List of tested models

mod1<-glm(elipic ~ ns(tempmin) + ns(tempmax) + Jours + mois + jours_fériés + Vacances + annee + lag_O3 + lag_PM10, family="binomial", data=data)

mod2<-glm(elipic ~ ns(tempmin) + ns(tempmax) + Jours + mois  + jours_fériés + Vacances + annee + lag_O3 + lag2_O3 + lag3_O3 + lag_PM10 + lag2_PM10 + lag3_PM10, family="binomial", data=data)

mod3<-glm(elipic ~ ns(tempmin,2) + ns(tempmax,2) + Jours + mois  + jours_fériés + Vacances + annee + lag_O3 + lag2_O3 + lag3_O3 + lag_PM10 + lag2_PM10 + lag3_PM10, family="binomial", data=data)

mod4<-glm(elipic ~ ns(tempmin,3) + ns(tempmax,3) + Jours + mois  + jours_fériés + Vacances + annee + lag_O3 + lag2_O3 + lag3_O3 + lag_PM10 + lag2_PM10 + lag3_PM10, family="binomial", data=data)

mod5<-glm(elipic ~ ns(tempmin,4) + ns(tempmax,4) + Jours + mois  + jours_fériés + Vacances + annee + lag_O3 + lag2_O3 + lag3_O3 + lag_PM10 + lag2_PM10 + lag3_PM10, family="binomial", data=data)

mod6<-glm(elipic ~ ns(tempmin) + ns(tempmax) + ns(tempmoy) + Jours + mois + jours_fériés + Vacances + annee + lag_O3 + lag_PM10, family="binomial", data=data)

mod7<-glm(elipic ~ ns(tempmin,2) + ns(tempmax,2) + ns(tempmoy,2) + Jours + mois  + jours_fériés + Vacances + annee + lag_O3 + lag2_O3 + lag3_O3 + lag_PM10 + lag2_PM10 + lag3_PM10, family="binomial", data=data)

mod8<-glm(elipic ~ ns(tempmoy,2) + Jours + mois  + jours_fériés + Vacances + annee + lag_O3 + lag2_O3 + lag3_O3 + lag_PM10 + lag2_PM10 + lag3_PM10, family="binomial", data=data)

mod9<-glm(elipic ~ ns(tempmax,2) + ns(tempmoy,2) + Jours + mois  + jours_fériés + Vacances + annee + lag_O3 + lag2_O3 + lag3_O3 + lag_PM10 + lag2_PM10 + lag3_PM10, family="binomial", data=data)

mod10<-glm(elipic ~ ns(tempmin,2) + ns(tempmax,2) + ns(tempmoy,2) + ns(tempmaxmoy7j,2) + Jours + mois  + jours_fériés + Vacances + annee + lag_O3 + lag2_O3 + lag3_O3 + lag_PM10 + lag2_PM10 + lag3_PM10, family="binomial", data=data)

mod11<-glm(elipic ~ ns(tempmin,2) + ns(tempmax,2) + ns(tempmaxmoy7j,2) + Jours + mois  + jours_fériés + Vacances + annee + lag_O3 + lag2_O3 + lag3_O3 + lag_PM10 + lag2_PM10 + lag3_PM10, family="binomial", data=data)

mod12<-glm(elipic ~ ns(tempmin,2) + ns(tempmoy,2) + ns(tempmaxmoy7j,2) + Jours + mois  + jours_fériés + Vacances + annee + lag_O3 + lag2_O3 + lag3_O3 + lag_PM10 + lag2_PM10 + lag3_PM10, family="binomial", data=data)

mod13<-glm(elipic ~ ns(tempmax,2) + ns(tempmoy,2) + ns(tempmaxmoy7j,2) + Jours + mois  + jours_fériés + Vacances + annee + lag_O3 + lag2_O3 + lag3_O3 + lag_PM10 + lag2_PM10 + lag3_PM10, family="binomial", data=data)

mod14<-glm(elipic ~ ns(tempmax,2) + ns(tempmoy,2) + ns(tempmaxmoy7j,2) + Jours + mois  + jours_fériés + Vacances + annee + ns(lag_O3,2) + ns(lag2_O3,2) + ns(lag3_O3,2) + ns(lag_PM10,2) + ns(lag2_PM10,2) + ns(lag3_PM10,2), family="binomial", data=data)

