Note: This is the R markdown of the manuscript “Associated Factors of Attention-Deficit/Hyperactivity Disorder Diagnosis and Psychostimulant Use: A Nationwide Representative Study”. Click run to reproduce all analyzes.
If you have any questions or queries, please reach me out at luisfca@puc-rio.br
last updated: 04 January, 2022
#get file
load(url("https://osf.io/uhavp/download"))pacman::p_load(tidyverse, janitor, arsenal, epiDisplay)ds %>% select(contains("snapp_")) %>%
alpha(.)
Reliability analysis
Call: alpha(x = .)
lower alpha upper 95% confidence boundaries
0.92 0.92 0.92
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
0 1 2 3 miss
snapp_1 0.30 0.51 0.14 0.05 0
snapp_2 0.54 0.31 0.11 0.04 0
snapp_3 0.53 0.30 0.12 0.06 0
snapp_4 0.57 0.28 0.10 0.05 0
snapp_5 0.52 0.31 0.12 0.06 0
snapp_6 0.49 0.32 0.13 0.06 0
snapp_7 0.49 0.31 0.14 0.07 0
snapp_8 0.35 0.41 0.17 0.07 0
snapp_9 0.48 0.34 0.12 0.06 0
snapp_10 0.40 0.34 0.17 0.09 0
snapp_11 0.53 0.30 0.11 0.06 0
snapp_12 0.66 0.20 0.09 0.05 0
snapp_13 0.70 0.20 0.07 0.04 0
snapp_14 0.48 0.30 0.13 0.09 0
snapp_15 0.33 0.33 0.20 0.14 0
snapp_16 0.45 0.33 0.14 0.08 0
snapp_17 0.41 0.34 0.16 0.10 0
snapp_18 0.35 0.35 0.17 0.12 0ds %>% select(contains("snapp_")) %>%
splitHalf()Split half reliabilities
Call: splitHalf(r = .)
Maximum split half reliability (lambda 4) = 0.95
Guttman lambda 6 = 0.93
Average split half reliability = 0.92
Guttman lambda 3 (alpha) = 0.92
Guttman lambda 2 = 0.92
Minimum split half reliability (beta) = 0.8
Average interitem r = 0.39 with median = 0.38ds %>% select(contains("snappro_")) %>%
pivot_longer(everything()) %>%
count(value) %>%
mutate(percent = (100 * n / sum(n)) %>% round(digits = 1))ds %>% select(contains("snappro_")) %>%
alpha(.)
Reliability analysis
Call: alpha(x = .)
lower alpha upper 95% confidence boundaries
0.96 0.96 0.96
Reliability if an item is dropped:
Item statistics
Non missing response frequency for each item
0 1 2 3 miss
snappro_1 0.34 0.39 0.19 0.07 0
snappro_2 0.47 0.31 0.16 0.06 0
snappro_3 0.58 0.26 0.11 0.05 0
snappro_4 0.53 0.27 0.13 0.07 0
snappro_5 0.50 0.29 0.14 0.06 0
snappro_6 0.52 0.27 0.14 0.07 0
snappro_7 0.61 0.26 0.09 0.04 0
snappro_8 0.30 0.40 0.19 0.10 0
snappro_9 0.50 0.30 0.13 0.06 0
snappro_10 0.56 0.27 0.11 0.06 0
snappro_11 0.59 0.25 0.10 0.06 0
snappro_12 0.75 0.16 0.06 0.03 0
snappro_13 0.69 0.20 0.08 0.03 0
snappro_14 0.68 0.21 0.07 0.04 0
snappro_15 0.49 0.31 0.14 0.06 0
snappro_16 0.63 0.24 0.09 0.04 0
snappro_17 0.62 0.24 0.09 0.05 0
snappro_18 0.63 0.23 0.09 0.05 0ds %>% select(contains("snappro_")) %>%
splitHalf()Split half reliabilities
Call: splitHalf(r = .)
