This week, we will look at the 2018 National Survey of Children’s Health. https://www.census.gov/data/datasets/2018/demo/nsch/nsch2018.html
suppressPackageStartupMessages(library(tidyverse))
library(lme4)Loading required package: Matrix
Attaching package: ‘Matrix’
The following objects are masked from ‘package:tidyr’:
expand, pack, unpacklibrary(Hmisc)Loading required package: lattice
Loading required package: survival
Loading required package: Formula
Attaching package: ‘Hmisc’
The following objects are masked from ‘package:dplyr’:
src, summarize
The following objects are masked from ‘package:base’:
format.pval, units
nsch <- haven::read_dta("nsch_2018_topical.dta")
glimpse(nsch)Rows: 30,530
Columns: 442
$ fipsst <dbl+lbl> 51, 48, 48, 21, 13, 27, 39, 28, 31, 21, 28,…
$ stratum <chr> "1", "1", "1", "2A", "1", "1", "2A", "1", "1", …
$ hhid <dbl+lbl> 18000001, 18000005, 18000008, 18000010, 180…
$ formtype <chr> "T1", "T3", "T3", "T1", "T3", "T2", "T3", "T3",…
$ totkids_r <dbl+lbl> 3, 2, 1, 2, 2, 2, 1, 2, 1, 1, 4, 3, 3, 1, 2…
$ tenure <dbl+lbl> 1, 1, 3, 1, 2, 1, 3, 1, 2, 1, 3, 3, 2, 1, 1…
$ hhlanguage <dbl+lbl> 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ sc_age_years <dbl+lbl> 3, 15, 16, 2, 17, 9, 17, 13, 1, 9, 6,…
$ sc_sex <dbl+lbl> 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 2, 1…
$ k2q35a_1_years <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ momage <dbl+lbl> 36, 38, 34, 31, 29, 28, 30, 30, 29, 41, 23,…
$ k6q41r_still <dbl+lbl> 2, NA(n), NA(n), 2, NA(n), NA(n), N…
$ k6q42r_never <dbl+lbl> 2, NA(n), NA(n), 1, NA(n), NA(n), N…
$ k6q43r_never <dbl+lbl> 2, NA(n), NA(n), 2, NA(n), NA(n), N…
$ k6q13a <dbl+lbl> NA(l), NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ k6q13b <dbl+lbl> NA(l), NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ k6q14a <dbl+lbl> NA(l), NA(n), NA(n), 1, NA(n), NA(n), N…
$ k6q14b <dbl+lbl> NA(l), NA(n), NA(n), 1, NA(n), NA(n), N…
$ k4q32x01 <dbl+lbl> 2, 2, NA(l), 1, 1, 2, …
$ k4q32x02 <dbl+lbl> 1, 1, NA(l), 2, 2, 1, …
$ k4q32x03 <dbl+lbl> 2, 2, NA(l), 2, 2, 2, …
$ k4q32x04 <dbl+lbl> 1, 2, NA(l), 2, 2, 1, …
$ k4q32x05 <dbl+lbl> 2, 2, NA(l), 2, 2, 2, …
$ dentalserv1 <dbl+lbl> NA(l), 1, 1, 1, 1, 1, …
$ dentalserv2 <dbl+lbl> NA(l), 1, 1, 2, 1, 1, …
$ dentalserv3 <dbl+lbl> NA(l), 2, 1, 1, 2, 1, …
$ dentalserv4 <dbl+lbl> NA(l), 1, 1, 2, 2, 1, …
$ dentalserv5 <dbl+lbl> NA(l), 2, 1, 2, 2, 2, …
$ dentalserv6 <dbl+lbl> NA(l), 2, 2, 2, 2, 2, …
$ dentalserv7 <dbl+lbl> NA(l), 2, 2, 2, 2, 2, …
$ k4q28x01 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k4q28x02 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k4q28x03 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k4q28x_ear <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k4q28x04 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k4q28x05 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ sesplanyr <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 3, NA(l), N…
$ sesplanmo <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(d), NA(l), N…
$ k4q37 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(m), NA(l), N…
$ spcservmo <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(m), NA(l), N…
$ liveusa_yr <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ liveusa_mo <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k11q43r <dbl+lbl> 2, 3, 6, 0, 2, 1, …
$ a1_age <dbl+lbl> 34, 53, 44, 33, 46, 37, 47, 44, 31, 50, 29,…
$ a1_liveusa <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ a2_age <dbl+lbl> NA(l), 75, 44, 32, 54, 37, N…
$ a2_liveusa <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ hhcount <dbl+lbl> 5, 4, 4, 4, 4, 4, 2, 3, 3, 4, 6, 6, 5, 3, 4…
$ famcount <dbl+lbl> 5, 4, 3, 4, 4, 4, 2, 3, 3, 4, 6, 6, 5, 3, 4…
$ breathing <dbl+lbl> 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ swallowing <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ stomach <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2…
$ physicalpain <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ hands <dbl+lbl> 2, NA(n), NA(n), 2, NA(n), NA(n), N…
$ coordination <dbl+lbl> 2, NA(n), NA(n), 2, NA(n), NA(n), N…
$ toothaches <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ gumbleed <dbl+lbl> 2, 2, 2, 2, 2, 2, …
$ cavities <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ memorycond <dbl+lbl> NA(n), 2, 2, NA(n), 2, 2, …
$ walkstairs <dbl+lbl> NA(n), 2, 2, NA(n), 2, 2, …
$ dressing <dbl+lbl> NA(n), 2, 2, NA(n), 2, 2, …
$ errandalone <dbl+lbl> NA(n), 2, 2, NA(n), 1, NA(n), …
$ k2q43b <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ blindness <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ allergies <dbl+lbl> 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2…
$ allergies_curr <dbl+lbl> NA(l), 1, 1, NA(l), NA(l), 1, N…
$ arthritis <dbl+lbl> 2, 2, 2, 2, 2, 2, …
$ arthritis_curr <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q40a <dbl+lbl> 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k2q40b <dbl+lbl> 2, 1, NA(l), NA(l), NA(l), 2, N…