mod15<-glm(elipic ~ ns(tempmax,2) + ns(tempmoy,2) + Jours + mois  + jours_fériés + Vacances + annee + ns(lag_O3,2) + ns(lag2_O3,2) + ns(lag3_O3,2) + ns(lag_PM10,2) + ns(lag2_PM10,2) + ns(lag3_PM10,2), family="binomial", data=data)

mod16<-glm(elipic ~ ns(tempmax,2) + ns(tempmoy,2) + Jours + mois  + jours_fériés + Vacances + annee + ns(lag_O3,3) + ns(lag2_O3,3) + ns(lag3_O3,3) + ns(lag_PM10,3) + ns(lag2_PM10,3) + ns(lag3_PM10,3), family="binomial", data=data)

mod17<-glm(elipic ~ ns(tempmax,2) + ns(tempmoy,2) + Jours + mois  + jours_fériés + Vacances + annee + ns(lag_O3,4) + ns(lag2_O3,4) + ns(lag3_O3,4) + ns(lag_PM10,4) + ns(lag2_PM10,4) + ns(lag3_PM10,4), family="binomial", data=data)

mod18<-glm(elipic ~ bs(tempmax,3) + bs(tempmoy,3) + Jours + mois  + jours_fériés + Vacances + annee + ns(lag_O3,2) + ns(lag2_O3,2) + ns(lag3_O3,2) + ns(lag_PM10,2) + ns(lag2_PM10,2) + ns(lag3_PM10,2), family="binomial", data=data)

mod19<-gam(elipic ~ s(tempmax,k=2,bs="tp") + s(tempmoy,k=2,bs="tp") + Jours + mois  + jours_fériés + Vacances + annee + ns(lag_O3,2) + ns(lag2_O3,2) + ns(lag3_O3,2) + ns(lag_PM10,2) + ns(lag2_PM10,2) + ns(lag3_PM10,2), family="binomial", data=data)

mod20<-gam(elipic ~ s(tempmax,k=3,bs="tp") + s(tempmoy,k=3,bs="tp") + Jours + mois  + jours_fériés + Vacances + annee + ns(lag_O3,2) + ns(lag2_O3,2) + ns(lag3_O3,2) + ns(lag_PM10,2) + ns(lag2_PM10,2) + ns(lag3_PM10,2), family="binomial", data=data)

mod21<-gam(elipic ~ s(tempmax,k=4,bs="tp") + s(tempmoy,k=4,bs="tp") + Jours + mois  + jours_fériés + Vacances + annee + ns(lag_O3,2) + ns(lag2_O3,2) + ns(lag3_O3,2) + ns(lag_PM10,2) + ns(lag2_PM10,2) + ns(lag3_PM10,2), family="binomial", data=data)

mod22<-gam(elipic ~ s(tempmax,k=5,bs="tp") + s(tempmoy,k=5,bs="tp") + Jours + mois  + jours_fériés + Vacances + annee + ns(lag_O3,2) + ns(lag2_O3,2) + ns(lag3_O3,2) + ns(lag_PM10,2) + ns(lag2_PM10,2) + ns(lag3_PM10,2), family="binomial", data=data)

mod23<-gam(elipic ~ s(tempmax,k=6,bs="tp") + s(tempmoy,k=6,bs="tp") + Jours + mois  + jours_fériés + Vacances + annee + ns(lag_O3,2) + ns(lag2_O3,2) + ns(lag3_O3,2) + ns(lag_PM10,2) + ns(lag2_PM10,2) + ns(lag3_PM10,2), family="binomial", data=data)

mod24<-gam(elipic ~ s(tempmax,k=7,bs="tp") + s(tempmoy,k=7,bs="tp") + Jours + mois  + jours_fériés + Vacances + annee + ns(lag_O3,2) + ns(lag2_O3,2) + ns(lag3_O3,2) + ns(lag_PM10,2) + ns(lag2_PM10,2) + ns(lag3_PM10,2), family="binomial", data=data)

