class: inverse, middle background-image: url(https://images.unsplash.com/photo-1586165368502-1bad197a6461?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=958&q=80) background-size: cover # Logit ### Gina Reynolds <br> <br> <br> --- count: false .panel1-hotel-auto[ ```r *library(tidyverse) ``` ] .panel2-hotel-auto[ ] --- count: false .panel1-hotel-auto[ ```r library(tidyverse) *library(broom) ``` ] .panel2-hotel-auto[ ] --- count: false .panel1-hotel-auto[ ```r library(tidyverse) library(broom) *read_csv('https://tidymodels.org/start/case-study/hotels.csv') ``` ] .panel2-hotel-auto[ ``` # A tibble: 50,000 × 23 hotel lead_time stays_in_weeken… stays_in_week_n… adults children meal country market_segment distribution_ch… is_repeated_gue… previous_cancel… previous_bookin… reserved_room_t… assigned_room_t… booking_changes deposit_type days_in_waiting… customer_type average_daily_r… required_car_pa… <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr> 1 City_Hotel 217 1 3 2 none BB DEU Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 80.8 none 2 City_Hotel 2 0 1 2 none BB PRT Direct Direct 0 0 0 D K 0 No_Deposit 0 Transient 170 none 3 Resort_Hotel 95 2 5 2 none BB GBR Online_TA TA/TO 0 0 0 A A 2 No_Deposit 0 Transient 8 none 4 Resort_Hotel 143 2 6 2 none HB ROU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 81 none 5 Resort_Hotel 136 1 4 2 none HB PRT Direct Direct 0 0 0 F F 0 No_Deposit 0 Transient 158. none 6 City_Hotel 67 2 2 2 none SC GBR Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 49.1 none 7 Resort_Hotel 47 0 2 2 children BB ESP Direct Direct 0 0 0 C C 0 No_Deposit 0 Transient 289 none 8 City_Hotel 56 0 3 0 children BB ESP Online_TA TA/TO 0 0 0 B A 0 No_Deposit 0 Transient 82.4 none 9 City_Hotel 80 0 4 2 none BB FRA Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 135 none 10 City_Hotel 6 2 2 2 children BB FRA Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 180 none 11 City_Hotel 130 1 2 2 none BB FRA Offline_TA/TO TA/TO 0 0 0 A D 0 No_Deposit 0 Transient 71 none 12 City_Hotel 27 0 1 1 none BB NLD Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 120. none 13 Resort_Hotel 16 1 2 2 none BB GBR Corporate Corporate 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 40 none 14 Resort_Hotel 46 0 2 2 none BB PRT Offline_TA/TO TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 162 none 15 City_Hotel 297 1 1 2 none BB FRA Groups TA/TO 0 0 0 A A 0 No_Deposit 236 Transient-Pa… 65 none 16 City_Hotel 423 1 1 2 none HB DEU Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 122. none 17 City_Hotel 22 1 2 2 none BB FRA Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 121. none 18 Resort_Hotel 96 4 7 2 none HB GBR Groups TA/TO 0 0 0 A A 4 No_Deposit 0 Transient-Pa… 74.2 none 19 City_Hotel 0 1 0 1 none BB PRT Corporate Corporate 0 0 0 E E 0 No_Deposit 0 Transient 139 parking 20 Resort_Hotel 7 1 2 2 none HB NULL Online_TA TA/TO 0 0 1 E I 1 No_Deposit 0 Transient 78.4 parking 21 Resort_Hotel 209 2 5 2 none BB GBR Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 76.2 none 22 City_Hotel 1 1 0 2 none SC CN Online_TA TA/TO 0 0 0 A B 0 No_Deposit 0 Transient 98 none 23 City_Hotel 205 0 5 2 none BB GBR Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 70.0 none 24 Resort_Hotel 45 0 1 2 none BB PRT Online_TA TA/TO 0 0 0 A D 1 No_Deposit 0 Transient-Pa… 65 none 25 City_Hotel 134 0 1 1 none BB PRT Groups TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 75 none 26 City_Hotel 179 2 5 2 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 107. none 27 City_Hotel 0 1 3 1 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Contract 91 none 28 City_Hotel 72 0 2 2 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 121. parking 29 City_Hotel 39 0 3 1 none BB PRT Online_TA TA/TO 0 0 0 A A 1 No_Deposit 0 Transient 94.5 none 30 City_Hotel 5 2 5 1 none BB ITA Aviation Corporate 0 0 0 D D 0 No_Deposit 0 Transient 110 none 31 City_Hotel 43 1 2 2 none BB NLD Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 68 none 32 City_Hotel 157 0 2 2 children BB BEL Online_TA TA/TO 0 0 0 A D 0 No_Deposit 0 Transient-Pa… 122. none 33 City_Hotel 0 0 1 2 none BB FRA Groups TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 124 none 34 Resort_Hotel 230 2 3 2 none BB GBR Offline_TA/TO TA/TO 0 0 0 E E 0 No_Deposit 0 Transient 46.8 none 35 Resort_Hotel 48 1 1 2 none BB ESP Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 206 none 36 Resort_Hotel 151 3 6 2 none HB GBR Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 167. none 37 Resort_Hotel 171 2 5 2 none BB GBR Online_TA TA/TO 0 0 0 D D 2 No_Deposit 0 Transient-Pa… 183. none 38 City_Hotel 1 0 1 2 none SC PRT Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 139 none 39 Resort_Hotel 39 2 3 2 none BB PRT Online_TA TA/TO 0 0 0 A B 0 No_Deposit 0 Transient 180. none 40 City_Hotel 61 2 0 2 none BB FRA Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 91 none 41 Resort_Hotel 127 2 5 2 children HB PRT Direct Direct 0 0 0 E E 4 No_Deposit 0 Transient 334. parking 42 Resort_Hotel 244 1 0 2 none BB HRV Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 84 parking 43 Resort_Hotel 4 0 1 1 none BB PRT Offline_TA/TO TA/TO 1 0 2 A D 0 No_Deposit 0 Transient 30 none 44 Resort_Hotel 4 1 1 1 none BB ESP Offline_TA/TO TA/TO 0 0 0 A D 0 No_Deposit 0 Transient 25 none 45 City_Hotel 134 0 1 1 none BB PRT Groups TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 75 none 46 Resort_Hotel 161 2 5 3 none BB IRL Online_TA TA/TO 0 0 0 E E 0 No_Deposit 0 Transient-Pa… 81.6 none 47 Resort_Hotel 210 4 10 2 none BB NOR Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 61.9 none 48 Resort_Hotel 96 2 5 2 none HB DEU Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 78.4 none 49 Resort_Hotel 83 2 5 1 none BB GBR Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Contract 