Read Me

This is the data analysis RMarkdown file for the QCQ data after ISIRC 2022.

Mainly changes: incorporating the contol of before and after COVID

Please read the subtitles and notes added as normal text in this document. Blocks with darker backgrounds are code chunks, mostly with their outputs. I tried to add #comments within the chunck just before the code line to explain the purpose of the code line. Meanwhile I explain the purpose of the section in the texts, so that is where to find information to give you fuller pictures.

# environment setup to run ordered logit properly
options(contrasts = rep("contr.treatment", 2))

This chunk loads all the packages to use

#packages for ordered logit
library(ordinal) # package for ordinal logit regression
library(brant) # brant test for the parallel assumption for ordered logit
library(MASS) # models that work with the brant test

library(tidyverse) # package for data cleaning and plotting
library(readxl) # package for reading excel file
library(broom) # extracting model summary as data frame
library(modelsummary) # deriving model tables
library(scales) # label percent
library(lubridate) # working with dates
library(marginaleffects) #to calculate marginal effects

Data Preparation

First, import the sampled and coded data set

#import the raw data file
socialcare_raw <- read_csv("data/sample_new_cleaned.csv")

Assign orders to the ordinal level variables and name the organizational form in a reader-friendly way.

#select relevant columns, rename and relabel 
socialcare <- socialcare_raw %>% 
  # recode legal form types to be more readable / easier to present
  mutate(location_id = factor(location_id),
         form = case_when(form_num == 1 ~ "FPO",
                          form_num == 2 ~ "NPO",
                          form_num == 3 ~ "GOV",
                          form_num == 4 ~ "CIC",
                          form_num == 5 ~ "IND",
                          TRUE ~ NA_character_),

         inherited = ifelse(inherited == "Y", TRUE, FALSE),
         rating = recode(rating, 
                         "Insufficient evidence to rate" = "NA",
                         "Requires improvement" = "Req improv"),
         publication_date = dmy(publication_date)) %>% 
  # set the order of the values in the factors 
  mutate(form = fct_relevel(form, "FPO"),
         form2 = fct_relevel(form, "NPO"),
         form3 = fct_relevel(form, "IND"),
         
         # assume the order of the ratings as follows but need to double check with the source 
         rating = ordered(rating, levels = c("Inadequate","Req improv", "Good", "Outstanding"))) %>% 
  
  # creating a new dummy variable for facility category
  mutate(category = case_when(primary_cat == "Community based adult social care services" ~ "community",
                              primary_cat == "Residential social care" ~ "residential",
                              TRUE ~ as.character(NA)),
         # deriving year column and dummy variable for before_covid
         year = year(publication_date),
         after_covid = ifelse(year >= 2020, TRUE, FALSE),
         before_covid = ifelse(year <= 2019, TRUE, FALSE)) %>%

  # converting the ordinal variable to numerical 
  mutate(rating_num = case_when(rating == "Inadequate" ~ 1,
                                rating == "Req improv" ~ 2,
                                rating == "Good" ~ 3,
                                rating == "Outstanding" ~ 4)) %>% 
  # derive the rating dummy
  mutate(rating_higher = ifelse(rating_num > 2, 1, 0))

# show first several rows of the data set derived 
head(socialcare)
## # A tibble: 6 × 42
##    ...1 location_id  `Location ODS Code` `Location Name` `Care Home` primary_cat
##   <dbl> <fct>        <chr>               <chr>           <chr>       <chr>      
## 1     1 1-100007925… VNH4P               69 Tenter Lane  N           Community …
## 2     2 1-100007925… VNH4P               69 Tenter Lane  N           Community …
## 3     3 1-100007925… VNH4P               69 Tenter Lane  N           Community …
## 4     4 1-100007925… VNH4P               69 Tenter Lane  N           Community …
## 5     5 1-100007925… VNH4P               69 Tenter Lane  N           Community …
## 6     6 1-100007925… VNH4P               69 Tenter Lane  N           Community …
## # ℹ 36 more variables: `Location Street Address` <chr>,
## #   `Location Address Line 2` <chr>, `Location City` <chr>,
## #   `Location Post Code` <chr>, `Location Local Authority` <chr>, region <chr>,
## #   `Location NHS Region` <chr>, `Location ONSPD CCG Code` <chr>,
## #   `Location ONSPD CCG` <chr>, `Location Commissioning CCG Code` <lgl>,
## #   `Location Commissioning CCG Name` <lgl>,
## #   `Service / Population Group` <chr>, domain <chr>, rating <ord>, …