mod25<-gam(elipic ~ s(tempmax,k=8,bs="tp") + s(tempmoy,k=8,bs="tp") + Jours + mois  + jours_fériés + Vacances + annee + ns(lag_O3,2) + ns(lag2_O3,2) + ns(lag3_O3,2) + ns(lag_PM10,2) + ns(lag2_PM10,2) + ns(lag3_PM10,2), family="binomial", data=data)

mod26<-gam(elipic ~ s(tempmax,k=9,bs="tp") + s(tempmoy,k=9,bs="tp") + Jours + mois  + jours_fériés + Vacances + annee + ns(lag_O3,2) + ns(lag2_O3,2) + ns(lag3_O3,2) + ns(lag_PM10,2) + ns(lag2_PM10,2) + ns(lag3_PM10,2), family="binomial", data=data)

mod27<-gam(elipic ~ s(tempmax,bs="tp") + s(tempmoy,bs="tp") + Jours + mois  + jours_fériés + Vacances + annee + ns(lag_O3,2) + ns(lag2_O3,2) + ns(lag3_O3,2) + ns(lag_PM10,2) + ns(lag2_PM10,2) + ns(lag3_PM10,2), family="binomial", data=data)

mod28<-gam(elipic ~ s(tempmax,k=7,bs="tp") + s(tempmoy,k=7, bs="tp") + Jours + mois  + jours_fériés + Vacances + annee + s(lag_O3,k=3) + s(lag2_O3,k=3) + s(lag3_O3,k=3) + s(lag_PM10,k=3) + s(lag2_PM10,k=3) + s(lag3_PM10,k=3), family="binomial", data=data)

mod29<-gam(elipic ~ s(tempmax,k=7,bs="tp") + s(tempmoy,k=7, bs="tp") + Jours + mois  + jours_fériés + Vacances + annee + s(lag_O3) + s(lag2_O3) + s(lag3_O3) + s(lag_PM10) + s(lag2_PM10) + s(lag3_PM10), family="binomial", data=data)

mod30<-gam(elipic ~ s(tempmax,k=7,bs="tp") + s(tempmoy,k=7, bs="tp") + Jours + mois  + jours_fériés + Vacances + annee + s(lag_O3,k=4) + s(lag2_O3,k=4) + s(lag3_O3,k=4) + s(lag_PM10,k=4) + s(lag2_PM10,k=4) + s(lag3_PM10,k=4), family="binomial", data=data)

mod31<-gam(elipic ~ s(tempmax,k=7,bs="tp") + s(tempmoy,k=7, bs="tp") + Jours + mois  + jours_fériés + Vacances + annee + s(lag_O3,k=5) + s(lag2_O3,k=5) + s(lag3_O3,k=5) + s(lag_PM10,k=5) + s(lag2_PM10,k=5) + s(lag3_PM10,k=5), family="binomial", data=data)

Faire un tableau avec AIC

Numèro modèle Tempmin, df Tempmax, df Tempmoy, df Tempmaxmoy7j, df lag03 lagPM10 lag1_3 O3 lag1_3 PM10 AIC
1 1 1 NA NA 1 1 NA NA 659.5702
2 1 1 NA NA 1 1 1 1 652.7396
3 2 2 NA NA 1 1 1 1 651.8525
4 3 3 NA NA 1 1 1 1 655.1385
5 4 4 NA NA 1 1 1 1 657.7451
6 1 1 1 NA 1 1 1 1 657.9681
7 2 2 2 NA 1 1 1 1 649.6752
8 NA NA 2 NA 1 1 1 1 675.855
9 NA 2 2 NA 1 1 1 1 647.8821
10 2 2 2 2 1 1 1 1 650.0199
11 2 2 NA 2 1 1 1 1 652.5364
12 2 NA 2 2 1 1 1 1 653.4476
13 NA 2 2 2 1 1 1 1 648.1115
14 NA 2 2 2 2 2 2 2 642.0257
15 NA 2 2 NA 2 2 2 2 639.35
16 NA 2 2 NA 3 3 3 3 644.2438
17 NA 2 2 NA 4 4 4 4 651.3597
18 NA 3, bspline 3,bspline NA 2 2 2 2 643,3485
19 NA 2, pen spline 2, pen spline NA 2 2 2 2 637.8742
20 NA 3, pen spline 3, pen spline NA 2 2 2 2 637.8742
21 NA 4, pen spline 4, pen spline NA 2 2 2 2 638.5838
22 NA 5, pen spline 5, pen spline NA 2 2 2 2 638.2126
23 NA 6, pen spline 6, pen spline NA 2 2 2 2 637.795
24 NA 7, pen spline 7, pen spline NA 2 2 2 2 637.5544
25 NA 8, pen spline 8, pen spline NA 2 2 2 2 637.6388
26 NA 9, pen spline 9, pen spline NA 2 2 2 2 637.6435
27 NA 10, pen spline 10, pen spline NA 2 2 2 2 637.6991
28 NA 7, pen spline 7, pen spline NA 3, pen spline 3, pen spline 3, pen spline 3, pen spline 631.735
29 NA 7, pen spline 7, pen spline NA 10, pen spline 10, pen spline 10, pen spline 10, pen spline 633.9455
30 NA 7, pen spline 7, pen spline NA 4, pen spline 4, pen spline 4, pen spline 4, pen spline 632.8304
31 NA 7, pen spline 7, pen spline NA 5, pen spline 5, pen spline 5, pen spline 5, pen spline 633.6836