$ k2q46a <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k2q46b <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q61a <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k2q61b <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q41a <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k2q41b <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q42a <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k2q42b <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ heart <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ heart_curr <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ headache <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ headache_curr <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q38a <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k2q38b <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q33a <dbl+lbl> 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2…
$ k2q33b <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 1, NA(l), N…
$ k2q32a <dbl+lbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k2q32b <dbl+lbl> NA(l), 2, NA(l), NA(l), NA(l), NA(l), N…
$ downsyn <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ blood <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ blood_screen <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ cystfib <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ cystfib_screen <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ genetic <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ genetic_screen <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ subabuse <dbl+lbl> NA(n), 2, 2, NA(n), 2, 2, …
$ subabuse_curr <dbl+lbl> NA(n), NA(l), NA(l), NA(n), NA(l), NA(l), N…
$ k2q34a <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k2q34b <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q36a <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k2q36b <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q60a <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k2q60b <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q37a <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k2q37b <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q30a <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k2q30b <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ anyother <dbl+lbl> 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ anyother_curr <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 1, NA(l), N…
$ k2q35a <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k2q35b <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ autismmed <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ autismtreat <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q31a <dbl+lbl> 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k2q31b <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 1, NA(l), N…
$ k2q31d <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 1, NA(l), N…
$ addtreat <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 1, NA(l), N…
$ k2q05 <dbl+lbl> 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 1, 2, 1…
$ k6q40 <dbl+lbl> 1, NA(n), NA(n), 1, NA(n), NA(n), N…
$ s4q01 <dbl+lbl> 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1…
$ docprivate <dbl+lbl> NA(n), NA(l), NA(l), NA(n), 1, NA(n), N…
$ overweight <dbl+lbl> 2, 2, 2, 2, 2, 2, …
$ k6q10 <dbl+lbl> 2, NA(n), NA(n), 2, NA(n), NA(n), N…
$ k6q12 <dbl+lbl> 2, NA(n), NA(n), 1, NA(n), NA(n), N…
$ k4q01 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2…
$ usualgo <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ usualsick <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1…
$ k4q31_r <dbl+lbl> 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2…
$ k4q23 <dbl+lbl> 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ althealth <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2…
$ k4q27 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ notelig <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ available <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ appointment <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ transportcc <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ notopen <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ issuecost <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ hospitalstay <dbl+lbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k6q15 <dbl+lbl> 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2…
$ sescurrsvc <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 1, NA(l), N…
$ k4q36 <dbl+lbl> 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2…
$ k4q38 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 2, NA(l), N…
$ k5q10 <dbl+lbl> 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2…
$ decisions <dbl+lbl> 2, NA(l), NA(l), 1, 2, 2, N…
$ k5q21 <dbl+lbl> NA(l), NA(l), NA(l), 2, 2, 2, N…
$ treatchild <dbl+lbl> NA(n), 1, 2, NA(n), 2, NA(n), …
$ treatadult <dbl+lbl> NA(n), 2, NA(l), NA(n), NA(l), NA(n), N…
$ medhistory <dbl+lbl> NA(n), 1, 2, NA(n), 1, NA(n), …
$ writeplan <dbl+lbl> NA(n), 2, 2, NA(n), 1, NA(n), …
$ receivecopy <dbl+lbl> NA(n), NA(l), NA(l), NA(n), 1, NA(n), N…
$ healthknow <dbl+lbl> NA(n), 2, 1, NA(n), 1, NA(n), …
$ keepinsadult <dbl+lbl> NA(n), 2, NA(l), NA(n), NA(l), NA(n), N…