34.2 none 50 Resort_Hotel 232 2 5 1 none BB AUT Offline_TA/TO TA/TO 0 0 0 A A 2 No_Deposit 0 Transient 25.5 none 51 City_Hotel 17 2 2 2 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 108. none 52 City_Hotel 40 0 4 2 none BB AUT Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 96.9 none 53 City_Hotel 141 1 0 2 none BB CN Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 90 none 54 Resort_Hotel 0 1 0 2 none BB BEL Direct Direct 0 0 0 G G 0 No_Deposit 0 Transient 116 parking 55 City_Hotel 181 0 2 1 none BB FRA Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 120 none # … with 49,945 more rows, and 2 more variables: total_of_special_requests <dbl>, arrival_date <date> ``` ] --- count: false .panel1-hotel-auto[ ```r library(tidyverse) library(broom) read_csv('https://tidymodels.org/start/case-study/hotels.csv') %>% * mutate(across(where(is.character), as.factor)) ``` ] .panel2-hotel-auto[ ``` # A tibble: 50,000 × 23 hotel lead_time stays_in_weeken… stays_in_week_n… adults children meal country market_segment distribution_ch… is_repeated_gue… previous_cancel… previous_bookin… reserved_room_t… assigned_room_t… booking_changes deposit_type days_in_waiting… customer_type average_daily_r… required_car_pa… <fct> <dbl> <dbl> <dbl> <dbl> <fct> <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <fct> <fct> <dbl> <fct> <dbl> <fct> <dbl> <fct> 1 City_Hotel 217 1 3 2 none BB DEU Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 80.8 none 2 City_Hotel 2 0 1 2 none BB PRT Direct Direct 0 0 0 D K 0 No_Deposit 0 Transient 170 none 3 Resort_Hotel 95 2 5 2 none BB GBR Online_TA TA/TO 0 0 0 A A 2 No_Deposit 0 Transient 8 none 4 Resort_Hotel 143 2 6 2 none HB ROU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 81 none 5 Resort_Hotel 136 1 4 2 none HB PRT Direct Direct 0 0 0 F F 0 No_Deposit 0 Transient 158. none 6 City_Hotel 67 2 2 2 none SC GBR Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 49.1 none 7 Resort_Hotel 47 0 2 2 children BB ESP Direct Direct 0 0 0 C C 0 No_Deposit 0 Transient 289 none 8 City_Hotel 56 0 3 0 children BB ESP Online_TA TA/TO 0 0 0 B A 0 No_Deposit 0 Transient 82.4 none 9 City_Hotel 80 0 4 2 none BB FRA Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 135 none 10 City_Hotel 6 2 2 2 children BB FRA Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 180 none 11 City_Hotel 130 1 2 2 none BB FRA Offline_TA/TO TA/TO 0 0 0 A D 0 No_Deposit 0 Transient 71 none 12 City_Hotel 27 0 1 1 none BB NLD Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 120. none 13 Resort_Hotel 16 1 2 2 none BB GBR Corporate Corporate 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 40 none 14 Resort_Hotel 46 0 2 2 none BB PRT Offline_TA/TO TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 162 none 15 City_Hotel 297 1 1 2 none BB FRA Groups TA/TO 0 0 0 A A 0 No_Deposit 236 Transient-Pa… 65 none 16 City_Hotel 423 1 1 2 none HB DEU Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 122. none 17 City_Hotel 22 1 2 2 none BB FRA Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 121. none 18 Resort_Hotel 96 4 7 2 none HB GBR Groups TA/TO 0 0 0 A A 4 No_Deposit 0 Transient-Pa… 74.2 none 19 City_Hotel 0 1 0 1 none BB PRT Corporate Corporate 0 0 0 E E 0 No_Deposit 0 Transient 139 parking 20 Resort_Hotel 7 1 2 2 none HB NULL Online_TA TA/TO 0 0 1 E I 1 No_Deposit 0 Transient 78.4 parking 21 Resort_Hotel 209 2 5 2 none BB GBR Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 76.2 none 22 City_Hotel 1 1 0 2 none SC CN Online_TA TA/TO 0 0 0 A B 0 No_Deposit 0 Transient 98 none 23 City_Hotel 205 0 5 2 none BB GBR Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 70.0 none 24 Resort_Hotel 45 0 1 2 none BB PRT Online_TA TA/TO 0 0 0 A D 1 No_Deposit 0 Transient-Pa… 65 none 25 City_Hotel 134 0 1 1 none BB PRT Groups TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 75 none 26 City_Hotel 179 2 5 2 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 107. none 27 City_Hotel 0 1 3 1 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Contract 91 none 28 City_Hotel 72 0 2 2 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 121. parking 29 City_Hotel 39 0 3 1 none BB PRT Online_TA TA/TO 0 0 0 A A 1 No_Deposit 0 Transient 94.5 none 30 City_Hotel 5 2 5 1 none BB ITA Aviation Corporate 0 0 0 D D 0 No_Deposit 0 Transient 110 none 31 City_Hotel 43 1 2 2 none BB NLD Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 68 none 32 City_Hotel 157 0 2 2 children BB BEL Online_TA TA/TO 0 0 0 A D 0 No_Deposit 0 Transient-Pa… 122. none 33 City_Hotel 0 0 1 2 none BB FRA Groups TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 124 none 34 Resort_Hotel 230 2 3 2 none BB GBR Offline_TA/TO TA/TO 0 0 0 E E 0 No_Deposit 0 Transient 46.8 none 35 Resort_Hotel 48 1 1 2 none BB ESP Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 206 none 36 Resort_Hotel 151 3 6 2 none HB GBR Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 167. none 37 Resort_Hotel 171 2 5 2 none BB GBR Online_TA TA/TO 0 0 0 D D 2 No_Deposit 0 Transient-Pa… 183. none 38 City_Hotel 1 0 1 2 none SC PRT Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 139 none 39 Resort_Hotel 39 2 3 2 none BB PRT Online_TA TA/TO 0 0 0 A B 0 No_Deposit 0 Transient 180. none 40 City_Hotel 61 2 0 2 none BB FRA Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 91 none 41 Resort_Hotel 127 2 5 2 children HB PRT Direct Direct 0 0 0 E E 4 No_Deposit 0 Transient 334. parking 42 Resort_Hotel 244 1 0 2 none BB HRV Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 84 parking 43 Resort_Hotel 4 0 1 1 none BB PRT Offline_TA/TO TA/TO 1 0 2 A D 0 No_Deposit 0 Transient 30 none 44 Resort_Hotel 4 1 1 1 none BB ESP Offline_TA/TO TA/TO 0 0 0 A D 0 No_Deposit 0 Transient 25 none 45 City_Hotel 134 0 1 1 none BB PRT Groups TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 