whole model without control - FPO as reference group

model_order_overall <- clm(rating ~ form + category + region + inherited,
                data = filter(socialcare, domain == "Overall"),
                link = "logit")
model_order_safe <- clm(rating ~ form + category + region + inherited,
                data = filter(socialcare, domain == "Safe"),
                link = "logit")
model_order_effective <- clm(rating ~ form + category + region + inherited,
                data = filter(socialcare, domain == "Effective"),
                link = "logit")
model_order_caring <- clm(rating ~ form + category + region + inherited,
                data = filter(socialcare, domain == "Caring"),
                link = "logit")
model_order_well_led <- clm(rating ~ form + category + region + inherited,
                data = filter(socialcare, domain == "Well-led"),
                link = "logit")
model_order_responsive <- clm(rating ~ form + category + region + inherited,
                data = filter(socialcare, domain == "Responsive"),
                link = "logit")
ordinal_models <- modelsummary(list("overall" = model_order_overall, "safe" = model_order_safe, 
                                    "effective" = model_order_effective, "caring"= model_order_caring, 
                                    "well-led" = model_order_well_led, "responsive" = model_order_responsive),
                               coef_omit = "region", exponentiate = F,
                               statistic = "({p.value}) {stars}")
ordinal_models
overall safe effective caring well-led responsive
Inadequate Req improv -4.449 -4.253 -5.932 -7.094 -4.102
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Req improv Good -1.702 -1.444 -2.079 -3.431 -1.334
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good Outstanding 3.144 5.856 4.395 3.024 3.178
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
formCIC 0.586 0.797 0.188 0.819 0.478 0.306
(0.045) * (0.040) * (0.622) (0.013) * (0.085) + (0.340)
formGOV 0.429 0.685 0.397 0.328 0.445 0.415
(0.001) ** (<0.001) *** (0.029) * (0.066) + (<0.001) *** (0.006) **
formIND -0.183 -0.117 -0.275 -0.050 -0.162 -0.057
(0.036) * (0.197) (0.011) * (0.710) (0.045) * (0.592)
formNPO 0.457 0.580 0.413 0.316 0.475 0.507
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
categoryresidential -0.387 -0.423 -0.374 -0.574 -0.302 -0.237
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
inheritedTRUE -0.108 -0.140 -0.288 -0.397 -0.076 -0.251
(0.238) (0.146) (0.010) ** (0.003) ** (0.372) (0.017) *
:———————- ————-: ————-: ————-: ————-: ————-: ————-:
Num.Obs. 12942 12966 12931 12927 12964 12953
AIC 16608.8 13440.5 10365.2 8969.0 18898.5 12726.4
BIC 16735.8 13567.5 10492.1 9096.0 19025.5 12853.4
RMSE 2.26 2.12 2.13 2.21 2.26 2.27

whole model without control - NPO as reference group

model_order_overall2 <- clm(rating ~ form2 + category + region + inherited,
                data = filter(socialcare, domain == "Overall"),
                link = "logit")
model_order_safe2 <- clm(rating ~ form2 + category + region + inherited,
                data = filter(socialcare, domain == "Safe"),
                link = "logit")
model_order_effective2 <- clm(rating ~ form2 + category + region + inherited,
                data = filter(socialcare, domain == "Effective"),
                link = "logit")
model_order_caring2 <- clm(rating ~ form2 + category + region + inherited,
                data = filter(socialcare, domain == "Caring"),
                link = "logit")
model_order_well_led2 <- clm(rating ~ form2 + category + region + inherited,
                data = filter(socialcare, domain == "Well-led"),
                link = "logit")
model_order_responsive2 <- clm(rating ~ form2 + category + region + inherited,
                data = filter(socialcare, domain == "Responsive"),
                link = "logit")
ordinal_models2 <- modelsummary(list("overall" = model_order_overall2, "safe" = model_order_safe2, 
                                    "effective" = model_order_effective2, "caring"= model_order_caring2, 
                                    "well-led" = model_order_well_led2, "responsive" = model_order_responsive2),
                               coef_omit = "region", exponentiate = F,
                               statistic = "({p.value}) {stars}")
ordinal_models2
overall safe effective caring well-led responsive
Inadequate Req improv -4.906 -4.833 -6.345 -7.410 -4.576
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Req improv Good -2.160 -2.024 -2.492 -3.747 -1.809
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good Outstanding 2.687 5.276 3.982 2.707 2.704
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
form2FPO -0.457 -0.580 -0.413 -0.316 -0.475 -0.507
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
form2CIC 0.129 0.217 -0.225 0.503 0.003 -0.201
(0.664) (0.581) (0.561) (0.134) (0.992) (0.537)
form2GOV -0.028 0.105 -0.017 0.012 -0.029 -0.092
(0.846) (0.554) (0.932) (0.950) (0.827) (0.569)
form2IND -0.640 -0.697 -0.688 -0.366 -0.637 -0.564
(<0.001) *** (<0.001) *** (<0.001) *** (0.015) * (<0.001) *** (<0.001) ***
categoryresidential -0.387 -0.423 -0.374 -0.574 -0.302 -0.237
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
inheritedTRUE -0.108 -0.140 -0.288 -0.397 -0.076 -0.251
(0.238) (0.146) (0.010) ** (0.003) ** (0.372) (0.017) *
:———————- ————-: ————-: ————-: ————-: ————-: ————-:
Num.Obs. 12942 12966 12931 12927 12964 12953
AIC 16608.8 13440.5 10365.2 8969.0 18898.5 12726.4
BIC 16735.8 13567.5 10492.1 9096.0 19025.5 12853.4
RMSE 2.26 2.12 2.13 2.21 2.26 2.27