Maximum split half reliability (lambda 4) = 0.98
Guttman lambda 6 = 0.97
Average split half reliability = 0.96
Guttman lambda 3 (alpha) = 0.96
Guttman lambda 2 = 0.96
Minimum split half reliability (beta) = 0.82
Average interitem r = 0.56 with median = 0.54cor(ds_selected$snapp_total, ds_selected$snapprof_total)
[1] 0.4666921tableby(~age_group + sex_male + race_white + economic_status + public_School + region + city_size, data = ds_selected) %>% summary()| Overall (N=7114) | |
|---|---|
| age_group | |
| 1 | 4239 (59.6%) |
| 2 | 2299 (32.3%) |
| 3 | 576 (8.1%) |
| sex_male | |
| female | 3562 (50.1%) |
| male | 3552 (49.9%) |
| race_white | |
| N-Miss | 278 |
| other | 2227 (32.6%) |
| white | 4609 (67.4%) |
| economic_status | |
| AB | 2635 (37.0%) |
| C | 3513 (49.4%) |
| DE | 966 (13.6%) |
| public_School | |
| public | 5993 (84.2%) |
| private | 1121 (15.8%) |
| region | |
| CO | 628 (8.8%) |
| NE | 872 (12.3%) |
| NO | 250 (3.5%) |
| SE | 2345 (33.0%) |
| SU | 3019 (42.4%) |
| city_size | |
| small | 2600 (36.5%) |
| medium | 3242 (45.6%) |
| big | 1272 (17.9%) |
NAThe prevalence of ADHD-report was 7.1%.
ds_selected %>% #<-- dataset with predictors
count(adhd_parent) %>%
mutate(prop = prop.table(n)) %>%
adorn_totals()The prevalence was higher in boys than in girls (relative risk [RR] = 1.76, 95% confidence interval [CI] = 1.48-2.10)
riskratio(ds_selected$sex_male,ds_selected$adhd_parent)
$data
Outcome
Predictor no yes Total
female 3379 183 3562
male 3230 322 3552
Total 6609 505 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
female 1.000000 NA NA
male 1.764517 1.480658 2.102793
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
female NA NA NA
male 9.128209e-11 1.08562e-10 1.117279e-10
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"and higher in children from upper upper-income classes A and B compared withthan in childred from lower lower-income classes D and E (RR = 1.40, 95% CI = 1.045-1.88).
riskratio(ds_selected$economic_status,ds_selected$adhd_parent, rev = "r")
$data
Outcome
Predictor no yes Total
DE 913 53 966
C 3264 249 3513
AB 2432 203 2635
Total 6609 505 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
DE 1.000000 NA NA
C 1.291881 0.9687814 1.722738
AB 1.404160 1.0473197 1.882583
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
DE NA NA NA
C 0.07497211 0.08217484 0.07876962
AB 0.01953964 0.02308901 0.02178100
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"The prevalence was higher in children who lived in the southern region of the country compared withthan in children who lived in the northeast region (RR = 1.59, 95% CI = 1.16-2.17).
riskratio(factor(ds_selected$region, levels=c("NE","SU","CO","NO","SE")),ds_selected$adhd_parent)
$data
Outcome
Predictor no yes Total
NE 827 45 872
SU 2802 217 3019
CO 593 35 628
NO 234 16 250
SE 2153 192 2345
Total 6609 505 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
NE 1.000000 NA NA
SU 1.392838 1.0194556 1.902974
CO 1.079972 0.7027771 1.659614
NO 1.240178 0.7134565 2.155760
SE 1.586581 1.1576768 2.174390
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
NE NA NA NA
SU 0.031930689 0.038077263 0.035361539
CO 0.723960605 0.728260479 0.725637019
NO 0.445173904 0.431718370 0.446085193
SE 0.002685312 0.003020571 0.003484157
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"The prevalence of ADHD-probable was 3.9%
ds_selected %>% #<-- dataset with predictors
count(adhd_risk) %>%
mutate(prop = prop.table(n)) %>%
adorn_totals()which was higher in younger children and boys (RR = 2.97, 95% CI = 2.27-3.87)
riskratio(ds_selected$sex_male,ds_selected$adhd_risk)
$data
Outcome
Predictor no yes Total
female 3492 70 3562
male 3345 207 3552
Total 6837 277 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
female 1.000000 NA NA
male 2.965468 2.270635 3.872926
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
female NA NA NA
male 0 1.054224e-17 3.748842e-17
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"riskratio(relevel(ds_selected$race_white, ref = "white"),ds_selected$adhd_risk)
$data
Outcome
Predictor no yes Total
white 4446 163 4609
other 2123 104 2227
Total 6569 267 6836
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
white 1.000000 NA NA
other 1.320481 1.03798 1.679869
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
white NA NA NA
other 0.02531767 0.02774041 0.02339304
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"The prevalence of ADHD-probable was 3.9%, which was higher in younger children and boys (RR = 2.97, 95% CI = 2.27-3.87)
riskratio(ds_selected$sex_male,ds_selected$adhd_risk)
$data
Outcome
Predictor no yes Total
female 3492 70 3562
male 3345 207 3552
Total 6837 277 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
female 1.000000 NA NA
male 2.965468 2.270635 3.872926
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
female NA NA NA
male 0 1.054224e-17 3.748842e-17
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"The prevalence of ADHD-pst was 1.9%
ds_selected %>% #entire sample to check use of psychotropics
drop_na() %>%
count(psychostimulant) %>%
mutate(prop = prop.table(n)) %>%
adorn_totals()
psychostimulant n prop
no 5823 0.98063321
yes 115 0.01936679
Total 5938 1.00000000The chi-square test was carried out to check the relationship between ADHD and Socioeconomic status. We could conclude that there’s no association between the two variables (X2(2) = 4.56, p = 0.1).
