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Exemple de modèles de régression de séries temporelles interrompues stratifiés par sexe et âge en utilisant la librairie ITS2ES
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| @@ -0,0 +1,283 @@ | ||
| library(its2es) | ||
| ###### Femmes #### | ||
| femmes_3039 <- data_std_ind_menseul_tsp %>% | ||
| dplyr::filter(territoire == "Eurométropole de Strasbourg") %>% | ||
| droplevels()%>% | ||
| dplyr::filter(sexe == "femmes" ) %>% | ||
| droplevels() %>% | ||
| dplyr::filter(âge == "30-39 ans") | ||
|
|
||
| femmes_3039$temps_obs <- as.numeric(femmes_3039$temps_obs) | ||
| femmes_3039 <- femmes_3039 %>% | ||
| mutate(covid = case_when( | ||
| temps_obs < 27 ~ "0", | ||
| temps_obs >= 27 ~ "1" | ||
| )) | ||
|
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||
| femmes_3039$mois <- as.numeric(femmes_3039$mois) | ||
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||
| N05A<- as.formula("N05A ~ temps_obs") | ||
| start_interr <- which(femmes_3039$année == "2020" & femmes_3039$mois == "3" | femmes_3039$année == "2021")[1] | ||
| fit <- its_poisson(data=femmes_3039,form=N05A,offset_name="pop_conso", | ||
| time_name = "temps_obs",intervention_start_ind=start_interr,over_dispersion=TRUE, | ||
| freq=12, seasonality= "full", impact_model = "full",counterfactual = TRUE) | ||
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||
|
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| femmes_0_29 <- data_std_ind_menseul_tsp %>% | ||
| dplyr::filter(territoire == "Eurométropole de Strasbourg") %>% | ||
| droplevels()%>% | ||
| dplyr::filter(sexe == "femmes" ) %>% | ||
| droplevels() %>% | ||
| dplyr::filter(âge == "0-29 ans") | ||
|
|
||
| femmes_0_29$temps_obs <- as.numeric(femmes_0_29$temps_obs) | ||
| femmes_0_29 <- femmes_0_29 %>% | ||
| mutate(covid = case_when( | ||
| temps_obs < 27 ~ "0", | ||
| temps_obs >= 27 ~ "1" | ||
| )) | ||
|
|
||
| femmes_0_29$mois <- as.numeric(femmes_0_29$mois) | ||
|
|
||
| N05A<- as.formula("N05A ~ temps_obs") | ||
| start_interr <- which(femmes_0_29$année == "2020" & femmes_0_29$mois == "3" | femmes_0_29$année == "2021")[1] | ||
|
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||
| fit <- its_poisson(data=femmes_0_29,form=N05A,offset_name="pop_conso", | ||
| time_name = "temps_obs",intervention_start_ind=start_interr,over_dispersion=TRUE, | ||
| freq=12, seasonality= "full", impact_model = "full",counterfactual = TRUE) | ||
|
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||
| femmes_4049 <- data_std_ind_menseul_tsp %>% | ||
| dplyr::filter(territoire == "Eurométropole de Strasbourg") %>% | ||
| droplevels()%>% | ||
| dplyr::filter(sexe == "femmes" ) %>% | ||
| droplevels() %>% | ||
| dplyr::filter(âge == "40-49 ans") | ||
|
|
||
| femmes_4049$temps_obs <- as.numeric(femmes_4049$temps_obs) | ||
| femmes_4049 <- femmes_4049 %>% | ||
| mutate(covid = case_when( | ||
| temps_obs < 27 ~ "0", | ||
| temps_obs >= 27 ~ "1" | ||
| )) | ||
|
|
||
| femmes_4049$mois <- as.numeric(femmes_4049$mois) | ||
|
|
||
| N05A<- as.formula("N05A ~ temps_obs") | ||
| start_interr <- which(femmes_4049$année == "2020" & femmes_4049$mois == "3" | femmes_4049$année == "2021")[1] | ||
