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Tweak vignette to make it independent
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@stefvanbuuren
stefvanbuuren committed Aug 23, 2023
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177 changes: 168 additions & 9 deletions vignettes/overweight-4y.Rmd
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Expand Up @@ -15,31 +15,190 @@ knitr::opts_chunk$set(
)
```

```{r setmodel}
library(ranger)
path <- path.expand("~/Project/zonmw_kansrijke_start/WP4/modellen_CBS/20230530/Overgewicht")
```{r setoutcome}
fn_model <- "20230525_rfr_mtry.rds"
fn_codebook <- "202211MH codebook JGZ predictors.xlsx"
model <- readRDS(file.path(path, fn_model))
outcome <- "overweight-4y"
```

```{r setmodel, eval = FALSE}
library(ranger)
path <- path.expand("~/Project/zonmw_kansrijke_start/WP4/modellen_CBS/20230530/Overgewicht")
model <- readRDS(file.path(path, fn_model))
```

## Description

BDS model ``r outcome`` predicts overweight risk around the age of 4 years given child data up to the age of 4 months. The model was fitted by Mirthe Hendriks for the C4PO project using combined CBS microdata.

This implementation is based on the random forest model version ``r fn_model`` and the code book ``r fn_codebook``. Here's a summary of the fitted model:

```{r}
model
```
Ranger result
Call:
ranger(overweight ~ ., data = overweight_train, num.trees = 1000, importance = "impurity", probability = TRUE)
Type: Probability estimation
Number of trees: 1000
Sample size: 13149
Number of independent variables: 138
Mtry: 11
Target node size: 10
Variable importance mode: impurity
Splitrule: gini
OOB prediction error (Brier s.): 0.0761
```

## Predictor names and importance

The model contains `r length(importance(model))` predictors. The names of the predictor variables, ordered in terms of their importance score, are
The model contains 138 predictors. The names of the predictor variables, ordered in terms of their importance score, are