#classe social e TDAH
CrossTable(ds_selected$adhd_parent,
ds_selected$economic_status,
prop.r = T, #row proportions
prop.c = F, #column proportion
total.r = T, #new column with totals
total.c = F,
prop.t = F,
expected = T,
chisq = T) #chi square valueThe prevalence was also higher in children who lived in the southeast region (RR = 4.37, 95% CI = 1.40-13.64) and the south region (RR = 3.22, 95% CI = 1.03-10.08) compared withthan in children who lived in the north region
riskratio(factor(ds_selected$region, levels=c("NO","SU","CO","NE","SE")),ds_selected$adhd_risk)
Warning in chisq.test(xx, correct = correction) :
Chi-squared approximation may be incorrect$data
Outcome
Predictor no yes Total
NO 247 3 250
SU 2902 117 3019
CO 619 9 628
NE 847 25 872
SE 2222 123 2345
Total 6837 277 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
NO 1.000000 NA NA
SU 3.229546 1.0341942 10.085116
CO 1.194268 0.3259983 4.375099
NE 2.389144 0.7273559 7.847613
SE 4.371002 1.4009316 13.637825
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
NO NA NA NA
SU 0.018147383 0.033181748 0.030628115
CO 0.829154795 1.000000000 0.788321775
NE 0.130927744 0.169800862 0.136333264
SE 0.001408455 0.002750785 0.004672068
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"ds_selected %>%
count(adhd_parent) %>%
mutate(prop=prop.table(n)*100)Age group
riskratio(ds_selected$age_group,ds_selected$adhd_parent, rev = "row") #older children are references
$data
Outcome
Predictor no yes Total
3 544 32 576
2 2123 176 2299
1 3942 297 4239
Total 6609 505 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
3 1.000000 NA NA
2 1.377990 0.9561945 1.985849
1 1.261146 0.8850567 1.797049
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
3 NA NA NA
2 0.07712139 0.08750415 0.08191846
1 0.19208807 0.21788824 0.19536606
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"Sex
riskratio(ds_selected$sex_male,ds_selected$adhd_parent)
$data
Outcome
Predictor no yes Total
female 3379 183 3562
male 3230 322 3552
Total 6609 505 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
female 1.000000 NA NA
male 1.764517 1.480658 2.102793
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
female NA NA NA
male 9.128209e-11 1.08562e-10 1.117279e-10
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"ds_selected %>%
tabyl(sex_male,adhd_parent) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 2) %>%
adorn_ns()
sex_male no yes
female 94.86% (3379) 5.14% (183)
male 90.93% (3230) 9.07% (322)ds_selected %>% #Relative Risk for gender
{CrossTable(.$sex_male,.$adhd_parent,
expected = T,
chisq = T)}Economic status
riskratio(ds_selected$economic_status,ds_selected$adhd_parent, rev = "r") #DE are reference
$data
Outcome
Predictor no yes Total
DE 913 53 966
C 3264 249 3513
AB 2432 203 2635
Total 6609 505 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
DE 1.000000 NA NA
C 1.291881 0.9687814 1.722738
AB 1.404160 1.0473197 1.882583
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
DE NA NA NA
C 0.07497211 0.08217484 0.07876962
AB 0.01953964 0.02308901 0.02178100
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"Race
riskratio(ds$cor_1_branca_2_nao_branca_3_nao_informou,ds$parent_reported_adhd) #change reference
$data
Outcome
Predictor 0 1 Total
1 4287 322 4609
2 2060 167 2227
3 262 16 278
Total 6609 505 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
1 1.000000 NA NA
2 1.073364 0.8966131 1.284959
3 0.823808 0.5060987 1.340963
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
1 NA NA NA
2 0.4403050 0.4526988 0.4409126
3 0.4424939 0.5418869 0.4321396
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"School type