Model 28 is retained.

data$SP<-NA
data$SP[4:nrow(data)]<-mod28$fitted
data<-data[-c(1:3),]
data$nrow<-c(1:nrow(data))

Matching

V1 - N=1, caliper = NULL, replacement=TRUE, ties=TRUE

var.match<-cbind(data$SP,data$period)
App1<-Match(Tr = data$elipic,X=var.match, ties=TRUE)

summary(App1)
## 
## Estimate...  0 
## SE.........  0 
## T-stat.....  NaN 
## p.val......  NA 
## 
## Original number of observations..............  6572 
## Original number of treated obs...............  158 
## Matched number of observations...............  158 
## Matched number of observations  (unweighted).  257
control=data.frame(App1$index.control)
treated=data.frame(App1$index.treated)

colnames(control) <- c("nrow")
colnames(treated) <- c("nrow")

treated$PAIR<-NA
treated$PAIR[1]<-1

for (i in (2:nrow(treated))) {
  treated$PAIR[i]<- if (treated$nrow[i] == treated$nrow[i-1]) treated$PAIR[i-1] else treated$PAIR[i-1]+1
} 


control$PAIR <- treated$PAIR
cas<-unique(treated)

casm<-merge(cas,data,by="nrow")
controlm<- merge(control,data, by ="nrow")

datam1<-rbind(casm,controlm)

datam1<-datam1[order(datam1[,"PAIR"]),]

 

V2 - N=1, caliper = NULL, replacement=TRUE, ties=FALSE

var.match<-cbind(data$SP,data$period)
App2<-Match(Tr = data$elipic,X=var.match, ties=FALSE)

summary(App2)
## 
## Estimate...  0 
## SE.........  0 
## T-stat.....  NaN 
## p.val......  NA 
## 
## Original number of observations..............  6572 
## Original number of treated obs...............  158 
## Matched number of observations...............  158 
## Matched number of observations  (unweighted).  158
control=data.frame(App2$index.control)
treated=data.frame(App2$index.treated)


colnames(control) <- c("nrow")
colnames(treated) <- c("nrow")

cas<- cbind(treated, PAIR = c(1:158))
control <- cbind(control, PAIR = c(1:158))

casm<-merge(cas,data,by="nrow")
controlm<- merge(control,data, by ="nrow")

datam2<-rbind(casm,controlm)

datam2<-datam2[order(datam2[,"PAIR"]),]

 

V3 - N=1, caliper = 0.10, replacement=TRUE, ties=FALSE

var.match<-cbind(data$SP,data$period)
App3<-Match(Tr = data$elipic,X=var.match, caliper=0.1,ties=FALSE)

summary(App3)
## 
## Estimate...  0 
## SE.........  0 
## T-stat.....  NaN 
## p.val......  NA 
## 
## Original number of observations..............  6572 
## Original number of treated obs...............  158 
## Matched number of observations...............  115 
## Matched number of observations  (unweighted).  115 
## 
## Caliper (SDs)........................................   0.1 0.1 
## Number of obs dropped by 'exact' or 'caliper'  43
control=data.frame(App3$index.control)
treated=data.frame(App3$index.treated)


colnames(control) <- c("nrow")
colnames(treated) <- c("nrow")

cas<- cbind(treated, PAIR = c(1:115))
control <- cbind(control, PAIR = c(1:115))

casm<-merge(cas,data,by="nrow")
controlm<- merge(control,data, by ="nrow")

datam3<-rbind(casm,controlm)

datam3<-datam3[order(datam3[,"PAIR"]),]

