length(time)/153))8 NA NA NA
## ns(time, df = round(2 * length(time)/153))9 NA NA NA
## ns(time, df = round(2 * length(time)/153))10 NA NA NA
## ns(time, df = round(2 * length(time)/153))11 NA NA NA
## ns(time, df = round(2 * length(time)/153))12 -0.187839 1.522451 -0.123
## ns(time, df = round(2 * length(time)/153))13 -0.342084 0.272070 -1.257
## ns(time, df = round(2 * length(time)/153))14 0.169110 0.150673 1.122
## ns(time, df = round(2 * length(time)/153))15 -0.270900 0.121518 -2.229
## ns(time, df = round(2 * length(time)/153))16 -0.033658 0.115599 -0.291
## ns(time, df = round(2 * length(time)/153))17 -0.120159 0.109769 -1.095
## ns(time, df = round(2 * length(time)/153))18 -0.056666 0.112894 -0.502
## ns(time, df = round(2 * length(time)/153))19 -0.138892 0.108983 -1.274
## ns(time, df = round(2 * length(time)/153))20 -0.062910 0.110876 -0.567
## ns(time, df = round(2 * length(time)/153))21 -0.162114 0.107179 -1.513
## ns(time, df = round(2 * length(time)/153))22 0.045924 0.111400 0.412
## ns(time, df = round(2 * length(time)/153))23 -0.233565 0.106672 -2.190
## ns(time, df = round(2 * length(time)/153))24 0.044616 0.111353 0.401
## ns(time, df = round(2 * length(time)/153))25 -0.166784 0.105835 -1.576
## ns(time, df = round(2 * length(time)/153))26 -0.037289 0.111848 -0.333
## ns(time, df = round(2 * length(time)/153))27 -0.079310 0.101732 -0.780
## ns(time, df = round(2 * length(time)/153))28 -0.033411 0.113605 -0.294
## ns(time, df = round(2 * length(time)/153))29 -0.145362 0.092123 -1.578
## ns(time, df = round(2 * length(time)/153))30 0.114362 0.129935 0.880
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi 0.041478 0.036769 1.128
## Jourslundi 0.047503 0.034454 1.379
## Joursmardi 0.053390 0.036295 1.471
## Joursmercredi 0.107310 0.036344 2.953
## Jourssamedi 0.081944 0.034833 2.353
## Joursvendredi 0.049508 0.036743 1.347
## Vacances1 -0.075246 0.023944 -3.143
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 0.24036
## no2moy 0.18900
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.90181
## ns(time, df = round(2 * length(time)/153))13 0.20863
## ns(time, df = round(2 * length(time)/153))14 0.26171
## ns(time, df = round(2 * length(time)/153))15 0.02579 *
## ns(time, df = round(2 * length(time)/153))16 0.77093
## ns(time, df = round(2 * length(time)/153))17 0.27367
## ns(time, df = round(2 * length(time)/153))18 0.61571
## ns(time, df = round(2 * length(time)/153))19 0.20251
## ns(time, df = round(2 * length(time)/153))20 0.57045
## ns(time, df = round(2 * length(time)/153))21 0.13039
## ns(time, df = round(2 * length(time)/153))22 0.68016
## ns(time, df = round(2 * length(time)/153))23 0.02856 *
## ns(time, df = round(2 * length(time)/153))24 0.68866
## ns(time, df = round(2 * length(time)/153))25 0.11505
## ns(time, df = round(2 * length(time)/153))26 0.73884
## ns(time, df = round(2 * length(time)/153))27 0.43563
## ns(time, df = round(2 * length(time)/153))28 0.76868
## ns(time, df = round(2 * length(time)/153))29 0.11458
## ns(time, df = round(2 * length(time)/153))30 0.37878
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.25929
## Jourslundi 0.16798
## Joursmardi 0.14128
## Joursmercredi 0.00315 **
## Jourssamedi 0.01865 *
## Joursvendredi 0.17785
## Vacances1 0.00167 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1340.4 on 1376 degrees of freedom
## Residual deviance: 1293.9 on 1348 degrees of freedom
## (1071 observations deleted due to missingness)
## AIC: 6869.4
##
## Number of Fisher Scoring iterations: 4
##
##
## $strasbourg
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3570 -0.7403 -0.0308 0.5992 3.6948
##
## Coefficients:
## Estimate Std. Error z value
## (Intercept) 1.894823 0.091723 20.658
## heat_wave 0.420064 0.050642 8.295
## no2moy 0.002884 0.001202 2.400
## ns(time, df = round(2 * length(time)/153))1 -0.121578 0.102577 -1.185
## ns(time, df = round(2 * length(time)/153))2 0.306127 0.139763 2.190
## ns(time, df = round(2 * length(time)/153))3 0.073988 0.115871 0.639
## ns(time, df = round(2 * length(time)/153))4 0.153223 0.136408 1.123
## ns(time, df = round(2 * length(time)/153))5 0.050074 0.120425 0.416
## ns(time, df = round(2 * length(time)/153))6 0.304980 0.131794 2.314
## ns(time, df = round(2 * length(time)/153))7 -0.073241 0.122741 -0.597
## ns(time, df = round(2 * length(time)/153))8 0.243553 0.134146 1.816
## ns(time, df = round(2 * length(time)/153))9 -0.201885 0.126151 -1.600
## ns(time, df = round(2 * length(time)/153))10 0.172631 0.135557 1.273
## ns(time, df = round(2 * length(time)/153))11 -0.207565 0.126036 -1.647
## ns(time, df = round(2 * length(time)/153))12 0.263511 0.132896 1.983
## ns(time, df = round(2 * length(time)/153))13 -0.067352 0.123237 -0.547
## ns(time, df = round(2 * length(time)/153))14 0.263755 0.132745 1.987
## ns(time, df = round(2 * length(time)/153))15 0.046676 0.122171 0.382
## ns(time, df = round(2 * length(time)/153))16 0.187585 0.133258 1.408
## ns(time, df = round(2 * length(time)/153))17 -0.028988 0.123416 -0.235
## ns(time, df = round(2 * length(time)/153))18 0.208140 0.134300 1.550
## ns(time, df = round(2 * length(time)/153))19 0.107393 0.122017 0.880
## ns(time, df = round(2 * length(time)/153))20 0.040637 0.133644 0.304
## ns(time, df = round(2 * length(time)/153))21 0.384537 0.120128 3.201
## ns(time, df = round(2 * length(time)/153))22 -0.147497 0.135353 -1.090
## ns(time, df = round(2 * length(time)/153))23 0.324455 0.121220 2.677
## ns(time, df = round(2 * length(time)/153))24 0.031509 0.132777 0.237
## ns(time, df = round(2 * length(time)/153))25 0.068700 0.122708 0.560
## ns(time, df = round(2 * length(time)/153))26 0.352634 0.130401 2.704
## ns(time, df = round(2 * length(time)/153))27 -0.100465 0.123222 -0.815
## ns(time, df = round(2 * length(time)/153))28 0.290971 0.129325 2.250
## ns(time, df = round(2 * length(time)/153))29 0.175968 0.120676 1.458
## ns(time, df = round(2 * length(time)/153))30 0.105776 0.103098 1.026
## ns(time, df = round(2 * length(time)/153))31 0.433073 0.217829 1.988
## ns(time, df = round(2 * length(time)/153))32 -0.053428 0.090863 -0.588
## Joursjeudi -0.033443 0.028535 -1.172
## Jourslundi 0.039035 0.026824 1.455
## Joursmardi 0.011489 0.027771 0.414
## Joursmercredi 0.031322 0.028037 1.117
## Jourssamedi -0.028183 0.027767 -1.015
## Joursvendredi -0.009874 0.028391 -0.348
## Vacances1 -0.045157 0.018806 -2.401
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave < 2e-16 ***
## no2moy 0.01639 *
## ns(time, df = round(2 * length(time)/153))1 0.23592
## ns(time, df = round(2 * length(time)/153))2 0.02850 *
## ns(time, df = round(2 * length(time)/153))3 0.52312
## ns(time, df = round(2 * length(time)/153))4 0.26132
## ns(time, df = round(2 * length(time)/153))5 0.67755
## ns(time, df = round(2 * length(time)/153))6 0.02066 *
## ns(time, df = round(2 * length(time)/153))7 0.55070
## ns(time, df = round(2 * length(time)/153))8 0.06943 .
## ns(time, df = round(2 * length(time)/153))9 0.10952
## ns(time, df = round(2 * length(time)/153))10 0.20284
## ns(time, df = round(2 * length(time)/153))11 0.09958 .
## ns(time, df = round(2 * length(time)/153))12 0.04739 *
## ns(time, df = round(2 * length(time)/153))13 0.58470
## ns(time, df = round(2 * length(time)/153))14 0.04693 *
## ns(time, df = round(2 * length(time)/153))15 0.70242
## ns(time, df = round(2 * length(time)/153))16 0.15923
## ns(time, df = round(2 * length(time)/153))17 0.81430
## ns(time, df = round(2 * length(time)/153))18 0.12119
## ns(time, df = round(2 * length(time)/153))19 0.37878
## ns(time, df = round(2 * length(time)/153))20 0.76108
## ns(time, df = round(2 * length(time)/153))21 0.00137 **
## ns(time, df = round(2 * length(time)/153))22 0.27583
## ns(time, df = round(2 * length(time)/153))23 0.00744 **
## ns(time, df = round(2 * length(time)/153))24 0.81242
## ns(time, df = round(2 * length(time)/153))25 0.57557
## ns(time, df = round(2 * length(time)/153))26 0.00685 **
## ns(time, df = round(2 * length(time)/153))27 0.41489
## ns(time, df = round(2 * length(time)/153))28 0.02445 *
## ns(time, df = round(2 * length(time)/153))29 0.14479
## ns(time, df = round(2 * length(time)/153))30 0.30490
## ns(time, df = round(2 * length(time)/153))31 0.04680 *
## ns(time, df = round(2 * length(time)/153))32 0.55653
## Joursjeudi 0.24120
## Jourslundi 0.14561
## Joursmardi 0.67910
## Joursmercredi 0.26391
## Jourssamedi 0.31011
## Joursvendredi 0.72800
## Vacances1 0.01634 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 2652.8 on 2443 degrees of freedom
## Residual deviance: 2476.5 on 2402 degrees of freedom
## (4 observations deleted due to missingness)
## AIC: 11990
##
## Number of Fisher Scoring iterations: 4
##
##
## $toulouse
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 *
## length(time)/153)) + Jours + Vacances, family = poisson,
## data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3211 -0.7438 -0.0517 0.6442 3.4584
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error z value
## (Intercept) 2.445996 0.060086 40.709
## heat_wave 0.081156 0.093078 0.872
## no2moy 0.001865 0.001399 1.333
## ns(time, df = round(2 * length(time)/153))1 NA NA NA
## ns(time, df = round(2 * length(time)/153))2 NA NA NA
## ns(time, df = round(2 * length(time)/153))3 NA NA NA
## ns(time, df = round(2 * length(time)/153))4 NA NA NA
## ns(time, df = round(2 * length(time)/153))5 NA NA NA
## ns(time, df = round(2 * length(time)/153))6 NA NA NA
## ns(time, df = round(2 * length(time)/153))7 NA NA NA
## ns(time, df = round(2 * length(time)/153))8 NA NA NA
## ns(time, df = round(2 * length(time)/153))9 NA NA NA
## ns(time, df = round(2 * length(time)/153))10 NA NA NA
## ns(time, df = round(2 * length(time)/153))11 NA NA NA
## ns(time, df = round(2 * length(time)/153))12 -2.920638 1.381131 -2.115
## ns(time, df = round(2 * length(time)/153))13 0.360370 0.245735 1.466
## ns(time, df = round(2 * length(time)/153))14 -0.340704 0.139401 -2.444
## ns(time, df = round(2 * length(time)/153))15 -0.075168 0.108224 -0.695
## ns(time, df = round(2 * length(time)/153))16 -0.089084 0.103604 -0.860
## ns(time, df = round(2 * length(time)/153))17 -0.121084 0.097842 -1.238
## ns(time, df = round(2 * length(time)/153))18 -0.049768 0.100760 -0.494
## ns(time, df = round(2 * length(time)/153))19 -0.215535 0.097272 -2.216
## ns(time, df = round(2 * length(time)/153))20 -0.034846 0.099197 -0.351
## ns(time, df = round(2 * length(time)/153))21 -0.054567 0.094357 -0.578
## ns(time, df = round(2 * length(time)/153))22 -0.072948 0.098277 -0.742
## ns(time, df = round(2 * length(time)/153))23 -0.003592 0.093873 -0.038
## ns(time, df = round(2 * length(time)/153))24 -0.186962 0.098118 -1.905
## ns(time, df = round(2 * length(time)/153))25 0.135384 0.091652 1.477
## ns(time, df = round(2 * length(time)/153))26 -0.142154 0.096768 -1.469
## ns(time, df = round(2 * length(time)/153))27 0.115173 0.089681 1.284
## ns(time, df = round(2 * length(time)/153))28 -0.260651 0.100686 -2.589
## ns(time, df = round(2 * length(time)/153))29 0.130194 0.082705 1.574
## ns(time, df = round(2 * length(time)/153))30 -0.149981 0.117216 -1.280
## ns(time, df = round(2 * length(time)/153))31 NA NA NA
## ns(time, df = round(2 * length(time)/153))32 NA NA NA
## Joursjeudi 0.014545 0.031528 0.461
## Jourslundi 0.045516 0.030299 1.502
## Joursmardi 0.010893 0.031190 0.349
## Joursmercredi 0.045470 0.031364 1.450
## Jourssamedi 0.020402 0.030690 0.665
## Joursvendredi 0.039047 0.031239 1.250
## Vacances1 -0.026472 0.021615 -1.225
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 0.38326
## no2moy 0.18238
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.03446 *
## ns(time, df = round(2 * length(time)/153))13 0.14251
## ns(time, df = round(2 * length(time)/153))14 0.01452 *
## ns(time, df = round(2 * length(time)/153))15 0.48733
## ns(time, df = round(2 * length(time)/153))16 0.38987
## ns(time, df = round(2 * length(time)/153))17 0.21588
## ns(time, df = round(2 * length(time)/153))18 0.62136
## ns(time, df = round(2 * length(time)/153))19 0.02671 *
## ns(time, df = round(2 * length(time)/153))20 0.72538
## ns(time, df = round(2 * length(time)/153))21 0.56306
## ns(time, df = round(2 * length(time)/153))22 0.45793
## ns(time, df = round(2 * length(time)/153))23 0.96948
## ns(time, df = round(2 * length(time)/153))24 0.05672 .
## ns(time, df = round(2 * length(time)/153))25 0.13964
## ns(time, df = round(2 * length(time)/153))26 0.14183
## ns(time, df = round(2 * length(time)/153))27 0.19905
## ns(time, df = round(2 * length(time)/153))28 0.00963 **
## ns(time, df = round(2 * length(time)/153))29 0.11544
## ns(time, df = round(2 * length(time)/153))30 0.20071
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.64457
## Jourslundi 0.13304
## Joursmardi 0.72691
## Joursmercredi 0.14712
## Jourssamedi 0.50620
## Joursvendredi 0.21132
## Vacances1 0.22068
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1478.9 on 1376 degrees of freedom
## Residual deviance: 1420.3 on 1348 degrees of freedom
## (1071 observations deleted due to missingness)
## AIC: 7290.8
##
## Number of Fisher Scoring iterations: 4REsults for gam function
lapply(m.outcome_g,function(x){summary(x)})## $BM
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 1.9226319 0.0821893 23.393
## heat_wave -0.0532369 0.1671523 -0.318
## no2moy 0.0012357 0.0018644 0.663
## ns(time, df = round(2 * length(time)/153))1 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))2 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))3 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))4 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))5 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))6 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))7 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))8 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))9 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))10 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))11 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))12 -0.0703808 1.6084924 -0.044
## ns(time, df = round(2 * length(time)/153))13 0.0880275 0.3026169 0.291
## ns(time, df = round(2 * length(time)/153))14 0.1423609 0.1851447 0.769
## ns(time, df = round(2 * length(time)/153))15 -0.0618071 0.1561045 -0.396
## ns(time, df = round(2 * length(time)/153))16 0.0547162 0.1426182 0.384
## ns(time, df = round(2 * length(time)/153))17 0.1373622 0.1328341 1.034
## ns(time, df = round(2 * length(time)/153))18 0.0028773 0.1349293 0.021
## ns(time, df = round(2 * length(time)/153))19 -0.0635845 0.1377579 -0.462
## ns(time, df = round(2 * length(time)/153))20 0.1727768 0.1295623 1.334
## ns(time, df = round(2 * length(time)/153))21 -0.0001943 0.1221734 -0.002
## ns(time, df = round(2 * length(time)/153))22 0.0881091 0.1268153 0.695
## ns(time, df = round(2 * length(time)/153))23 -0.0217584 0.1194120 -0.182
## ns(time, df = round(2 * length(time)/153))24 0.2740305 0.1245856 2.200
## ns(time, df = round(2 * length(time)/153))25 -0.1466061 0.1219284 -1.202
## ns(time, df = round(2 * length(time)/153))26 0.1791592 0.1269295 1.411
## ns(time, df = round(2 * length(time)/153))27 -0.2458586 0.1223640 -2.009
## ns(time, df = round(2 * length(time)/153))28 0.2705559 0.1314532 2.058
## ns(time, df = round(2 * length(time)/153))29 -0.0644477 0.1474195 -0.437
## ns(time, df = round(2 * length(time)/153))30 0.4368027 0.2988683 1.462
## ns(time, df = round(2 * length(time)/153))31 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))32 0.0000000 0.0000000 NA
## Joursjeudi 0.0552982 0.0395789 1.397
## Jourslundi 0.0899628 0.0388819 2.314
## Joursmardi 0.0976611 0.0392444 2.489
## Joursmercredi 0.0913028 0.0395432 2.309
## Jourssamedi 0.0234589 0.0395555 0.593
## Joursvendredi 0.1171899 0.0392499 2.986
## Vacances1 -0.0106470 0.0278878 -0.382
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 0.75011
## no2moy 0.50745
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.96510
## ns(time, df = round(2 * length(time)/153))13 0.77114
## ns(time, df = round(2 * length(time)/153))14 0.44194
## ns(time, df = round(2 * length(time)/153))15 0.69215
## ns(time, df = round(2 * length(time)/153))16 0.70123
## ns(time, df = round(2 * length(time)/153))17 0.30109
## ns(time, df = round(2 * length(time)/153))18 0.98299
## ns(time, df = round(2 * length(time)/153))19 0.64439
## ns(time, df = round(2 * length(time)/153))20 0.18235
## ns(time, df = round(2 * length(time)/153))21 0.99873
## ns(time, df = round(2 * length(time)/153))22 0.48719
## ns(time, df = round(2 * length(time)/153))23 0.85542
## ns(time, df = round(2 * length(time)/153))24 0.02784 *
## ns(time, df = round(2 * length(time)/153))25 0.22921
## ns(time, df = round(2 * length(time)/153))26 0.15810
## ns(time, df = round(2 * length(time)/153))27 0.04451 *
## ns(time, df = round(2 * length(time)/153))28 0.03957 *
## ns(time, df = round(2 * length(time)/153))29 0.66199
## ns(time, df = round(2 * length(time)/153))30 0.14387
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.16236
## Jourslundi 0.02068 *
## Joursmardi 0.01283 *
## Joursmercredi 0.02095 *
## Jourssamedi 0.55314
## Joursvendredi 0.00283 **
## Vacances1 0.70262
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 29/42
## R-sq.(adj) = 0.00983 Deviance explained = 3.14%
## UBRE = 0.12371 Scale est. = 1 n = 1211
##
## $bordeaux
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 2.406333 0.060046 40.075
## heat_wave 0.214455 0.109573 1.957
## no2moy 0.002257 0.001737 1.300
## ns(time, df = round(2 * length(time)/153))1 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))2 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))3 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))4 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))5 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))6 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))7 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))8 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))9 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))10 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))11 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))12 -0.355271 1.296334 -0.274
## ns(time, df = round(2 * length(time)/153))13 0.120255 0.237558 0.506
## ns(time, df = round(2 * length(time)/153))14 -0.171739 0.134904 -1.273
## ns(time, df = round(2 * length(time)/153))15 -0.011240 0.106168 -0.106
## ns(time, df = round(2 * length(time)/153))16 0.010237 0.103572 0.099
## ns(time, df = round(2 * length(time)/153))17 -0.152227 0.098259 -1.549
## ns(time, df = round(2 * length(time)/153))18 -0.044714 0.100300 -0.446
## ns(time, df = round(2 * length(time)/153))19 -0.015040 0.096022 -0.157
## ns(time, df = round(2 * length(time)/153))20 0.035776 0.098922 0.362
## ns(time, df = round(2 * length(time)/153))21 -0.029963 0.095243 -0.315
## ns(time, df = round(2 * length(time)/153))22 0.001445 0.099723 0.014
## ns(time, df = round(2 * length(time)/153))23 -0.078848 0.095474 -0.826
## ns(time, df = round(2 * length(time)/153))24 -0.074488 0.098820 -0.754
## ns(time, df = round(2 * length(time)/153))25 0.104838 0.091127 1.150
## ns(time, df = round(2 * length(time)/153))26 -0.054977 0.096850 -0.568
## ns(time, df = round(2 * length(time)/153))27 0.056275 0.090110 0.625
## ns(time, df = round(2 * length(time)/153))28 -0.037238 0.100418 -0.371
## ns(time, df = round(2 * length(time)/153))29 0.128647 0.081225 1.584
## ns(time, df = round(2 * length(time)/153))30 0.010018 0.116864 0.086
## ns(time, df = round(2 * length(time)/153))31 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))32 0.000000 0.000000 NA
## Joursjeudi 0.049104 0.031439 1.562
## Jourslundi 0.064325 0.029856 2.155
## Joursmardi 0.042604 0.030940 1.377
## Joursmercredi 0.029669 0.031525 0.941
## Jourssamedi 0.044057 0.030434 1.448
## Joursvendredi 0.045112 0.031414 1.436
## Vacances1 -0.013433 0.021144 -0.635
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.0503 .