riskratio(ds_selected$public_School,ds_selected$adhd_parent)
$data
Outcome
Predictor no yes Total
public 5576 417 5993
private 1033 88 1121
Total 6609 505 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
public 1.000000 NA NA
private 1.128198 0.9045846 1.407088
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
public NA NA NA
private 0.2873481 0.2817659 0.285776
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"Brazilian region
riskratio(relevel(ds_selected$region, ref = "NE"),ds_selected$adhd_parent) #need to change the reference$data
Outcome
Predictor no yes Total
NE 827 45 872
CO 593 35 628
NO 234 16 250
SE 2153 192 2345
SU 2802 217 3019
Total 6609 505 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
NE 1.000000 NA NA
CO 1.079972 0.7027771 1.659614
NO 1.240178 0.7134565 2.155760
SE 1.586581 1.1576768 2.174390
SU 1.392838 1.0194556 1.902974
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
NE NA NA NA
CO 0.723960605 0.728260479 0.725637019
NO 0.445173904 0.431718370 0.446085193
SE 0.002685312 0.003020571 0.003484157
SU 0.031930689 0.038077263 0.035361539
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"ds_selected %>%
tabyl(region, adhd_parent) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
region no yes
CO 94.4% (593) 5.6% (35)
NE 94.8% (827) 5.2% (45)
NO 93.6% (234) 6.4% (16)
SE 91.8% (2153) 8.2% (192)
SU 92.8% (2802) 7.2% (217)
Total 92.9% (6609) 7.1% (505)riskratio(ds_selected$city_size,ds_selected$adhd_parent, rev = "r") Totals
ds_selected %>% #<-- dataset with predictors
count(adhd_risk) %>%
mutate(prop = prop.table(n)) %>%
adorn_totals()
adhd_risk n prop
no 6837 0.96106269
yes 277 0.03893731
Total 7114 1.00000000Age group
riskratio(ds_selected$age_group,ds_selected$adhd_risk, rev = "r")
$data
Outcome
Predictor no yes Total
3 564 12 576
2 2210 89 2299
1 4063 176 4239
Total 6837 277 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
3 1.000000 NA NA
2 1.858199 1.024123 3.371573
1 1.992923 1.117789 3.553212
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
3 NA NA NA
2 0.03049368 0.04193336 0.03714494
1 0.01076177 0.01543821 0.01617942
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"ds_selected %>%
tabyl(age_group, adhd_risk) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
age_group no yes
1 95.8% (4063) 4.2% (176)
2 96.1% (2210) 3.9% (89)
3 97.9% (564) 2.1% (12)
Total 96.1% (6837) 3.9% (277)Sex
riskratio(ds_selected$sex_male,ds_selected$adhd_risk) $data
Outcome
Predictor no yes Total
female 3492 70 3562
male 3345 207 3552
Total 6837 277 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
female 1.000000 NA NA
male 2.965468 2.270635 3.872926
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
female NA NA NA
male 0 1.054224e-17 3.748842e-17
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"ds_selected %>%
tabyl(sex_male, adhd_risk) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
sex_male no yes
female 98.0% (3492) 2.0% (70)
male 94.2% (3345) 5.8% (207)
Total 96.1% (6837) 3.9% (277)Race/Ethniticy
riskratio(ds$cor_1_branca_2_nao_branca_3_nao_informou,ds$adhd_risk_dsm_5_0_nao_1_sim)
$data
Outcome
Predictor 0 1 Total
1 4446 163 4609
2 2123 104 2227
3 268 10 278
Total 6837 277 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
1 1.000000 NA NA
2 1.320481 1.0379801 1.679869
3 1.017125 0.5433693 1.903941
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
1 NA NA NA
2 0.02531767 0.02774041 0.02339304
3 0.92300151 0.86789609 0.95767709
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"ds %>%
tabyl(cor_1_branca_2_nao_branca_3_nao_informou, adhd_risk_dsm_5_0_nao_1_sim) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