 

V4 - N=1, caliper = 0.20, replacement=TRUE, ties=FALSE

var.match<-cbind(data$SP,data$period)
App4<-Match(Tr = data$elipic,X=var.match, caliper=0.2,ties=FALSE)

summary(App4)
## 
## Estimate...  0 
## SE.........  0 
## T-stat.....  NaN 
## p.val......  NA 
## 
## Original number of observations..............  6572 
## Original number of treated obs...............  158 
## Matched number of observations...............  135 
## Matched number of observations  (unweighted).  135 
## 
## Caliper (SDs)........................................   0.2 0.2 
## Number of obs dropped by 'exact' or 'caliper'  23
control=data.frame(App4$index.control)
treated=data.frame(App4$index.treated)


colnames(control) <- c("nrow")
colnames(treated) <- c("nrow")

cas<- cbind(treated, PAIR = c(1:135))
control <- cbind(control, PAIR = c(1:135))

casm<-merge(cas,data,by="nrow")
controlm<- merge(control,data, by ="nrow")

datam4<-rbind(casm,controlm)

datam4<-datam4[order(datam4[,"PAIR"]),]

 

V5 - N=2, caliper = NULL, replacement=TRUE, ties=FALSE

var.match<-cbind(data$SP,data$period)
App5<-Match(Tr = data$elipic,X=var.match, M=2,ties=FALSE)

summary(App5)
## 
## Estimate...  0 
## SE.........  0 
## T-stat.....  NaN 
## p.val......  NA 
## 
## Original number of observations..............  6572 
## Original number of treated obs...............  158 
## Matched number of observations...............  158 
## Matched number of observations  (unweighted).  316
control=data.frame(App5$index.control)
treated=data.frame(App5$index.treated)


colnames(control) <- c("nrow")
colnames(treated) <- c("nrow")

cas<-unique(treated)

cas$PAIR<-c(1:158)

treated<-merge(treated,cas,by="nrow")

control$PAIR <- treated$PAIR

casm<-merge(cas,data,by="nrow")
controlm<- merge(control,data, by ="nrow")

datam5<-rbind(casm,controlm)

datam5<-datam5[order(datam5[,"PAIR"]),]

 

V6 - N=2, caliper = 0.1, replacement=TRUE, ties=FALSE

var.match<-cbind(data$SP,data$period)
App6<-Match(Tr = data$elipic,X=var.match, M=2, ties=FALSE, caliper=0.1)

summary(App6)
## 
## Estimate...  0 
## SE.........  0 
## T-stat.....  NaN 
## p.val......  NA 
## 
## Original number of observations..............  6572 
## Original number of treated obs...............  158 
## Matched number of observations...............  85 
## Matched number of observations  (unweighted).  170 
## 
## Caliper (SDs)........................................   0.1 0.1 
## Number of obs dropped by 'exact' or 'caliper'  73
control=data.frame(App6$index.control)
treated=data.frame(App6$index.treated)


colnames(control) <- c("nrow")
colnames(treated) <- c("nrow")

cas<-unique(treated)

cas$PAIR<-c(1:85)

treated<-merge(treated,cas,by="nrow")

control$PAIR <- treated$PAIR

casm<-merge(cas,data,by="nrow")
controlm<- merge(control,data, by ="nrow")

datam6<-rbind(casm,controlm)

datam6<-datam6[order(datam6[,"PAIR"]),]