75 none 46 Resort_Hotel 161 2 5 3 none BB IRL Online_TA TA/TO 0 0 0 E E 0 No_Deposit 0 Transient-Pa… 81.6 none 47 Resort_Hotel 210 4 10 2 none BB NOR Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 61.9 none 48 Resort_Hotel 96 2 5 2 none HB DEU Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 78.4 none 49 Resort_Hotel 83 2 5 1 none BB GBR Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Contract 34.2 none 50 Resort_Hotel 232 2 5 1 none BB AUT Offline_TA/TO TA/TO 0 0 0 A A 2 No_Deposit 0 Transient 25.5 none 51 City_Hotel 17 2 2 2 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 108. none 52 City_Hotel 40 0 4 2 none BB AUT Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 96.9 none 53 City_Hotel 141 1 0 2 none BB CN Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 90 none 54 Resort_Hotel 0 1 0 2 none BB BEL Direct Direct 0 0 0 G G 0 No_Deposit 0 Transient 116 parking 55 City_Hotel 181 0 2 1 none BB FRA Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 120 none # … with 49,945 more rows, and 2 more variables: total_of_special_requests <dbl>, arrival_date <date> ``` ] --- count: false .panel1-hotel-auto[ ```r library(tidyverse) library(broom) read_csv('https://tidymodels.org/start/case-study/hotels.csv') %>% mutate(across(where(is.character), as.factor)) -> *hotels ``` ] .panel2-hotel-auto[ ] --- count: false .panel1-hotel-auto[ ```r library(tidyverse) library(broom) read_csv('https://tidymodels.org/start/case-study/hotels.csv') %>% mutate(across(where(is.character), as.factor)) -> hotels *dim(hotels) ``` ] .panel2-hotel-auto[ ``` [1] 50000 23 ``` ] --- count: false .panel1-hotel-auto[ ```r library(tidyverse) library(broom) read_csv('https://tidymodels.org/start/case-study/hotels.csv') %>% mutate(across(where(is.character), as.factor)) -> hotels dim(hotels) *hotels ``` ] .panel2-hotel-auto[ ``` [1] 50000 23 ``` ``` # A tibble: 50,000 × 23 hotel lead_time stays_in_weeken… stays_in_week_n… adults children meal country market_segment distribution_ch… is_repeated_gue… previous_cancel… previous_bookin… reserved_room_t… assigned_room_t… booking_changes deposit_type days_in_waiting… customer_type average_daily_r… required_car_pa… <fct> <dbl> <dbl> <dbl> <dbl> <fct> <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <fct> <fct> <dbl> <fct> <dbl> <fct> <dbl> <fct> 1 City_Hotel 217 1 3 2 none BB DEU Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 80.8 none 2 City_Hotel 2 0 1 2 none BB PRT Direct Direct 0 0 0 D K 0 No_Deposit 0 Transient 170 none 3 Resort_Hotel 95 2 5 2 none BB GBR Online_TA TA/TO 0 0 0 A A 2 No_Deposit 0 Transient 8 none 4 Resort_Hotel 143 2 6 2 none HB ROU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 81 none 5 Resort_Hotel 136 1 4 2 none HB PRT Direct Direct 0 0 0 F F 0 No_Deposit 0 Transient 158. none 6 City_Hotel 67 2 2 2 none SC GBR Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 49.1 none 7 Resort_Hotel 47 0 2 2 children BB ESP Direct Direct 0 0 0 C C 0 No_Deposit 0 Transient 289 none 8 City_Hotel 56 0 3 0 children BB ESP Online_TA TA/TO 0 0 0 B A 0 No_Deposit 0 Transient 82.4 none 9 City_Hotel 80 0 4 2 none BB FRA Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 135 none 10 City_Hotel 6 2 2 2 children BB FRA Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 180 none 11 City_Hotel 130 1 2 2 none BB FRA Offline_TA/TO TA/TO 0 0 0 A D 0 No_Deposit 0 Transient 71 none 12 City_Hotel 27 0 1 1 none BB NLD Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 120. none 13 Resort_Hotel 16 1 2 2 none BB GBR Corporate Corporate 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 40 none 14 Resort_Hotel 46 0 2 2 none BB PRT Offline_TA/TO TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 162 none 15 City_Hotel 297 1 1 2 none BB FRA Groups TA/TO 0 0 0 A A 0 No_Deposit 236 Transient-Pa… 65 none 16 City_Hotel 423 1 1 2 none HB DEU Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 122. none 17 City_Hotel 22 1 2 2 none BB FRA Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 121. none 18 Resort_Hotel 96 4 7 2 none HB GBR Groups TA/TO 0 0 0 A A 4 No_Deposit 0 Transient-Pa… 74.2 none 19 City_Hotel 0 1 0 1 none BB PRT Corporate Corporate 0 0 0 E E 0 No_Deposit 0 Transient 139 parking 20 Resort_Hotel 7 1 2 2 none HB NULL Online_TA TA/TO 0 0 1 E I 1 No_Deposit 0 Transient 78.4 parking 21 Resort_Hotel 209 2 5 2 none BB GBR Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 76.2 none 22 City_Hotel 1 1 0 2 none SC CN Online_TA TA/TO 0 0 0 A B 0 No_Deposit 0 Transient 98 none 23 City_Hotel 205 0 5 2 none BB GBR Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 70.0 none 24 Resort_Hotel 45 0 1 2 none BB PRT Online_TA TA/TO 0 0 0 A D 1 No_Deposit 0 Transient-Pa… 65 none 25 City_Hotel 134 0 1 1 none BB PRT Groups TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 75 none 26 City_Hotel 179 2 5 2 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 107. none 27 City_Hotel 0 1 3 1 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Contract 91 none 28 City_Hotel 72 0 2 2 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 121. parking 29 City_Hotel 39 0 3 1 none BB PRT Online_TA TA/TO 0 0 0 A A 1 No_Deposit 0 Transient 94.5 none 30 City_Hotel 5 2 5 1 none BB ITA Aviation Corporate 0 0 0 D D 0 No_Deposit 0 Transient 110 none 31 City_Hotel 43 1 2 2 none BB NLD Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 68 none 32 City_Hotel 157 0 2 2 children BB BEL Online_TA TA/TO 0 0 0 A D 0 No_Deposit 0 Transient-Pa… 122. none 33 City_Hotel 0 0 1 2 none BB FRA Groups TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 124 none 34 Resort_Hotel 230 2 3 2 none BB GBR Offline_TA/TO TA/TO 0 0 0 E E 0 No_Deposit 0 Transient 46.8 none 35 Resort_Hotel 48 1 1 2 none BB ESP Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 206 none 36 Resort_Hotel 151 3 