$ k12q01_a <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k12q01_b <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k12q01_c <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k12q01_d <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k12q01_e <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k12q01_f <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k12q01_g <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ currcov <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ k12q03 <dbl+lbl> 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1…
$ k12q04 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2…
$ k12q12 <dbl+lbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2…
$ tricare <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k11q03r <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ hccovoth <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k3q25 <dbl+lbl> 2, NA(l), 2, 2, 2, 2, N…
$ stopwork <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2…
$ cuthours <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ avoidchg <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ oneword <dbl+lbl> 1, NA(n), NA(n), 1, NA(n), NA(n), N…
$ twowords <dbl+lbl> 1, NA(n), NA(n), 1, NA(n), NA(n), N…
$ threewords <dbl+lbl> 1, NA(n), NA(n), 1, NA(n), NA(n), N…
$ askquestion <dbl+lbl> 1, NA(n), NA(n), 1, NA(n), NA(n), N…
$ askquestion2 <dbl+lbl> 1, NA(n), NA(n), 1, NA(n), NA(n), N…
$ tellstory <dbl+lbl> 1, NA(n), NA(n), 2, NA(n), NA(n), N…
$ understand <dbl+lbl> 1, NA(n), NA(n), 1, NA(n), NA(n), N…
$ directions <dbl+lbl> 1, NA(n), NA(n), 1, NA(n), NA(n), N…
$ point <dbl+lbl> 1, NA(n), NA(n), 1, NA(n), NA(n), N…
$ directions2 <dbl+lbl> 1, NA(n), NA(n), 1, NA(n), NA(n), N…
$ understand2 <dbl+lbl> 1, NA(n), NA(n), 1, NA(n), NA(n), N…
$ rhymeword <dbl+lbl> 2, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ repeated <dbl+lbl> NA(n), 2, 2, NA(n), 2, 2, …
$ k7q30 <dbl+lbl> NA(n), 1, 1, NA(n), 2, 1, …
$ k7q31 <dbl+lbl> NA(n), 1, 1, NA(n), 1, 1, …
$ k7q32 <dbl+lbl> NA(n), 1, 1, NA(n), 1, 1, …
$ k7q37 <dbl+lbl> NA(n), 1, 2, NA(n), 1, 1, …
$ k7q38 <dbl+lbl> NA(n), 1, 2, NA(n), 2, 2, …
$ bornusa <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ k8q35 <dbl+lbl> 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ emosupspo <dbl+lbl> 1, NA(l), 1, 1, 1, 1, …
$ emosupfam <dbl+lbl> 1, NA(l), 1, 1, 1, 2, …
$ emosuphcp <dbl+lbl> 1, NA(l), 2, 2, 1, 2, …
$ emosupwor <dbl+lbl> 1, NA(l), 2, 2, 1, 2, …
$ emosupadv <dbl+lbl> 1, NA(l), 2, 2, 2, 2, …
$ emosuppeer <dbl+lbl> 1, NA(l), 2, 2, 2, 2, …
$ emosupmhp <dbl+lbl> 1, NA(l), 2, 2, 2, 2, …
$ emosupoth <dbl+lbl> 2, NA(l), 2, 2, 2, 2, …
$ k6q20 <dbl+lbl> 2, NA(n), NA(n), 1, NA(n), NA(n), N…
$ k6q27 <dbl+lbl> 2, NA(n), NA(n), 2, NA(n), NA(n), N…
$ k9q40 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2…
$ k9q41 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ mold <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k11q60 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k11q61 <dbl+lbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2…
$ k11q62 <dbl+lbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2…
$ s9q34 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k10q11 <dbl+lbl> 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1…
$ k10q12 <dbl+lbl> 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1…
$ k10q13 <dbl+lbl> 2, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2…
$ k10q14 <dbl+lbl> 1, 2, 2, 1, 2, 1, …
$ k10q20 <dbl+lbl> 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ k10q22 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2…
$ k10q23 <dbl+lbl> 2, 2, 2, 2, 2, 2, …
$ k9q96 <dbl+lbl> NA(n), 1, 1, NA(n), 1, 1, …
$ ace3 <dbl+lbl> 2, 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2…
$ ace4 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ ace5 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2…
$ ace6 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ ace7 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ ace8 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2…
$ ace9 <dbl+lbl> 2, 2, 2, 2, 2, 2, …
$ ace10 <dbl+lbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ a1_k11q50_r <dbl+lbl> 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1…
$ a1_deplstat <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ a2_k11q50_r <dbl+lbl> NA(l), 2, 1, 1, 1, 1, N…
$ a2_deplstat <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q01 <dbl+lbl> 1, 3, 1, 1, 1, 1, …
$ k2q01_d <dbl+lbl> 1, 2, 1, 1, 1, 1, …
$ k6q70_r <dbl+lbl> 1, NA(n), NA(n), 1, NA(n), NA(n), N…
$ k6q73_r <dbl+lbl> 1, NA(n), NA(n), 1, NA(n), NA(n), N…
$ k6q71_r <dbl+lbl> 1, 1, 1, 1, 1, 3, …
$ k6q72_r <dbl+lbl> 1, NA(n), NA(n), 1, NA(n), NA(n), N…
$ k7q84_r <dbl+lbl> NA(n), 1, 2, NA(n), 2, 2, …
$ k7q85_r <dbl+lbl> NA(n), 2, 1, NA(n), 1, 2, …
$ k7q82_r <dbl+lbl> NA(n), 1, 2, NA(n), 2, 1, …
$ k7q83_r <dbl+lbl> NA(n), 1, 2, NA(n), 1, 1, …
$ k7q70_r <dbl+lbl> NA(n), 3, 4, NA(n), 4, 4, …
$ k5q40 <dbl+lbl> 1, NA(l), NA(l), 1, 1, 1, N…
$ k5q41 <dbl+lbl> 1, NA(l), NA(l), 1, 1, 1, N…
$ k5q42 <dbl+lbl> 1, NA(l), NA(l), 1, 1, 1, N…
$ k5q43 <dbl+lbl> 1, NA(l), NA(l), 1, 1, 1, N…
$ k5q44 <dbl+lbl> 1, NA(l), NA(l), 1, 1, 1, N…
$ discussopt <dbl+lbl> NA(l), NA(l), NA(l), 1, NA(l), NA(l), N…
$ raiseconc <dbl+lbl> NA(l), NA(l), NA(l), 1, NA(l), NA(l), N…
$ bestforchild <dbl+lbl> NA(l), NA(l), NA(l), 1, NA(l), NA(l), N…
$ k3q20 <dbl+lbl> 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1…
$ k3q22 <dbl+lbl> 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1…