## no2moy 0.1937
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.7840
## ns(time, df = round(2 * length(time)/153))13 0.6127
## ns(time, df = round(2 * length(time)/153))14 0.2030
## ns(time, df = round(2 * length(time)/153))15 0.9157
## ns(time, df = round(2 * length(time)/153))16 0.9213
## ns(time, df = round(2 * length(time)/153))17 0.1213
## ns(time, df = round(2 * length(time)/153))18 0.6557
## ns(time, df = round(2 * length(time)/153))19 0.8755
## ns(time, df = round(2 * length(time)/153))20 0.7176
## ns(time, df = round(2 * length(time)/153))21 0.7531
## ns(time, df = round(2 * length(time)/153))22 0.9884
## ns(time, df = round(2 * length(time)/153))23 0.4089
## ns(time, df = round(2 * length(time)/153))24 0.4510
## ns(time, df = round(2 * length(time)/153))25 0.2500
## ns(time, df = round(2 * length(time)/153))26 0.5703
## ns(time, df = round(2 * length(time)/153))27 0.5323
## ns(time, df = round(2 * length(time)/153))28 0.7108
## ns(time, df = round(2 * length(time)/153))29 0.1132
## ns(time, df = round(2 * length(time)/153))30 0.9317
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.1183
## Jourslundi 0.0312 *
## Joursmardi 0.1685
## Joursmercredi 0.3466
## Jourssamedi 0.1477
## Joursvendredi 0.1510
## Vacances1 0.5252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 29/42
## R-sq.(adj) = 0.0102 Deviance explained = 2.98%
## UBRE = 0.092221 Scale est. = 1 n = 1377
##
## $clermont
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 1.802e+00 1.035e-01 17.410
## heat_wave 3.754e-01 7.836e-02 4.791
## no2moy 1.006e-03 1.578e-03 0.637
## ns(time, df = round(2 * length(time)/153))1 -4.221e-03 1.252e-01 -0.034
## ns(time, df = round(2 * length(time)/153))2 -4.299e-01 1.713e-01 -2.510
## ns(time, df = round(2 * length(time)/153))3 -1.136e-01 1.424e-01 -0.797
## ns(time, df = round(2 * length(time)/153))4 -1.559e-01 1.615e-01 -0.965
## ns(time, df = round(2 * length(time)/153))5 -2.770e-01 1.462e-01 -1.895
## ns(time, df = round(2 * length(time)/153))6 -7.394e-02 1.574e-01 -0.470
## ns(time, df = round(2 * length(time)/153))7 -1.905e-02 1.448e-01 -0.132
## ns(time, df = round(2 * length(time)/153))8 -3.251e-01 1.595e-01 -2.039
## ns(time, df = round(2 * length(time)/153))9 -1.708e-01 1.481e-01 -1.153
## ns(time, df = round(2 * length(time)/153))10 -2.449e-01 1.590e-01 -1.540
## ns(time, df = round(2 * length(time)/153))11 -1.690e-01 1.485e-01 -1.138
## ns(time, df = round(2 * length(time)/153))12 -3.312e-01 1.599e-01 -2.072
## ns(time, df = round(2 * length(time)/153))13 -1.596e-01 1.483e-01 -1.076
## ns(time, df = round(2 * length(time)/153))14 -2.124e-01 1.603e-01 -1.325
## ns(time, df = round(2 * length(time)/153))15 -3.318e-01 1.492e-01 -2.224
## ns(time, df = round(2 * length(time)/153))16 -4.655e-02 1.577e-01 -0.295
## ns(time, df = round(2 * length(time)/153))17 -2.499e-01 1.465e-01 -1.707
## ns(time, df = round(2 * length(time)/153))18 -5.361e-02 1.584e-01 -0.339
## ns(time, df = round(2 * length(time)/153))19 -1.522e-01 1.448e-01 -1.051
## ns(time, df = round(2 * length(time)/153))20 -6.131e-02 1.561e-01 -0.393
## ns(time, df = round(2 * length(time)/153))21 -2.005e-01 1.454e-01 -1.379
## ns(time, df = round(2 * length(time)/153))22 -9.564e-02 1.571e-01 -0.609
## ns(time, df = round(2 * length(time)/153))23 -2.914e-01 1.467e-01 -1.986
## ns(time, df = round(2 * length(time)/153))24 -2.184e-02 1.563e-01 -0.140
## ns(time, df = round(2 * length(time)/153))25 -2.543e-01 1.462e-01 -1.739
## ns(time, df = round(2 * length(time)/153))26 -9.181e-05 1.539e-01 -0.001
## ns(time, df = round(2 * length(time)/153))27 -3.029e-01 1.467e-01 -2.064
## ns(time, df = round(2 * length(time)/153))28 -3.936e-02 1.542e-01 -0.255
## ns(time, df = round(2 * length(time)/153))29 -2.189e-01 1.452e-01 -1.508
## ns(time, df = round(2 * length(time)/153))30 6.225e-02 1.264e-01 0.493
## ns(time, df = round(2 * length(time)/153))31 -4.470e-01 2.554e-01 -1.750
## ns(time, df = round(2 * length(time)/153))32 -7.470e-02 1.153e-01 -0.648
## Joursjeudi -9.711e-03 3.488e-02 -0.278
## Jourslundi 2.025e-02 3.321e-02 0.610
## Joursmardi 8.673e-03 3.422e-02 0.253
## Joursmercredi 1.824e-02 3.465e-02 0.526
## Jourssamedi -8.247e-03 3.414e-02 -0.242
## Joursvendredi 1.632e-02 3.489e-02 0.468
## Vacances1 -1.720e-02 2.294e-02 -0.750
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 1.66e-06 ***
## no2moy 0.5240
## ns(time, df = round(2 * length(time)/153))1 0.9731
## ns(time, df = round(2 * length(time)/153))2 0.0121 *
## ns(time, df = round(2 * length(time)/153))3 0.4253
## ns(time, df = round(2 * length(time)/153))4 0.3344
## ns(time, df = round(2 * length(time)/153))5 0.0581 .
## ns(time, df = round(2 * length(time)/153))6 0.6385
## ns(time, df = round(2 * length(time)/153))7 0.8953
## ns(time, df = round(2 * length(time)/153))8 0.0415 *
## ns(time, df = round(2 * length(time)/153))9 0.2489
## ns(time, df = round(2 * length(time)/153))10 0.1236
## ns(time, df = round(2 * length(time)/153))11 0.2553
## ns(time, df = round(2 * length(time)/153))12 0.0383 *
## ns(time, df = round(2 * length(time)/153))13 0.2818
## ns(time, df = round(2 * length(time)/153))14 0.1852
## ns(time, df = round(2 * length(time)/153))15 0.0261 *
## ns(time, df = round(2 * length(time)/153))16 0.7678
## ns(time, df = round(2 * length(time)/153))17 0.0879 .
## ns(time, df = round(2 * length(time)/153))18 0.7349
## ns(time, df = round(2 * length(time)/153))19 0.2932
## ns(time, df = round(2 * length(time)/153))20 0.6944
## ns(time, df = round(2 * length(time)/153))21 0.1678
## ns(time, df = round(2 * length(time)/153))22 0.5427
## ns(time, df = round(2 * length(time)/153))23 0.0470 *
## ns(time, df = round(2 * length(time)/153))24 0.8888
## ns(time, df = round(2 * length(time)/153))25 0.0821 .
## ns(time, df = round(2 * length(time)/153))26 0.9995
## ns(time, df = round(2 * length(time)/153))27 0.0390 *
## ns(time, df = round(2 * length(time)/153))28 0.7985
## ns(time, df = round(2 * length(time)/153))29 0.1316
## ns(time, df = round(2 * length(time)/153))30 0.6224
## ns(time, df = round(2 * length(time)/153))31 0.0801 .
## ns(time, df = round(2 * length(time)/153))32 0.5172
## Joursjeudi 0.7807
## Jourslundi 0.5420
## Joursmardi 0.7999
## Joursmercredi 0.5986
## Jourssamedi 0.8091
## Joursvendredi 0.6400
## Vacances1 0.4534
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## R-sq.(adj) = 0.016 Deviance explained = 2.93%
## UBRE = 0.018023 Scale est. = 1 n = 2431
##
## $dijon
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 1.2020639 0.1255473 9.575
## heat_wave 0.3188149 0.0789417 4.039
## no2moy 0.0069614 0.0020802 3.346
## ns(time, df = round(2 * length(time)/153))1 0.0172788 0.1442802 0.120
## ns(time, df = round(2 * length(time)/153))2 0.0911124 0.1961564 0.464
## ns(time, df = round(2 * length(time)/153))3 -0.1446592 0.1650666 -0.876
## ns(time, df = round(2 * length(time)/153))4 0.2717373 0.1873806 1.450
## ns(time, df = round(2 * length(time)/153))5 -0.3411138 0.1712132 -1.992
## ns(time, df = round(2 * length(time)/153))6 0.4630186 0.1827020 2.534
## ns(time, df = round(2 * length(time)/153))7 -0.0608143 0.1688945 -0.360
## ns(time, df = round(2 * length(time)/153))8 -0.1208891 0.1857430 -0.651
## ns(time, df = round(2 * length(time)/153))9 -0.0724796 0.1744139 -0.416
## ns(time, df = round(2 * length(time)/153))10 0.1790974 0.1859112 0.963
## ns(time, df = round(2 * length(time)/153))11 -0.3468612 0.1771495 -1.958
## ns(time, df = round(2 * length(time)/153))12 0.2202232 0.1838580 1.198
## ns(time, df = round(2 * length(time)/153))13 -0.1380913 0.1726901 -0.800
## ns(time, df = round(2 * length(time)/153))14 0.1718846 0.1850753 0.929
## ns(time, df = round(2 * length(time)/153))15 -0.0064035 0.1699390 -0.038
## ns(time, df = round(2 * length(time)/153))16 0.1706140 0.1830471 0.932
## ns(time, df = round(2 * length(time)/153))17 0.0318045 0.1689820 0.188
## ns(time, df = round(2 * length(time)/153))18 0.2147801 0.1855697 1.157
## ns(time, df = round(2 * length(time)/153))19 -0.0860551 0.1720303 -0.500
## ns(time, df = round(2 * length(time)/153))20 0.2069020 0.1841970 1.123
## ns(time, df = round(2 * length(time)/153))21 0.1344377 0.1691257 0.795
## ns(time, df = round(2 * length(time)/153))22 0.1270269 0.1813229 0.701
## ns(time, df = round(2 * length(time)/153))23 0.2045805 0.1647623 1.242
## ns(time, df = round(2 * length(time)/153))24 0.3118946 0.1799032 1.734
## ns(time, df = round(2 * length(time)/153))25 -0.0444794 0.1671433 -0.266
## ns(time, df = round(2 * length(time)/153))26 0.3862037 0.1786292 2.162
## ns(time, df = round(2 * length(time)/153))27 0.0049116 0.1677407 0.029
## ns(time, df = round(2 * length(time)/153))28 0.3180926 0.1778763 1.788
## ns(time, df = round(2 * length(time)/153))29 0.1399787 0.1666157 0.840
## ns(time, df = round(2 * length(time)/153))30 0.2276601 0.1429924 1.592
## ns(time, df = round(2 * length(time)/153))31 0.1947785 0.3040791 0.641
## ns(time, df = round(2 * length(time)/153))32 0.3019432 0.1222140 2.471
## Joursjeudi 0.0120091 0.0395842 0.303
## Jourslundi 0.0249575 0.0377694 0.661
## Joursmardi 0.0377725 0.0388586 0.972
## Joursmercredi 0.0250714 0.0394444 0.636
## Jourssamedi 0.0485215 0.0383913 1.264
## Joursvendredi 0.0359819 0.0397767 0.905
## Vacances1 0.0003654 0.0257666 0.014
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 5.38e-05 ***
## no2moy 0.000819 ***
## ns(time, df = round(2 * length(time)/153))1 0.904675
## ns(time, df = round(2 * length(time)/153))2 0.642298
## ns(time, df = round(2 * length(time)/153))3 0.380830
## ns(time, df = round(2 * length(time)/153))4 0.147006
## ns(time, df = round(2 * length(time)/153))5 0.046334 *
## ns(time, df = round(2 * length(time)/153))6 0.011268 *
## ns(time, df = round(2 * length(time)/153))7 0.718793
## ns(time, df = round(2 * length(time)/153))8 0.515149
## ns(time, df = round(2 * length(time)/153))9 0.677732
## ns(time, df = round(2 * length(time)/153))10 0.335372
## ns(time, df = round(2 * length(time)/153))11 0.050228 .
## ns(time, df = round(2 * length(time)/153))12 0.230999
## ns(time, df = round(2 * length(time)/153))13 0.423915
## ns(time, df = round(2 * length(time)/153))14 0.353030
## ns(time, df = round(2 * length(time)/153))15 0.969942
## ns(time, df = round(2 * length(time)/153))16 0.351297
## ns(time, df = round(2 * length(time)/153))17 0.850710
## ns(time, df = round(2 * length(time)/153))18 0.247105
## ns(time, df = round(2 * length(time)/153))19 0.616911
## ns(time, df = round(2 * length(time)/153))20 0.261325
## ns(time, df = round(2 * length(time)/153))21 0.426673
## ns(time, df = round(2 * length(time)/153))22 0.483580
## ns(time, df = round(2 * length(time)/153))23 0.214358
## ns(time, df = round(2 * length(time)/153))24 0.082975 .
## ns(time, df = round(2 * length(time)/153))25 0.790150
## ns(time, df = round(2 * length(time)/153))26 0.030615 *
## ns(time, df = round(2 * length(time)/153))27 0.976641
## ns(time, df = round(2 * length(time)/153))28 0.073731 .
## ns(time, df = round(2 * length(time)/153))29 0.400836
## ns(time, df = round(2 * length(time)/153))30 0.111359
## ns(time, df = round(2 * length(time)/153))31 0.521814
## ns(time, df = round(2 * length(time)/153))32 0.013488 *
## Joursjeudi 0.761600
## Jourslundi 0.508749
## Joursmardi 0.331027
## Joursmercredi 0.525028
## Jourssamedi 0.206277
## Joursvendredi 0.365679
## Vacances1 0.988686
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## R-sq.(adj) = 0.031 Deviance explained = 4.29%
## UBRE = 0.011835 Scale est. = 1 n = 2417
##
## $grenoble
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 2.056765 0.075448 27.261
## heat_wave 0.114980 0.092664 1.241
## no2moy 0.008719 0.002585 3.373
## ns(time, df = round(2 * length(time)/153))1 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))2 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))3 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))4 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))5 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))6 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))7 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))8 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))9 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))10 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))11 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))12 -0.847711 1.781648 -0.476
## ns(time, df = round(2 * length(time)/153))13 -0.373198 0.317203 -1.177
## ns(time, df = round(2 * length(time)/153))14 -0.241921 0.173950 -1.391
## ns(time, df = round(2 * length(time)/153))15 -0.321894 0.135164 -2.382
## ns(time, df = round(2 * length(time)/153))16 -0.114496 0.129658 -0.883
## ns(time, df = round(2 * length(time)/153))17 -0.186402 0.119784 -1.556
## ns(time, df = round(2 * length(time)/153))18 -0.150188 0.125399 -1.198
## ns(time, df = round(2 * length(time)/153))19 -0.288523 0.119669 -2.411
## ns(time, df = round(2 * length(time)/153))20 -0.095634 0.122265 -0.782
## ns(time, df = round(2 * length(time)/153))21 -0.185639 0.115776 -1.603
## ns(time, df = round(2 * length(time)/153))22 -0.071266 0.122342 -0.583
## ns(time, df = round(2 * length(time)/153))23 -0.233828 0.116005 -2.016
## ns(time, df = round(2 * length(time)/153))24 -0.070739 0.119855 -0.590
## ns(time, df = round(2 * length(time)/153))25 -0.143490 0.112180 -1.279
## ns(time, df = round(2 * length(time)/153))26 0.035204 0.119061 0.296
## ns(time, df = round(2 * length(time)/153))27 -0.072588 0.107899 -0.673
## ns(time, df = round(2 * length(time)/153))28 -0.092633 0.121559 -0.762
## ns(time, df = round(2 * length(time)/153))29 -0.164095 0.100746 -1.629
## ns(time, df = round(2 * length(time)/153))30 0.074303 0.141862 0.524
## ns(time, df = round(2 * length(time)/153))31 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))32 0.000000 0.000000 NA