cor_1_branca_2_nao_branca_3_nao_informou 0 1
1 96.5% (4446) 3.5% (163)
2 95.3% (2123) 4.7% (104)
3 96.4% (268) 3.6% (10)
Total 96.1% (6837) 3.9% (277)riskratio(ds_selected$race_white,ds_selected$adhd_risk, rev = "r")
$data
Outcome
Predictor no yes Total
white 4446 163 4609
other 2123 104 2227
Total 6569 267 6836
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
white 1.000000 NA NA
other 1.320481 1.03798 1.679869
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
white NA NA NA
other 0.02531767 0.02774041 0.02339304
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"Income class
riskratio(ds_selected$economic_status,ds_selected$adhd_risk)
$data
Outcome
Predictor no yes Total
AB 2567 68 2635
C 3366 147 3513
DE 904 62 966
Total 6837 277 7114
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
AB 1.000000 NA NA
C 1.621477 1.221850 2.151810
DE 2.487060 1.776992 3.480864
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
AB NA NA NA
C 6.119647e-04 7.272842e-04 7.050421e-04
DE 2.407965e-07 2.699206e-07 4.512870e-08
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"ds_selected %>%
tabyl(economic_status, adhd_risk) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
economic_status no yes
AB 97.4% (2567) 2.6% (68)
C 95.8% (3366) 4.2% (147)
DE 93.6% (904) 6.4% (62)
Total 96.1% (6837) 3.9% (277)Brazilian region
ds_selected %>%
tabyl(economic_status, adhd_risk) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
economic_status no yes
AB 97.4% (2567) 2.6% (68)
C 95.8% (3366) 4.2% (147)
DE 93.6% (904) 6.4% (62)
Total 96.1% (6837) 3.9% (277)ds_selected %>%
tabyl(region, adhd_risk) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
region no yes
CO 98.6% (619) 1.4% (9)
NE 97.1% (847) 2.9% (25)
NO 98.8% (247) 1.2% (3)
SE 94.8% (2222) 5.2% (123)
SU 96.1% (2902) 3.9% (117)
Total 96.1% (6837) 3.9% (277)School type
riskratio(ds_selected$public_School,ds_selected$adhd_risk, rev = "r") #check referenceds_selected %>%
tabyl(public_School, adhd_risk) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
public_School no yes
public 95.8% (5739) 4.2% (254)
private 97.9% (1098) 2.1% (23)
Total 96.1% (6837) 3.9% (277)City Size
riskratio(ds_selected$city_size, ds_selected$adhd_risk, rev = "r") #check referenceds_selected %>%
tabyl(city_size, adhd_risk) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
city_size no yes
small 95.0% (2471) 5.0% (129)
medium 96.8% (3138) 3.2% (104)
big 96.5% (1228) 3.5% (44)
Total 96.1% (6837) 3.9% (277)Age group
riskratio(ds_selected$age_group, ds_selected$psychostimulant, rev = "r") #check reference
$data
Outcome
Predictor no yes Total
3 566 9 575
2 2239 52 2291
1 4157 74 4231
Total 6962 135 7097
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
3 1.000000 NA NA
2 1.450119 0.7189038 2.925071
1 1.117414 0.5624866 2.219810
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
3 NA NA NA
2 0.2998557 0.3361292 0.2953127
1 0.7828656 0.8654783 0.7509478
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"ds_selected %>%
tabyl(age_group, psychostimulant) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
age_group no yes NA_
1 98.1% (4157) 1.7% (74) 0.2% (8)
2 97.4% (2239) 2.3% (52) 0.3% (8)
3 98.3% (566) 1.6% (9) 0.2% (1)
Total 97.9% (6962) 1.9% (135) 0.2% (17)sex
riskratio(ds_selected$sex_male, ds_selected$psychostimulant) #check reference
$data
Outcome
Predictor no yes Total
female 3515 41 3556
male 3447 94 3541
Total 6962 135 7097
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
female 1.000000 NA NA
male 2.302395 1.600135 3.31286
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
female NA NA NA
male 3.009571e-06 3.251082e-06 3.650795e-06