6 2 none HB GBR Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 167. none 37 Resort_Hotel 171 2 5 2 none BB GBR Online_TA TA/TO 0 0 0 D D 2 No_Deposit 0 Transient-Pa… 183. none 38 City_Hotel 1 0 1 2 none SC PRT Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 139 none 39 Resort_Hotel 39 2 3 2 none BB PRT Online_TA TA/TO 0 0 0 A B 0 No_Deposit 0 Transient 180. none 40 City_Hotel 61 2 0 2 none BB FRA Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 91 none 41 Resort_Hotel 127 2 5 2 children HB PRT Direct Direct 0 0 0 E E 4 No_Deposit 0 Transient 334. parking 42 Resort_Hotel 244 1 0 2 none BB HRV Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 84 parking 43 Resort_Hotel 4 0 1 1 none BB PRT Offline_TA/TO TA/TO 1 0 2 A D 0 No_Deposit 0 Transient 30 none 44 Resort_Hotel 4 1 1 1 none BB ESP Offline_TA/TO TA/TO 0 0 0 A D 0 No_Deposit 0 Transient 25 none 45 City_Hotel 134 0 1 1 none BB PRT Groups TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 75 none 46 Resort_Hotel 161 2 5 3 none BB IRL Online_TA TA/TO 0 0 0 E E 0 No_Deposit 0 Transient-Pa… 81.6 none 47 Resort_Hotel 210 4 10 2 none BB NOR Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 61.9 none 48 Resort_Hotel 96 2 5 2 none HB DEU Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 78.4 none 49 Resort_Hotel 83 2 5 1 none BB GBR Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Contract 34.2 none 50 Resort_Hotel 232 2 5 1 none BB AUT Offline_TA/TO TA/TO 0 0 0 A A 2 No_Deposit 0 Transient 25.5 none 51 City_Hotel 17 2 2 2 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 108. none 52 City_Hotel 40 0 4 2 none BB AUT Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 96.9 none 53 City_Hotel 141 1 0 2 none BB CN Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 90 none 54 Resort_Hotel 0 1 0 2 none BB BEL Direct Direct 0 0 0 G G 0 No_Deposit 0 Transient 116 parking 55 City_Hotel 181 0 2 1 none BB FRA Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 120 none # … with 49,945 more rows, and 2 more variables: total_of_special_requests <dbl>, arrival_date <date> ``` ] --- count: false .panel1-hotel-auto[ ```r library(tidyverse) library(broom) read_csv('https://tidymodels.org/start/case-study/hotels.csv') %>% mutate(across(where(is.character), as.factor)) -> hotels dim(hotels) hotels %>% * mutate(ind_children = as.numeric(children == "children") ) ``` ] .panel2-hotel-auto[ ``` [1] 50000 23 ``` ``` # A tibble: 50,000 × 24 hotel lead_time stays_in_weeken… stays_in_week_n… adults children meal country market_segment distribution_ch… is_repeated_gue… previous_cancel… previous_bookin… reserved_room_t… assigned_room_t… booking_changes deposit_type days_in_waiting… customer_type average_daily_r… required_car_pa… <fct> <dbl> <dbl> <dbl> <dbl> <fct> <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <fct> <fct> <dbl> <fct> <dbl> <fct> <dbl> <fct> 1 City_Hotel 217 1 3 2 none BB DEU Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 80.8 none 2 City_Hotel 2 0 1 2 none BB PRT Direct Direct 0 0 0 D K 0 No_Deposit 0 Transient 170 none 3 Resort_Hotel 95 2 5 2 none BB GBR Online_TA TA/TO 0 0 0 A A 2 No_Deposit 0 Transient 8 none 4 Resort_Hotel 143 2 6 2 none HB ROU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 81 none 5 Resort_Hotel 136 1 4 2 none HB PRT Direct Direct 0 0 0 F F 0 No_Deposit 0 Transient 158. none 6 City_Hotel 67 2 2 2 none SC GBR Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 49.1 none 7 Resort_Hotel 47 0 2 2 children BB ESP Direct Direct 0 0 0 C C 0 No_Deposit 0 Transient 289 none 8 City_Hotel 56 0 3 0 children BB ESP Online_TA TA/TO 0 0 0 B A 0 No_Deposit 0 Transient 82.4 none 9 City_Hotel 80 0 4 2 none BB FRA Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 135 none 10 City_Hotel 6 2 2 2 children BB FRA Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 180 none 11 City_Hotel 130 1 2 2 none BB FRA Offline_TA/TO TA/TO 0 0 0 A D 0 No_Deposit 0 Transient 71 none 12 City_Hotel 27 0 1 1 none BB NLD Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 120. none 13 Resort_Hotel 16 1 2 2 none BB GBR Corporate Corporate 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 40 none 14 Resort_Hotel 46 0 2 2 none BB PRT Offline_TA/TO TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 162 none 15 City_Hotel 297 1 1 2 none BB FRA Groups TA/TO 0 0 0 A A 0 No_Deposit 236 Transient-Pa… 65 none 16 City_Hotel 423 1 1 2 none HB DEU Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 122. none 17 City_Hotel 22 1 2 2 none BB FRA Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 121. none 18 Resort_Hotel 96 4 7 2 none HB GBR Groups TA/TO 0 0 0 A A 4 No_Deposit 0 Transient-Pa… 74.2 none 19 City_Hotel 0 1 0 1 none BB PRT Corporate Corporate 0 0 0 E E 0 No_Deposit 0 Transient 139 parking 20 Resort_Hotel 7 1 2 2 none HB NULL Online_TA TA/TO 0 0 1 E I 1 No_Deposit 0 Transient 78.4 parking 21 Resort_Hotel 209 2 5 2 none BB GBR Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 76.2 none 22 City_Hotel 1 1 0 2 none SC CN Online_TA TA/TO 0 0 0 A B 0 No_Deposit 0 Transient 98 none 23 City_Hotel 205 0 5 2 none BB GBR Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 70.0 none 24 Resort_Hotel 45 0 1 2 none BB PRT Online_TA TA/TO 0 0 0 A D 1 No_Deposit 0 Transient-Pa… 65 none 25 City_Hotel 134 0 1 1 none BB PRT Groups TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 75 none 26 City_Hotel 179 2 5 2 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 107. none 27 City_Hotel 0 1 3 1 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Contract 91 none 28 City_Hotel 72 0 2 2 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 121. parking 29 City_Hotel 39 0 3 1 none BB PRT Online_TA TA/TO 0 0 0 A A 1 No_Deposit 0 Transient 94.5 none 30 City_Hotel 5 2 5 1 none BB ITA Aviation Corporate 0 0 0 D D 0 No_Deposit 0 Transient 110 none 