whole model without control - IND as reference group

model_order_overall3 <- clm(rating ~ form3 + category + region + inherited,
                data = filter(socialcare, domain == "Overall"),
                link = "logit")
model_order_safe3 <- clm(rating ~ form3 + category + region + inherited,
                data = filter(socialcare, domain == "Safe"),
                link = "logit")
model_order_effective3 <- clm(rating ~ form3 + category + region + inherited,
                data = filter(socialcare, domain == "Effective"),
                link = "logit")
model_order_caring3 <- clm(rating ~ form3 + category + region + inherited,
                data = filter(socialcare, domain == "Caring"),
                link = "logit")
model_order_well_led3 <- clm(rating ~ form3 + category + region + inherited,
                data = filter(socialcare, domain == "Well-led"),
                link = "logit")
model_order_responsive3 <- clm(rating ~ form3 + category + region + inherited,
                data = filter(socialcare, domain == "Responsive"),
                link = "logit")
ordinal_models3 <- modelsummary(list("overall" = model_order_overall3, "safe" = model_order_safe3, 
                                    "effective" = model_order_effective3, "caring"= model_order_caring3, 
                                    "well-led" = model_order_well_led3, "responsive" = model_order_responsive3),
                               coef_omit = "region", exponentiate = F,
                               statistic = "({p.value}) {stars}")
ordinal_models3
overall safe effective caring well-led responsive
Inadequate Req improv -4.266 -4.136 -5.657 -7.044 -3.940
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Req improv Good -1.519 -1.327 -1.804 -3.381 -1.172
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good Outstanding 3.327 5.973 4.670 3.073 3.340
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
form3FPO 0.183 0.117 0.275 0.050 0.162 0.057
(0.036) * (0.197) (0.011) * (0.710) (0.045) * (0.592)
form3CIC 0.770 0.914 0.462 0.869 0.640 0.363
(0.011) * (0.021) * (0.241) (0.014) * (0.026) * (0.280)
form3GOV 0.612 0.802 0.671 0.378 0.607 0.472
(<0.001) *** (<0.001) *** (0.001) ** (0.083) + (<0.001) *** (0.009) **
form3NPO 0.640 0.697 0.688 0.366 0.637 0.564
(<0.001) *** (<0.001) *** (<0.001) *** (0.015) * (<0.001) *** (<0.001) ***
categoryresidential -0.387 -0.423 -0.374 -0.574 -0.302 -0.237
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
inheritedTRUE -0.108 -0.140 -0.288 -0.397 -0.076 -0.251
(0.238) (0.146) (0.010) ** (0.003) ** (0.372) (0.017) *
:———————- ————-: ————-: ————-: ————-: ————-: ————-:
Num.Obs. 12942 12966 12931 12927 12964 12953
AIC 16608.8 13440.5 10365.2 8969.0 18898.5 12726.4
BIC 16735.8 13567.5 10492.1 9096.0 19025.5 12853.4
RMSE 2.26 2.12 2.13 2.21 2.26 2.27