| fit <- its_poisson(data=femmes_4049,form=N05A,offset_name="pop_conso", | ||
| time_name = "temps_obs",intervention_start_ind=start_interr,over_dispersion=TRUE, | ||
| freq=12, seasonality= "full", impact_model = "full",counterfactual = TRUE) | ||
|
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||
|
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||
| femmes_5059 <- data_std_ind_menseul_tsp %>% | ||
| dplyr::filter(territoire == "Eurométropole de Strasbourg") %>% | ||
| droplevels()%>% | ||
| dplyr::filter(sexe == "femmes" ) %>% | ||
| droplevels() %>% | ||
| dplyr::filter(âge == "50-59 ans") | ||
|
|
||
| femmes_5059$temps_obs <- as.numeric(femmes_5059$temps_obs) | ||
| femmes_5059 <- femmes_5059 %>% | ||
| mutate(covid = case_when( | ||
| temps_obs < 27 ~ "0", | ||
| temps_obs >= 27 ~ "1" | ||
| )) | ||
|
|
||
| femmes_5059$mois <- as.numeric(femmes_5059$mois) | ||
|
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||
| N05A<- as.formula("N05A ~ temps_obs") | ||
| start_interr <- which(femmes_5059$année == "2020" & femmes_5059$mois == "3" | femmes_5059$année == "2021")[1] | ||
| fit <- its_poisson(data=femmes_5059,form=N05A,offset_name="pop_conso", | ||
| time_name = "temps_obs",intervention_start_ind=start_interr,over_dispersion=TRUE, | ||
| freq=12, seasonality= "full", impact_model = "full",counterfactual = TRUE) | ||
|
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||
|
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||
| femmes_6069 <- data_std_ind_menseul_tsp %>% | ||
| dplyr::filter(territoire == "Eurométropole de Strasbourg") %>% | ||
| droplevels()%>% | ||
| dplyr::filter(sexe == "femmes" ) %>% | ||
| droplevels() %>% | ||
| dplyr::filter(âge == "60-69 ans") | ||
|
|
||
| femmes_6069$temps_obs <- as.numeric(femmes_6069$temps_obs) | ||
| femmes_6069 <- femmes_6069 %>% | ||
| mutate(covid = case_when( | ||
| temps_obs < 27 ~ "0", | ||
| temps_obs >= 27 ~ "1" | ||
| )) | ||
|
|
||
| femmes_6069$mois <- as.numeric(femmes_6069$mois) | ||
|
|
||
| N05A<- as.formula("N05A ~ temps_obs") | ||
| start_interr <- which(femmes_6069$année == "2020" & femmes_6069$mois == "3" | femmes_6069$année == "2021")[1] | ||
| fit <- its_poisson(data=femmes_6069,form=N05A,offset_name="pop_conso", | ||
| time_name = "temps_obs",intervention_start_ind=start_interr,over_dispersion=TRUE, | ||
| freq=12, seasonality= "full", impact_model = "full",counterfactual = TRUE) | ||
|
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| femmes_70 <- data_std_ind_menseul_tsp %>% | ||
| dplyr::filter(territoire == "Eurométropole de Strasbourg") %>% | ||
| droplevels()%>% | ||
| dplyr::filter(sexe == "femmes" ) %>% | ||
| droplevels() %>% | ||
| dplyr::filter(âge == "70 ans ou +") | ||
|
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||
| femmes_70$temps_obs <- as.numeric(femmes_70$temps_obs) | ||
| femmes_70 <- femmes_70 %>% | ||
| mutate(covid = case_when( | ||
| temps_obs < 27 ~ "0", | ||
| temps_obs >= 27 ~ "1" | ||
| )) | ||
|
|
||
| femmes_70$mois <- as.numeric(femmes_70$mois) | ||
|
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||
| N05A<- as.formula("N05A ~ temps_obs") | ||
| start_interr <- which(femmes_70$année == "2020" & femmes_70$mois == "3" | femmes_70$année == "2021")[1] | ||