31 City_Hotel 43 1 2 2 none BB NLD Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 68 none 32 City_Hotel 157 0 2 2 children BB BEL Online_TA TA/TO 0 0 0 A D 0 No_Deposit 0 Transient-Pa… 122. none 33 City_Hotel 0 0 1 2 none BB FRA Groups TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 124 none 34 Resort_Hotel 230 2 3 2 none BB GBR Offline_TA/TO TA/TO 0 0 0 E E 0 No_Deposit 0 Transient 46.8 none 35 Resort_Hotel 48 1 1 2 none BB ESP Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 206 none 36 Resort_Hotel 151 3 6 2 none HB GBR Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 167. none 37 Resort_Hotel 171 2 5 2 none BB GBR Online_TA TA/TO 0 0 0 D D 2 No_Deposit 0 Transient-Pa… 183. none 38 City_Hotel 1 0 1 2 none SC PRT Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 139 none 39 Resort_Hotel 39 2 3 2 none BB PRT Online_TA TA/TO 0 0 0 A B 0 No_Deposit 0 Transient 180. none 40 City_Hotel 61 2 0 2 none BB FRA Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 91 none 41 Resort_Hotel 127 2 5 2 children HB PRT Direct Direct 0 0 0 E E 4 No_Deposit 0 Transient 334. parking 42 Resort_Hotel 244 1 0 2 none BB HRV Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 84 parking 43 Resort_Hotel 4 0 1 1 none BB PRT Offline_TA/TO TA/TO 1 0 2 A D 0 No_Deposit 0 Transient 30 none 44 Resort_Hotel 4 1 1 1 none BB ESP Offline_TA/TO TA/TO 0 0 0 A D 0 No_Deposit 0 Transient 25 none 45 City_Hotel 134 0 1 1 none BB PRT Groups TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 75 none 46 Resort_Hotel 161 2 5 3 none BB IRL Online_TA TA/TO 0 0 0 E E 0 No_Deposit 0 Transient-Pa… 81.6 none 47 Resort_Hotel 210 4 10 2 none BB NOR Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 61.9 none 48 Resort_Hotel 96 2 5 2 none HB DEU Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 78.4 none 49 Resort_Hotel 83 2 5 1 none BB GBR Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Contract 34.2 none 50 Resort_Hotel 232 2 5 1 none BB AUT Offline_TA/TO TA/TO 0 0 0 A A 2 No_Deposit 0 Transient 25.5 none 51 City_Hotel 17 2 2 2 none BB DEU Online_TA TA/TO 0 0 0 A A 0 No_Deposit 0 Transient 108. none 52 City_Hotel 40 0 4 2 none BB AUT Online_TA TA/TO 0 0 0 D D 0 No_Deposit 0 Transient 96.9 none 53 City_Hotel 141 1 0 2 none BB CN Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 90 none 54 Resort_Hotel 0 1 0 2 none BB BEL Direct Direct 0 0 0 G G 0 No_Deposit 0 Transient 116 parking 55 City_Hotel 181 0 2 1 none BB FRA Offline_TA/TO TA/TO 0 0 0 A A 0 No_Deposit 0 Transient-Pa… 120 none # … with 49,945 more rows, and 3 more variables: total_of_special_requests <dbl>, arrival_date <date>, ind_children <dbl> ``` ] --- count: false .panel1-hotel-auto[ ```r library(tidyverse) library(broom) read_csv('https://tidymodels.org/start/case-study/hotels.csv') %>% mutate(across(where(is.character), as.factor)) -> hotels dim(hotels) hotels %>% mutate(ind_children = as.numeric(children == "children") ) %>% * glm(ind_children ~ lead_time, * family = binomial(link = "logit"), * data = .) ``` ] .panel2-hotel-auto[ ``` [1] 50000 23 ``` ``` Call: glm(formula = ind_children ~ lead_time, family = binomial(link = "logit"), data = .) Coefficients: (Intercept) lead_time -2.3906349 -0.0005293 Degrees of Freedom: 49999 Total (i.e. Null); 49998 Residual Null Deviance: 28060 Residual Deviance: 28050 AIC: 28060 ``` ] --- count: false .panel1-hotel-auto[ ```r library(tidyverse) library(broom) read_csv('https://tidymodels.org/start/case-study/hotels.csv') %>% mutate(across(where(is.character), as.factor)) -> hotels dim(hotels) hotels %>% mutate(ind_children = as.numeric(children == "children") ) %>% glm(ind_children ~ lead_time, family = binomial(link = "logit"), data = .) %>% * augment() ``` ] .panel2-hotel-auto[ ``` [1] 50000 23 ``` ``` # A tibble: 50,000 × 8 ind_children lead_time .fitted .resid .std.resid .hat .sigma .cooksd <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 0 217 -2.51 -0.396 -0.396 0.0000663 0.749 0.00000271 2 0 2 -2.39 -0.418 -0.418 0.0000354 0.749 0.00000162 3 0 95 -2.44 -0.409 -0.409 0.0000207 0.749 0.000000902 4 0 143 -2.47 -0.404 -0.404 0.0000304 0.749 0.00000129 5 0 136 -2.46 -0.404 -0.404 0.0000283 0.749 0.00000121 6 0 67 -2.43 -0.412 -0.412 0.0000203 0.749 0.000000899 7 1 47 -2.42 2.24 2.24 0.0000225 0.749 0.000126 8 1 56 -2.42 2.24 2.24 0.0000213 0.749 0.000120 9 0 80 -2.43 -0.410 -0.410 0.0000200 0.749 0.000000879 10 1 6 -2.39 2.23 2.23 0.0000338 0.749 0.000185 11 0 130 -2.46 -0.405 -0.405 0.0000267 0.749 0.00000114 12 0 27 -2.40 -0.416 -0.416 0.0000269 0.749 0.00000121 13 0 16 -2.40 -0.417 -0.417 0.0000302 0.749 0.00000137 14 0 46 -2.41 -0.414 -0.414 0.0000227 0.749 0.00000101 15 0 297 -2.55 -0.388 -0.388 0.000131 0.749 0.00000512 16 0 423 -2.61 -0.376 -0.376 0.000280 0.749 0.0000103 17 0 22 -2.40 -0.416 -0.416 0.0000283 0.749 0.00000128 18 0 96 -2.44 -0.409 -0.409 0.0000208 0.749 0.000000906 19 0 0 -2.39 -0.419 -0.419 0.0000362 0.749 0.00000166 20 0 7 -2.39 -0.418 -0.418 0.0000334 0.749 0.00000152 21 0 209 -2.50 -0.397 -0.397 0.0000613 0.749 0.00000251 22 0 1 -2.39 -0.419 -0.419 0.0000358 0.749 0.00000164 23 0 205 -2.50 -0.397 -0.397 0.0000588 0.749 0.00000242 24 0 45 -2.41 -0.414 -0.414 0.0000229 0.749 0.00000102 25 0 134 -2.46 -0.405 -0.405 0.0000278 0.749 0.00000118 26 0 179 -2.49 -0.400 -0.400 0.0000448 0.749 0.00000187 27 0 0 -2.39 -0.419 -0.419 0.0000362 0.749 0.00000166 28 0 72 -2.43 -0.411 -0.411 0.0000201 0.749 0.000000886 29 0 39 -2.41 -0.414 -0.415 0.0000240 0.749 0.00000108 30 0 5 -2.39 -0.418 -0.418 0.0000342 0.749 0.00000156 31 0 43 -2.41 -0.414 -0.414 0.0000232 0.749 0.00000104 32 1 157 -2.47 2.26 2.26 0.0000353 0.749 0.000210 33 0 0 -2.39 -0.419 -0.419 0.0000362 0.749 0.00000166 34 0 230 -2.51 -0.395 -0.395 0.0000750 0.749 0.00000304 35 0 48 -2.42 -0.414 -0.414 0.0000224 0.749 0.000000999 36 0 151 -2.47 -0.403 -0.403 0.0000331 0.749 0.00000140 37 0 171 -2.48 -0.401 -0.401 0.0000411 0.749 0.00000172 38 0 1 -2.39 -0.419 -0.419 0.0000358 0.749 0.00000164 39 0 39 -2.41 -0.414 -0.415 0.0000240 0.749 0.00000108 40 0 61 -2.42 -0.412 -0.412 0.0000208 0.749 0.000000921 41 1 127 -2.46 2.25 2.25 0.0000260 0.749 0.000152 42 0 244 -2.52 -0.393 -0.393 0.0000852 0.749 0.00000343 43 0 4 -2.39 -0.418 -0.418 0.0000346 0.749 0.00000158 44 0 4 -2.39 -0.418 -0.418 0.0000346 0.749 0.00000158 45 0 134 -2.46 -0.405 -0.405 0.0000278 0.749 0.00000118 46 0 161 -2.48 -0.402 -0.402 0.0000369 0.749 0.00000155 47 0 210 -2.50 -0.397 -0.397 0.0000619 0.749 0.00000253 48 0 96 -2.44 -0.409 -0.409 0.0000208 0.749 0.000000906 49 0 83 -2.43 -0.410 -0.410 0.0000201 0.749 0.000000879 50 0 232 -2.51 -0.395 -0.395 0.0000764 0.749 0.00000310 51 0 17 -2.40 -0.417 -0.417 0.0000299 0.749 0.00000135 52 0 40 -2.41 -0.414 -0.414 0.0000238 0.749 0.00000107 53 0 141 -2.47 -0.404 -0.404 0.0000298 0.749 0.00000127 54 0 0 -2.39 -0.419 -0.419 0.0000362 0.749 0.00000166 55 0 181 -2.49 -0.400 -0.400 0.0000458 0.749 0.00000191 # … with 49,945 more rows ``` ] --- count: false .panel1-hotel-auto[ ```r library(tidyverse) library(broom) read_csv('https://tidymodels.org/start/case-study/hotels.csv') %>% mutate(across(where(is.character), as.factor)) -> hotels dim(hotels) hotels %>% mutate(ind_children = as.numeric(children == "children") ) %>% glm(ind_children ~ lead_time, family = binomial(link = "logit"), data = .) %>% augment() %>% * ggplot() ``` ] .panel2-hotel-auto[ ``` [1] 50000 23 ``` <!-- --> ] --- count: false .panel1-hotel-auto[ ```r library(tidyverse) library(broom) read_csv('https://tidymodels.org/start/case-study/hotels.csv') %>% mutate(across(where(is.character), as.factor)) -> hotels dim(hotels) hotels %>% mutate(ind_children = as.numeric(children == "children") ) %>% glm(ind_children ~ lead_time, family = binomial(link = "logit"), data = .) %>% augment() %>% ggplot() + * aes(x = lead_time, y = ind_children) ``` ] .panel2-hotel-auto[ ``` [1] 50000 23 ``` <!-- --> ] --- count: false .panel1-hotel-auto[ ```r library(tidyverse) library(broom) read_csv('https://tidymodels.org/start/case-study/hotels.csv') %>% mutate(across(where(is.character), as.factor)) -> hotels dim(hotels) hotels %>% mutate(ind_children = as.numeric(children == "children") ) %>% glm(ind_children ~ lead_time, family = binomial(link = "logit"), data = .) %>% augment() %>% ggplot() + aes(x = lead_time, y = ind_children) + * geom_jitter(height = .1) ``` ] .panel2-hotel-auto[ ``` [1] 50000 23 ``` <!-- --> ] --- count: false .panel1-hotel-auto[ ```r library(tidyverse) library(broom) read_csv('https://tidymodels.org/start/case-study/hotels.csv') %>% mutate(across(where(is.character), as.factor)) -> hotels dim(hotels) hotels %>% mutate(ind_children = as.numeric(children == "children") ) %>% glm(ind_children ~ lead_time, family = binomial(link = "logit"), data = .) %>% augment() %>% ggplot() + aes(x = lead_time, y = ind_children) + geom_jitter(height = .1) + * geom_point(aes(y = exp(.fitted)/(1+exp(.fitted))), alpha = .01, color = "blue") ``` ] .panel2-hotel-auto[ ``` [1] 50000 23 ``` <!-- --> ] <style> .panel1-hotel-auto { color: black; width: 38.6060606060606%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-hotel-auto { color: black; width: 59.3939393939394%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-hotel-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style> --- count: false .panel1-mtcars-auto[ ```r *library(tidyverse) ``` ] .panel2-mtcars-auto[ ] --- count: false .panel1-mtcars-auto[ ```r library(tidyverse) *library(broom) ``` ] .panel2-mtcars-auto[ ] --- count: false .panel1-mtcars-auto[ ```r library(tidyverse) library(broom) *glm(am ~ disp, * family = binomial(link = "logit"), * data = mtcars *) ``` ] .panel2-mtcars-auto[ ``` Call: glm(formula = am ~ disp, family = binomial(link = "logit"), data = mtcars) Coefficients: (Intercept) disp 2.6308 -0.0146 Degrees of Freedom: 31 Total (i.e. Null); 30 Residual Null Deviance: 43.23 Residual Deviance: 29.73 AIC: 33.73 ``` ] --- count: false .panel1-mtcars-auto[ ```r library(tidyverse) library(broom) glm(am ~ disp, family = binomial(link = "logit"), data = mtcars ) %>% * augment() ``` ] .panel2-mtcars-auto[ ``` # A tibble: 32 × 9 .rownames am disp .fitted .resid .std.resid .hat .sigma .cooksd <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 Mazda RX4 1 160 0.294 1.06 1.08 0.0539 0.992 0.0224 2 Mazda RX4 Wag 1 160 0.294 1.06 1.08 0.0539 0.992 0.0224 3 Datsun 710 1 108 1.05 0.773 0.801 0.0685 1.00 0.0138 4 Hornet 4 Drive 0 258 -1.14 -0.746 -0.772 0.0653 1.00 0.0120 5 Hornet Sportabout 0 360 -2.63 -0.374 -0.387 0.0654 1.01 0.00271 6 Valiant 0 225 -0.655 -0.915 -0.942 0.0568 0.997 0.0166 7 Duster 360 0 360 -2.63 -0.374 -0.387 0.0654 1.01 0.00271 8 Merc 240D 0 147. 0.488 -1.39 -1.43 0.0569 0.977 0.0522 9 Merc 230 0 141. 0.575 -1.43 -1.47 0.0586 0.975 0.0587 10 Merc 280 0 168. 0.183 -1.26 -1.29 0.0527 0.984 0.0352 11 Merc 280C 0 168. 0.183 -1.26 -1.29 0.0527 0.984 0.0352 12 Merc 450SE 0 276. -1.40 -0.665 -0.689 0.0691 1.00 0.00986 13 Merc 450SL 0 276. -1.40 -0.665 -0.689 0.0691 1.00 0.00986 14 Merc 450SLC 0 276. -1.40 -0.665 -0.689 0.0691 1.00 0.00986 15 Cadillac Fleetwood 0 472 -4.26 -0.167 -0.170 0.0333 1.01 0.000251 16 Lincoln Continental 0 460 -4.09 -0.182 -0.186 0.0366 1.01 0.000331 17 Chrysler Imperial 0 440 -3.80 -0.211 -0.215 0.0423 1.01 0.000518 18 Fiat 128 1 78.7 1.48 0.640 0.666 0.0751 1.00 0.00998 19 Honda Civic 1 75.7 1.53 0.627 0.653 0.0756 1.01 0.00962 20 Toyota Corolla 1 71.1 1.59 0.609 0.633 0.0761 1.01 0.00907 21 Toyota Corona 0 120. 0.877 -1.57 -1.62 0.0648 0.967 0.0891 22 Dodge Challenger 0 318 -2.01 -0.501 -0.520 0.0718 1.01 0.00556 23 AMC Javelin 0 304 -1.81 -0.551 -0.572 0.0720 1.01 0.00685 24 Camaro Z28 0 350 -2.48 -0.401 -0.415 0.0676 1.01 0.00325 25 Pontiac Firebird 0 400 -3.21 -0.281 -0.289 0.0544 1.01 0.00123 26 Fiat X1-9 1 79 1.48 0.641 0.667 0.0751 1.00 0.0100 27 Porsche 914-2 1 120. 