whole model with control - FPO as reference group

model_order_overall_covid <- clm(rating ~ form + after_covid + 
                                   category + region + inherited,
                data = filter(socialcare, domain == "Overall"),
                link = "logit")
model_order_safe_covid <- clm(rating ~ form + after_covid +
                                category + region + inherited,
                data = filter(socialcare, domain == "Safe"),
                link = "logit")
model_order_effective_covid <- clm(rating ~ form + after_covid +
                                     category + region + inherited,
                data = filter(socialcare, domain == "Effective"),
                link = "logit")
model_order_caring_covid <- clm(rating ~ form + after_covid + 
                                  category + region + inherited,
                data = filter(socialcare, domain == "Caring"),
                link = "logit")
model_order_well_led_covid <- clm(rating ~ form + after_covid +
                                    category + region + inherited,
                data = filter(socialcare, domain == "Well-led"),
                link = "logit")
model_order_responsive_covid <- clm(rating ~ form + after_covid +
                                      category + region + inherited,
                data = filter(socialcare, domain == "Responsive"),
                link = "logit")
ordinal_models_covid <- modelsummary(list("overall" = model_order_overall_covid, "safe" = model_order_safe_covid, 
                                    "effective" = model_order_effective_covid, "caring"= model_order_caring_covid, 
                                    "well-led" = model_order_well_led_covid, "responsive" = model_order_responsive_covid),
                               coef_omit = "region", exponentiate = F,
                               statistic = "({p.value}) {stars}")
ordinal_models_covid
overall safe effective caring well-led responsive
Inadequate Req improv -5.398 -5.000 -6.333 -7.446 -4.796
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Req improv Good -2.498 -2.079 -2.442 -3.765 -1.905
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good Outstanding 2.794 5.545 4.214 2.847 2.866
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
formCIC 0.494 0.756 0.183 0.802 0.408 0.296
(0.097) + (0.056) + (0.633) (0.015) * (0.146) (0.354)
formGOV 0.290 0.561 0.337 0.290 0.316 0.378
(0.036) * (0.001) ** (0.065) + (0.107) (0.014) * (0.012) *
formIND -0.339 -0.248 -0.344 -0.097 -0.282 -0.097
(<0.001) *** (0.009) ** (0.002) ** (0.469) (<0.001) *** (0.364)
formNPO 0.307 0.450 0.350 0.274 0.357 0.467
(<0.001) *** (<0.001) *** (<0.001) *** (0.002) ** (<0.001) *** (<0.001) ***
after_covidTRUE -1.776 -1.496 -1.132 -1.042 -1.412 -0.843
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
categoryresidential -0.377 -0.420 -0.386 -0.587 -0.294 -0.244
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
inheritedTRUE -0.322 -0.335 -0.398 -0.460 -0.238 -0.306
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (0.007) ** (0.004) **
:———————- ————-: ————-: ————-: ————-: ————-: ————-:
Num.Obs. 12942 12966 12931 12927 12964 12953
AIC 15296.7 12507.3 10045.6 8808.0 17890.6 12548.2
BIC 15431.1 12641.8 10180.0 8942.4 18025.1 12682.7
RMSE 2.24 2.10 2.13 2.21 2.25 2.27

whole model with control - NPO as reference group

model_order_overall_covid2 <- clm(rating ~ form2 + after_covid + 
                                   category + region + inherited,
                data = filter(socialcare, domain == "Overall"),
                link = "logit")
model_order_safe_covid2 <- clm(rating ~ form2 + after_covid +
                                category + region + inherited,
                data = filter(socialcare, domain == "Safe"),
                link = "logit")
model_order_effective_covid2 <- clm(rating ~ form2 + after_covid +
                                     category + region + inherited,
                data = filter(socialcare, domain == "Effective"),
                link = "logit")
model_order_caring_covid2 <- clm(rating ~ form2 + after_covid + 
                                  category + region + inherited,
                data = filter(socialcare, domain == "Caring"),
                link = "logit")
model_order_well_led_covid2 <- clm(rating ~ form2 + after_covid +
                                    category + region + inherited,
                data = filter(socialcare, domain == "Well-led"),
                link = "logit")
model_order_responsive_covid2 <- clm(rating ~ form2 + after_covid +
                                      category + region + inherited,
                data = filter(socialcare, domain == "Responsive"),
                link = "logit")
ordinal_models_covid2 <- modelsummary(list("overall" = model_order_overall_covid2, "safe" = model_order_safe_covid2, 
                                    "effective" = model_order_effective_covid2, "caring"= model_order_caring_covid2, 
                                    "well-led" = model_order_well_led_covid2, "responsive" = model_order_responsive_covid2),
                               coef_omit = "region", exponentiate = F,
                               statistic = "({p.value}) {stars}")
ordinal_models_covid2
overall safe effective caring well-led responsive
Inadequate Req improv -5.705 -5.450 -6.682 -7.721 -5.152
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Req improv Good -2.805 -2.528 -2.792 -4.039 -2.262
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good Outstanding 2.488 5.096 3.864 2.573 2.510
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
form2FPO -0.307 -0.450 -0.350 -0.274 -0.357 -0.467
(<0.001) *** (<0.001) *** (<0.001) *** (0.002) ** (<0.001) *** (<0.001) ***
form2CIC 0.187 0.306 -0.167 0.528 0.051 -0.171
(0.536) (0.446) (0.668) (0.119) (0.857) (0.600)
form2GOV -0.017 0.111 -0.013 0.016 -0.041 -0.089
(0.910) (0.543) (0.948) (0.935) (0.767) (0.580)
form2IND -0.645 -0.698 -0.694 -0.371 -0.638 -0.564
(<0.001) *** (<0.001) *** (<0.001) *** (0.014) * (<0.001) *** (<0.001) ***
after_covidTRUE -1.776 -1.496 -1.132 -1.042 -1.412 -0.843
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
categoryresidential -0.377 -0.420 -0.386 -0.587 -0.294 -0.244
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
inheritedTRUE -0.322 -0.335 -0.398 -0.460 -0.238 -0.306
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (0.007) ** (0.004) **
:———————- ————-: ————-: ————-: ————-: ————-: ————-:
Num.Obs. 12942 12966 12931 12927 12964 12953
AIC 15296.7 12507.3 10045.6 8808.0 17890.6 12548.2
BIC 15431.1 12641.8 10180.0 8942.4 18025.1 12682.7
RMSE 2.24 2.10 2.13 2.21 2.25 2.27