## Joursjeudi -0.041093 0.039522 -1.040
## Jourslundi -0.029132 0.037547 -0.776
## Joursmardi -0.071709 0.039735 -1.805
## Joursmercredi -0.051252 0.039783 -1.288
## Jourssamedi 0.009798 0.037745 0.260
## Joursvendredi -0.047649 0.039708 -1.200
## Vacances1 -0.044894 0.026709 -1.681
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 0.214673
## no2moy 0.000744 ***
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.634216
## ns(time, df = round(2 * length(time)/153))13 0.239384
## ns(time, df = round(2 * length(time)/153))14 0.164302
## ns(time, df = round(2 * length(time)/153))15 0.017242 *
## ns(time, df = round(2 * length(time)/153))16 0.377201
## ns(time, df = round(2 * length(time)/153))17 0.119674
## ns(time, df = round(2 * length(time)/153))18 0.231041
## ns(time, df = round(2 * length(time)/153))19 0.015909 *
## ns(time, df = round(2 * length(time)/153))20 0.434105
## ns(time, df = round(2 * length(time)/153))21 0.108840
## ns(time, df = round(2 * length(time)/153))22 0.560221
## ns(time, df = round(2 * length(time)/153))23 0.043835 *
## ns(time, df = round(2 * length(time)/153))24 0.555051
## ns(time, df = round(2 * length(time)/153))25 0.200862
## ns(time, df = round(2 * length(time)/153))26 0.767472
## ns(time, df = round(2 * length(time)/153))27 0.501114
## ns(time, df = round(2 * length(time)/153))28 0.446037
## ns(time, df = round(2 * length(time)/153))29 0.103355
## ns(time, df = round(2 * length(time)/153))30 0.600438
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.298466
## Jourslundi 0.437824
## Joursmardi 0.071124 .
## Joursmercredi 0.197645
## Jourssamedi 0.795174
## Joursvendredi 0.230147
## Vacances1 0.092797 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 29/42
## R-sq.(adj) = 0.0407 Deviance explained = 5.82%
## UBRE = 0.069151 Scale est. = 1 n = 1377
##
## $lehavre
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 1.689863 0.086531 19.529
## heat_wave 0.188521 0.152470 1.236
## no2moy 0.002378 0.001645 1.446
## ns(time, df = round(2 * length(time)/153))1 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))2 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))3 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))4 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))5 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))6 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))7 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))8 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))9 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))10 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))11 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))12 1.081215 2.025433 0.534
## ns(time, df = round(2 * length(time)/153))13 -0.481348 0.361768 -1.331
## ns(time, df = round(2 * length(time)/153))14 0.193238 0.199015 0.971
## ns(time, df = round(2 * length(time)/153))15 -0.185487 0.159780 -1.161
## ns(time, df = round(2 * length(time)/153))16 0.010392 0.151859 0.068
## ns(time, df = round(2 * length(time)/153))17 -0.012538 0.146274 -0.086
## ns(time, df = round(2 * length(time)/153))18 -0.032894 0.148837 -0.221
## ns(time, df = round(2 * length(time)/153))19 -0.037785 0.141318 -0.267
## ns(time, df = round(2 * length(time)/153))20 0.016982 0.144286 0.118
## ns(time, df = round(2 * length(time)/153))21 -0.023956 0.139255 -0.172
## ns(time, df = round(2 * length(time)/153))22 0.039813 0.144088 0.276
## ns(time, df = round(2 * length(time)/153))23 -0.058861 0.139279 -0.423
## ns(time, df = round(2 * length(time)/153))24 -0.013532 0.145109 -0.093
## ns(time, df = round(2 * length(time)/153))25 -0.101316 0.147799 -0.685
## ns(time, df = round(2 * length(time)/153))26 0.030102 0.145998 0.206
## ns(time, df = round(2 * length(time)/153))27 -0.004336 0.134260 -0.032
## ns(time, df = round(2 * length(time)/153))28 -0.075751 0.147616 -0.513
## ns(time, df = round(2 * length(time)/153))29 0.143777 0.119021 1.208
## ns(time, df = round(2 * length(time)/153))30 0.017265 0.169846 0.102
## ns(time, df = round(2 * length(time)/153))31 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))32 0.000000 0.000000 NA
## Joursjeudi -0.029075 0.044270 -0.657
## Jourslundi -0.043060 0.044069 -0.977
## Joursmardi 0.026681 0.043600 0.612
## Joursmercredi -0.025979 0.044304 -0.586
## Jourssamedi -0.069627 0.044622 -1.560
## Joursvendredi -0.031317 0.044343 -0.706
## Vacances1 -0.030141 0.031395 -0.960
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.216
## no2moy 0.148
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.593
## ns(time, df = round(2 * length(time)/153))13 0.183
## ns(time, df = round(2 * length(time)/153))14 0.332
## ns(time, df = round(2 * length(time)/153))15 0.246
## ns(time, df = round(2 * length(time)/153))16 0.945
## ns(time, df = round(2 * length(time)/153))17 0.932
## ns(time, df = round(2 * length(time)/153))18 0.825
## ns(time, df = round(2 * length(time)/153))19 0.789
## ns(time, df = round(2 * length(time)/153))20 0.906
## ns(time, df = round(2 * length(time)/153))21 0.863
## ns(time, df = round(2 * length(time)/153))22 0.782
## ns(time, df = round(2 * length(time)/153))23 0.673
## ns(time, df = round(2 * length(time)/153))24 0.926
## ns(time, df = round(2 * length(time)/153))25 0.493
## ns(time, df = round(2 * length(time)/153))26 0.837
## ns(time, df = round(2 * length(time)/153))27 0.974
## ns(time, df = round(2 * length(time)/153))28 0.608
## ns(time, df = round(2 * length(time)/153))29 0.227
## ns(time, df = round(2 * length(time)/153))30 0.919
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.511
## Jourslundi 0.329
## Joursmardi 0.541
## Joursmercredi 0.558
## Jourssamedi 0.119
## Joursvendredi 0.480
## Vacances1 0.337
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 29/42
## R-sq.(adj) = -0.00464 Deviance explained = 1.55%
## UBRE = 0.072209 Scale est. = 1 n = 1351
##
## $lille
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 2.9301643 0.0461883 63.440
## heat_wave 0.0568800 0.0566277 1.004
## no2moy 0.0030755 0.0010057 3.058
## ns(time, df = round(2 * length(time)/153))1 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))2 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))3 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))4 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))5 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))6 -0.4173841 1.0011442 -0.417
## ns(time, df = round(2 * length(time)/153))7 0.0522596 0.1826347 0.286
## ns(time, df = round(2 * length(time)/153))8 -0.0895261 0.1012971 -0.884
## ns(time, df = round(2 * length(time)/153))9 -0.0023044 0.0809648 -0.028
## ns(time, df = round(2 * length(time)/153))10 -0.0347031 0.0773453 -0.449
## ns(time, df = round(2 * length(time)/153))11 -0.0239223 0.0751314 -0.318
## ns(time, df = round(2 * length(time)/153))12 -0.0135597 0.0766903 -0.177
## ns(time, df = round(2 * length(time)/153))13 0.0129686 0.0732767 0.177
## ns(time, df = round(2 * length(time)/153))14 -0.0939691 0.0753948 -1.246
## ns(time, df = round(2 * length(time)/153))15 -0.0243957 0.0744723 -0.328
## ns(time, df = round(2 * length(time)/153))16 -0.0837097 0.0757357 -1.105
## ns(time, df = round(2 * length(time)/153))17 -0.0353626 0.0737123 -0.480
## ns(time, df = round(2 * length(time)/153))18 -0.0300079 0.0754776 -0.398
## ns(time, df = round(2 * length(time)/153))19 -0.0921934 0.0735197 -1.254
## ns(time, df = round(2 * length(time)/153))20 0.0686105 0.0746496 0.919
## ns(time, df = round(2 * length(time)/153))21 -0.1028769 0.0728181 -1.413
## ns(time, df = round(2 * length(time)/153))22 0.0090584 0.0746651 0.121
## ns(time, df = round(2 * length(time)/153))23 -0.1052407 0.0725068 -1.451
## ns(time, df = round(2 * length(time)/153))24 0.0014422 0.0748668 0.019
## ns(time, df = round(2 * length(time)/153))25 -0.0449458 0.0715982 -0.628
## ns(time, df = round(2 * length(time)/153))26 0.0504185 0.0742851 0.679
## ns(time, df = round(2 * length(time)/153))27 -0.1461649 0.0703003 -2.079
## ns(time, df = round(2 * length(time)/153))28 0.0980994 0.0771729 1.271
## ns(time, df = round(2 * length(time)/153))29 -0.0809418 0.0636213 -1.272
## ns(time, df = round(2 * length(time)/153))30 0.0562432 0.0902751 0.623
## ns(time, df = round(2 * length(time)/153))31 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))32 0.0000000 0.0000000 NA
## Joursjeudi -0.0005368 0.0210316 -0.026
## Jourslundi 0.0471171 0.0198702 2.371
## Joursmardi 0.0228186 0.0204975 1.113
## Joursmercredi -0.0007559 0.0209003 -0.036
## Jourssamedi -0.0291911 0.0205450 -1.421
## Joursvendredi -0.0070078 0.0211021 -0.332
## Vacances1 -0.0162439 0.0142787 -1.138
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 0.31516
## no2moy 0.00223 **
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 0.67675
## ns(time, df = round(2 * length(time)/153))7 0.77477
## ns(time, df = round(2 * length(time)/153))8 0.37681
## ns(time, df = round(2 * length(time)/153))9 0.97729
## ns(time, df = round(2 * length(time)/153))10 0.65366
## ns(time, df = round(2 * length(time)/153))11 0.75018
## ns(time, df = round(2 * length(time)/153))12 0.85966
## ns(time, df = round(2 * length(time)/153))13 0.85952
## ns(time, df = round(2 * length(time)/153))14 0.21263
## ns(time, df = round(2 * length(time)/153))15 0.74323
## ns(time, df = round(2 * length(time)/153))16 0.26904
## ns(time, df = round(2 * length(time)/153))17 0.63141
## ns(time, df = round(2 * length(time)/153))18 0.69094
## ns(time, df = round(2 * length(time)/153))19 0.20984
## ns(time, df = round(2 * length(time)/153))20 0.35804
## ns(time, df = round(2 * length(time)/153))21 0.15772
## ns(time, df = round(2 * length(time)/153))22 0.90344
## ns(time, df = round(2 * length(time)/153))23 0.14665
## ns(time, df = round(2 * length(time)/153))24 0.98463
## ns(time, df = round(2 * length(time)/153))25 0.53017
## ns(time, df = round(2 * length(time)/153))26 0.49732
## ns(time, df = round(2 * length(time)/153))27 0.03760 *
## ns(time, df = round(2 * length(time)/153))28 0.20367
## ns(time, df = round(2 * length(time)/153))29 0.20329
## ns(time, df = round(2 * length(time)/153))30 0.53327
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.97964
## Jourslundi 0.01773 *
## Joursmardi 0.26561
## Joursmercredi 0.97115
## Jourssamedi 0.15536
## Joursvendredi 0.73982
## Vacances1 0.25527
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 35/42
## R-sq.(adj) = 0.00836 Deviance explained = 2.64%
## UBRE = 0.059941 Scale est. = 1 n = 1834
##
## $lyon
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 2.9125595 0.0473853 61.465
## heat_wave 0.1563655 0.0739074 2.116
## no2moy 0.0013012 0.0009089 1.432
## ns(time, df = round(2 * length(time)/153))1 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))2 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))3 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))4 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))5 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))6 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))7 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))8 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))9 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))10 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))11 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))12 -0.7298697 1.1247438 -0.649
## ns(time, df = round(2 * length(time)/153))13 -0.2277132 0.2019194 -1.128
## ns(time, df = round(2 * length(time)/153))14 -0.1367086 0.1109097 -1.233
## ns(time, df = round(2 * length(time)/153))15 -0.1799387 0.0870755 -2.066
## ns(time, df = round(2 * length(time)/153))16 -0.0237138 0.0824077 -0.288
## ns(time, df = round(2 * length(time)/153))17 -0.2322444 0.0792241 -2.931
## ns(time, df = round(2 * length(time)/153))18 -0.0972782 0.0807360 -1.205
## ns(time, df = round(2 * length(time)/153))19 -0.1711809 0.0779121 -2.197
## ns(time, df = round(2 * length(time)/153))20 -0.0598599 0.0790243 -0.757
## ns(time, df = round(2 * length(time)/153))21 -0.1250276 0.0767600 -1.629
## ns(time, df = round(2 * length(time)/153))22 -0.1781182 0.0798667 -2.230
## ns(time, df = round(2 * length(time)/153))23 -0.0527890 0.0769501 -0.686
## ns(time, df = round(2 * length(time)/153))24 -0.1190518 0.0790032 -1.507
## ns(time, df = round(2 * length(time)/153))25 -0.1286481 0.0757797 -1.698
## ns(time, df = round(2 * length(time)/153))26 -0.1928600 0.0796651 -2.421
## ns(time, df = round(2 * length(time)/153))27 -0.1322194 0.0741284 -1.784
## ns(time, df = round(2 * length(time)/153))28 -0.1853229 0.0816273 -2.270
## ns(time, df = round(2 * length(time)/153))29 -0.0589414 0.0677450 -0.870
## ns(time, df = round(2 * length(time)/153))30 -0.1172928 0.0948979 -1.236
## ns(time, df = round(2 * length(time)/153))31 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))32 0.0000000 0.0000000 NA
## Joursjeudi 0.0312913 0.0259773 1.205
## Jourslundi 0.0200727 0.0247807 0.810
## Joursmardi 0.0393040 0.0253448 1.551
## Joursmercredi 0.0095416 0.0258789 0.369
## Jourssamedi -0.0200380 0.0253538 -0.790
## Joursvendredi 0.0722111 0.0256371 2.817
## Vacances1 -0.0215830 0.0175241 -1.232
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 0.03437 *
## no2moy 0.15225
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.51639
## ns(time, df = round(2 * length(time)/153))13 0.25943
## ns(time, df = round(2 * length(time)/153))14 0.21772
## ns(time, df = round(2 * length(time)/153))15 0.03878 *
## ns(time, df = round(2 * length(time)/153))16 0.77353
## ns(time, df = round(2 * length(time)/153))17 0.00337 **
## ns(time, df = round(2 * length(time)/153))18 0.22824
## ns(time, df = round(2 * length(time)/153))19 0.02801 *
## ns(time, df = round(2 * length(time)/153))20 0.44876
## ns(time, df = round(2 * length(time)/153))21 0.10335
## ns(time, df = round(2 * length(time)/153))22 0.02573 *
## ns(time, df = round(2 * length(time)/153))23 0.49270
## ns(time, df = round(2 * length(time)/153))24 0.13183
## ns(time, df = round(2 * length(time)/153))25 0.08957 .
## ns(time, df = round(2 * length(time)/153))26 0.01548 *
## ns(time, df = round(2 * length(time)/153))27 0.07448 .
## ns(time, df = round(2 * length(time)/153))28 0.02319 *
## ns(time, df = round(2 * length(time)/153))29 0.38427
## ns(time, df = round(2 * length(time)/153))30 0.21646
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.22837
## Jourslundi 0.41793
## Joursmardi 0.12096
## Joursmercredi 0.71235
## Jourssamedi 0.42933
## Joursvendredi 0.00485 **
## Vacances1 0.21809
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 29/42
## R-sq.(adj) = 0.027 Deviance explained = 4.62%
## UBRE = 0.077458 Scale est. = 1 n = 1377
##
## $marseille
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 2.9438244 0.0463758 63.478
## heat_wave -0.0061396 0.0692970 -0.089
## no2moy 0.0010167 0.0005496 1.850
## ns(time, df = round(2 * length(time)/153))1 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))2 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))3 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))4 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))5 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))6 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))7 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))8 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))9 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))10 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))11 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))12 1.1729170 1.0095439 1.162
## ns(time, df = round(2 * length(time)/153))13 -0.2493245 0.1854388 -1.345
## ns(time, df = round(2 * length(time)/153))14 0.1044123 0.1018025 1.026
## ns(time, df = round(2 * length(time)/153))15 -0.0477790 0.0806113 -0.593
## ns(time, df = round(2 * length(time)/153))16 0.0739550 0.0765966 0.966
## ns(time, df = round(2 * length(time)/153))17 0.0116577 0.0726962 0.160
## ns(time, df = round(2 * length(time)/153))18 0.0420978 0.0744723 0.565
## ns(time, df = round(2 * length(time)/153))19 -0.0000861 0.0727438 -0.001
## ns(time, df = round(2 * length(time)/153))20 0.0085915 0.0757071 0.113
## ns(time, df = round(2 * length(time)/153))21 -0.0414456 0.0731189 -0.567
## ns(time, df = round(2 * length(time)/153))22 -0.0178394 0.0748848 -0.238
## ns(time, df = round(2 * length(time)/153))23 -0.0166517 0.0715315 -0.233
## ns(time, df = round(2 * length(time)/153))24 0.0716058 0.0732643 0.977
## ns(time, df = round(2 * length(time)/153))25 -0.0105791 0.0709071 -0.149
## ns(time, df = round(2 * length(time)/153))26 0.0812486 0.0736330 1.103
## ns(time, df = round(2 * length(time)/153))27 -0.0568571 0.0695597 -0.817
## ns(time, df = round(2 * length(time)/153))28 0.0203177 0.0765947 0.265
## ns(time, df = round(2 * length(time)/153))29 -0.1028092 0.0635562 -1.618
## ns(time, df = round(2 * length(time)/153))30 0.1350026 0.0892668 1.512
## ns(time, df = round(2 * length(time)/153))31 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))32 0.0000000 0.0000000 NA