$ k3q21b <dbl+lbl> 2, NA(l), 2, 2, 3, 2, N…
$ bullied_r <dbl+lbl> NA(n), 1, 1, NA(n), 1, 1, …
$ bully <dbl+lbl> NA(n), 1, 1, NA(n), 1, 1, …
$ allergies_desc <dbl+lbl> NA(l), 1, 1, NA(l), NA(l), 1, N…
$ arthritis_desc <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q40c <dbl+lbl> NA(l), 2, NA(l), NA(l), NA(l), NA(l), N…
$ k2q46c <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ cerpals_desc <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q41c <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q42c <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ heart_desc <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ headache_desc <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q38c <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q33c <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 2, NA(l), N…
$ k2q32c <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ blood_desc <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ genetic_desc <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q34c <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q36c <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q60c <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q37c <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q30c <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ anyother_desc <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 2, NA(l), N…
$ k2q35c <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k2q31c <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 1, NA(l), N…
$ recogbegin <dbl+lbl> 2, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ clearexp <dbl+lbl> 1, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ writename <dbl+lbl> 2, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ recshapes <dbl+lbl> 2, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ distracted <dbl+lbl> 3, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ worktofin <dbl+lbl> 2, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ simpleinst <dbl+lbl> 1, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ playwell <dbl+lbl> 2, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ newactivity <dbl+lbl> 3, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ hurtsad <dbl+lbl> 1, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ calmdown <dbl+lbl> 2, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ temper <dbl+lbl> 3, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ sitstill <dbl+lbl> 2, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ recogabc <dbl+lbl> 1, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ talkabout <dbl+lbl> 1, 3, 2, 1, 1, 3, 1, 1, 2, 3, 2, 2, 1, 2, 2…
$ wktosolve <dbl+lbl> 1, 3, 1, 1, 1, 3, 1, 1, 2, 3, 1, 2, 1, 2, 2…
$ strengths <dbl+lbl> 1, 2, 2, 1, 1, 4, 1, 1, 2, 3, 2, 2, 1, 1, 2…
$ hopeful <dbl+lbl> 1, 1, 1, 1, 1, 4, 1, 1, 2, 2, 1, 2, 1, 1, 2…
$ a1_physhealth <dbl+lbl> 1, 4, 3, 1, 2, 2, 1, 3, 2, 3, 2, 3, 1, 2, 2…
$ a1_menthealth <dbl+lbl> 2, 3, 2, 1, 1, 2, 1, 3, 2, 3, 3, 2, 1, 2, 2…
$ a2_physhealth <dbl+lbl> NA(l), 4, 2, 1, 3, 3, N…
$ a2_menthealth <dbl+lbl> NA(l), 2, 2, 1, 3, 3, N…
$ k10q30 <dbl+lbl> 1, 1, 4, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2…
$ k10q31 <dbl+lbl> 1, 2, 4, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1…
$ k10q40_r <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ goforhelp <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1…
$ k10q41_r <dbl+lbl> NA(n), 1, 1, NA(n), 1, 1, …
$ k8q31 <dbl+lbl> 2, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1…
$ k8q32 <dbl+lbl> 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2…
$ k8q34 <dbl+lbl> 2, 2, 1, 1, 1, 2, 1, 2, 1, 2, 2, 2, 1, 1, 2…
$ a1_relation <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ a1_sex <dbl+lbl> 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1…
$ a1_born <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1…
$ a1_grade <dbl+lbl> 8, 3, 8, 8, 7, 6, 7, 7, 9, 9, 5, 5, 8, 7, 7…
$ a1_marital <dbl+lbl> 2, 5, 1, 1, 1, 1, 4, 4, 1, 2, 1, 1, 1, 2, 1…
$ a2_relation <dbl+lbl> 8, 3, 1, 1, 1, 1, 8, 8, 1, 1, 1, 1, 1, 1, 1…
$ a2_sex <dbl+lbl> NA(l), 2, 1, 1, 1, 1, N…
$ a2_born <dbl+lbl> NA(l), 1, 1, 1, 1, 1, N…
$ a2_grade <dbl+lbl> NA(l), 6, 6, 7, 2, 7, N…
$ a2_marital <dbl+lbl> NA(l), 6, 4, 1, 1, 1, N…
$ a1_active <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ a2_active <dbl+lbl> NA(l), 1, 1, 1, 1, 1, N…
$ howmuch <dbl+lbl> 4, 1, 5, 3, 4, 4, 1, 5, 2, 3, 2, 1, 2, 1, 3…
$ athomehc <dbl+lbl> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6…
$ arrangehc <dbl+lbl> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6…
$ k7q02r_r <dbl+lbl> NA(n), 2, 3, NA(n), 1, 1, …
$ k7q04r_r <dbl+lbl> NA(n), 1, 1, NA(n), 3, 1, …
$ physactiv <dbl+lbl> NA(n), 2, 3, NA(n), 3, 4, …
$ hoursleep05 <dbl+lbl> 6, NA(n), NA(n), 5, NA(n), NA(n), N…
$ hoursleep <dbl+lbl> NA(n), 3, 6, NA(n), 4, 6, …
$ screentime <dbl+lbl> 3, 5, 5, 3, 2, 3, 3, 4, 1, 4, 2, 3, 1, 1, 3…
$ k6q60_r <dbl+lbl> 4, NA(n), NA(n), 4, NA(n), NA(n), N…
$ k6q61_r <dbl+lbl> 4, NA(n), NA(n), 4, NA(n), NA(n), N…
$ k8q11 <dbl+lbl> 3, 4, 2, 3, 3, 4, 3, 2, 4, 2, 4, 4, 4, 4, 4…
$ foodsit <dbl+lbl> 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 2, 1, 1, 1, 1…
$ pesticide <dbl+lbl> 5, 7, 6, 6, 3, 7, 7, 5, 7, 7, 5, 5, 6, 7, 7…