whole model with control - IND as reference group

model_order_overall_covid3 <- clm(rating ~ form3 + after_covid + 
                                   category + region + inherited,
                data = filter(socialcare, domain == "Overall"),
                link = "logit")
model_order_safe_covid3 <- clm(rating ~ form3 + after_covid +
                                category + region + inherited,
                data = filter(socialcare, domain == "Safe"),
                link = "logit")
model_order_effective_covid3 <- clm(rating ~ form3 + after_covid +
                                     category + region + inherited,
                data = filter(socialcare, domain == "Effective"),
                link = "logit")
model_order_caring_covid3 <- clm(rating ~ form3 + after_covid + 
                                  category + region + inherited,
                data = filter(socialcare, domain == "Caring"),
                link = "logit")
model_order_well_led_covid3 <- clm(rating ~ form3 + after_covid +
                                    category + region + inherited,
                data = filter(socialcare, domain == "Well-led"),
                link = "logit")
model_order_responsive_covid3 <- clm(rating ~ form3 + after_covid +
                                      category + region + inherited,
                data = filter(socialcare, domain == "Responsive"),
                link = "logit")
ordinal_models_covid3 <- modelsummary(list("overall" = model_order_overall_covid3, "safe" = model_order_safe_covid3, 
                                    "effective" = model_order_effective_covid3, "caring"= model_order_caring_covid3, 
                                    "well-led" = model_order_well_led_covid3, "responsive" = model_order_responsive_covid3),
                               coef_omit = "region", exponentiate = F,
                               statistic = "({p.value}) {stars}")
ordinal_models_covid3
overall safe effective caring well-led responsive
Inadequate Req improv -5.060 -4.751 -5.989 -7.349 -4.514
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Req improv Good -2.160 -1.830 -2.099 -3.668 -1.624
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
Good Outstanding 3.133 5.794 4.558 2.944 3.148
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
form3FPO 0.339 0.248 0.344 0.097 0.282 0.097
(<0.001) *** (0.009) ** (0.002) ** (0.469) (<0.001) *** (0.364)
form3CIC 0.832 1.004 0.526 0.899 0.690 0.393
(0.007) ** (0.013) * (0.184) (0.011) * (0.018) * (0.241)
form3GOV 0.628 0.810 0.681 0.387 0.597 0.474
(<0.001) *** (<0.001) *** (0.001) ** (0.077) + (<0.001) *** (0.009) **
form3NPO 0.645 0.698 0.694 0.371 0.638 0.564
(<0.001) *** (<0.001) *** (<0.001) *** (0.014) * (<0.001) *** (<0.001) ***
after_covidTRUE -1.776 -1.496 -1.132 -1.042 -1.412 -0.843
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
categoryresidential -0.377 -0.420 -0.386 -0.587 -0.294 -0.244
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (<0.001) ***
inheritedTRUE -0.322 -0.335 -0.398 -0.460 -0.238 -0.306
(<0.001) *** (<0.001) *** (<0.001) *** (<0.001) *** (0.007) ** (0.004) **
:———————- ————-: ————-: ————-: ————-: ————-: ————-:
Num.Obs. 12942 12966 12931 12927 12964 12953
AIC 15296.7 12507.3 10045.6 8808.0 17890.6 12548.2
BIC 15431.1 12641.8 10180.0 8942.4 18025.1 12682.7
RMSE 2.24 2.10 2.13 2.21 2.25 2.27