## Joursjeudi 0.0348800 0.0230216 1.515
## Jourslundi 0.0106272 0.0228441 0.465
## Joursmardi 0.0178174 0.0229298 0.777
## Joursmercredi 0.0505939 0.0228933 2.210
## Jourssamedi 0.0236652 0.0228738 1.035
## Joursvendredi 0.0374145 0.0229958 1.627
## Vacances1 -0.0334064 0.0162029 -2.062
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.9294
## no2moy 0.0643 .
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.2453
## ns(time, df = round(2 * length(time)/153))13 0.1788
## ns(time, df = round(2 * length(time)/153))14 0.3051
## ns(time, df = round(2 * length(time)/153))15 0.5534
## ns(time, df = round(2 * length(time)/153))16 0.3343
## ns(time, df = round(2 * length(time)/153))17 0.8726
## ns(time, df = round(2 * length(time)/153))18 0.5719
## ns(time, df = round(2 * length(time)/153))19 0.9991
## ns(time, df = round(2 * length(time)/153))20 0.9096
## ns(time, df = round(2 * length(time)/153))21 0.5708
## ns(time, df = round(2 * length(time)/153))22 0.8117
## ns(time, df = round(2 * length(time)/153))23 0.8159
## ns(time, df = round(2 * length(time)/153))24 0.3284
## ns(time, df = round(2 * length(time)/153))25 0.8814
## ns(time, df = round(2 * length(time)/153))26 0.2698
## ns(time, df = round(2 * length(time)/153))27 0.4137
## ns(time, df = round(2 * length(time)/153))28 0.7908
## ns(time, df = round(2 * length(time)/153))29 0.1057
## ns(time, df = round(2 * length(time)/153))30 0.1304
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.1297
## Jourslundi 0.6418
## Joursmardi 0.4371
## Joursmercredi 0.0271 *
## Jourssamedi 0.3009
## Joursvendredi 0.1037
## Vacances1 0.0392 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 29/42
## R-sq.(adj) = 0.00611 Deviance explained = 2.66%
## UBRE = 0.038438 Scale est. = 1 n = 1370
##
## $montpellier
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 1.867209 0.080556 23.179
## heat_wave 0.094337 0.125066 0.754
## no2moy 0.002230 0.001518 1.469
## ns(time, df = round(2 * length(time)/153))1 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))2 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))3 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))4 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))5 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))6 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))7 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))8 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))9 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))10 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))11 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))12 1.703083 1.851482 0.920
## ns(time, df = round(2 * length(time)/153))13 -0.490328 0.342765 -1.431
## ns(time, df = round(2 * length(time)/153))14 -0.140765 0.189340 -0.743
## ns(time, df = round(2 * length(time)/153))15 -0.284779 0.152544 -1.867
## ns(time, df = round(2 * length(time)/153))16 -0.163013 0.142297 -1.146
## ns(time, df = round(2 * length(time)/153))17 -0.113334 0.135098 -0.839
## ns(time, df = round(2 * length(time)/153))18 -0.146302 0.135702 -1.078
## ns(time, df = round(2 * length(time)/153))19 -0.128420 0.132372 -0.970
## ns(time, df = round(2 * length(time)/153))20 -0.175653 0.134572 -1.305
## ns(time, df = round(2 * length(time)/153))21 -0.006325 0.128852 -0.049
## ns(time, df = round(2 * length(time)/153))22 -0.232699 0.137708 -1.690
## ns(time, df = round(2 * length(time)/153))23 -0.143804 0.131709 -1.092
## ns(time, df = round(2 * length(time)/153))24 -0.106862 0.134346 -0.795
## ns(time, df = round(2 * length(time)/153))25 -0.087953 0.128022 -0.687
## ns(time, df = round(2 * length(time)/153))26 -0.253850 0.134703 -1.885
## ns(time, df = round(2 * length(time)/153))27 -0.064197 0.124108 -0.517
## ns(time, df = round(2 * length(time)/153))28 -0.142462 0.136128 -1.047
## ns(time, df = round(2 * length(time)/153))29 0.030188 0.111554 0.271
## ns(time, df = round(2 * length(time)/153))30 0.041435 0.158406 0.262
## ns(time, df = round(2 * length(time)/153))31 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))32 0.000000 0.000000 NA
## Joursjeudi 0.001458 0.042230 0.035
## Jourslundi -0.045309 0.041759 -1.085
## Joursmardi -0.009648 0.041863 -0.230
## Joursmercredi -0.007036 0.042235 -0.167
## Jourssamedi -0.076159 0.042457 -1.794
## Joursvendredi 0.029509 0.041887 0.704
## Vacances1 0.004159 0.029590 0.141
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.4507
## no2moy 0.1419
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.3577
## ns(time, df = round(2 * length(time)/153))13 0.1526
## ns(time, df = round(2 * length(time)/153))14 0.4572
## ns(time, df = round(2 * length(time)/153))15 0.0619 .
## ns(time, df = round(2 * length(time)/153))16 0.2520
## ns(time, df = round(2 * length(time)/153))17 0.4015
## ns(time, df = round(2 * length(time)/153))18 0.2810
## ns(time, df = round(2 * length(time)/153))19 0.3320
## ns(time, df = round(2 * length(time)/153))20 0.1918
## ns(time, df = round(2 * length(time)/153))21 0.9608
## ns(time, df = round(2 * length(time)/153))22 0.0911 .
## ns(time, df = round(2 * length(time)/153))23 0.2749
## ns(time, df = round(2 * length(time)/153))24 0.4264
## ns(time, df = round(2 * length(time)/153))25 0.4921
## ns(time, df = round(2 * length(time)/153))26 0.0595 .
## ns(time, df = round(2 * length(time)/153))27 0.6050
## ns(time, df = round(2 * length(time)/153))28 0.2953
## ns(time, df = round(2 * length(time)/153))29 0.7867
## ns(time, df = round(2 * length(time)/153))30 0.7936
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.9725
## Jourslundi 0.2779
## Joursmardi 0.8177
## Joursmercredi 0.8677
## Jourssamedi 0.0728 .
## Joursvendredi 0.4811
## Vacances1 0.8882
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 29/42
## R-sq.(adj) = 0.0194 Deviance explained = 3.78%
## UBRE = 0.029158 Scale est. = 1 n = 1374
##
## $nancy
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 1.834708 0.083264 22.035
## heat_wave 0.098727 0.106288 0.929
## no2moy 0.001206 0.001922 0.628
## ns(time, df = round(2 * length(time)/153))1 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))2 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))3 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))4 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))5 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))6 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))7 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))8 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))9 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))10 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))11 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))12 -2.809303 1.848438 -1.520
## ns(time, df = round(2 * length(time)/153))13 0.322093 0.326819 0.986
## ns(time, df = round(2 * length(time)/153))14 -0.193081 0.183362 -1.053
## ns(time, df = round(2 * length(time)/153))15 -0.008448 0.146195 -0.058
## ns(time, df = round(2 * length(time)/153))16 -0.137363 0.140392 -0.978
## ns(time, df = round(2 * length(time)/153))17 -0.097259 0.133486 -0.729
## ns(time, df = round(2 * length(time)/153))18 0.002130 0.135340 0.016
## ns(time, df = round(2 * length(time)/153))19 -0.188359 0.131637 -1.431
## ns(time, df = round(2 * length(time)/153))20 0.238769 0.128679 1.856
## ns(time, df = round(2 * length(time)/153))21 -0.099640 0.126438 -0.788
## ns(time, df = round(2 * length(time)/153))22 0.097217 0.130992 0.742
## ns(time, df = round(2 * length(time)/153))23 -0.081745 0.127119 -0.643
## ns(time, df = round(2 * length(time)/153))24 0.040640 0.129536 0.314
## ns(time, df = round(2 * length(time)/153))25 -0.012969 0.124106 -0.105
## ns(time, df = round(2 * length(time)/153))26 0.040635 0.129386 0.314
## ns(time, df = round(2 * length(time)/153))27 0.075839 0.120105 0.631
## ns(time, df = round(2 * length(time)/153))28 -0.082754 0.134255 -0.616
## ns(time, df = round(2 * length(time)/153))29 0.019538 0.110541 0.177
## ns(time, df = round(2 * length(time)/153))30 0.206773 0.156194 1.324
## ns(time, df = round(2 * length(time)/153))31 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))32 0.000000 0.000000 NA
## Joursjeudi 0.007604 0.041888 0.182
## Jourslundi 0.052132 0.039783 1.310
## Joursmardi 0.008518 0.041168 0.207
## Joursmercredi 0.028408 0.041490 0.685
## Jourssamedi -0.044056 0.041516 -1.061
## Joursvendredi 0.037797 0.041781 0.905
## Vacances1 -0.028393 0.028427 -0.999
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.3530
## no2moy 0.5303
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.1286
## ns(time, df = round(2 * length(time)/153))13 0.3244
## ns(time, df = round(2 * length(time)/153))14 0.2923
## ns(time, df = round(2 * length(time)/153))15 0.9539
## ns(time, df = round(2 * length(time)/153))16 0.3279
## ns(time, df = round(2 * length(time)/153))17 0.4662
## ns(time, df = round(2 * length(time)/153))18 0.9874
## ns(time, df = round(2 * length(time)/153))19 0.1525
## ns(time, df = round(2 * length(time)/153))20 0.0635 .
## ns(time, df = round(2 * length(time)/153))21 0.4307
## ns(time, df = round(2 * length(time)/153))22 0.4580
## ns(time, df = round(2 * length(time)/153))23 0.5202
## ns(time, df = round(2 * length(time)/153))24 0.7537
## ns(time, df = round(2 * length(time)/153))25 0.9168
## ns(time, df = round(2 * length(time)/153))26 0.7535
## ns(time, df = round(2 * length(time)/153))27 0.5278
## ns(time, df = round(2 * length(time)/153))28 0.5376
## ns(time, df = round(2 * length(time)/153))29 0.8597
## ns(time, df = round(2 * length(time)/153))30 0.1856
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.8560
## Jourslundi 0.1900
## Joursmardi 0.8361
## Joursmercredi 0.4935
## Jourssamedi 0.2886
## Joursvendredi 0.3657
## Vacances1 0.3179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 29/42
## R-sq.(adj) = 0.0124 Deviance explained = 3.15%
## UBRE = 0.054509 Scale est. = 1 n = 1375
##
## $nantes
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 2.313e+00 6.365e-02 36.341
## heat_wave 1.451e-03 1.894e-01 0.008
## no2moy 7.072e-04 2.361e-03 0.300
## ns(time, df = round(2 * length(time)/153))1 0.000e+00 0.000e+00 NA
## ns(time, df = round(2 * length(time)/153))2 0.000e+00 0.000e+00 NA
## ns(time, df = round(2 * length(time)/153))3 0.000e+00 0.000e+00 NA
## ns(time, df = round(2 * length(time)/153))4 0.000e+00 0.000e+00 NA
## ns(time, df = round(2 * length(time)/153))5 0.000e+00 0.000e+00 NA
## ns(time, df = round(2 * length(time)/153))6 0.000e+00 0.000e+00 NA
## ns(time, df = round(2 * length(time)/153))7 0.000e+00 0.000e+00 NA
## ns(time, df = round(2 * length(time)/153))8 0.000e+00 0.000e+00 NA
## ns(time, df = round(2 * length(time)/153))9 0.000e+00 0.000e+00 NA
## ns(time, df = round(2 * length(time)/153))10 0.000e+00 0.000e+00 NA
## ns(time, df = round(2 * length(time)/153))11 0.000e+00 0.000e+00 NA
## ns(time, df = round(2 * length(time)/153))12 -1.688e+00 1.418e+00 -1.191
## ns(time, df = round(2 * length(time)/153))13 2.804e-01 2.597e-01 1.080
## ns(time, df = round(2 * length(time)/153))14 -2.140e-01 1.470e-01 -1.456
## ns(time, df = round(2 * length(time)/153))15 2.829e-02 1.211e-01 0.234
## ns(time, df = round(2 * length(time)/153))16 -2.121e-01 1.126e-01 -1.883
## ns(time, df = round(2 * length(time)/153))17 1.397e-02 1.081e-01 0.129
## ns(time, df = round(2 * length(time)/153))18 -1.450e-01 1.142e-01 -1.270
## ns(time, df = round(2 * length(time)/153))19 -4.267e-02 1.073e-01 -0.398
## ns(time, df = round(2 * length(time)/153))20 -9.187e-06 1.085e-01 0.000
## ns(time, df = round(2 * length(time)/153))21 -1.114e-01 1.060e-01 -1.051
## ns(time, df = round(2 * length(time)/153))22 -8.017e-02 1.154e-01 -0.695
## ns(time, df = round(2 * length(time)/153))23 9.803e-03 1.050e-01 0.093
## ns(time, df = round(2 * length(time)/153))24 -8.299e-02 1.077e-01 -0.771
## ns(time, df = round(2 * length(time)/153))25 -7.010e-03 1.013e-01 -0.069
## ns(time, df = round(2 * length(time)/153))26 -3.424e-02 1.128e-01 -0.304
## ns(time, df = round(2 * length(time)/153))27 -2.956e-02 9.890e-02 -0.299
## ns(time, df = round(2 * length(time)/153))28 1.994e-02 1.096e-01 0.182
## ns(time, df = round(2 * length(time)/153))29 -1.462e-01 9.055e-02 -1.614
## ns(time, df = round(2 * length(time)/153))30 1.210e-01 1.272e-01 0.951
## ns(time, df = round(2 * length(time)/153))31 0.000e+00 0.000e+00 NA
## ns(time, df = round(2 * length(time)/153))32 0.000e+00 0.000e+00 NA
## Joursjeudi 4.213e-02 3.431e-02 1.228
## Jourslundi 4.235e-02 3.366e-02 1.258
## Joursmardi 6.232e-02 3.412e-02 1.826
## Joursmercredi 2.132e-02 3.433e-02 0.621
## Jourssamedi 4.105e-03 3.410e-02 0.120
## Joursvendredi 3.819e-02 3.462e-02 1.103
## Vacances1 -4.280e-02 2.329e-02 -1.838
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.9939
## no2moy 0.7645
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.2338
## ns(time, df = round(2 * length(time)/153))13 0.2803
## ns(time, df = round(2 * length(time)/153))14 0.1453
## ns(time, df = round(2 * length(time)/153))15 0.8152
## ns(time, df = round(2 * length(time)/153))16 0.0597 .
## ns(time, df = round(2 * length(time)/153))17 0.8972
## ns(time, df = round(2 * length(time)/153))18 0.2042
## ns(time, df = round(2 * length(time)/153))19 0.6908
## ns(time, df = round(2 * length(time)/153))20 0.9999
## ns(time, df = round(2 * length(time)/153))21 0.2931
## ns(time, df = round(2 * length(time)/153))22 0.4873
## ns(time, df = round(2 * length(time)/153))23 0.9256
## ns(time, df = round(2 * length(time)/153))24 0.4408
## ns(time, df = round(2 * length(time)/153))25 0.9449
## ns(time, df = round(2 * length(time)/153))26 0.7614
## ns(time, df = round(2 * length(time)/153))27 0.7651
## ns(time, df = round(2 * length(time)/153))28 0.8556
## ns(time, df = round(2 * length(time)/153))29 0.1064
## ns(time, df = round(2 * length(time)/153))30 0.3415
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.2195
## Jourslundi 0.2084
## Joursmardi 0.0678 .
## Joursmercredi 0.5346
## Jourssamedi 0.9042
## Joursvendredi 0.2700
## Vacances1 0.0660 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 29/42
## R-sq.(adj) = 0.00286 Deviance explained = 2.38%
## UBRE = 0.071107 Scale est. = 1 n = 1313
##
## $nice
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 2.3755509 0.0709423 33.486
## heat_wave 0.0056155 0.1025785 0.055
## no2moy 0.0004202 0.0017629 0.238
## ns(time, df = round(2 * length(time)/153))1 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))2 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))3 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))4 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))5 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))6 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))7 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))8 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))9 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))10 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))11 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))12 0.0084291 1.3740329 0.006
## ns(time, df = round(2 * length(time)/153))13 -0.0310653 0.2503218 -0.124
## ns(time, df = round(2 * length(time)/153))14 -0.1534734 0.1399368 -1.097
## ns(time, df = round(2 * length(time)/153))15 0.0410992 0.1096023 0.375
## ns(time, df = round(2 * length(time)/153))16 -0.0521964 0.1097823 -0.475
## ns(time, df = round(2 * length(time)/153))17 0.0076135 0.0985664 0.077
## ns(time, df = round(2 * length(time)/153))18 0.0110507 0.1033049 0.107
## ns(time, df = round(2 * length(time)/153))19 -0.0123503 0.0988010 -0.125
## ns(time, df = round(2 * length(time)/153))20 -0.1340540 0.1033003 -1.298
## ns(time, df = round(2 * length(time)/153))21 0.0522221 0.0985708 0.530
## ns(time, df = round(2 * length(time)/153))22 -0.1879697 0.1040421 -1.807
## ns(time, df = round(2 * length(time)/153))23 0.1249519 0.0974231 1.283
## ns(time, df = round(2 * length(time)/153))24 -0.2242161 0.1020967 -2.196
## ns(time, df = round(2 * length(time)/153))25 0.1831806 0.0962511 1.903
## ns(time, df = round(2 * length(time)/153))26 -0.1401847 0.1006560 -1.393
## ns(time, df = round(2 * length(time)/153))27 0.0155690 0.0936435 0.166
## ns(time, df = round(2 * length(time)/153))28 0.0016791 0.1022708 0.016
## ns(time, df = round(2 * length(time)/153))29 -0.0484968 0.0849792 -0.571
## ns(time, df = round(2 * length(time)/153))30 0.0181329 0.1193208 0.152
## ns(time, df = round(2 * length(time)/153))31 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))32 0.0000000 0.0000000 NA
## Joursjeudi -0.0109434 0.0328211 -0.333
## Jourslundi 0.0455709 0.0308760 1.476
## Joursmardi 0.0123528 0.0321719 0.384
## Joursmercredi 0.0075825 0.0326404 0.232
## Jourssamedi 0.0442128 0.0316191 1.398
## Joursvendredi 0.0661642 0.0323895 2.043
## Vacances1 0.0112118 0.0222758 0.503
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.9563
## no2moy 0.8116
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.9951
## ns(time, df = round(2 * length(time)/153))13 0.9012
## ns(time, df = round(2 * length(time)/153))14 0.2728
## ns(time, df = round(2 * length(time)/153))15 0.7077
## ns(time, df = round(2 * length(time)/153))16 0.6345
## ns(time, df = round(2 * length(time)/153))17 0.9384
## ns(time, df = round(2 * length(time)/153))18 0.9148
## ns(time, df = round(2 * length(time)/153))19 0.9005
## ns(time, df = round(2 * length(time)/153))20 0.1944
## ns(time, df = round(2 * length(time)/153))21 0.5963
## ns(time, df = round(2 * length(time)/153))22 0.0708 .
## ns(time, df = round(2 * length(time)/153))23 0.1996
## ns(time, df = round(2 * length(time)/153))24 0.0281 *
## ns(time, df = round(2 * length(time)/153))25 0.0570 .
## ns(time, df = round(2 * length(time)/153))26 0.1637
## ns(time, df = round(2 * length(time)/153))27 0.8680
## ns(time, df = round(2 * length(time)/153))28 0.9869
## ns(time, df = round(2 * length(time)/153))29 0.5682
## ns(time, df = round(2 * length(time)/153))30 0.8792
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.7388
## Jourslundi 0.1400
## Joursmardi 0.7010
## Joursmercredi 0.8163
## Jourssamedi 0.1620
## Joursvendredi 0.0411 *
## Vacances1 0.6147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 29/42
## R-sq.(adj) = 0.00015 Deviance explained = 2.04%
## UBRE = 0.078912 Scale est. = 1 n = 1359
##
## $paris
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 4.4622057 0.0218318 204.390
## heat_wave 0.2105394 0.0296090 7.111
## no2moy 0.0030195 0.0003559 8.484
## ns(time, df = round(2 * length(time)/153))1 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))2 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))3 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))4 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))5 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))6 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))7 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))8 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))9 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))10 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))11 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))12 0.5134040 0.4527567 1.134
## ns(time, df = round(2 * length(time)/153))13 -0.1508802 0.0823362 -1.832
## ns(time, df = round(2 * length(time)/153))14 0.1110419 0.0465671 2.385
## ns(time, df = round(2 * length(time)/153))15 -0.0931674 0.0369533 -2.521
## ns(time, df = round(2 * length(time)/153))16 0.1164288 0.0358935 3.244
## ns(time, df = round(2 * length(time)/153))17 -0.0866836 0.0337168 -2.571
## ns(time, df = round(2 * length(time)/153))18 0.0867078 0.0348089 2.491
## ns(time, df = round(2 * length(time)/153))19 -0.0497632 0.0332638 -1.496
## ns(time, df = round(2 * length(time)/153))20 0.0724571 0.0342344 2.116
## ns(time, df = round(2 * length(time)/153))21 -0.0266357 0.0329402 -0.809
## ns(time, df = round(2 * length(time)/153))22 0.0902264 0.0343832 2.624
## ns(time, df = round(2 * length(time)/153))23 -0.0085582 0.0326267 -0.262
## ns(time, df = round(2 * length(time)/153))24 0.0666488 0.0338982 1.966
## ns(time, df = round(2 * length(time)/153))25 -0.0401769 0.0321449 -1.250
## ns(time, df = round(2 * length(time)/153))26 0.1048259 0.0338546 3.096
## ns(time, df = round(2 * length(time)/153))27 -0.0969348 0.0319516 -3.034
## ns(time, df = round(2 * length(time)/153))28 0.0581616 0.0349793 1.663
## ns(time, df = round(2 * length(time)/153))29 -0.0014165 0.0288877 -0.049
## ns(time, df = round(2 * length(time)/153))30 0.0743031 0.0412569 1.801
## ns(time, df = round(2 * length(time)/153))31 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))32 0.0000000 0.0000000 NA