$ poschoice <dbl+lbl> NA(n), 1, 2, NA(n), 1, NA(n), …
$ gainskills <dbl+lbl> NA(n), 1, 2, NA(n), 1, NA(n), …
$ changeage <dbl+lbl> NA(n), 2, 2, NA(n), 1, NA(n), …
$ k2q35d <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ hcability <dbl+lbl> 1, 2, 2, 1, 4, 1, 1, 1, 2, 3, 1, 1, 1, 1, 1…
$ hcextent <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 2, NA(l), N…
$ k4q20r <dbl+lbl> 2, NA(l), NA(l), 3, 3, 2, N…
$ docroom <dbl+lbl> 2, NA(l), NA(l), 2, 1, 3, N…
$ wgtconc <dbl+lbl> 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3…
$ k4q02_r <dbl+lbl> 1, 1, 1, 1, 1, 1, …
$ dentistvisit <dbl+lbl> NA(l), 3, 3, 2, 3, 2, …
$ k4q22_r <dbl+lbl> 3, 3, 3, 3, 1, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3…
$ treatneed <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 1, NA(l), N…
$ k4q24_r <dbl+lbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3…
$ k4q26 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ c4q04 <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1…
$ hospitaler <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1…
$ k4q04_r <dbl+lbl> 1, 2, 1, 1, 2, 1, 3, 1, 1, 1, 1, 1, 1, 3, 1…
$ k5q11 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), 1, N…
$ k5q20_r <dbl+lbl> 3, NA(l), NA(l), 2, 2, 2, N…
$ k5q22 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k5q30 <dbl+lbl> NA(l), NA(l), NA(l), 1, 1, 1, N…
$ k5q32 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ k5q31_r <dbl+lbl> 3, NA(l), NA(l), 3, 3, 2, N…
$ k8q21 <dbl+lbl> NA(n), 1, 1, NA(n), 1, 1, …
$ k8q30 <dbl+lbl> 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1…
$ countto <dbl+lbl> 4, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ k7q33 <dbl+lbl> NA(n), 2, 2, NA(n), 1, 1, …
$ bedtime <dbl+lbl> 2, 3, 3, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 3, 2…
$ k3q04_r <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ k6q08_r <dbl+lbl> 3, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ confident <dbl+lbl> 1, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ ace1 <dbl+lbl> 2, 1, 1, 1, 1, 1, 4, 2, 1, 2, 3, 2, 1, 1, 1…
$ usepencil <dbl+lbl> 1, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ makefriend <dbl+lbl> 1, 1, 1, NA(l), 2, 1, …
$ sleeppos <dbl+lbl> NA(l), NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ color <dbl+lbl> 1, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ k4q30_r <dbl+lbl> 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 1…
$ startschool <dbl+lbl> 1, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ menbevcov <dbl+lbl> 5, 5, 5, 5, 3, 5, 5, 5, 5, 1, 5, 5, 5, 5, 5…
$ planneeds_r <dbl+lbl> NA(n), NA(l), NA(l), NA(n), 1, NA(n), N…
$ year <dbl+lbl> 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2…
$ cbsafp_yn <dbl+lbl> 1, 1, 1, 1, 1, 1, …
$ metro_yn <dbl+lbl> NA(d), 2, 1, 2, 1, 1, …
$ mpc_yn <dbl+lbl> NA(d), 2, 2, 2, 2, 2, …
$ totage_0_5 <dbl+lbl> 1, 0, 0, 2, 0, 0, 0, 0, 1, 0, 1, 0, 3, 1, 0…
$ totage_6_11 <dbl+lbl> 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 2, 1, 0, 0, 0…
$ totage_12_17 <dbl+lbl> 2, 2, 1, 0, 2, 0, 1, 2, 0, 0, 1, 2, 0, 0, 2…
$ totcshcn <dbl+lbl> 1, 2, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0…
$ totnonshcn <dbl+lbl> 2, 0, 0, 2, 1, 1, 1, 2, 1, 0, 3, 2, 3, 1, 2…
$ sc_race_r <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1…
$ sc_hispanic_r <dbl+lbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ sc_cshcn <dbl+lbl> 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2…
$ sc_k2q10 <dbl+lbl> 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ sc_k2q11 <dbl+lbl> NA(l), 1, 1, NA(l), 1, NA(l), N…
$ sc_k2q12 <dbl+lbl> NA(l), 1, 1, NA(l), 1, NA(l), N…
$ sc_k2q13 <dbl+lbl> 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ sc_k2q14 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 1, NA(l), N…
$ sc_k2q15 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 1, NA(l), N…
$ sc_k2q16 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ sc_k2q17 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ sc_k2q18 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ sc_k2q19 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2…
$ sc_k2q20 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ sc_k2q21 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ sc_k2q22 <dbl+lbl> 2, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2…
$ sc_k2q23 <dbl+lbl> NA(l), NA(l), NA(l), NA(l), 1, NA(l), N…
$ sc_age_lt4 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2…
$ sc_age_lt6 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2…
$ sc_age_lt9 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2…
$ sc_age_lt10 <dbl+lbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2…
$ agepos4 <dbl+lbl> 4, 3, 1, 2, 2, 2, 1, 3, 1, 1, 4, 2, 3, 1, 2…
$ tenure_if <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ totmale <dbl+lbl> 1, 0, 1, 2, 2, 1, 1, 0, 1, 0, 1, 1, 1, 0, 2…
$ totfemale <dbl+lbl> 2, 2, 0, 0, 0, 1, 0, 2, 0, 1, 3, 2, 2, 1, 0…
$ sc_race_r_if <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ sc_racer <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1…
$ sc_raceasia <dbl+lbl> 1, NA(l), NA(l), NA(l), NA(l), 1, N…
$ sc_raceaian <dbl+lbl> NA(l), NA(l), NA(l), NA(l), NA(l), NA(l), N…