0.874 0.835 0.864 0.0648 1.00 0.0155 28 Lotus Europa 1 95.1 1.24 0.712 0.739 0.0719 1.00 0.0121 29 Ford Pantera L 1 351 -2.50 2.27 2.35 0.0674 0.914 0.470 30 Ferrari Dino 1 145 0.513 0.969 0.998 0.0574 0.995 0.0193 31 Maserati Bora 1 301 -1.77 1.96 2.04 0.0719 0.939 0.244 32 Volvo 142E 1 121 0.864 0.839 0.867 0.0646 1.00 0.0156 ``` ] --- count: false .panel1-mtcars-auto[ ```r library(tidyverse) library(broom) glm(am ~ disp, family = binomial(link = "logit"), data = mtcars ) %>% augment() %>% * mutate(linked = * exp(.fitted)/(1+exp(.fitted))) ``` ] .panel2-mtcars-auto[ ``` # A tibble: 32 × 10 .rownames am disp .fitted .resid .std.resid .hat .sigma .cooksd linked <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 Mazda RX4 1 160 0.294 1.06 1.08 0.0539 0.992 0.0224 0.573 2 Mazda RX4 Wag 1 160 0.294 1.06 1.08 0.0539 0.992 0.0224 0.573 3 Datsun 710 1 108 1.05 0.773 0.801 0.0685 1.00 0.0138 0.741 4 Hornet 4 Drive 0 258 -1.14 -0.746 -0.772 0.0653 1.00 0.0120 0.243 5 Hornet Sportabout 0 360 -2.63 -0.374 -0.387 0.0654 1.01 0.00271 0.0674 6 Valiant 0 225 -0.655 -0.915 -0.942 0.0568 0.997 0.0166 0.342 7 Duster 360 0 360 -2.63 -0.374 -0.387 0.0654 1.01 0.00271 0.0674 8 Merc 240D 0 147. 0.488 -1.39 -1.43 0.0569 0.977 0.0522 0.620 9 Merc 230 0 141. 0.575 -1.43 -1.47 0.0586 0.975 0.0587 0.640 10 Merc 280 0 168. 0.183 -1.26 -1.29 0.0527 0.984 0.0352 0.546 11 Merc 280C 0 168. 0.183 -1.26 -1.29 0.0527 0.984 0.0352 0.546 12 Merc 450SE 0 276. -1.40 -0.665 -0.689 0.0691 1.00 0.00986 0.198 13 Merc 450SL 0 276. -1.40 -0.665 -0.689 0.0691 1.00 0.00986 0.198 14 Merc 450SLC 0 276. -1.40 -0.665 -0.689 0.0691 1.00 0.00986 0.198 15 Cadillac Fleetwood 0 472 -4.26 -0.167 -0.170 0.0333 1.01 0.000251 0.0139 16 Lincoln Continental 0 460 -4.09 -0.182 -0.186 0.0366 1.01 0.000331 0.0165 17 Chrysler Imperial 0 440 -3.80 -0.211 -0.215 0.0423 1.01 0.000518 0.0220 18 Fiat 128 1 78.7 1.48 0.640 0.666 0.0751 1.00 0.00998 0.815 19 Honda Civic 1 75.7 1.53 0.627 0.653 0.0756 1.01 0.00962 0.821 20 Toyota Corolla 1 71.1 1.59 0.609 0.633 0.0761 1.01 0.00907 0.831 21 Toyota Corona 0 120. 0.877 -1.57 -1.62 0.0648 0.967 0.0891 0.706 22 Dodge Challenger 0 318 -2.01 -0.501 -0.520 0.0718 1.01 0.00556 0.118 23 AMC Javelin 0 304 -1.81 -0.551 -0.572 0.0720 1.01 0.00685 0.141 24 Camaro Z28 0 350 -2.48 -0.401 -0.415 0.0676 1.01 0.00325 0.0772 25 Pontiac Firebird 0 400 -3.21 -0.281 -0.289 0.0544 1.01 0.00123 0.0388 26 Fiat X1-9 1 79 1.48 0.641 0.667 0.0751 1.00 0.0100 0.814 27 Porsche 914-2 1 120. 0.874 0.835 0.864 0.0648 1.00 0.0155 0.706 28 Lotus Europa 1 95.1 1.24 0.712 0.739 0.0719 1.00 0.0121 0.776 29 Ford Pantera L 1 351 -2.50 2.27 2.35 0.0674 0.914 0.470 0.0762 30 Ferrari Dino 1 145 0.513 0.969 0.998 0.0574 0.995 0.0193 0.626 31 Maserati Bora 1 301 -1.77 1.96 2.04 0.0719 0.939 0.244 0.146 32 Volvo 142E 1 121 0.864 0.839 0.867 0.0646 1.00 0.0156 0.703 ``` ] --- count: false .panel1-mtcars-auto[ ```r library(tidyverse) library(broom) glm(am ~ disp, family = binomial(link = "logit"), data = mtcars ) %>% augment() %>% mutate(linked = exp(.fitted)/(1+exp(.fitted))) %>% * mutate(force = (linked > .5) %>% * as.numeric()) ``` ] .panel2-mtcars-auto[ ``` # A tibble: 32 × 11 .rownames am disp .fitted .resid .std.resid .hat .sigma .cooksd linked force <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 Mazda RX4 1 160 0.294 1.06 1.08 0.0539 0.992 0.0224 0.573 1 2 Mazda RX4 Wag 1 160 0.294 1.06 1.08 0.0539 0.992 0.0224 0.573 1 3 Datsun 710 1 108 1.05 0.773 0.801 0.0685 1.00 0.0138 0.741 1 4 Hornet 4 Drive 0 258 -1.14 -0.746 -0.772 0.0653 1.00 0.0120 0.243 0 5 Hornet Sportabout 0 360 -2.63 -0.374 -0.387 0.0654 1.01 0.00271 0.0674 0 6 Valiant 0 225 -0.655 -0.915 -0.942 0.0568 0.997 0.0166 0.342 0 7 Duster 360 0 360 -2.63 -0.374 -0.387 0.0654 1.01 0.00271 0.0674 0 8 Merc 240D 0 147. 0.488 -1.39 -1.43 0.0569 0.977 0.0522 0.620 1 9 Merc 230 0 141. 0.575 -1.43 -1.47 0.0586 0.975 0.0587 0.640 1 10 Merc 280 0 168. 0.183 -1.26 -1.29 0.0527 0.984 0.0352 0.546 1 11 Merc 280C 0 168. 0.183 -1.26 -1.29 0.0527 0.984 0.0352 0.546 1 12 Merc 450SE 0 276. -1.40 -0.665 -0.689 0.0691 1.00 0.00986 0.198 0 13 Merc 450SL 0 276. -1.40 -0.665 -0.689 0.0691 1.00 0.00986 0.198 0 14 Merc 450SLC 0 276. -1.40 -0.665 -0.689 0.0691 1.00 0.00986 0.198 0 15 Cadillac Fleetwood 0 472 -4.26 -0.167 -0.170 0.0333 1.01 0.000251 0.0139 0 16 Lincoln Continental 0 460 -4.09 -0.182 -0.186 0.0366 1.01 0.000331 0.0165 0 17 Chrysler Imperial 0 440 -3.80 -0.211 -0.215 0.0423 1.01 0.000518 0.0220 0 18 Fiat 128 1 78.7 1.48 0.640 0.666 0.0751 1.00 0.00998 0.815 1 19 Honda Civic 1 75.7 1.53 0.627 0.653 0.0756 1.01 0.00962 0.821 1 20 Toyota Corolla 1 71.1 1.59 0.609 0.633 0.0761 1.01 0.00907 0.831 1 21 Toyota Corona 0 120. 0.877 -1.57 -1.62 0.0648 0.967 0.0891 0.706 1 22 Dodge Challenger 0 318 -2.01 -0.501 -0.520 0.0718 1.01 0.00556 0.118 0 23 AMC Javelin 0 304 -1.81 -0.551 -0.572 0.0720 1.01 0.00685 0.141 0 24 Camaro Z28 0 350 -2.48 -0.401 -0.415 0.0676 1.01 0.00325 0.0772 0 25 Pontiac Firebird 0 400 -3.21 -0.281 -0.289 0.0544 1.01 0.00123 0.0388 0 26 Fiat X1-9 1 79 1.48 0.641 0.667 0.0751 1.00 0.0100 0.814 1 27 Porsche 914-2 1 120. 