## Joursjeudi 0.0359921 0.0107097 3.361
## Jourslundi 0.0455830 0.0103614 4.399
## Joursmardi 0.0288158 0.0106208 2.713
## Joursmercredi 0.0180862 0.0107540 1.682
## Jourssamedi 0.0185546 0.0105251 1.763
## Joursvendredi 0.0138401 0.0107701 1.285
## Vacances1 -0.0376135 0.0073780 -5.098
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 1.15e-12 ***
## no2moy < 2e-16 ***
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.256815
## ns(time, df = round(2 * length(time)/153))13 0.066878 .
## ns(time, df = round(2 * length(time)/153))14 0.017100 *
## ns(time, df = round(2 * length(time)/153))15 0.011695 *
## ns(time, df = round(2 * length(time)/153))16 0.001180 **
## ns(time, df = round(2 * length(time)/153))17 0.010142 *
## ns(time, df = round(2 * length(time)/153))18 0.012740 *
## ns(time, df = round(2 * length(time)/153))19 0.134649
## ns(time, df = round(2 * length(time)/153))20 0.034302 *
## ns(time, df = round(2 * length(time)/153))21 0.418742
## ns(time, df = round(2 * length(time)/153))22 0.008687 **
## ns(time, df = round(2 * length(time)/153))23 0.793085
## ns(time, df = round(2 * length(time)/153))24 0.049282 *
## ns(time, df = round(2 * length(time)/153))25 0.211348
## ns(time, df = round(2 * length(time)/153))26 0.001959 **
## ns(time, df = round(2 * length(time)/153))27 0.002415 **
## ns(time, df = round(2 * length(time)/153))28 0.096364 .
## ns(time, df = round(2 * length(time)/153))29 0.960893
## ns(time, df = round(2 * length(time)/153))30 0.071705 .
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.000777 ***
## Jourslundi 1.09e-05 ***
## Joursmardi 0.006665 **
## Joursmercredi 0.092605 .
## Jourssamedi 0.077918 .
## Joursvendredi 0.198776
## Vacances1 3.43e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 29/42
## R-sq.(adj) = 0.102 Deviance explained = 11.9%
## UBRE = 0.31869 Scale est. = 1 n = 1377
##
## $rennes
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 1.3847088 0.1030165 13.442
## heat_wave -0.2123030 0.3376930 -0.629
## no2moy -0.0038778 0.0031209 -1.243
## ns(time, df = round(2 * length(time)/153))1 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))2 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))3 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))4 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))5 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))6 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))7 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))8 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))9 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))10 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))11 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))12 1.0974901 2.4203477 0.453
## ns(time, df = round(2 * length(time)/153))13 -0.3829354 0.4462781 -0.858
## ns(time, df = round(2 * length(time)/153))14 -0.0852518 0.2447435 -0.348
## ns(time, df = round(2 * length(time)/153))15 -0.1116258 0.1924717 -0.580
## ns(time, df = round(2 * length(time)/153))16 -0.0306236 0.1824195 -0.168
## ns(time, df = round(2 * length(time)/153))17 -0.2703200 0.1749612 -1.545
## ns(time, df = round(2 * length(time)/153))18 -0.0889802 0.1786988 -0.498
## ns(time, df = round(2 * length(time)/153))19 0.0106399 0.1704359 0.062
## ns(time, df = round(2 * length(time)/153))20 -0.4235258 0.1783610 -2.375
## ns(time, df = round(2 * length(time)/153))21 0.1390606 0.1669863 0.833
## ns(time, df = round(2 * length(time)/153))22 -0.0984123 0.1744131 -0.564
## ns(time, df = round(2 * length(time)/153))23 -0.0007603 0.1662300 -0.005
## ns(time, df = round(2 * length(time)/153))24 -0.0871773 0.1775245 -0.491
## ns(time, df = round(2 * length(time)/153))25 0.0279932 0.1651045 0.170
## ns(time, df = round(2 * length(time)/153))26 -0.0213547 0.1712875 -0.125
## ns(time, df = round(2 * length(time)/153))27 -0.0628965 0.1592353 -0.395
## ns(time, df = round(2 * length(time)/153))28 0.1182774 0.1737929 0.681
## ns(time, df = round(2 * length(time)/153))29 -0.1725963 0.1492222 -1.157
## ns(time, df = round(2 * length(time)/153))30 -0.0214462 0.2047797 -0.105
## ns(time, df = round(2 * length(time)/153))31 0.0000000 0.0000000 NA
## ns(time, df = round(2 * length(time)/153))32 0.0000000 0.0000000 NA
## Joursjeudi 0.1175346 0.0562069 2.091
## Jourslundi 0.1160030 0.0532411 2.179
## Joursmardi 0.0978408 0.0553762 1.767
## Joursmercredi 0.0141606 0.0568255 0.249
## Jourssamedi 0.0969454 0.0547865 1.770
## Joursvendredi -0.0179802 0.0582983 -0.308
## Vacances1 -0.0465582 0.0379966 -1.225
## Pr(>|z|)
## (Intercept) <2e-16 ***
## heat_wave 0.5296
## no2moy 0.2140
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.6502
## ns(time, df = round(2 * length(time)/153))13 0.3909
## ns(time, df = round(2 * length(time)/153))14 0.7276
## ns(time, df = round(2 * length(time)/153))15 0.5619
## ns(time, df = round(2 * length(time)/153))16 0.8667
## ns(time, df = round(2 * length(time)/153))17 0.1223
## ns(time, df = round(2 * length(time)/153))18 0.6185
## ns(time, df = round(2 * length(time)/153))19 0.9502
## ns(time, df = round(2 * length(time)/153))20 0.0176 *
## ns(time, df = round(2 * length(time)/153))21 0.4050
## ns(time, df = round(2 * length(time)/153))22 0.5726
## ns(time, df = round(2 * length(time)/153))23 0.9964
## ns(time, df = round(2 * length(time)/153))24 0.6234
## ns(time, df = round(2 * length(time)/153))25 0.8654
## ns(time, df = round(2 * length(time)/153))26 0.9008
## ns(time, df = round(2 * length(time)/153))27 0.6928
## ns(time, df = round(2 * length(time)/153))28 0.4961
## ns(time, df = round(2 * length(time)/153))29 0.2474
## ns(time, df = round(2 * length(time)/153))30 0.9166
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.0365 *
## Jourslundi 0.0293 *
## Joursmardi 0.0773 .
## Joursmercredi 0.8032
## Jourssamedi 0.0768 .
## Joursvendredi 0.7578
## Vacances1 0.2205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 29/42
## R-sq.(adj) = 0.0126 Deviance explained = 3.33%
## UBRE = -0.028814 Scale est. = 1 n = 1334
##
## $rouen
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 2.204044 0.070154 31.417
## heat_wave 0.155263 0.132243 1.174
## no2moy 0.002787 0.002122 1.314
## ns(time, df = round(2 * length(time)/153))1 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))2 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))3 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))4 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))5 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))6 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))7 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))8 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))9 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))10 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))11 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))12 -0.187839 1.522453 -0.123
## ns(time, df = round(2 * length(time)/153))13 -0.342084 0.272070 -1.257
## ns(time, df = round(2 * length(time)/153))14 0.169110 0.150673 1.122
## ns(time, df = round(2 * length(time)/153))15 -0.270900 0.121518 -2.229
## ns(time, df = round(2 * length(time)/153))16 -0.033658 0.115599 -0.291
## ns(time, df = round(2 * length(time)/153))17 -0.120159 0.109769 -1.095
## ns(time, df = round(2 * length(time)/153))18 -0.056666 0.112895 -0.502
## ns(time, df = round(2 * length(time)/153))19 -0.138892 0.108983 -1.274
## ns(time, df = round(2 * length(time)/153))20 -0.062910 0.110876 -0.567
## ns(time, df = round(2 * length(time)/153))21 -0.162114 0.107179 -1.513
## ns(time, df = round(2 * length(time)/153))22 0.045924 0.111400 0.412
## ns(time, df = round(2 * length(time)/153))23 -0.233565 0.106672 -2.190
## ns(time, df = round(2 * length(time)/153))24 0.044616 0.111353 0.401
## ns(time, df = round(2 * length(time)/153))25 -0.166784 0.105835 -1.576
## ns(time, df = round(2 * length(time)/153))26 -0.037289 0.111848 -0.333
## ns(time, df = round(2 * length(time)/153))27 -0.079310 0.101732 -0.780
## ns(time, df = round(2 * length(time)/153))28 -0.033411 0.113605 -0.294
## ns(time, df = round(2 * length(time)/153))29 -0.145362 0.092123 -1.578
## ns(time, df = round(2 * length(time)/153))30 0.114362 0.129935 0.880
## ns(time, df = round(2 * length(time)/153))31 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))32 0.000000 0.000000 NA
## Joursjeudi 0.041478 0.036769 1.128
## Jourslundi 0.047503 0.034454 1.379
## Joursmardi 0.053390 0.036295 1.471
## Joursmercredi 0.107310 0.036344 2.953
## Jourssamedi 0.081944 0.034833 2.353
## Joursvendredi 0.049508 0.036743 1.347
## Vacances1 -0.075246 0.023944 -3.143
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 0.24036
## no2moy 0.18900
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.90181
## ns(time, df = round(2 * length(time)/153))13 0.20863
## ns(time, df = round(2 * length(time)/153))14 0.26171
## ns(time, df = round(2 * length(time)/153))15 0.02579 *
## ns(time, df = round(2 * length(time)/153))16 0.77093
## ns(time, df = round(2 * length(time)/153))17 0.27367
## ns(time, df = round(2 * length(time)/153))18 0.61571
## ns(time, df = round(2 * length(time)/153))19 0.20251
## ns(time, df = round(2 * length(time)/153))20 0.57045
## ns(time, df = round(2 * length(time)/153))21 0.13039
## ns(time, df = round(2 * length(time)/153))22 0.68016
## ns(time, df = round(2 * length(time)/153))23 0.02856 *
## ns(time, df = round(2 * length(time)/153))24 0.68866
## ns(time, df = round(2 * length(time)/153))25 0.11505
## ns(time, df = round(2 * length(time)/153))26 0.73884
## ns(time, df = round(2 * length(time)/153))27 0.43563
## ns(time, df = round(2 * length(time)/153))28 0.76868
## ns(time, df = round(2 * length(time)/153))29 0.11458
## ns(time, df = round(2 * length(time)/153))30 0.37878
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.25929
## Jourslundi 0.16798
## Joursmardi 0.14128
## Joursmercredi 0.00315 **
## Jourssamedi 0.01865 *
## Joursvendredi 0.17785
## Vacances1 0.00167 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 29/42
## R-sq.(adj) = 0.0152 Deviance explained = 3.47%
## UBRE = -0.01825 Scale est. = 1 n = 1377
##
## $strasbourg
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 1.894823 0.091723 20.658
## heat_wave 0.420064 0.050642 8.295
## no2moy 0.002884 0.001202 2.400
## ns(time, df = round(2 * length(time)/153))1 -0.121578 0.102577 -1.185
## ns(time, df = round(2 * length(time)/153))2 0.306127 0.139763 2.190
## ns(time, df = round(2 * length(time)/153))3 0.073988 0.115872 0.639
## ns(time, df = round(2 * length(time)/153))4 0.153223 0.136408 1.123
## ns(time, df = round(2 * length(time)/153))5 0.050074 0.120425 0.416
## ns(time, df = round(2 * length(time)/153))6 0.304980 0.131794 2.314
## ns(time, df = round(2 * length(time)/153))7 -0.073241 0.122742 -0.597
## ns(time, df = round(2 * length(time)/153))8 0.243553 0.134146 1.816
## ns(time, df = round(2 * length(time)/153))9 -0.201885 0.126151 -1.600
## ns(time, df = round(2 * length(time)/153))10 0.172631 0.135558 1.273
## ns(time, df = round(2 * length(time)/153))11 -0.207565 0.126036 -1.647
## ns(time, df = round(2 * length(time)/153))12 0.263511 0.132896 1.983
## ns(time, df = round(2 * length(time)/153))13 -0.067352 0.123237 -0.547
## ns(time, df = round(2 * length(time)/153))14 0.263755 0.132745 1.987
## ns(time, df = round(2 * length(time)/153))15 0.046676 0.122171 0.382
## ns(time, df = round(2 * length(time)/153))16 0.187585 0.133259 1.408
## ns(time, df = round(2 * length(time)/153))17 -0.028988 0.123416 -0.235
## ns(time, df = round(2 * length(time)/153))18 0.208140 0.134301 1.550
## ns(time, df = round(2 * length(time)/153))19 0.107393 0.122017 0.880
## ns(time, df = round(2 * length(time)/153))20 0.040637 0.133644 0.304
## ns(time, df = round(2 * length(time)/153))21 0.384537 0.120128 3.201
## ns(time, df = round(2 * length(time)/153))22 -0.147497 0.135353 -1.090
## ns(time, df = round(2 * length(time)/153))23 0.324455 0.121221 2.677
## ns(time, df = round(2 * length(time)/153))24 0.031509 0.132777 0.237
## ns(time, df = round(2 * length(time)/153))25 0.068700 0.122708 0.560
## ns(time, df = round(2 * length(time)/153))26 0.352634 0.130402 2.704
## ns(time, df = round(2 * length(time)/153))27 -0.100465 0.123222 -0.815
## ns(time, df = round(2 * length(time)/153))28 0.290971 0.129325 2.250
## ns(time, df = round(2 * length(time)/153))29 0.175968 0.120676 1.458
## ns(time, df = round(2 * length(time)/153))30 0.105776 0.103098 1.026
## ns(time, df = round(2 * length(time)/153))31 0.433073 0.217830 1.988
## ns(time, df = round(2 * length(time)/153))32 -0.053428 0.090863 -0.588
## Joursjeudi -0.033443 0.028535 -1.172
## Jourslundi 0.039035 0.026824 1.455
## Joursmardi 0.011489 0.027771 0.414
## Joursmercredi 0.031322 0.028037 1.117
## Jourssamedi -0.028183 0.027767 -1.015
## Joursvendredi -0.009874 0.028391 -0.348
## Vacances1 -0.045157 0.018806 -2.401
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave < 2e-16 ***
## no2moy 0.01639 *
## ns(time, df = round(2 * length(time)/153))1 0.23592
## ns(time, df = round(2 * length(time)/153))2 0.02850 *
## ns(time, df = round(2 * length(time)/153))3 0.52312
## ns(time, df = round(2 * length(time)/153))4 0.26132
## ns(time, df = round(2 * length(time)/153))5 0.67755
## ns(time, df = round(2 * length(time)/153))6 0.02066 *
## ns(time, df = round(2 * length(time)/153))7 0.55070
## ns(time, df = round(2 * length(time)/153))8 0.06944 .
## ns(time, df = round(2 * length(time)/153))9 0.10952
## ns(time, df = round(2 * length(time)/153))10 0.20285
## ns(time, df = round(2 * length(time)/153))11 0.09958 .
## ns(time, df = round(2 * length(time)/153))12 0.04739 *
## ns(time, df = round(2 * length(time)/153))13 0.58470
## ns(time, df = round(2 * length(time)/153))14 0.04693 *
## ns(time, df = round(2 * length(time)/153))15 0.70242
## ns(time, df = round(2 * length(time)/153))16 0.15923
## ns(time, df = round(2 * length(time)/153))17 0.81430
## ns(time, df = round(2 * length(time)/153))18 0.12119
## ns(time, df = round(2 * length(time)/153))19 0.37878
## ns(time, df = round(2 * length(time)/153))20 0.76108
## ns(time, df = round(2 * length(time)/153))21 0.00137 **
## ns(time, df = round(2 * length(time)/153))22 0.27584
## ns(time, df = round(2 * length(time)/153))23 0.00744 **
## ns(time, df = round(2 * length(time)/153))24 0.81242
## ns(time, df = round(2 * length(time)/153))25 0.57557
## ns(time, df = round(2 * length(time)/153))26 0.00685 **
## ns(time, df = round(2 * length(time)/153))27 0.41489
## ns(time, df = round(2 * length(time)/153))28 0.02445 *
## ns(time, df = round(2 * length(time)/153))29 0.14479
## ns(time, df = round(2 * length(time)/153))30 0.30491
## ns(time, df = round(2 * length(time)/153))31 0.04680 *
## ns(time, df = round(2 * length(time)/153))32 0.55653
## Joursjeudi 0.24120
## Jourslundi 0.14561
## Joursmardi 0.67910
## Joursmercredi 0.26391
## Jourssamedi 0.31011
## Joursvendredi 0.72800
## Vacances1 0.01634 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## R-sq.(adj) = 0.0575 Deviance explained = 6.64%
## UBRE = 0.047678 Scale est. = 1 n = 2444
##
## $toulouse
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + ns(time, df = round(2 * length(time)/153)) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value
## (Intercept) 2.445996 0.060086 40.709
## heat_wave 0.081156 0.093078 0.872
## no2moy 0.001865 0.001399 1.333
## ns(time, df = round(2 * length(time)/153))1 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))2 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))3 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))4 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))5 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))6 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))7 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))8 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))9 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))10 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))11 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))12 -2.920638 1.381133 -2.115