$ sc_hispanic_r_if <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ sc_sex_if <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ a2_if <dbl+lbl> 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0…
$ birthwt_oz_s <dbl+lbl> 105, 144, 135, 102, 95, 107, …
$ breastfedend_day_s <dbl+lbl> NA(d), NA(n), NA(n), NA(d), NA(n), NA(n), N…
$ breastfedend_wk_s <dbl+lbl> NA(d), NA(n), NA(n), NA(d), NA(n), NA(n), N…
$ breastfedend_mo_s <dbl+lbl> 25, NA(n), NA(n), 11, NA(n), NA(n), N…
$ frstformula_day_s <dbl+lbl> NA(d), NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ frstformula_wk_s <dbl+lbl> NA(d), NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ frstformula_mo_s <dbl+lbl> 5, NA(n), NA(n), NA(l), NA(n), NA(n), N…
$ frstsolids_day_s <dbl+lbl> NA(d), NA(n), NA(n), NA(d), NA(n), NA(n), N…
$ frstsolids_wk_s <dbl+lbl> NA(d), NA(n), NA(n), NA(d), NA(n), NA(n), N…
$ frstsolids_mo_s <dbl+lbl> 6, NA(n), NA(n), 11, NA(n), NA(n), N…
$ house_gen <dbl+lbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3…
$ family_r <dbl+lbl> 6, 5, 2, 1, 1, 1, 5, 5, 1, 2, 1, 1, 1, 2, 1…
$ currins <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ insgap <dbl+lbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ instype <dbl+lbl> 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2…
$ higrade <dbl+lbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3…
$ higrade_tvis <dbl+lbl> 4, 3, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4…
$ birthwt_vl <dbl+lbl> 2, 2, 2, 2, 2, 2, …
$ birthwt_l <dbl+lbl> 2, 2, 2, 2, 2, 2, …
$ birthwt <dbl+lbl> 3, 3, 3, 3, 3, 3, …
$ fpl_if <dbl+lbl> 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0…
$ a1_grade_if <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ bmiclass <dbl+lbl> NA(n), 4, 2, NA(n), 3, NA(n), …
$ hhcount_if <dbl+lbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ fpl_i1 <dbl+lbl> 325, 87, 400, 241, 71, 160, 184, 140, 400…
$ fpl_i2 <dbl+lbl> 325, 63, 400, 241, 100, 78, 184, 209, 400…
$ fpl_i3 <dbl+lbl> 325, 71, 400, 241, 126, 125, 184, 173, 400…
$ fpl_i4 <dbl+lbl> 325, 50, 400, 241, 400, 95, 184, 351, 400…
$ fpl_i5 <dbl+lbl> 325, 50, 400, 241, 180, 101, 184, 400, 400…
$ fpl_i6 <dbl+lbl> 325, 75, 400, 241, 50, 108, 184, 273, 400…
$ fwc <dbl+lbl> 6132.6177, 12203.8996, 3124.5496, 2935.6…select function is really useful, especially with really big datasets.First, we narrow down the data by selecting only the variables we need. Then, we use the as_factor function to convert all variable to factors ahead of time.
glimpse(nsch.2)
Rows: 30,530
Columns: 6
$ fipsst <fct> Virginia, Texas, Texas, Kentucky, Georgia, Minnesota, Oh…
$ hhid <fct> 18000001, 18000005, 18000008, 18000010, 18000015, 180000…
$ k2q40a <fct> Yes, Yes, No, No, No, Yes, No, No, No, No, No, No, No, N…
$ k9q41 <fct> Logical skip, Logical skip, Logical skip, Logical skip, …
$ mold <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, No, …
$ physactiv <fct> Not in universe, 1 - 3 days, 4 - 6 days, Not in universe…table function is a great way to figure out how variables are labelled…table(nsch.2$mold)
Yes No
2853 27188
No valid response Suppressed for confidentiality
489 0
Logical skip Not in universe
0 0 filter to only keep observations that have valid categories…nsch.2.clean <- nsch.2 %>%
filter(!(k2q40a %in%
c("No valid response", "Suppressed for confidentiality", "Not in universe", "Logical skip"))) %>%
filter(!(k9q41 %in%
c("No valid response", "Suppressed for confidentiality", "Not in universe", "Logical skip"))) %>%
filter(!(physactiv %in%
c("No valid response", "Suppressed for confidentiality", "Not in universe", "Logical skip"))) %>%
filter(!(mold %in%
c("No valid response", "Suppressed for confidentiality", "Not in universe", "Logical skip"))) %>%
rename(.,
asthma = k2q40a,
smoke_house = k9q41) %>%
mutate(.,
asthma.num = as.numeric(asthma),
asthma.recode = case_when(
asthma.num == 2 ~ 0,
asthma.num == 1 ~ 1
))
glimpse(nsch.2.clean)Rows: 3,346
Columns: 8
$ fipsst <fct> Kentucky, Oklahoma, New Jersey, California, Wyoming,…
$ hhid <fct> 18000030, 18000113, 18000217, 18000230, 18000315, 18…
$ asthma <fct> No, No, No, Yes, No, No, No, Yes, No, No, No, Yes, N…
$ smoke_house <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, …
$ mold <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, …
$ physactiv <fct> Every day, Every day, 1 - 3 days, 1 - 3 days, 0 days…
$ asthma.num <dbl> 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2…
$ asthma.recode <dbl> 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0…describe function from Hmisc is a nice codebook…Hmisc::describe(nsch.2.clean)nsch.2.clean
8 Variables 3346 Observations
-----------------------------------------------------------------------------
fipsst : State FIPS Code
n missing distinct
3346 0 51
lowest : Alabama Alaska Arizona Arkansas California
highest: Virginia Washington West Virginia Wisconsin Wyoming
-----------------------------------------------------------------------------
hhid : Unique Household ID
n missing distinct
3346 0 3346
lowest : 18000030 18000113 18000217 18000230 18000315
highest: 18175731 18175815 18175981 18175986 18176000
-----------------------------------------------------------------------------
asthma : Asthma
n missing distinct
3346 0 2
Value Yes No
Frequency 582 2764
Proportion 0.174 0.826
-----------------------------------------------------------------------------