| fit <- its_poisson(data=femmes_70,form=N05A,offset_name="pop_conso", | ||
| time_name = "temps_obs",intervention_start_ind=start_interr,over_dispersion=TRUE, | ||
| freq=12, seasonality= "full", impact_model = "full",counterfactual = TRUE) | ||
|
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||
|
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||
|
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||
| ##### hommes #### | ||
|
|
||
| hommes_3039 <- data_std_ind_menseul_tsp %>% | ||
| dplyr::filter(territoire == "Eurométropole de Strasbourg") %>% | ||
| droplevels()%>% | ||
| dplyr::filter(sexe == "hommes" ) %>% | ||
| droplevels() %>% | ||
| dplyr::filter(âge == "30-39 ans") | ||
|
|
||
| hommes_3039$temps_obs <- as.numeric(hommes_3039$temps_obs) | ||
| hommes_3039 <- hommes_3039 %>% | ||
| mutate(covid = case_when( | ||
| temps_obs < 27 ~ "0", | ||
| temps_obs >= 27 ~ "1" | ||
| )) | ||
|
|
||
| hommes_3039$mois <- as.numeric(hommes_3039$mois) | ||
|
|
||
| N05A<- as.formula("N05A ~ temps_obs") | ||
| start_interr <- which(hommes_3039$année == "2020" & hommes_3039$mois == "3" | hommes_3039$année == "2021")[1] | ||
| fit <- its_poisson(data=hommes_3039,form=N05A,offset_name="pop_conso", | ||
| time_name = "temps_obs",intervention_start_ind=start_interr,over_dispersion=TRUE, | ||
| freq=12, seasonality= "full", impact_model = "full",counterfactual = TRUE) | ||
|
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||
|
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||
| hommes_0_29 <- data_std_ind_menseul_tsp %>% | ||
| dplyr::filter(territoire == "Eurométropole de Strasbourg") %>% | ||
| droplevels()%>% | ||
| dplyr::filter(sexe == "hommes" ) %>% | ||
| droplevels() %>% | ||
| dplyr::filter(âge == "0-29 ans") | ||
|
|
||
| hommes_0_29$temps_obs <- as.numeric(hommes_0_29$temps_obs) | ||
| hommes_0_29 <- hommes_0_29 %>% | ||
| mutate(covid = case_when( | ||
| temps_obs < 27 ~ "0", | ||
| temps_obs >= 27 ~ "1" | ||
| )) | ||
|
|
||
| hommes_0_29$mois <- as.numeric(hommes_0_29$mois) | ||
|
|
||
| N05A<- as.formula("N05A ~ temps_obs") | ||
| start_interr <- which(hommes_0_29$année == "2020" & hommes_0_29$mois == "3" | hommes_0_29$année == "2021")[1] | ||
|
|
||
| fit <- its_poisson(data=hommes_0_29,form=N05A,offset_name="pop_conso", | ||
| time_name = "temps_obs",intervention_start_ind=start_interr,over_dispersion=TRUE, | ||
| freq=12, seasonality= "full", impact_model = "full",counterfactual = TRUE) | ||
|
|
||
| hommes_4049 <- data_std_ind_menseul_tsp %>% | ||
| dplyr::filter(territoire == "Eurométropole de Strasbourg") %>% | ||
| droplevels()%>% | ||
| dplyr::filter(sexe == "hommes" ) %>% | ||
| droplevels() %>% | ||
| dplyr::filter(âge == "40-49 ans") | ||
|
|
||
| hommes_4049$temps_obs <- as.numeric(hommes_4049$temps_obs) | ||
| hommes_4049 <- hommes_4049 %>% | ||
| mutate(covid = case_when( | ||
| temps_obs < 27 ~ "0", | ||
| temps_obs >= 27 ~ "1" | ||
| )) | ||
|
|
||
| hommes_4049$mois <- as.numeric(hommes_4049$mois) | ||
|
|
||
| N05A<- as.formula("N05A ~ temps_obs") | ||
| start_interr <- which(hommes_4049$année == "2020" & hommes_4049$mois == "3" | hommes_4049$année == "2021")[1] | ||
| fit <- its_poisson(data=hommes_4049,form=N05A,offset_name="pop_conso", | ||