## ns(time, df = round(2 * length(time)/153))13 0.360370 0.245735 1.466
## ns(time, df = round(2 * length(time)/153))14 -0.340704 0.139401 -2.444
## ns(time, df = round(2 * length(time)/153))15 -0.075168 0.108224 -0.695
## ns(time, df = round(2 * length(time)/153))16 -0.089084 0.103604 -0.860
## ns(time, df = round(2 * length(time)/153))17 -0.121084 0.097842 -1.238
## ns(time, df = round(2 * length(time)/153))18 -0.049768 0.100760 -0.494
## ns(time, df = round(2 * length(time)/153))19 -0.215535 0.097272 -2.216
## ns(time, df = round(2 * length(time)/153))20 -0.034846 0.099197 -0.351
## ns(time, df = round(2 * length(time)/153))21 -0.054567 0.094357 -0.578
## ns(time, df = round(2 * length(time)/153))22 -0.072948 0.098277 -0.742
## ns(time, df = round(2 * length(time)/153))23 -0.003592 0.093873 -0.038
## ns(time, df = round(2 * length(time)/153))24 -0.186962 0.098118 -1.905
## ns(time, df = round(2 * length(time)/153))25 0.135384 0.091652 1.477
## ns(time, df = round(2 * length(time)/153))26 -0.142154 0.096768 -1.469
## ns(time, df = round(2 * length(time)/153))27 0.115173 0.089681 1.284
## ns(time, df = round(2 * length(time)/153))28 -0.260651 0.100686 -2.589
## ns(time, df = round(2 * length(time)/153))29 0.130194 0.082705 1.574
## ns(time, df = round(2 * length(time)/153))30 -0.149981 0.117216 -1.280
## ns(time, df = round(2 * length(time)/153))31 0.000000 0.000000 NA
## ns(time, df = round(2 * length(time)/153))32 0.000000 0.000000 NA
## Joursjeudi 0.014545 0.031528 0.461
## Jourslundi 0.045516 0.030299 1.502
## Joursmardi 0.010893 0.031190 0.349
## Joursmercredi 0.045470 0.031364 1.450
## Jourssamedi 0.020402 0.030690 0.665
## Joursvendredi 0.039047 0.031239 1.250
## Vacances1 -0.026472 0.021615 -1.225
## Pr(>|z|)
## (Intercept) < 2e-16 ***
## heat_wave 0.38326
## no2moy 0.18238
## ns(time, df = round(2 * length(time)/153))1 NA
## ns(time, df = round(2 * length(time)/153))2 NA
## ns(time, df = round(2 * length(time)/153))3 NA
## ns(time, df = round(2 * length(time)/153))4 NA
## ns(time, df = round(2 * length(time)/153))5 NA
## ns(time, df = round(2 * length(time)/153))6 NA
## ns(time, df = round(2 * length(time)/153))7 NA
## ns(time, df = round(2 * length(time)/153))8 NA
## ns(time, df = round(2 * length(time)/153))9 NA
## ns(time, df = round(2 * length(time)/153))10 NA
## ns(time, df = round(2 * length(time)/153))11 NA
## ns(time, df = round(2 * length(time)/153))12 0.03446 *
## ns(time, df = round(2 * length(time)/153))13 0.14251
## ns(time, df = round(2 * length(time)/153))14 0.01452 *
## ns(time, df = round(2 * length(time)/153))15 0.48733
## ns(time, df = round(2 * length(time)/153))16 0.38987
## ns(time, df = round(2 * length(time)/153))17 0.21588
## ns(time, df = round(2 * length(time)/153))18 0.62136
## ns(time, df = round(2 * length(time)/153))19 0.02671 *
## ns(time, df = round(2 * length(time)/153))20 0.72538
## ns(time, df = round(2 * length(time)/153))21 0.56306
## ns(time, df = round(2 * length(time)/153))22 0.45793
## ns(time, df = round(2 * length(time)/153))23 0.96948
## ns(time, df = round(2 * length(time)/153))24 0.05672 .
## ns(time, df = round(2 * length(time)/153))25 0.13964
## ns(time, df = round(2 * length(time)/153))26 0.14183
## ns(time, df = round(2 * length(time)/153))27 0.19905
## ns(time, df = round(2 * length(time)/153))28 0.00963 **
## ns(time, df = round(2 * length(time)/153))29 0.11544
## ns(time, df = round(2 * length(time)/153))30 0.20071
## ns(time, df = round(2 * length(time)/153))31 NA
## ns(time, df = round(2 * length(time)/153))32 NA
## Joursjeudi 0.64457
## Jourslundi 0.13304
## Joursmardi 0.72691
## Joursmercredi 0.14712
## Jourssamedi 0.50620
## Joursvendredi 0.21132
## Vacances1 0.22068
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Rank: 29/42
## R-sq.(adj) = 0.0202 Deviance explained = 3.97%
## UBRE = 0.073554 Scale est. = 1 n = 1377## to get AIC of the model
lapply(m.outcome_g,function(x){AIC(x)})## $BM
## [1] 6015.081
##
## $bordeaux
## [1] 7370.001
##
## $clermont
## [1] 10794.4
##
## $dijon
## [1] 10169.21
##
## $grenoble
## [1] 6677.972
##
## $lehavre
## [1] 6099.378
##
## $lille
## [1] 10709.97
##
## $lyon
## [1] 7879.635
##
## $marseille
## [1] 8019.308
##
## $montpellier
## [1] 6293.925
##
## $nancy
## [1] 6445.964
##
## $nantes
## [1] 6772.152
##
## $nice
## [1] 7158.469
##
## $paris
## [1] 10631.65
##
## $rennes
## [1] 5369.516
##
## $rouen
## [1] 6869.369
##
## $strasbourg
## [1] 11990.35
##
## $toulouse
## [1] 7290.771Results are identical for glm and gam function. In some cities, we identify a problem of collinearity for the coefficients linked to the different basis function of the time variable; this might be due to the fact that in some cities with les variablity in mortality, the model is overparametrized (i.e. too many dfs for time variable).
Model with 2 temporal indicators, one for the season (day of the season with 4 df), one for long-term trend (1 df every 5 or 10 years).
see Guo et al. 2017, Hajat et al. 2006, Gasparrini and Armstrong 2011, Zeng et al. 2014
## Create variable for day of the season, going from 1 at the 1st of may of every year and until 153 for 30th of september (except of bisextiles years, 2000 and 2004)
for (i in 1:18){
villes_s[[i]]$day<-ifelse(villes_s[[i]]$annee %in% c("2000","2004"),as.numeric(strftime(villes_s[[i]]$Dates,format="%j"))-121,as.numeric(strftime(villes_s[[i]]$Dates, format = "%j"))-120)
}
lapply(villes_s,function(x){summary(x$day)})## $BM
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $bordeaux
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $clermont
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $dijon
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $grenoble
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $lehavre
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $lille
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $lyon
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $marseille
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $montpellier
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $nancy
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $nantes
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $nice
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $paris
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $rennes
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $rouen
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $strasbourg
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00
##
## $toulouse
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 39.00 77.00 77.12 115.00 154.00## Create a variable going for the year going from 1 in 2000 to 16 in 2015
for (i in 1:18){
villes_s[[i]]$year<-year(villes_s[[i]]$Dates)-1999
}
lapply(villes_s,function(x){summary(x$year)})## $BM
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $bordeaux
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $clermont
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $dijon
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $grenoble
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $lehavre
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $lille
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $lyon
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $marseille
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $montpellier
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $nancy
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $nantes
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $nice
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $paris
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $rennes
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $rouen
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $strasbourg
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00
##
## $toulouse
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 4.75 8.50 8.50 12.25 16.00# Model
m2.outcome<-list()
for (i in 1:18){
m2.outcome[[i]]<-glm(nocc_tot~heat_wave+no2moy+ns(year,df=4)+ns(day,df=4)+Jours+Vacances,data=villes_s[[i]],family=poisson)}
names(m2.outcome)<-names
lapply(m2.outcome,function(x){summary(x)})## $BM
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1792 -0.7722 -0.0304 0.6327 2.9593
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.744e+01 2.974e+02 0.126 0.89982
## heat_wave -4.896e-02 1.665e-01 -0.294 0.76869
## no2moy 7.985e-04 1.859e-03 0.430 0.66751
## ns(year, df = 4)1 -3.539e+01 2.973e+02 -0.119 0.90524
## ns(year, df = 4)2 -2.544e+01 2.135e+02 -0.119 0.90512
## ns(year, df = 4)3 -6.543e+01 5.493e+02 -0.119 0.90519
## ns(year, df = 4)4 -1.541e+01 1.295e+02 -0.119 0.90525
## ns(day, df = 4)1 5.238e-02 6.437e-02 0.814 0.41581
## ns(day, df = 4)2 -1.471e-01 6.618e-02 -2.222 0.02629 *
## ns(day, df = 4)3 -1.439e-01 1.178e-01 -1.221 0.22203
## ns(day, df = 4)4 -1.400e-02 5.158e-02 -0.271 0.78610
## Joursjeudi 5.945e-02 3.964e-02 1.500 0.13369
## Jourslundi 9.274e-02 3.890e-02 2.384 0.01712 *
## Joursmardi 1.018e-01 3.930e-02 2.591 0.00958 **
## Joursmercredi 9.553e-02 3.963e-02 2.410 0.01594 *
## Jourssamedi 2.564e-02 3.958e-02 0.648 0.51704
## Joursvendredi 1.207e-01 3.927e-02 3.075 0.00211 **
## Vacances1 1.457e-02 4.254e-02 0.343 0.73194
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1345.0 on 1210 degrees of freedom
## Residual deviance: 1314.8 on 1193 degrees of freedom
## (1237 observations deleted due to missingness)
## AIC: 6005
##
## Number of Fisher Scoring iterations: 4
##
##
## $bordeaux
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.5320 -0.7444 -0.0394 0.6352 2.7813
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 7.702e+01 2.198e+02 0.350 0.7261
## heat_wave 2.059e-01 1.092e-01 1.885 0.0594 .
## no2moy 2.826e-03 1.892e-03 1.494 0.1353
## ns(year, df = 4)1 -7.459e+01 2.198e+02 -0.339 0.7343
## ns(year, df = 4)2 -5.350e+01 1.578e+02 -0.339 0.7346
## ns(year, df = 4)3 -1.378e+02 4.060e+02 -0.339 0.7343
## ns(year, df = 4)4 -3.235e+01 9.569e+01 -0.338 0.7353
## ns(day, df = 4)1 -3.254e-02 4.801e-02 -0.678 0.4979
## ns(day, df = 4)2 -3.299e-02 4.937e-02 -0.668 0.5040
## ns(day, df = 4)3 -1.319e-01 8.732e-02 -1.510 0.1311
## ns(day, df = 4)4 -1.883e-02 4.282e-02 -0.440 0.6602
## Joursjeudi 4.509e-02 3.180e-02 1.418 0.1562
## Jourslundi 6.322e-02 2.988e-02 2.115 0.0344 *
## Joursmardi 3.898e-02 3.118e-02 1.250 0.2111
## Joursmercredi 2.566e-02 3.187e-02 0.805 0.4207
## Jourssamedi 4.172e-02 3.057e-02 1.365 0.1723
## Joursvendredi 4.096e-02 3.175e-02 1.290 0.1970
## Vacances1 -2.125e-02 3.312e-02 -0.642 0.5211
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1490.4 on 1376 degrees of freedom
## Residual deviance: 1459.4 on 1359 degrees of freedom
## (1071 observations deleted due to missingness)
## AIC: 7361.4
##
## Number of Fisher Scoring iterations: 4
##
##
## $clermont
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1919 -0.7209 -0.0711 0.6168 3.3903
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.6957409 0.0602713 28.135 < 2e-16 ***
## heat_wave 0.4333304 0.0744939 5.817 5.99e-09 ***
## no2moy 0.0016609 0.0016936 0.981 0.3267
## ns(year, df = 4)1 -0.0523191 0.0441386 -1.185 0.2359
## ns(year, df = 4)2 0.0825395 0.0410473 2.011 0.0443 *
## ns(year, df = 4)3 0.0345834 0.0776617 0.445 0.6561
## ns(year, df = 4)4 0.0226835 0.0388630 0.584 0.5594
## ns(day, df = 4)1 0.0377468 0.0549301 0.687 0.4920
## ns(day, df = 4)2 -0.0707475 0.0554120 -1.277 0.2017
## ns(day, df = 4)3 -0.2364428 0.0977144 -2.420 0.0155 *
## ns(day, df = 4)4 -0.0008405 0.0473609 -0.018 0.9858
## Joursjeudi -0.0158980 0.0350886 -0.453 0.6505
## Jourslundi 0.0196361 0.0332218 0.591 0.5545
## Joursmardi 0.0044504 0.0343666 0.129 0.8970
## Joursmercredi 0.0135356 0.0348625 0.388 0.6978
## Jourssamedi -0.0117581 0.0342687 -0.343 0.7315
## Joursvendredi 0.0107253 0.0351337 0.305 0.7602
## Vacances1 -0.0462484 0.0376776 -1.227 0.2196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 2463.1 on 2430 degrees of freedom
## Residual deviance: 2412.8 on 2413 degrees of freedom
## (17 observations deleted due to missingness)
## AIC: 10768
##
## Number of Fisher Scoring iterations: 4
##
##
## $dijon
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.0388 -0.7376 -0.0748 0.6090 3.2072
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.234734 0.069816 17.686 < 2e-16 ***
## heat_wave 0.410229 0.073409 5.588 2.29e-08 ***
## no2moy 0.003953 0.002007 1.970 0.048891 *
## ns(year, df = 4)1 -0.003958 0.049481 -0.080 0.936250
## ns(year, df = 4)2 0.170393 0.046978 3.627 0.000287 ***
## ns(year, df = 4)3 0.216440 0.087488 2.474 0.013363 *
## ns(year, df = 4)4 0.147837 0.049023 3.016 0.002564 **
## ns(day, df = 4)1 0.062744 0.062762 1.000 0.317455
## ns(day, df = 4)2 -0.054957 0.062709 -0.876 0.380827
## ns(day, df = 4)3 -0.018124 0.109205 -0.166 0.868185
## ns(day, df = 4)4 0.019414 0.051321 0.378 0.705212
## Joursjeudi 0.032246 0.039464 0.817 0.413881
## Jourslundi 0.030016 0.037765 0.795 0.426721
## Joursmardi 0.054433 0.038770 1.404 0.160321
## Joursmercredi 0.044326 0.039357 1.126 0.260058
## Jourssamedi 0.062642 0.038300 1.636 0.101934
## Joursvendredi 0.057507 0.039582 1.453 0.146261
## Vacances1 0.026180 0.042556 0.615 0.538432
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 2467.5 on 2416 degrees of freedom
## Residual deviance: 2394.7 on 2399 degrees of freedom
## (31 observations deleted due to missingness)
## AIC: 10154
##
## Number of Fisher Scoring iterations: 4
##
##
## $grenoble
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.9344 -0.7214 -0.0354 0.5913 3.1636
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.577e+02 2.801e+02 -1.277 0.20171
## heat_wave 1.348e-01 8.929e-02 1.510 0.13115
## no2moy 7.333e-03 2.775e-03 2.643 0.00823 **
## ns(year, df = 4)1 3.594e+02 2.801e+02 1.283 0.19945
## ns(year, df = 4)2 2.582e+02 2.011e+02 1.284 0.19918
## ns(year, df = 4)3 6.636e+02 5.174e+02 1.283 0.19965
## ns(year, df = 4)4 1.569e+02 1.220e+02 1.286 0.19828
## ns(day, df = 4)1 7.596e-02 6.021e-02 1.262 0.20707
## ns(day, df = 4)2 6.134e-02 6.170e-02 0.994 0.32016
## ns(day, df = 4)3 2.434e-01 1.137e-01 2.141 0.03230 *
## ns(day, df = 4)4 4.202e-02 5.208e-02 0.807 0.41971
## Joursjeudi -3.283e-02 3.993e-02 -0.822 0.41096
## Jourslundi -2.649e-02 3.760e-02 -0.705 0.48109
## Joursmardi -6.428e-02 4.010e-02 -1.603 0.10892
## Joursmercredi -4.312e-02 4.021e-02 -1.072 0.28350
## Jourssamedi 1.509e-02 3.793e-02 0.398 0.69064
## Joursvendredi -3.919e-02 4.014e-02 -0.976 0.32891
## Vacances1 -1.917e-02 4.162e-02 -0.460 0.64518
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1501.6 on 1376 degrees of freedom
## Residual deviance: 1422.0 on 1359 degrees of freedom
## (1071 observations deleted due to missingness)
## AIC: 6663.7
##
## Number of Fisher Scoring iterations: 4
##
##
## $lehavre
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3272 -0.7441 -0.0974 0.6271 3.3028
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.401e+02 3.252e+02 1.046 0.296
## heat_wave 1.682e-01 1.508e-01 1.115 0.265
## no2moy 2.683e-03 1.684e-03 1.593 0.111
## ns(year, df = 4)1 -3.383e+02 3.251e+02 -1.040 0.298
## ns(year, df = 4)2 -2.428e+02 2.334e+02 -1.040 0.298
## ns(year, df = 4)3 -6.253e+02 6.006e+02 -1.041 0.298
## ns(year, df = 4)4 -1.470e+02 1.416e+02 -1.039 0.299
## ns(day, df = 4)1 -2.011e-03 7.130e-02 -0.028 0.977
## ns(day, df = 4)2 -3.653e-02 7.379e-02 -0.495 0.621
## ns(day, df = 4)3 -8.803e-02 1.343e-01 -0.655 0.512
## ns(day, df = 4)4 -2.654e-02 6.137e-02 -0.432 0.665
## Joursjeudi -3.005e-02 4.436e-02 -0.677 0.498
## Jourslundi -4.224e-02 4.408e-02 -0.958 0.338
## Joursmardi 2.637e-02 4.364e-02 0.604 0.546
## Joursmercredi -2.701e-02 4.439e-02 -0.608 0.543
## Jourssamedi -7.053e-02 4.465e-02 -1.580 0.114
## Joursvendredi -3.240e-02 4.440e-02 -0.730 0.466
## Vacances1 -1.416e-02 4.947e-02 -0.286 0.775
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1412.4 on 1350 degrees of freedom
## Residual deviance: 1395.0 on 1333 degrees of freedom
## (1097 observations deleted due to missingness)
## AIC: 6081.8
##
## Number of Fisher Scoring iterations: 4
##
##
## $lille
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.0319 -0.7115 -0.0315 0.6285 3.7576
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.592980 0.316632 8.189 2.63e-16 ***
## heat_wave 0.066780 0.054723 1.220 0.222338
## no2moy 0.003737 0.001015 3.681 0.000233 ***
## ns(year, df = 4)1 0.289766 0.297335 0.975 0.329787
## ns(year, df = 4)2 0.203379 0.220618 0.922 0.356602
## ns(year, df = 4)3 0.663272 0.628505 1.055 0.291280
## ns(year, df = 4)4 0.146264 0.108112 1.353 0.176088
## ns(day, df = 4)1 -0.010334 0.033074 -0.312 0.754705
## ns(day, df = 4)2 -0.016022 0.033516 -0.478 0.632619
## ns(day, df = 4)3 -0.080613 0.058978 -1.367 0.171680
## ns(day, df = 4)4 -0.062959 0.027487 -2.291 0.021991 *
## Joursjeudi -0.004626 0.021068 -0.220 0.826212
## Jourslundi 0.046239 0.019877 2.326 0.020003 *
## Joursmardi 0.019728 0.020528 0.961 0.336545
## Joursmercredi -0.004341 0.020934 -0.207 0.835712
## Jourssamedi -0.031927 0.020563 -1.553 0.120496
## Joursvendredi -0.011440 0.021133 -0.541 0.588271
## Vacances1 -0.015311 0.022508 -0.680 0.496366
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1924.7 on 1833 degrees of freedom
## Residual deviance: 1875.6 on 1816 degrees of freedom
## (614 observations deleted due to missingness)
## AIC: 10678
##
## Number of Fisher Scoring iterations: 4
##
##
## $lyon
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1685 -0.7627 -0.0855 0.6703 2.8358
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.059e+01 1.797e+02 -0.226 0.82133
## heat_wave 1.492e-01 7.210e-02 2.069 0.03856 *
## no2moy 1.883e-03 9.572e-04 1.967 0.04920 *
## ns(year, df = 4)1 4.344e+01 1.797e+02 0.242 0.80897
## ns(year, df = 4)2 3.121e+01 1.290e+02 0.242 0.80884
## ns(year, df = 4)3 7.976e+01 3.320e+02 0.240 0.81013
## ns(year, df = 4)4 1.917e+01 7.824e+01 0.245 0.80645
## ns(day, df = 4)1 -2.831e-02 3.931e-02 -0.720 0.47149
## ns(day, df = 4)2 -8.622e-02 4.058e-02 -2.124 0.03363 *
## ns(day, df = 4)3 -2.396e-02 7.336e-02 -0.327 0.74393
## ns(day, df = 4)4 -2.293e-03 3.392e-02 -0.068 0.94609
## Joursjeudi 2.790e-02 2.616e-02 1.066 0.28623
## Jourslundi 1.992e-02 2.479e-02 0.803 0.42177
## Joursmardi 3.766e-02 2.544e-02 1.480 0.13880
## Joursmercredi 7.374e-03 2.603e-02 0.283 0.77693
## Jourssamedi -2.240e-02 2.541e-02 -0.881 0.37814
## Joursvendredi 6.827e-02 2.580e-02 2.646 0.00815 **
## Vacances1 2.054e-02 2.733e-02 0.752 0.45233
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1494.7 on 1376 degrees of freedom
## Residual deviance: 1443.3 on 1359 degrees of freedom
## (1071 observations deleted due to missingness)
## AIC: 7875.3
##
## Number of Fisher Scoring iterations: 4
##
##
## $marseille
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4485 -0.6755 -0.0352 0.6332 3.1461
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.700e+02 1.647e+02 -1.032 0.30190
## heat_wave 2.023e-02 6.797e-02 0.298 0.76599
## no2moy 1.465e-03 5.455e-04 2.686 0.00723 **
## ns(year, df = 4)1 1.730e+02 1.647e+02 1.051 0.29339
## ns(year, df = 4)2 1.242e+02 1.182e+02 1.051 0.29347
## ns(year, df = 4)3 3.195e+02 3.042e+02 1.050 0.29367
## ns(year, df = 4)4 7.539e+01 7.170e+01 1.052 0.29301
## ns(day, df = 4)1 1.710e-02 3.624e-02 0.472 0.63693
## ns(day, df = 4)2 -3.978e-02 3.724e-02 -1.068 0.28547
## ns(day, df = 4)3 -1.350e-01 6.821e-02 -1.980 0.04775 *
## ns(day, df = 4)4 -2.766e-02 2.988e-02 -0.926 0.35450
## Joursjeudi 3.185e-02 2.304e-02 1.382 0.16698
## Jourslundi 1.013e-02 2.285e-02 0.443 0.65764
## Joursmardi 1.574e-02 2.294e-02 0.686 0.49253
## Joursmercredi 4.847e-02 2.291e-02 2.116 0.03438 *
## Jourssamedi 2.136e-02 2.288e-02 0.934 0.35037
## Joursvendredi 3.382e-02 2.300e-02 1.471 0.14138
## Vacances1 -3.571e-02 2.532e-02 -1.410 0.15844
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1401.9 on 1369 degrees of freedom
## Residual deviance: 1372.9 on 1352 degrees of freedom
## (1078 observations deleted due to missingness)
## AIC: 8005.6
##
## Number of Fisher Scoring iterations: 4
##
##
## $montpellier
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3764 -0.7399 -0.0579 0.5850 3.6815
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.535e+02 3.086e+02 1.145 0.2521