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"ds_selected %>%
tabyl(sex_male, psychostimulant) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
sex_male no yes NA_
female 98.7% (3515) 1.2% (41) 0.2% (6)
male 97.0% (3447) 2.6% (94) 0.3% (11)
Total 97.9% (6962) 1.9% (135) 0.2% (17)income
riskratio(ds_selected$economic_status, ds_selected$psychostimulant, rev = "r") #check reference
$data
Outcome
Predictor no yes Total
DE 955 11 966
C 3436 65 3501
AB 2571 59 2630
Total 6962 135 7097
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
DE 1.000000 NA NA
C 1.630443 0.8640134 3.076741
AB 1.970066 1.0393727 3.734136
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
DE NA NA NA
C 0.12053424 0.15884492 0.12665034
AB 0.02820677 0.04000511 0.03356871
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"ds_selected %>%
tabyl(economic_status, psychostimulant) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
economic_status no yes NA_
AB 97.6% (2571) 2.2% (59) 0.2% (5)
C 97.8% (3436) 1.9% (65) 0.3% (12)
DE 98.9% (955) 1.1% (11) 0.0% (0)
Total 97.9% (6962) 1.9% (135) 0.2% (17)City size
riskratio(ds_selected$city_size, ds_selected$psychostimulant, rev = "r") #check reference
$data
Outcome
Predictor no yes Total
big 1257 15 1272
medium 3167 75 3242
small 2538 45 2583
Total 6962 135 7097
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
big 1.000000 NA NA
medium 1.961752 1.1312034 3.402103
small 1.477352 0.8267709 2.639871
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
big NA NA NA
medium 0.01108165 0.01280013 0.01419559
small 0.18407298 0.21371351 0.18429815
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"ds_selected %>%
tabyl(city_size, psychostimulant) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
city_size no yes NA_
small 97.6% (2538) 1.7% (45) 0.7% (17)
medium 97.7% (3167) 2.3% (75) 0.0% (0)
big 98.8% (1257) 1.2% (15) 0.0% (0)
Total 97.9% (6962) 1.9% (135) 0.2% (17)Cor da pele (que analise estupida..)
riskratio(ds$cor_1_branca_2_nao_branca_3_nao_informou, ds_selected$psychostimulant, rev = "r") #check reference
Warning in chisq.test(xx, correct = correction) :
Chi-squared approximation may be incorrect$data
Outcome
Predictor no yes Total
3 273 5 278
2 2189 36 2225
1 4500 94 4594
Total 6962 135 7097
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
3 1.0000000 NA NA
2 0.8995955 0.3559811 2.273357
1 1.1376578 0.4665557 2.774085
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
3 NA NA NA
2 0.7854924 0.8009775 0.8230331
1 0.8243120 1.0000000 0.7763272
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"ds %>%
tabyl(cor_1_branca_2_nao_branca_3_nao_informou, uso_atual_de_psicoestimulante_0_nao_1_sim_2_nao_informado) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
cor_1_branca_2_nao_branca_3_nao_informou 0 1 2
1 97.6% (4500) 2.0% (94) 0.3% (15)
2 98.3% (2189) 1.6% (36) 0.1% (2)
3 98.2% (273) 1.8% (5) 0.0% (0)
Total 97.9% (6962) 1.9% (135) 0.2% (17)Tipo de escola
riskratio(ds_selected$public_School, ds_selected$psychostimulant) #check referenceds_selected %>%
tabyl(public_School, psychostimulant) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
public_School no yes NA_
public 97.8% (5862) 1.9% (114) 0.3% (17)
private 98.1% (1100) 1.9% (21) 0.0% (0)
Total 97.9% (6962) 1.9% (135) 0.2% (17)Regiao
riskratio(relevel(ds_selected$region, ref = "NE"), ds_selected$psychostimulant) #check reference
Warning in chisq.test(xx, correct = correction) :
Chi-squared approximation may be incorrect$data
Outcome
Predictor no yes Total
NE 869 3 872
CO 619 9 628
NO 248 2 250
SE 2290 45 2335
SU 2936 76 3012
Total 6962 135 7097
$measure
risk ratio with 95% C.I.