smoke_house : Anyone Smoke Inside of Home
n missing distinct
3346 0 2
Value Yes No
Frequency 503 2843
Proportion 0.15 0.85
-----------------------------------------------------------------------------
mold : Mold Inside of Home
n missing distinct
3346 0 2
Value Yes No
Frequency 449 2897
Proportion 0.134 0.866
-----------------------------------------------------------------------------
physactiv : Exercise, Play Sport, or Physical Activity for 60 Minutes
n missing distinct
3346 0 4
Value 0 days 1 - 3 days 4 - 6 days Every day
Frequency 392 1333 863 758
Proportion 0.117 0.398 0.258 0.227
-----------------------------------------------------------------------------
asthma.num
n missing distinct Info Mean Gmd
3346 0 2 0.431 1.826 0.2875
Value 1 2
Frequency 582 2764
Proportion 0.174 0.826
-----------------------------------------------------------------------------
asthma.recode
n missing distinct Info Sum Mean Gmd
3346 0 2 0.431 582 0.1739 0.2875
-----------------------------------------------------------------------------glmer instead of lmer now that we have a binary outcome…model.null <- glmer(asthma.recode ~ (1|fipsst), family = binomial, data = nsch.2.clean)
summary(model.null)Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: asthma.recode ~ (1 | fipsst)
Data: nsch.2.clean
AIC BIC logLik deviance df.resid
3095.5 3107.7 -1545.7 3091.5 3344
Scaled residuals:
Min 1Q Median 3Q Max
-0.4805 -0.4639 -0.4553 -0.4391 2.3156
Random effects:
Groups Name Variance Std.Dev.
fipsst (Intercept) 0.01808 0.1345
Number of obs: 3346, groups: fipsst, 51
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.562 0.050 -31.23 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1null.icc
[1] 0.005465624model.2 <- glmer(asthma.recode ~ smoke_house + (1|fipsst), family = binomial, data = nsch.2.clean)
summary(model.2)Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: asthma.recode ~ smoke_house + (1 | fipsst)
Data: nsch.2.clean
AIC BIC logLik deviance df.resid
3087.3 3105.7 -1540.7 3081.3 3343
Scaled residuals:
Min 1Q Median 3Q Max
-0.5655 -0.4526 -0.4428 -0.4272 2.4132
Random effects:
Groups Name Variance Std.Dev.
fipsst (Intercept) 0.01995 0.1413
Number of obs: 3346, groups: fipsst, 51
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.2380 0.1096 -11.300 <2e-16 ***
smoke_houseNo -0.3877 0.1188 -3.263 0.0011 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
smoke_housN -0.888These estimates are logit coefficients. Long story short, negative numbers suggest lower odds relative to the reference group, and positive number suggest higher odds. So here, we would say that children in houses without a smoker have lower odds of having asthma. But that’s about all we can say without some more math.
We can exponentiate this coefficient to get an odds ratio- this makes things a little easier to interpret.
smoke.odds.ratio
[1] 0.6786159When the OR is less than one (.68 in this case), we subtract 1 - OR which equals .32. This means that for children in houses without a smoker, the odds of having asthma are 32% lower than for children who live in houses with a smoker.
We can also get predicted probabilities associated with each estimate. To do this, we need to add up the logits and plug them into the formula below:
exp(-1.2380)/(1+exp(-1.2380))
[1] 0.2247843So, for a child in a house with a smoker, the predicted probability of having asthma is 22%.
What about for a child in a house that doesn’t have a smoker?
exp(-1.6257)/(1+exp(-1.6257))
[1] 0.1644203For a child in a house without a smoker, the predicted probability of having asthma is 16%.
mold…model.3 <- glmer(asthma.recode ~ smoke_house + mold + (1|fipsst), family = binomial, data = nsch.2.clean)
summary(model.3)Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: asthma.recode ~ smoke_house + mold + (1 | fipsst)
Data: nsch.2.clean
AIC BIC logLik deviance df.resid
3075.9 3100.3 -1533.9 3067.9 3342
Scaled residuals:
Min 1Q Median 3Q Max
-0.6790 -0.4484 -0.4290 -0.4121 2.4910
Random effects:
Groups Name Variance Std.Dev.
fipsst (Intercept) 0.02022 0.1422
Number of obs: 3346, groups: fipsst, 51
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.8724 0.1451 -6.011 1.85e-09 ***
smoke_houseNo -0.3590 0.1194 -3.007 0.002641 **
moldNo -0.4603 0.1221 -3.768 0.000164 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) smk_hN
smoke_housN -0.629
moldNo -0.653 -0.064The estimate for smoke_house looks very similar (-.35). So, the calculations we ran for the first model (ORs, predicted probabilities) would still hold. But, we can do the same thing for the mold variable:
First, we can exponentiate the coefficient to get an odds ratio:
mold.odds.ratio <- exp(-0.4672)
mold.odds.ratio[1] 0.6267547Pretty similar to smoke! So, the odds of having asthma for a child in a house without mold (mold = No) are about 37 percent lower than the odds for a child in a house with mold.
We can also get predicted probabilities associated with each estimate. To do this, we need to add up the logits and plug them into the formula below:
exp(-0.8724)/(1+exp(-0.8724))
[1] 0.2947552So, for a child in a house with a smoker AND mold, the predicted probability of having asthma is 29%.
What about for a child in a house that doesn’t have a smoker OR mold?