## heat_wave 1.129e-01 1.213e-01 0.931 0.3517
## no2moy 2.134e-03 1.564e-03 1.364 0.1726
## ns(year, df = 4)1 -3.515e+02 3.085e+02 -1.139 0.2546
## ns(year, df = 4)2 -2.523e+02 2.215e+02 -1.139 0.2546
## ns(year, df = 4)3 -6.501e+02 5.700e+02 -1.141 0.2541
## ns(year, df = 4)4 -1.526e+02 1.343e+02 -1.136 0.2560
## ns(day, df = 4)1 -5.285e-02 6.665e-02 -0.793 0.4278
## ns(day, df = 4)2 -3.339e-02 6.806e-02 -0.491 0.6237
## ns(day, df = 4)3 -8.614e-02 1.243e-01 -0.693 0.4884
## ns(day, df = 4)4 1.419e-04 5.598e-02 0.003 0.9980
## Joursjeudi 2.149e-03 4.230e-02 0.051 0.9595
## Jourslundi -4.489e-02 4.176e-02 -1.075 0.2825
## Joursmardi -8.170e-03 4.189e-02 -0.195 0.8454
## Joursmercredi -6.028e-03 4.228e-02 -0.143 0.8866
## Jourssamedi -7.618e-02 4.248e-02 -1.793 0.0729 .
## Joursvendredi 2.974e-02 4.196e-02 0.709 0.4784
## Vacances1 1.263e-02 4.620e-02 0.273 0.7845
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1409.4 on 1373 degrees of freedom
## Residual deviance: 1363.5 on 1356 degrees of freedom
## (1074 observations deleted due to missingness)
## AIC: 6279.4
##
## Number of Fisher Scoring iterations: 4
##
##
## $nancy
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.87349 -0.78012 -0.08301 0.64760 2.79998
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.001e+02 3.003e+02 1.332 0.183
## heat_wave 1.358e-01 1.038e-01 1.308 0.191
## no2moy 1.282e-03 2.024e-03 0.634 0.526
## ns(year, df = 4)1 -3.983e+02 3.002e+02 -1.327 0.185
## ns(year, df = 4)2 -2.858e+02 2.155e+02 -1.326 0.185
## ns(year, df = 4)3 -7.358e+02 5.547e+02 -1.327 0.185
## ns(year, df = 4)4 -1.733e+02 1.307e+02 -1.325 0.185
## ns(day, df = 4)1 5.642e-02 6.415e-02 0.879 0.379
## ns(day, df = 4)2 -9.234e-02 6.622e-02 -1.395 0.163
## ns(day, df = 4)3 1.347e-01 1.221e-01 1.103 0.270
## ns(day, df = 4)4 3.648e-02 5.559e-02 0.656 0.512
## Joursjeudi 1.062e-02 4.212e-02 0.252 0.801
## Jourslundi 5.273e-02 3.979e-02 1.325 0.185
## Joursmardi 9.798e-03 4.130e-02 0.237 0.812
## Joursmercredi 3.019e-02 4.168e-02 0.724 0.469
## Jourssamedi -4.339e-02 4.163e-02 -1.042 0.297
## Joursvendredi 3.965e-02 4.203e-02 0.943 0.346
## Vacances1 2.722e-02 4.455e-02 0.611 0.541
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1437.2 on 1374 degrees of freedom
## Residual deviance: 1401.9 on 1357 degrees of freedom
## (1073 observations deleted due to missingness)
## AIC: 6433.9
##
## Number of Fisher Scoring iterations: 4
##
##
## $nantes
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3355 -0.7045 -0.0361 0.6052 3.8097
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.625e+02 2.455e+02 1.069 0.2849
## heat_wave -3.173e-02 1.877e-01 -0.169 0.8658
## no2moy 4.295e-03 2.729e-03 1.574 0.1155
## ns(year, df = 4)1 -2.602e+02 2.454e+02 -1.060 0.2891
## ns(year, df = 4)2 -1.868e+02 1.762e+02 -1.060 0.2892
## ns(year, df = 4)3 -4.806e+02 4.535e+02 -1.060 0.2893
## ns(year, df = 4)4 -1.132e+02 1.069e+02 -1.060 0.2893
## ns(day, df = 4)1 -1.830e-02 5.261e-02 -0.348 0.7279
## ns(day, df = 4)2 -1.396e-01 5.495e-02 -2.541 0.0110 *
## ns(day, df = 4)3 -1.918e-01 9.625e-02 -1.993 0.0463 *
## ns(day, df = 4)4 -3.800e-02 5.015e-02 -0.758 0.4485
## Joursjeudi 2.765e-02 3.473e-02 0.796 0.4259
## Jourslundi 3.898e-02 3.370e-02 1.157 0.2474
## Joursmardi 4.972e-02 3.438e-02 1.446 0.1481
## Joursmercredi 8.019e-03 3.468e-02 0.231 0.8171
## Jourssamedi -5.815e-03 3.428e-02 -0.170 0.8653
## Joursvendredi 2.198e-02 3.514e-02 0.626 0.5316
## Vacances1 -2.169e-02 3.654e-02 -0.594 0.5528
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1381.2 on 1312 degrees of freedom
## Residual deviance: 1342.2 on 1295 degrees of freedom
## (1135 observations deleted due to missingness)
## AIC: 6744
##
## Number of Fisher Scoring iterations: 4
##
##
## $nice
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4136 -0.7459 -0.0684 0.6635 3.3109
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.511e+02 2.304e+02 -0.656 0.5119
## heat_wave 3.627e-02 1.016e-01 0.357 0.7211
## no2moy -1.996e-04 1.722e-03 -0.116 0.9077
## ns(year, df = 4)1 1.536e+02 2.303e+02 0.667 0.5050
## ns(year, df = 4)2 1.102e+02 1.654e+02 0.667 0.5050
## ns(year, df = 4)3 2.833e+02 4.255e+02 0.666 0.5055
## ns(year, df = 4)4 6.704e+01 1.003e+02 0.669 0.5038
## ns(day, df = 4)1 -3.664e-02 4.911e-02 -0.746 0.4556
## ns(day, df = 4)2 -3.635e-02 5.053e-02 -0.719 0.4719
## ns(day, df = 4)3 -1.038e-01 9.252e-02 -1.122 0.2621
## ns(day, df = 4)4 3.358e-02 4.061e-02 0.827 0.4082
## Joursjeudi -7.283e-03 3.274e-02 -0.222 0.8240
## Jourslundi 4.576e-02 3.088e-02 1.482 0.1384
## Joursmardi 1.439e-02 3.210e-02 0.448 0.6540
## Joursmercredi 1.086e-02 3.254e-02 0.334 0.7385
## Jourssamedi 4.630e-02 3.158e-02 1.466 0.1427
## Joursvendredi 6.999e-02 3.229e-02 2.168 0.0302 *
## Vacances1 -4.429e-03 3.426e-02 -0.129 0.8972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1437.6 on 1358 degrees of freedom
## Residual deviance: 1418.1 on 1341 degrees of freedom
## (1089 observations deleted due to missingness)
## AIC: 7146.3
##
## Number of Fisher Scoring iterations: 4
##
##
## $paris
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2600 -0.8119 -0.0253 0.7216 3.9448
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -57.552542 75.280886 -0.765 0.44457
## heat_wave 0.205802 0.029358 7.010 2.38e-12 ***
## no2moy 0.003221 0.000375 8.590 < 2e-16 ***
## ns(year, df = 4)1 62.035059 75.260833 0.824 0.40979
## ns(year, df = 4)2 44.561061 54.032405 0.825 0.40954
## ns(year, df = 4)3 114.518454 139.047642 0.824 0.41017
## ns(year, df = 4)4 27.069876 32.769941 0.826 0.40877
## ns(day, df = 4)1 0.034865 0.016737 2.083 0.03724 *
## ns(day, df = 4)2 -0.044518 0.017409 -2.557 0.01055 *
## ns(day, df = 4)3 -0.092045 0.030376 -3.030 0.00244 **
## ns(day, df = 4)4 -0.033879 0.014614 -2.318 0.02043 *
## Joursjeudi 0.034876 0.010775 3.237 0.00121 **
## Jourslundi 0.045538 0.010368 4.392 1.12e-05 ***
## Joursmardi 0.028416 0.010663 2.665 0.00770 **
## Joursmercredi 0.017402 0.010815 1.609 0.10761
## Jourssamedi 0.017450 0.010550 1.654 0.09812 .
## Joursvendredi 0.012312 0.010829 1.137 0.25558
## Vacances1 -0.027028 0.011526 -2.345 0.01903 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1995.9 on 1376 degrees of freedom
## Residual deviance: 1746.2 on 1359 degrees of freedom
## (1071 observations deleted due to missingness)
## AIC: 10598
##
## Number of Fisher Scoring iterations: 4
##
##
## $rennes
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7793 -0.7885 -0.1147 0.6015 3.7935
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.809e+02 3.966e+02 -1.465 0.1430
## heat_wave -2.256e-01 3.360e-01 -0.671 0.5019
## no2moy -4.251e-03 3.179e-03 -1.337 0.1812
## ns(year, df = 4)1 5.819e+02 3.965e+02 1.468 0.1422
## ns(year, df = 4)2 4.179e+02 2.847e+02 1.468 0.1421
## ns(year, df = 4)3 1.076e+03 7.326e+02 1.468 0.1420
## ns(year, df = 4)4 2.534e+02 1.726e+02 1.468 0.1422
## ns(day, df = 4)1 -2.045e-02 8.552e-02 -0.239 0.8110
## ns(day, df = 4)2 -1.299e-01 8.984e-02 -1.446 0.1482
## ns(day, df = 4)3 -5.702e-02 1.592e-01 -0.358 0.7201
## ns(day, df = 4)4 6.703e-02 7.469e-02 0.897 0.3695
## Joursjeudi 1.219e-01 5.642e-02 2.160 0.0308 *
## Jourslundi 1.164e-01 5.324e-02 2.187 0.0287 *
## Joursmardi 1.007e-01 5.544e-02 1.816 0.0693 .
## Joursmercredi 1.868e-02 5.695e-02 0.328 0.7429
## Jourssamedi 9.884e-02 5.484e-02 1.802 0.0715 .
## Joursvendredi -1.509e-02 5.846e-02 -0.258 0.7963
## Vacances1 -3.572e-03 5.952e-02 -0.060 0.9521
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1280.2 on 1333 degrees of freedom
## Residual deviance: 1245.8 on 1316 degrees of freedom
## (1114 observations deleted due to missingness)
## AIC: 5355.7
##
## Number of Fisher Scoring iterations: 4
##
##
## $rouen
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3053 -0.6869 -0.0927 0.6022 3.6039
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.426e+02 2.475e+02 0.980 0.32709
## heat_wave 1.175e-01 1.306e-01 0.899 0.36857
## no2moy 4.840e-03 2.305e-03 2.100 0.03574 *
## ns(year, df = 4)1 -2.404e+02 2.474e+02 -0.972 0.33129
## ns(year, df = 4)2 -1.726e+02 1.776e+02 -0.972 0.33120
## ns(year, df = 4)3 -4.441e+02 4.572e+02 -0.971 0.33131
## ns(year, df = 4)4 -1.046e+02 1.077e+02 -0.971 0.33177
## ns(day, df = 4)1 3.533e-02 5.384e-02 0.656 0.51167
## ns(day, df = 4)2 -1.330e-01 5.568e-02 -2.388 0.01693 *
## ns(day, df = 4)3 -8.991e-02 1.009e-01 -0.891 0.37306
## ns(day, df = 4)4 -3.618e-02 4.865e-02 -0.744 0.45711
## Joursjeudi 3.074e-02 3.730e-02 0.824 0.40987
## Jourslundi 4.520e-02 3.452e-02 1.310 0.19035
## Joursmardi 4.365e-02 3.669e-02 1.190 0.23423
## Joursmercredi 9.684e-02 3.688e-02 2.626 0.00864 **
## Jourssamedi 7.445e-02 3.505e-02 2.124 0.03364 *
## Joursvendredi 3.697e-02 3.728e-02 0.992 0.32144
## Vacances1 -1.880e-02 3.743e-02 -0.502 0.61546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1340.4 on 1376 degrees of freedom
## Residual deviance: 1290.1 on 1359 degrees of freedom
## (1071 observations deleted due to missingness)
## AIC: 6843.6
##
## Number of Fisher Scoring iterations: 4
##
##
## $strasbourg
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2143 -0.7537 -0.0338 0.6033 3.9679
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.045999 0.053061 38.559 < 2e-16 ***
## heat_wave 0.428621 0.048577 8.824 < 2e-16 ***
## no2moy 0.002866 0.001238 2.315 0.02063 *
## ns(year, df = 4)1 -0.054382 0.035664 -1.525 0.12730
## ns(year, df = 4)2 0.006667 0.034711 0.192 0.84769
## ns(year, df = 4)3 -0.096197 0.062648 -1.536 0.12466
## ns(year, df = 4)4 0.086502 0.032060 2.698 0.00697 **
## ns(day, df = 4)1 0.093010 0.045788 2.031 0.04222 *
## ns(day, df = 4)2 -0.028200 0.045741 -0.617 0.53756
## ns(day, df = 4)3 -0.048604 0.078840 -0.616 0.53757
## ns(day, df = 4)4 0.018223 0.036685 0.497 0.61937
## Joursjeudi -0.033571 0.028650 -1.172 0.24129
## Jourslundi 0.038508 0.026831 1.435 0.15123
## Joursmardi 0.011085 0.027842 0.398 0.69054
## Joursmercredi 0.031227 0.028141 1.110 0.26716
## Jourssamedi -0.028417 0.027807 -1.022 0.30681
## Joursvendredi -0.010227 0.028485 -0.359 0.71958
## Vacances1 -0.042888 0.031046 -1.381 0.16715
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 2652.8 on 2443 degrees of freedom
## Residual deviance: 2531.3 on 2426 degrees of freedom
## (4 observations deleted due to missingness)
## AIC: 11997
##
## Number of Fisher Scoring iterations: 4
##
##
## $toulouse
##
## Call:
## glm(formula = nocc_tot ~ heat_wave + no2moy + ns(year, df = 4) +
## ns(day, df = 4) + Jours + Vacances, family = poisson, data = villes_s[[i]])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3675 -0.7421 -0.0525 0.6487 3.3187
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.564e+02 2.230e+02 -0.701 0.483
## heat_wave 9.891e-02 9.266e-02 1.067 0.286
## no2moy 1.816e-03 1.480e-03 1.227 0.220
## ns(year, df = 4)1 1.587e+02 2.230e+02 0.712 0.477
## ns(year, df = 4)2 1.140e+02 1.601e+02 0.712 0.476
## ns(year, df = 4)3 2.933e+02 4.120e+02 0.712 0.477
## ns(year, df = 4)4 6.921e+01 9.709e+01 0.713 0.476
## ns(day, df = 4)1 -3.182e-02 4.837e-02 -0.658 0.511
## ns(day, df = 4)2 -4.446e-02 4.966e-02 -0.895 0.371
## ns(day, df = 4)3 -1.228e-01 9.026e-02 -1.361 0.174
## ns(day, df = 4)4 3.068e-02 4.179e-02 0.734 0.463
## Joursjeudi 1.396e-02 3.172e-02 0.440 0.660
## Jourslundi 4.543e-02 3.032e-02 1.498 0.134
## Joursmardi 9.546e-03 3.132e-02 0.305 0.761
## Joursmercredi 4.468e-02 3.155e-02 1.416 0.157
## Jourssamedi 2.003e-02 3.075e-02 0.651 0.515
## Joursvendredi 3.899e-02 3.141e-02 1.242 0.214
## Vacances1 -4.267e-02 3.345e-02 -1.276 0.202
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 1478.9 on 1376 degrees of freedom
## Residual deviance: 1439.2 on 1359 degrees of freedom
## (1071 observations deleted due to missingness)
## AIC: 7287.6
##
## Number of Fisher Scoring iterations: 4I tried to fit a model with 5 df for the long term trend but there were still some collineatiry problems in some cities. A model with 4 df for long term trend seems OK.