| time_name = "temps_obs",intervention_start_ind=start_interr,over_dispersion=TRUE, | ||
| freq=12, seasonality= "full", impact_model = "full",counterfactual = TRUE) | ||
|
|
||
|
|
||
| hommes_5059 <- data_std_ind_menseul_tsp %>% | ||
| dplyr::filter(territoire == "Eurométropole de Strasbourg") %>% | ||
| droplevels()%>% | ||
| dplyr::filter(sexe == "hommes" ) %>% | ||
| droplevels() %>% | ||
| dplyr::filter(âge == "50-59 ans") | ||
|
|
||
| hommes_5059$temps_obs <- as.numeric(hommes_5059$temps_obs) | ||
| hommes_5059 <- hommes_5059 %>% | ||
| mutate(covid = case_when( | ||
| temps_obs < 27 ~ "0", | ||
| temps_obs >= 27 ~ "1" | ||
| )) | ||
|
|
||
| hommes_5059$mois <- as.numeric(hommes_5059$mois) | ||
|
|
||
| N05A<- as.formula("N05A ~ temps_obs") | ||
| start_interr <- which(hommes_5059$année == "2020" & hommes_5059$mois == "3" | hommes_5059$année == "2021")[1] | ||
| fit <- its_poisson(data=hommes_5059,form=N05A,offset_name="pop_conso", | ||
| time_name = "temps_obs",intervention_start_ind=start_interr,over_dispersion=TRUE, | ||
| freq=12, seasonality= "full", impact_model = "full",counterfactual = TRUE) | ||
|
|
||
|
|
||
| hommes_6069 <- data_std_ind_menseul_tsp %>% | ||
| dplyr::filter(territoire == "Eurométropole de Strasbourg") %>% | ||
| droplevels()%>% | ||
| dplyr::filter(sexe == "hommes" ) %>% | ||
| droplevels() %>% | ||
| dplyr::filter(âge == "60-69 ans") | ||
|
|
||
| hommes_6069$temps_obs <- as.numeric(hommes_6069$temps_obs) | ||
| hommes_6069 <- hommes_6069 %>% | ||
| mutate(covid = case_when( | ||
| temps_obs < 27 ~ "0", | ||
| temps_obs >= 27 ~ "1" | ||
| )) | ||
|
|
||
| hommes_6069$mois <- as.numeric(hommes_6069$mois) | ||
|
|
||
| N05A<- as.formula("N05A ~ temps_obs") | ||
| start_interr <- which(hommes_6069$année == "2020" & hommes_6069$mois == "3" | hommes_6069$année == "2021")[1] | ||
| fit <- its_poisson(data=hommes_6069,form=N05A,offset_name="pop_conso", | ||
| time_name = "temps_obs",intervention_start_ind=start_interr,over_dispersion=TRUE, | ||
| freq=12, seasonality= "full", impact_model = "full",counterfactual = TRUE) | ||
|
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||
|
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||
|
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| hommes_70 <- data_std_ind_menseul_tsp %>% | ||
| dplyr::filter(territoire == "Eurométropole de Strasbourg") %>% | ||
| droplevels()%>% | ||
| dplyr::filter(sexe == "hommes" ) %>% | ||
| droplevels() %>% | ||
| dplyr::filter(âge == "70 ans ou +") | ||
|
|
||
| hommes_70$temps_obs <- as.numeric(hommes_70$temps_obs) | ||
| hommes_70 <- hommes_70 %>% | ||
| mutate(covid = case_when( | ||
| temps_obs < 27 ~ "0", | ||
| temps_obs >= 27 ~ "1" | ||
| )) | ||
|
|
||
| hommes_70$mois <- as.numeric(hommes_70$mois) | ||
|
|
||
| N05A<- as.formula("N05A ~ temps_obs") | ||
| start_interr <- which(hommes_70$année == "2020" & hommes_70$mois == "3" | hommes_70$année == "2021")[1] | ||
| fit <- its_poisson(data=hommes_70,form=N05A,offset_name="pop_conso", | ||
| time_name = "temps_obs",intervention_start_ind=start_interr,over_dispersion=TRUE, | ||
| freq=12, seasonality= "full", impact_model = "full",counterfactual = TRUE) | ||
|
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||
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