# Predicted probability for child in house without smoker (logit = -0.8724 + -0.3590 + -0.4603 = -1.6917)
exp(-1.6917)/(1+exp(-1.6917))[1] 0.1555524For a child in a house without a smoker OR mold, the predicted probability of having asthma is 15%. Other combinations can be found by adding or removing the logits for each coefficient.
physactiv…model.4 <- glmer(asthma.recode ~ smoke_house + mold + physactiv + (1|fipsst), family = binomial, data = nsch.2.clean)
summary(model.4)Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: asthma.recode ~ smoke_house + mold + physactiv + (1 | fipsst)
Data: nsch.2.clean
AIC BIC logLik deviance df.resid
3079.8 3122.7 -1532.9 3065.8 3339
Scaled residuals:
Min 1Q Median 3Q Max
-0.6976 -0.4550 -0.4300 -0.4064 2.6157
Random effects:
Groups Name Variance Std.Dev.
fipsst (Intercept) 0.02058 0.1435
Number of obs: 3346, groups: fipsst, 51
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.85916 0.18199 -4.721 2.35e-06 ***
smoke_houseNo -0.34851 0.12030 -2.897 0.003767 **
moldNo -0.45557 0.12230 -3.725 0.000195 ***
physactiv1 - 3 days 0.04014 0.14958 0.268 0.788427
physactiv4 - 6 days -0.12221 0.16223 -0.753 0.451273
physactivEvery day -0.05369 0.16391 -0.328 0.743262
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) smk_hN moldNo ph1-3d ph4-6d
smoke_housN -0.435
moldNo -0.501 -0.060
physctv1-3d -0.575 -0.088 -0.024
physctv4-6d -0.506 -0.116 -0.037 0.716
physctvEvrd -0.517 -0.089 -0.030 0.707 0.654mold by smoke_house interaction?model.5 <- glmer(asthma.recode ~ smoke_house + mold + smoke_house:mold + (1|fipsst), family = binomial, data = nsch.2.clean)
summary(model.5)Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: asthma.recode ~ smoke_house + mold + smoke_house:mold + (1 |
fipsst)
Data: nsch.2.clean
AIC BIC logLik deviance df.resid
3077.4 3107.9 -1533.7 3067.4 3341
Scaled residuals:
Min 1Q Median 3Q Max
-0.6366 -0.4471 -0.4275 -0.4104 2.5025
Random effects:
Groups Name Variance Std.Dev.
fipsst (Intercept) 0.02053 0.1433
Number of obs: 3346, groups: fipsst, 51
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.0033 0.2357 -4.257 2.07e-05 ***
smoke_houseNo -0.1890 0.2664 -0.709 0.478
moldNo -0.2933 0.2639 -1.111 0.266
smoke_houseNo:moldNo -0.2137 0.2977 -0.718 0.473
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) smk_hN moldNo
smoke_housN -0.878
moldNo -0.885 0.784
smk_hsN:mlN 0.785 -0.894 -0.887moldmodel.6 <- glmer(asthma.recode ~ smoke_house + mold + (mold|fipsst), family = binomial, data = nsch.2.clean)boundary (singular) fit: see ?isSingularsummary(model.6)Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: asthma.recode ~ smoke_house + mold + (mold | fipsst)
Data: nsch.2.clean
AIC BIC logLik deviance df.resid
3079.8 3116.5 -1533.9 3067.8 3340
Scaled residuals:
Min 1Q Median 3Q Max
-0.6905 -0.4473 -0.4298 -0.4124 2.4853
Random effects:
Groups Name Variance Std.Dev. Corr
fipsst (Intercept) 0.03305 0.18180
moldNo 0.00214 0.04626 -1.00
Number of obs: 3346, groups: fipsst, 51
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.8742 0.1465 -5.967 2.42e-09 ***
smoke_houseNo -0.3593 0.1194 -3.008 0.002630 **
moldNo -0.4574 0.1232 -3.714 0.000204 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) smk_hN
smoke_housN -0.623
moldNo -0.660 -0.064
convergence code: 0
boundary (singular) fit: see ?isSingularrand won’t work with GLMMs, but we can compare AIC and BICHere, we see that both AIC and BIC went up, which is not a good sign…We probably don’t need the random slopes.
model.6.bic - model.5.bic
[1] 8.6modelsummary and broom.mixed Packages to Organize Your Results:| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| (Intercept) | -1.562 | -1.238 | -0.872 | -0.859 | -1.003 | -0.874 |
| (0.050) | (0.110) | (0.145) | (0.182) | (0.236) | (0.147) | |
| sd__(Intercept) | 0.134 | 0.141 | 0.142 | 0.143 | 0.143 | 0.182 |
| smoke_houseNo | -0.388 | -0.359 | -0.349 | -0.189 | -0.359 | |
| (0.119) | (0.119) | (0.120) | (0.266) | (0.119) | ||
| moldNo | -0.460 | -0.456 | -0.293 | -0.457 | ||
| (0.122) | (0.122) | (0.264) | (0.123) | |||
| physactiv1 - 3 days | 0.040 | |||||
| (0.150) | ||||||
| physactiv4 - 6 days | -0.122 | |||||
| (0.162) | ||||||
| physactivEvery day | -0.054 | |||||
| (0.164) | ||||||
| smoke_houseNo × moldNo | -0.214 | |||||
| (0.298) | ||||||
| cor__(Intercept).moldNo | -1.000 | |||||
| sd__moldNo | 0.046 | |||||
| AIC | 3095.5 | 3087.3 | 3075.9 | 3079.8 | 3077.4 | 3079.8 |
| BIC | 3107.7 | 3105.7 | 3100.3 | 3122.7 | 3107.9 | 3116.5 |
| Log.Lik. | -1545.742 | -1540.660 | -1533.940 | -1532.923 | -1533.680 | -1533.911 |