A penalized cubic spline function of time to control for seasonality and for the long-term temporal trend.
The degree of smoothness is determined by minimization of the absolute value of the sum of the partial autocorrelation function of the model’s residuals from lag 0 to lag 30. The model is constrained to use at least 3 degrees of freedom per year in case the minimization of the sum of the partial autocorrelation function led to smaller values
see Hertel et al. 2009, Pascal et al. 2021, Touloumi et al. 2006. This is SpF’s approach
Estimation of smoothing parameter:
## Indicate the location of the script of the PSAS function
source('C:/Users/Anna/Dropbox/Noemie/PSAS_optimate_pacf-3df.r')
Sparameter<- matrix(NA,length(villes_s),1,
dimnames=list(names))
for (i in 1:18){
# EXTRACT THE DATA
data <- villes_s[[i]]
data<-na.omit(data[, c("nocc_tot", "heat_wave", "no2moy", "time", "Jours", "Vacances")])
data$morta<-data$nocc_tot
mod.formula<-quote(morta~heat_wave+no2moy+s(time,k=nk,bs="cr",fx=FALSE)+Jours+Vacances)
# fonction for selection of smoothing parameter
mod <- iterate.model(model.dat = data, pind = 0:30, dsp = c(2, 4, 6), formul = mod.formula)
#Save value of smoothing parameter for each city
Sparameter[i,]<-mod$bestspc
}## Warning in gam.fit3(x = args$X, y = args$y, sp = lsp, Eb = args$Eb, UrS =
## args$UrS, : Algorithm did not convergeMODEL WITH SELECTED SMOOTHING PARAMETER:
m3.outcome<-list()
nk<-round(20*length(villes_s[[i]]$nocc_tot)/153.25) # max 20 df per warm season
for (i in 1:18){
m3.outcome[[i]]<-gam(nocc_tot~heat_wave+no2moy+s(time,k=nk,bs="cr",fx=FALSE)+Jours+Vacances,sp = 10^Sparameter[i,], data=villes_s[[i]],family=poisson)
}
names(m3.outcome)<-names
lapply(m3.outcome,function(x){summary(x)})## $BM
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.960926 0.040816 48.043 < 2e-16 ***
## heat_wave -0.034981 0.167269 -0.209 0.83435
## no2moy 0.001260 0.001804 0.698 0.48494
## Joursjeudi 0.057627 0.039469 1.460 0.14427
## Jourslundi 0.091476 0.038872 2.353 0.01861 *
## Joursmardi 0.099309 0.039174 2.535 0.01124 *
## Joursmercredi 0.093617 0.039447 2.373 0.01763 *
## Jourssamedi 0.024402 0.039525 0.617 0.53699
## Joursvendredi 0.118516 0.039173 3.025 0.00248 **
## Vacances1 0.020375 0.028572 0.713 0.47578
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 20.92 26.09 29.34 0.305
##
## R-sq.(adj) = 0.0189 Deviance explained = 4.15%
## UBRE = 0.11569 Scale est. = 1 n = 1211
##
## $bordeaux
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.393451 0.028821 83.045 <2e-16 ***
## heat_wave 0.214685 0.109797 1.955 0.0505 .
## no2moy 0.002226 0.001698 1.310 0.1901
## Joursjeudi 0.048951 0.031285 1.565 0.1177
## Jourslundi 0.064198 0.029841 2.151 0.0314 *
## Joursmardi 0.042308 0.030820 1.373 0.1698
## Joursmercredi 0.029528 0.031370 0.941 0.3466
## Jourssamedi 0.044083 0.030391 1.451 0.1469
## Joursvendredi 0.045081 0.031295 1.441 0.1497
## Vacances1 -0.017546 0.019826 -0.885 0.3762
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 17.59 21.94 31.26 0.0865 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0167 Deviance explained = 3.5%
## UBRE = 0.084555 Scale est. = 1 n = 1377
##
## $clermont
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.6322987 0.0340091 47.996 < 2e-16 ***
## heat_wave 0.3971615 0.0766642 5.181 2.21e-07 ***
## no2moy 0.0008577 0.0015172 0.565 0.572
## Joursjeudi -0.0085380 0.0346962 -0.246 0.806
## Jourslundi 0.0207839 0.0332065 0.626 0.531
## Joursmardi 0.0099898 0.0340921 0.293 0.770
## Joursmercredi 0.0197694 0.0344866 0.573 0.566
## Jourssamedi -0.0070525 0.0340421 -0.207 0.836
## Joursvendredi 0.0178925 0.0346995 0.516 0.606
## Vacances1 -0.0150403 0.0196428 -0.766 0.444
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 21.05 26.26 24.74 0.561
##
## R-sq.(adj) = 0.0176 Deviance explained = 2.66%
## UBRE = 0.011752 Scale est. = 1 n = 2431
##
## $dijon
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.298021 0.041416 31.341 < 2e-16 ***
## heat_wave 0.376669 0.076170 4.945 7.61e-07 ***
## no2moy 0.005053 0.001948 2.594 0.00948 **
## Joursjeudi 0.025490 0.039257 0.649 0.51613
## Jourslundi 0.028457 0.037745 0.754 0.45089
## Joursmardi 0.048837 0.038627 1.264 0.20611
## Joursmercredi 0.038217 0.039146 0.976 0.32893
## Jourssamedi 0.057696 0.038219 1.510 0.13115
## Joursvendredi 0.049907 0.039411 1.266 0.20539
## Vacances1 0.028999 0.021647 1.340 0.18038
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 20.88 26.05 45.54 0.0107 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.031 Deviance explained = 3.87%
## UBRE = 0.0069713 Scale est. = 1 n = 2417
##
## $grenoble
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.915749 0.042343 45.244 <2e-16 ***
## heat_wave 0.119402 0.091784 1.301 0.1933
## no2moy 0.008165 0.002522 3.238 0.0012 **
## Joursjeudi -0.037549 0.039309 -0.955 0.3395
## Jourslundi -0.027763 0.037516 -0.740 0.4593
## Joursmardi -0.068325 0.039523 -1.729 0.0839 .
## Joursmercredi -0.047735 0.039552 -1.207 0.2275
## Jourssamedi 0.011966 0.037668 0.318 0.7507
## Joursvendredi -0.044201 0.039522 -1.118 0.2634
## Vacances1 -0.032640 0.025216 -1.294 0.1955
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 17.28 21.55 68.64 8.99e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0455 Deviance explained = 6.16%
## UBRE = 0.062953 Scale est. = 1 n = 1377
##
## $lehavre
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.663836 0.041222 40.362 <2e-16 ***
## heat_wave 0.194262 0.151772 1.280 0.201
## no2moy 0.002414 0.001630 1.481 0.139
## Joursjeudi -0.028138 0.044224 -0.636 0.525
## Jourslundi -0.042561 0.044061 -0.966 0.334
## Joursmardi 0.027754 0.043546 0.637 0.524
## Joursmercredi -0.024828 0.044246 -0.561 0.575
## Jourssamedi -0.069211 0.044605 -1.552 0.121
## Joursvendredi -0.030690 0.044307 -0.693 0.489
## Vacances1 -0.009456 0.029930 -0.316 0.752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 19.11 23.87 13.56 0.953
##
## R-sq.(adj) = 0.00353 Deviance explained = 2.33%
## UBRE = 0.064208 Scale est. = 1 n = 1351
##
## $lille
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.8997674 0.0223114 129.968 < 2e-16 ***
## heat_wave 0.0626723 0.0556270 1.127 0.25989
## no2moy 0.0029463 0.0009497 3.103 0.00192 **
## Joursjeudi 0.0007684 0.0208749 0.037 0.97064
## Jourslundi 0.0475343 0.0198549 2.394 0.01666 *
## Joursmardi 0.0238807 0.0203980 1.171 0.24170
## Joursmercredi 0.0004466 0.0207619 0.022 0.98284
## Jourssamedi -0.0284817 0.0204927 -1.390 0.16457
## Joursvendredi -0.0057894 0.0209544 -0.276 0.78233
## Vacances1 -0.0097087 0.0124170 -0.782 0.43428
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 18.82 23.46 14.65 0.93
##
## R-sq.(adj) = 0.0119 Deviance explained = 2.67%
## UBRE = 0.052897 Scale est. = 1 n = 1834
##
## $lyon
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.7753268 0.0250934 110.600 < 2e-16 ***
## heat_wave 0.1475810 0.0734406 2.010 0.04448 *
## no2moy 0.0015494 0.0009026 1.716 0.08608 .
## Joursjeudi 0.0296039 0.0259161 1.142 0.25333
## Jourslundi 0.0199157 0.0247749 0.804 0.42147
## Joursmardi 0.0382665 0.0252951 1.513 0.13033
## Joursmercredi 0.0081095 0.0258196 0.314 0.75346
## Jourssamedi -0.0210685 0.0253366 -0.832 0.40567
## Joursvendredi 0.0704334 0.0255936 2.752 0.00592 **
## Vacances1 -0.0097522 0.0159895 -0.610 0.54192
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 17.39 21.7 38.86 0.0136 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0348 Deviance explained = 5.26%
## UBRE = 0.068193 Scale est. = 1 n = 1377
##
## $marseille
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.9428365 0.0229967 127.968 <2e-16 ***
## heat_wave 0.0012538 0.0690344 0.018 0.9855
## no2moy 0.0010823 0.0005494 1.970 0.0488 *
## Joursjeudi 0.0357184 0.0230020 1.553 0.1205
## Jourslundi 0.0111037 0.0228415 0.486 0.6269
## Joursmardi 0.0186111 0.0229181 0.812 0.4168
## Joursmercredi 0.0516450 0.0228740 2.258 0.0240 *
## Jourssamedi 0.0237333 0.0228696 1.038 0.2994
## Joursvendredi 0.0375135 0.0229863 1.632 0.1027
## Vacances1 -0.0176099 0.0143464 -1.227 0.2196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 16.87 21.04 17.56 0.672
##
## R-sq.(adj) = 0.0116 Deviance explained = 3.03%
## UBRE = 0.031527 Scale est. = 1 n = 1370
##
## $montpellier
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.7355312 0.0432335 40.143 <2e-16 ***
## heat_wave 0.0893222 0.1238388 0.721 0.4707
## no2moy 0.0023962 0.0015111 1.586 0.1128
## Joursjeudi 0.0008739 0.0421739 0.021 0.9835
## Jourslundi -0.0454405 0.0417534 -1.088 0.2765
## Joursmardi -0.0097971 0.0418114 -0.234 0.8147
## Joursmercredi -0.0074736 0.0421751 -0.177 0.8593
## Jourssamedi -0.0767754 0.0424347 -1.809 0.0704 .
## Joursvendredi 0.0286448 0.0418444 0.685 0.4936
## Vacances1 0.0067220 0.0267355 0.251 0.8015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 17.04 21.25 39.75 0.00879 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0225 Deviance explained = 3.94%
## UBRE = 0.024751 Scale est. = 1 n = 1374
##
## $nancy
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.8250390 0.0470506 38.789 <2e-16 ***
## heat_wave 0.1118143 0.1056383 1.058 0.290
## no2moy 0.0009841 0.0018996 0.518 0.604
## Joursjeudi 0.0094699 0.0417739 0.227 0.821
## Jourslundi 0.0525954 0.0397725 1.322 0.186
## Joursmardi 0.0093100 0.0410715 0.227 0.821
## Joursmercredi 0.0295712 0.0413677 0.715 0.475
## Jourssamedi -0.0427881 0.0414682 -1.032 0.302
## Joursvendredi 0.0398174 0.0416770 0.955 0.339
## Vacances1 -0.0186873 0.0259093 -0.721 0.471
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 16.88 21.05 29.07 0.112
##
## R-sq.(adj) = 0.0167 Deviance explained = 3.37%
## UBRE = 0.049067 Scale est. = 1 n = 1375
##
## $nantes
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.257007 0.033461 67.452 <2e-16 ***
## heat_wave -0.003881 0.188806 -0.021 0.9836
## no2moy 0.001524 0.002293 0.665 0.5063
## Joursjeudi 0.038837 0.034146 1.137 0.2554
## Jourslundi 0.041272 0.033640 1.227 0.2199
## Joursmardi 0.058714 0.033964 1.729 0.0839 .
## Joursmercredi 0.017907 0.034159 0.524 0.6001
## Jourssamedi 0.001980 0.034036 0.058 0.9536
## Joursvendredi 0.034672 0.034459 1.006 0.3143
## Vacances1 -0.037882 0.022658 -1.672 0.0946 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 18.27 22.79 16.84 0.807
##
## R-sq.(adj) = 0.00857 Deviance explained = 2.88%
## UBRE = 0.06472 Scale est. = 1 n = 1313
##
## $nice
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.3496714 0.0404270 58.121 <2e-16 ***
## heat_wave 0.0121829 0.1025389 0.119 0.9054
## no2moy 0.0006866 0.0017411 0.394 0.6933
## Joursjeudi -0.0127974 0.0327325 -0.391 0.6958
## Jourslundi 0.0444377 0.0308636 1.440 0.1499
## Joursmardi 0.0101839 0.0320985 0.317 0.7510
## Joursmercredi 0.0054781 0.0325423 0.168 0.8663
## Jourssamedi 0.0427854 0.0315858 1.355 0.1756
## Joursvendredi 0.0644747 0.0323195 1.995 0.0461 *
## Vacances1 -0.0051745 0.0195157 -0.265 0.7909
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 16.94 21.13 13.95 0.876
##
## R-sq.(adj) = 0.00318 Deviance explained = 2.18%
## UBRE = 0.074442 Scale est. = 1 n = 1359
##
## $paris
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.4741762 0.0114780 389.804 < 2e-16 ***
## heat_wave 0.2172572 0.0295601 7.350 1.99e-13 ***
## no2moy 0.0028079 0.0003472 8.087 6.13e-16 ***
## Joursjeudi 0.0389631 0.0106668 3.653 0.000259 ***
## Jourslundi 0.0466236 0.0103570 4.502 6.74e-06 ***
## Joursmardi 0.0316313 0.0105858 2.988 0.002807 **
## Joursmercredi 0.0212186 0.0107093 1.981 0.047556 *
## Jourssamedi 0.0199926 0.0105134 1.902 0.057220 .
## Joursvendredi 0.0163264 0.0107368 1.521 0.128362
## Vacances1 -0.0168272 0.0068664 -2.451 0.014259 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 17.29 21.57 36.24 0.025 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.107 Deviance explained = 12.3%
## UBRE = 0.31152 Scale est. = 1 n = 1377
##
## $rennes
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.305296 0.051557 25.318 <2e-16 ***
## heat_wave -0.211266 0.337507 -0.626 0.5313
## no2moy -0.003458 0.003026 -1.143 0.2531
## Joursjeudi 0.115910 0.055968 2.071 0.0384 *
## Jourslundi 0.115086 0.053210 2.163 0.0306 *
## Joursmardi 0.096293 0.055156 1.746 0.0808 .
## Joursmercredi 0.013119 0.056579 0.232 0.8166
## Jourssamedi 0.095242 0.054652 1.743 0.0814 .
## Joursvendredi -0.020420 0.058040 -0.352 0.7250
## Vacances1 -0.040034 0.035513 -1.127 0.2596
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 16.98 21.16 20.54 0.498
##
## R-sq.(adj) = 0.0175 Deviance explained = 3.64%
## UBRE = -0.034831 Scale est. = 1 n = 1334
##
## $rouen
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.114649 0.040884 51.723 <2e-16 ***
## heat_wave 0.156501 0.131729 1.188 0.2348
## no2moy 0.002599 0.002029 1.281 0.2002
## Joursjeudi 0.044606 0.036406 1.225 0.2205
## Jourslundi 0.049090 0.034402 1.427 0.1536
## Joursmardi 0.056223 0.035973 1.563 0.1181
## Joursmercredi 0.110583 0.035953 3.076 0.0021 **
## Jourssamedi 0.083440 0.034710 2.404 0.0162 *
## Joursvendredi 0.051751 0.036435 1.420 0.1555
## Vacances1 -0.040124 0.021992 -1.824 0.0681 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 17.27 21.54 22.68 0.401
##
## R-sq.(adj) = 0.0234 Deviance explained = 4.18%
## UBRE = -0.027652 Scale est. = 1 n = 1377
##
## $strasbourg
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.007759 0.031261 64.226 <2e-16 ***
## heat_wave 0.426074 0.049199 8.660 <2e-16 ***
## no2moy 0.002427 0.001157 2.098 0.0359 *
## Joursjeudi -0.028982 0.028397 -1.021 0.3074
## Jourslundi 0.039899 0.026816 1.488 0.1368
## Joursmardi 0.015057 0.027673 0.544 0.5864
## Joursmercredi 0.035908 0.027897 1.287 0.1980
## Jourssamedi -0.025635 0.027703 -0.925 0.3548
## Joursvendredi -0.005768 0.028257 -0.204 0.8382
## Vacances1 -0.019862 0.015766 -1.260 0.2077
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 20.16 25.15 44.24 0.0113 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0526 Deviance explained = 5.75%
## UBRE = 0.047649 Scale est. = 1 n = 2444
##
## $toulouse
##
## Family: poisson
## Link function: log
##
## Formula:
## nocc_tot ~ heat_wave + no2moy + s(time, k = nk, bs = "cr", fx = FALSE) +
## Jours + Vacances
##
## Parametric coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.381047 0.028681 83.018 <2e-16 ***
## heat_wave 0.092850 0.093415 0.994 0.3202
## no2moy 0.001935 0.001397 1.385 0.1662
## Joursjeudi 0.013012 0.031491 0.413 0.6795
## Jourslundi 0.044996 0.030294 1.485 0.1375
## Joursmardi 0.008981 0.031156 0.288 0.7732
## Joursmercredi 0.043643 0.031323 1.393 0.1635
## Jourssamedi 0.019681 0.030681 0.641 0.5212
## Joursvendredi 0.038186 0.031216 1.223 0.2212
## Vacances1 -0.044426 0.019810 -2.243 0.0249 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df Chi.sq p-value
## s(time) 17.53 21.87 34.25 0.0436 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0205 Deviance explained = 3.89%
## UBRE = 0.072207 Scale est. = 1 n = 1377lapply(m3.outcome,function(x){AIC(x)})## $BM
## [1] 6005.373
##
## $bordeaux
## [1] 7359.445
##
## $clermont
## [1] 10779.16
##
## $dijon
## [1] 10157.45
##
## $grenoble
## [1] 6669.436
##
## $lehavre
## [1] 6088.569
##
## $lille
## [1] 10697.05
##
## $lyon
## [1] 7866.877
##
## $marseille
## [1] 8009.84
##
## $montpellier
## [1] 6287.869
##
## $nancy
## [1] 6438.482
##
## $nantes
## [1] 6763.765
##
## $nice
## [1] 7152.394
##
## $paris
## [1] 10621.77
##
## $rennes
## [1] 5361.49
##
## $rouen
## [1] 6856.424
##
## $strasbourg
## [1] 11990.28
##
## $toulouse
## [1] 7288.916There is also a last approach with GEE, that assumed independence between successive warm seasons and specified a first-order autoregressive structure to account for the correlation of daily outcomes within each warm season. (Williams et al. 2012, D’Ippoliti et al. 2012, Baccini et al. 2008; Fouillet et al. 2007, Michelozzi et al. 2009)