0.874 0.835 0.864 0.0648 1.00 0.0155 0.706 1 28 Lotus Europa 1 95.1 1.24 0.712 0.739 0.0719 1.00 0.0121 0.776 1 29 Ford Pantera L 1 351 -2.50 2.27 2.35 0.0674 0.914 0.470 0.0762 0 30 Ferrari Dino 1 145 0.513 0.969 0.998 0.0574 0.995 0.0193 0.626 1 31 Maserati Bora 1 301 -1.77 1.96 2.04 0.0719 0.939 0.244 0.146 0 32 Volvo 142E 1 121 0.864 0.839 0.867 0.0646 1.00 0.0156 0.703 1 ``` ] --- count: false .panel1-mtcars-auto[ ```r library(tidyverse) library(broom) glm(am ~ disp, family = binomial(link = "logit"), data = mtcars ) %>% augment() %>% mutate(linked = exp(.fitted)/(1+exp(.fitted))) %>% mutate(force = (linked > .5) %>% as.numeric()) -> *logistic_augmented ``` ] .panel2-mtcars-auto[ ] --- count: false .panel1-mtcars-auto[ ```r library(tidyverse) library(broom) glm(am ~ disp, family = binomial(link = "logit"), data = mtcars ) %>% augment() %>% mutate(linked = exp(.fitted)/(1+exp(.fitted))) %>% mutate(force = (linked > .5) %>% as.numeric()) -> logistic_augmented *logistic_augmented ``` ] .panel2-mtcars-auto[ ``` # A tibble: 32 × 11 .rownames am disp .fitted .resid .std.resid .hat .sigma .cooksd linked force <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 Mazda RX4 1 160 0.294 1.06 1.08 0.0539 0.992 0.0224 0.573 1 2 Mazda RX4 Wag 1 160 0.294 1.06 1.08 0.0539 0.992 0.0224 0.573 1 3 Datsun 710 1 108 1.05 0.773 0.801 0.0685 1.00 0.0138 0.741 1 4 Hornet 4 Drive 0 258 -1.14 -0.746 -0.772 0.0653 1.00 0.0120 0.243 0 5 Hornet Sportabout 0 360 -2.63 -0.374 -0.387 0.0654 1.01 0.00271 0.0674 0 6 Valiant 0 225 -0.655 -0.915 -0.942 0.0568 0.997 0.0166 0.342 0 7 Duster 360 0 360 -2.63 -0.374 -0.387 0.0654 1.01 0.00271 0.0674 0 8 Merc 240D 0 147. 0.488 -1.39 -1.43 0.0569 0.977 0.0522 0.620 1 9 Merc 230 0 141. 0.575 -1.43 -1.47 0.0586 0.975 0.0587 0.640 1 10 Merc 280 0 168. 0.183 -1.26 -1.29 0.0527 0.984 0.0352 0.546 1 11 Merc 280C 0 168. 0.183 -1.26 -1.29 0.0527 0.984 0.0352 0.546 1 12 Merc 450SE 0 276. -1.40 -0.665 -0.689 0.0691 1.00 0.00986 0.198 0 13 Merc 450SL 0 276. -1.40 -0.665 -0.689 0.0691 1.00 0.00986 0.198 0 14 Merc 450SLC 0 276. -1.40 -0.665 -0.689 0.0691 1.00 0.00986 0.198 0 15 Cadillac Fleetwood 0 472 -4.26 -0.167 -0.170 0.0333 1.01 0.000251 0.0139 0 16 Lincoln Continental 0 460 -4.09 -0.182 -0.186 0.0366 1.01 0.000331 0.0165 0 17 Chrysler Imperial 0 440 -3.80 -0.211 -0.215 0.0423 1.01 0.000518 0.0220 0 18 Fiat 128 1 78.7 1.48 0.640 0.666 0.0751 1.00 0.00998 0.815 1 19 Honda Civic 1 75.7 1.53 0.627 0.653 0.0756 1.01 0.00962 0.821 1 20 Toyota Corolla 1 71.1 1.59 0.609 0.633 0.0761 1.01 0.00907 0.831 1 21 Toyota Corona 0 120. 0.877 -1.57 -1.62 0.0648 0.967 0.0891 0.706 1 22 Dodge Challenger 0 318 -2.01 -0.501 -0.520 0.0718 1.01 0.00556 0.118 0 23 AMC Javelin 0 304 -1.81 -0.551 -0.572 0.0720 1.01 0.00685 0.141 0 24 Camaro Z28 0 350 -2.48 -0.401 -0.415 0.0676 1.01 0.00325 0.0772 0 25 Pontiac Firebird 0 400 -3.21 -0.281 -0.289 0.0544 1.01 0.00123 0.0388 0 26 Fiat X1-9 1 79 1.48 0.641 0.667 0.0751 1.00 0.0100 0.814 1 27 Porsche 914-2 1 120. 0.874 0.835 0.864 0.0648 1.00 0.0155 0.706 1 28 Lotus Europa 1 95.1 1.24 0.712 0.739 0.0719 1.00 0.0121 0.776 1 29 Ford Pantera L 1 351 -2.50 2.27 2.35 0.0674 0.914 0.470 0.0762 0 30 Ferrari Dino 1 145 0.513 0.969 0.998 0.0574 0.995 0.0193 0.626 1 31 Maserati Bora 1 301 -1.77 1.96 2.04 0.0719 0.939 0.244 0.146 0 32 Volvo 142E 1 121 0.864 0.839 0.867 0.0646 1.00 0.0156 0.703 1 ``` ] --- count: false .panel1-mtcars-auto[ ```r library(tidyverse) library(broom) glm(am ~ disp, family = binomial(link = "logit"), data = mtcars ) %>% augment() %>% mutate(linked = exp(.fitted)/(1+exp(.fitted))) %>% mutate(force = (linked > .5) %>% as.numeric()) -> logistic_augmented logistic_augmented %>% * ggplot() ``` ] .panel2-mtcars-auto[ <!-- --> ] --- count: false .panel1-mtcars-auto[ ```r library(tidyverse) library(broom) glm(am ~ disp, family = binomial(link = "logit"), data = mtcars ) %>% augment() %>% mutate(linked = exp(.fitted)/(1+exp(.fitted))) %>% mutate(force = (linked > .5) %>% as.numeric()) -> logistic_augmented logistic_augmented %>% ggplot() + * aes(x = disp, y = am, color = am) ``` ] .panel2-mtcars-auto[ <!-- --> ] --- count: false .panel1-mtcars-auto[ ```r library(tidyverse) library(broom) glm(am ~ disp, family = binomial(link = "logit"), data = mtcars ) %>% augment() %>% mutate(linked = exp(.fitted)/(1+exp(.fitted))) %>% mutate(force = (linked > .5) %>% as.numeric()) -> logistic_augmented logistic_augmented %>% ggplot() + aes(x = disp, y = am, color = am) + * geom_point() ``` ] .panel2-mtcars-auto[ <!-- --> ] --- count: false .panel1-mtcars-auto[ ```r library(tidyverse) library(broom) glm(am ~ disp, family = binomial(link = "logit"), data = mtcars ) %>% augment() %>% mutate(linked = exp(.fitted)/(1+exp(.fitted))) %>% mutate(force = (linked > .5) %>% as.numeric()) -> logistic_augmented logistic_augmented %>% ggplot() + aes(x = disp, y = am, color = am) + geom_point() + * geom_point(aes(y = linked, * shape = am == force)) ``` ] .panel2-mtcars-auto[ <!-- --> ] --- count: false .panel1-mtcars-auto[ ```r library(tidyverse) library(broom) glm(am ~ disp, family = binomial(link = "logit"), data = mtcars ) %>% augment() %>% mutate(linked = exp(.fitted)/(1+exp(.fitted))) %>% mutate(force = (linked > .5) %>% as.numeric()) -> logistic_augmented logistic_augmented %>% ggplot() + aes(x = disp, y = am, color = am) + geom_point() + geom_point(aes(y = linked, shape = am == force)) + * scale_shape_manual(values = c(4, 1)) ``` ] .panel2-mtcars-auto[ <!-- --> ] --- count: false .panel1-mtcars-auto[ ```r library(tidyverse) library(broom) glm(am ~ disp, family = binomial(link = "logit"), data = mtcars ) %>% augment() %>% mutate(linked = exp(.fitted)/(1+exp(.fitted))) %>% mutate(force = (linked > .5) %>% as.numeric()) -> logistic_augmented logistic_augmented %>% ggplot() + aes(x = disp, y = am, color = am) + geom_point() + geom_point(aes(y = linked, shape = am == force)) + scale_shape_manual(values = c(4, 1)) + * scale_color_viridis_c(end = .7) ``` ] .panel2-mtcars-auto[ <!-- --> ] <style> .panel1-mtcars-auto { color: black; width: 38.6060606060606%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel2-mtcars-auto { color: black; width: 59.3939393939394%; hight: 32%; float: left; padding-left: 1%; font-size: 80% } .panel3-mtcars-auto { color: black; width: NA%; hight: 33%; float: left; padding-left: 1%; font-size: 80% } </style>