V7 - N=2, caliper = 0.2, replacement=TRUE, ties=FALSE

var.match<-cbind(data$SP,data$period)
App7<-Match(Tr = data$elipic,X=var.match, M=2, ties=FALSE, caliper=0.2)

summary(App7)
## 
## Estimate...  0 
## SE.........  0 
## T-stat.....  NaN 
## p.val......  NA 
## 
## Original number of observations..............  6572 
## Original number of treated obs...............  158 
## Matched number of observations...............  112 
## Matched number of observations  (unweighted).  224 
## 
## Caliper (SDs)........................................   0.2 0.2 
## Number of obs dropped by 'exact' or 'caliper'  46
control=data.frame(App7$index.control)
treated=data.frame(App7$index.treated)


colnames(control) <- c("nrow")
colnames(treated) <- c("nrow")

cas<-unique(treated)

cas$PAIR<-c(1:112)

treated<-merge(treated,cas,by="nrow")

control$PAIR <- treated$PAIR

casm<-merge(cas,data,by="nrow")
controlm<- merge(control,data, by ="nrow")

datam7<-rbind(casm,controlm)

datam7<-datam7[order(datam7[,"PAIR"]),]

V8 - N=3, caliper = NULL, replacement=TRUE, ties=FALSE

var.match<-cbind(data$SP,data$period)
App8<-Match(Tr = data$elipic,X=var.match, M=3, ties=FALSE)

summary(App8)
## 
## Estimate...  0 
## SE.........  0 
## T-stat.....  NaN 
## p.val......  NA 
## 
## Original number of observations..............  6572 
## Original number of treated obs...............  158 
## Matched number of observations...............  158 
## Matched number of observations  (unweighted).  474
control=data.frame(App8$index.control)
treated=data.frame(App8$index.treated)


colnames(control) <- c("nrow")
colnames(treated) <- c("nrow")

cas<-unique(treated)

cas$PAIR<-c(1:158)

treated<-merge(treated,cas,by="nrow")

control$PAIR <- treated$PAIR

casm<-merge(cas,data,by="nrow")
controlm<- merge(control,data, by ="nrow")

datam8<-rbind(casm,controlm)

datam8<-datam8[order(datam8[,"PAIR"]),]

V9 - N=3, caliper = 0.1, replacement=TRUE, ties=FALSE

var.match<-cbind(data$SP,data$period)
App9<-Match(Tr = data$elipic,X=var.match, M=3, caliper=0.1, ties=FALSE)

summary(App9)
## 
## Estimate...  0 
## SE.........  0 
## T-stat.....  NaN 
## p.val......  NA 
## 
## Original number of observations..............  6572 
## Original number of treated obs...............  158 
## Matched number of observations...............  62 
## Matched number of observations  (unweighted).  186 
## 
## Caliper (SDs)........................................   0.1 0.1 
## Number of obs dropped by 'exact' or 'caliper'  96
control=data.frame(App9$index.control)
treated=data.frame(App9$index.treated)


colnames(control) <- c("nrow")
colnames(treated) <- c("nrow")

cas<-unique(treated)

cas$PAIR<-c(1:nrow(cas))

treated<-merge(treated,cas,by="nrow")

control$PAIR <- treated$PAIR

casm<-merge(cas,data,by="nrow")
controlm<- merge(control,data, by ="nrow")

datam9<-rbind(casm,controlm)

datam9<-datam9[order(datam9[,"PAIR"]),]

 

V10 - N=3, caliper = 0.2, replacement=TRUE, ties=FALSE

var.match<-cbind(data$SP,data$period)
App10<-Match(Tr = data$elipic,X=var.match, M=3, caliper=0.2, ties=FALSE)

summary(App10)
## 
## Estimate...  0 
## SE.........  0 
## T-stat.....  NaN 
## p.val......  NA 
## 
## Original number of observations..............  6572 
## Original number of treated obs...............  158 
## Matched number of observations...............  87 
## Matched number of observations  (unweighted).  261 
## 
## Caliper (SDs)........................................   0.2 0.2 
## Number of obs dropped by 'exact' or 'caliper'  71
control=data.frame(App9$index.control)
treated=data.frame(App9$index.treated)

colnames(control) <- c("nrow")
colnames(treated) <- c("nrow")

cas<-unique(treated)

cas$PAIR<-c(1:nrow(cas))

treated<-merge(treated,cas,by="nrow")

control$PAIR <- treated$PAIR

casm<-merge(cas,data,by="nrow")
controlm<- merge(control,data, by ="nrow")

datam10<-rbind(casm,controlm)

datam10<-datam10[order(datam10[,"PAIR"]),]

 