```{r}
sort(round(importance(model), 1), decreasing = TRUE)
```{r eval = FALSE}
v <- sort(round(importance(model), 1), decreasing = TRUE)
data.frame(importance = v)
```

```
importance
bmi_4mnd_znl 103.1
gewicht_4mnd_znl 84.0
bmi_3mnd_znl 64.8
gewicht_3mnd_znl 56.5
bmi_8wks_znl 49.6
gewicht_8wks_znl 43.7
bmi_4wks_znl 41.7
PRNL_birthweight 39.3
lengte_4mnd_znl 37.6
gewicht_4wks_znl 36.6
lengte_3mnd_znl 34.8
lengte_8wks_znl 34.1
ZVWK_sumtotal_mo 33.1
income_fa 32.9
lengte_4wks_znl 32.0
income_parents 32.0
ZVWK_hospital_mo 31.9
ZVWK_GP_other_fa 31.2
income_mo 30.9
ZVWK_GP_other_mo 30.9
ZVWK_GP_consult_mo 29.8
ZVWK_pharmacy_mo 29.1
ED_woz 29.0
ZVWK_sumtotal_fa 28.7
age_at_birth_mo 27.4
ZVWK_birth_obstetrician_mo 26.3
ZVWK_birth_maternitycare_mo 26.1
SPOLIS_wages_fa 26.0
age_at_birth_fa 25.9
ZVWK_GP_regist_mo 25.1
hh_vermogen 25.0
ZVWK_GP_regist_fa 24.1
ZVWK_GP_consult_fa 22.9
SPOLIS_wages_mo 22.8
SPOLIS_paidhours_fa 21.9
SPOLIS_paidhours_mo 21.2
ZVWK_pharmacy_fa 20.5
income_hh 18.8
educationlevel_mo 18.3
PRNL_gestational_age_week_f 17.5
ZVWK_appliances_mo 16.7
educationlevel_fa 15.2
ZVWK_hospital_fa 14.9
opl1 11.8
ZVWK_other_mo 10.4
ZVWK_other_fa 10.1
opl2 9.7
geslacht 9.2
STED 9.1
PRNL_parity 8.8
LAND_ETNG_fa 8.7
ZVWK_appliances_fa 8.1
LAND_ACHTS_fa 6.8
ZVWK_patient_transport_lie_mo 6.8
LAND_ETNG_mo 6.7
LAND_ACHTS_mo 6.7
SECM_mo 6.7
SECM_fa 6.3
ZVWK_physical_other_mo 6.2
SPOLIS_contract_mo 5.7
SPOLIS_contract_fa 5.6
LAND_ETNG_gebl2 5.4
LAND_ACHTS_gebl2 5.4
LAND_ACHTS_gebl1 5.3
ED_rentown 5.2
LAND_ETNG_gebl1 5.0
GBA_generation_fa 4.7
income_hh_source 4.6
house_ownership 4.4
ZVWK_dentalcare_mo 4.0
ZVWK_physical_therapy_mo 4.0
ZVWK_mentalhealth_bas_mo 4.0
residence_same_for_parents 3.9
GBA_generation_mo 3.8
ZVWK_mentalhealth_spec_mo 3.7
ZVWK_mentalh_spec_nostay_inst_mo 3.5
SECM_disability_mo 3.1
GBA_generation_kid 3.0
premature_birth 2.9
ZVWK_mentalh_spec_nostay_ind_mo 2.9
l_income_hh_pov_binary 2.8
SECM_selfemployed_fa 2.8
ZVWK_GP_basic_mo 2.7
SECM_student_fa 2.6
l_income_hh_min_binary 2.5
SECM_selfemployed_mo 2.5
SECM_otherwork_mo 2.5
SECM_socialassistance_mo 2.5
ZVWK_mentalhealth_bas_fa 2.5
SECM_employee_fa 2.4
NA_dummy_bmi_4wks_znl 2.4
NA_dummy_bmi_4mnd_znl 2.4
l_income_hh_pov_4j_binary 2.3
l_income_hh_min_4j_binary 2.3
SECM_unemployed_mo 2.3
ZVWK_dentalcare_fa 2.3
ZVWK_abroad_fa 2.3
ZVWK_GP_basic_fa 2.2
ZVWK_patient_transport_lie_fa 2.2
NA_dummy_SPOLIS_wages_mo 2.2
SECM_employee_mo 2.1
SECM_otherwork_fa 2.1
SECM_unemployed_fa 2.1
SECM_disability_fa 2.0
NA_dummy_SPOLIS_wages_fa 2.0
PRNL_multiples 1.9
ZVWK_abroad_mo 1.9
NA_dummy_income_hh 1.9
ZVWK_physical_therapy_fa 1.8
SECM_student_mo 1.7
ZVWK_mentalhealth_spec_fa 1.7
SECM_otherassistance_fa 1.6
NA_dummy_ZVWK_mentalh_spec_other_fa 1.6
SECM_director_fa 1.5
ZVWK_physical_other_fa 1.5
NA_dummy_ZVWK_GP_basic_fa 1.5
SECM_familywork_fa 1.4
ZVWK_mentalh_spec_nostay_inst_fa 1.4
SECM_otherassistance_mo 1.3
SECM_socialassistance_fa 1.2
NA_dummy_ZVWK_GP_basic_mo 1.1
NA_dummy_ZVWK_mentalh_spec_other_mo 1.1
SECM_familywork_mo 1.0
NA_dummy_income_mo 1.0
SECM_retirement_mo 0.9
SECM_retirement_fa 0.9
ZVWK_mentalh_spec_nostay_ind_fa 0.9
ZVWK_patient_transport_sit_mo 0.5
ZVWK_birth_obstetrician_fa 0.5
NA_dummy_income_fa 0.5
ZVWK_birth_maternitycare_fa 0.4
NA_dummy_age_at_birth_fa 0.4
SECM_director_mo 0.3
ZVWK_mentalhealth_spec_stay_fa 0.3
ZVWK_mentalhealth_spec_stay_mo 0.0
ZVWK_mentalh_spec_other_mo 0.0
ZVWK_patient_transport_sit_fa 0.0
ZVWK_mentalh_spec_other_fa 0.0
```

## Connection between predictors and Basisdataset
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