V11 - N=3, caliper = NULL, replacement=FALSE, ties=FALSE

var.match<-cbind(data$SP,data$period)
App11<-Match(Tr = data$elipic,X=var.match, M=3, ties=FALSE,replace = FALSE)

summary(App11)
## 
## Estimate...  0 
## SE.........  0 
## T-stat.....  NaN 
## p.val......  NA 
## 
## Original number of observations..............  6572 
## Original number of treated obs...............  158 
## Matched number of observations...............  158 
## Matched number of observations  (unweighted).  474
control=data.frame(App11$index.control)
treated=data.frame(App11$index.treated)


colnames(control) <- c("nrow")
colnames(treated) <- c("nrow")

cas<-unique(treated)

cas$PAIR<-c(1:nrow(cas))

treated<-merge(treated,cas,by="nrow")

control$PAIR <- treated$PAIR

casm<-merge(cas,data,by="nrow")
controlm<- merge(control,data, by ="nrow")

datam11<-rbind(casm,controlm)

datam11<-datam11[order(datam11[,"PAIR"]),]

 

V12 - N=4, caliper = NULL, replacement=TRUE, ties=FALSE

var.match<-cbind(data$SP,data$period)
App12<-Match(Tr = data$elipic,X=var.match, M=4, ties=FALSE)

summary(App12)
## 
## Estimate...  0 
## SE.........  0 
## T-stat.....  NaN 
## p.val......  NA 
## 
## Original number of observations..............  6572 
## Original number of treated obs...............  158 
## Matched number of observations...............  158 
## Matched number of observations  (unweighted).  632
control=data.frame(App12$index.control)
treated=data.frame(App12$index.treated)


colnames(control) <- c("nrow")
colnames(treated) <- c("nrow")

cas<-unique(treated)

cas$PAIR<-c(1:nrow(cas))

treated<-merge(treated,cas,by="nrow")

control$PAIR <- treated$PAIR

casm<-merge(cas,data,by="nrow")
controlm<- merge(control,data, by ="nrow")

datam12<-rbind(casm,controlm)

datam11<-datam12[order(datam12[,"PAIR"]),]

 

Balance statistics for matching

 

Standardized mean differences for continuos variables after matching :

temp min temp max lag_O3 lag2_O3 lag3_O3 lag_PM10 lag2_PM10 lag3_PM10 tot_SMD
N=1
Match 1 caliper=NULL tail=TRUE 0.0603 0.0530 0.0677 0.0588 0.0774 0.0345 0.0385 0.0595 0.4497
Match 2 caliper=NULL 0.0225 0.0189 0.0466 0.0539 0.0361 0.0321 0.0428 0.0229 0.2758
Match 3 caliper=0.1 0.0543 0.0576 0.0323 0.0374 0.0326 0.0277 0.0468 0.0206 0.3093
Match 4 caliper=0.2 0.0331 0.0351 0.0379 0.0356 0.0254 0.0298 0.0520 0.0326 0.2815
N=2
Match 5 caliper=NULL 0.0204 0.0207 0.0361 0.0317 0.0392 0.0240 0.0355 0.0218 0.2294
Match 6 caliper=0.1 0.0576 0.0627 0.0532 0.0480 0.0533 0.0366 0.0437 0.0675 0.4226
Match 7 caliper=0.2 0.0517 0.0571 0.0415 0.0378 0.0306 0.0246 0.0382 0.0220 0.3035
N=3
Match 8 caliper=NULL 0.0197 0.0196 0.0302 0.0246 0.0345 0.0197 0.0304 0.0223 0.2010
Match 9 caliper=0.1 0.0233 0.0299 0.0526 0.0346 0.0967 0.0352 0.0399 0.0462 0.3584
Match 10 caliper=0.2 0.0354 0.0431 0.0336 0.0300 0.0473 0.0207 0.0438 0.0326 0.2865
Match 11 caliper=NULL replace=FALSE 0.0464 0.0713 0.0239 0.0191 0.0168 0.2509 0.1593 0.1195 0.7072
N=4
Match 12 caliper=NULL replace=FALSE 0.0180 0.0195 0.0364 0.0333 0.0427 0.0156 0.0327 0.0220 0.2202
Note:
By default: tail=FALSE and replace=TRUE, except when different arguments are indicated in the 3rd columns