Predictor estimate lower upper
NE 1.000000 NA NA
CO 4.165605 1.1323007 15.32479
NO 2.325333 0.3907071 13.83946
SE 5.601713 1.7453711 17.97852
SU 7.334219 2.3194080 23.19159
$p.value
two-sided
Predictor midp.exact fisher.exact chi.square
NE NA NA NA
CO 2.494347e-02 3.494130e-02 1.949677e-02
NO 3.864003e-01 3.099012e-01 3.399785e-01
SE 2.801815e-04 4.408481e-04 1.018602e-03
SU 3.980458e-06 7.180548e-06 5.957003e-05
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"ds_selected %>%
tabyl(region, psychostimulant) %>%
adorn_totals("row") %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
region no yes NA_
CO 98.6% (619) 1.4% (9) 0.0% (0)
NE 99.7% (869) 0.3% (3) 0.0% (0)
NO 99.2% (248) 0.8% (2) 0.0% (0)
SE 97.7% (2290) 1.9% (45) 0.4% (10)
SU 97.3% (2936) 2.5% (76) 0.2% (7)
Total 97.9% (6962) 1.9% (135) 0.2% (17)Row = ADHD Report
ds_selected %>%
tabyl(adhd_parent, adhd_risk) %>%
adorn_totals(where = c("row","col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
adhd_parent no yes Total
no 97.5% (6446) 2.5% (163) 100.0% (6609)
yes 77.4% (391) 22.6% (114) 100.0% (505)
Total 96.1% (6837) 3.9% (277) 100.0% (7114)114/(114+391)
[1] 0.2257426ds_selected %>%
tabyl(adhd_parent, psychostimulant) %>%
adorn_totals(where = c("row","col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
adhd_parent no yes NA_ Total
no 99.2% (6553) 0.8% (56) 0.0% (0) 100.0% (6609)
yes 81.0% (409) 15.6% (79) 3.4% (17) 100.0% (505)
Total 97.9% (6962) 1.9% (135) 0.2% (17) 100.0% (7114)Row = ADHD Probable
ds_selected %>%
tabyl(adhd_risk, adhd_parent) %>%
adorn_totals(where = c("row","col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
adhd_risk no yes Total
no 94.3% (6446) 5.7% (391) 100.0% (6837)
yes 58.8% (163) 41.2% (114) 100.0% (277)
Total 92.9% (6609) 7.1% (505) 100.0% (7114)ds_selected %>%
tabyl(adhd_risk, psychostimulant) %>%
adorn_totals(where = c("row","col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
adhd_risk no yes NA_ Total
no 98.3% (6724) 1.4% (99) 0.2% (14) 100.0% (6837)
yes 85.9% (238) 13.0% (36) 1.1% (3) 100.0% (277)
Total 97.9% (6962) 1.9% (135) 0.2% (17) 100.0% (7114)ds_selected %>%
tabyl(adhd_risk, psychostimulant) %>%
adorn_percentages() %>%
adorn_ns(.)Row = Psychostimulant
ds_selected %>%
tabyl(psychostimulant, adhd_parent) %>%
adorn_totals(where = c("row","col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
psychostimulant no yes Total
no 94.1% (6553) 5.9% (409) 100.0% (6962)
yes 41.5% (56) 58.5% (79) 100.0% (135)
<NA> 0.0% (0) 100.0% (17) 100.0% (17)
Total 92.9% (6609) 7.1% (505) 100.0% (7114)ds_selected %>%
tabyl(psychostimulant, adhd_risk) %>%
adorn_totals(where = c("row","col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 1) %>%
adorn_ns
psychostimulant no yes Total
no 96.6% (6724) 3.4% (238) 100.0% (6962)
yes 73.3% (99) 26.7% (36) 100.0% (135)
<NA> 82.4% (14) 17.6% (3) 100.0% (17)
Total 96.1% (6837) 3.9% (277) 100.0% (7114)ds_selected %>%
tabyl(adhd_parent, adhd_risk) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(digits = 2) %>%
adorn_ns()CrossTable(ds_selected$adhd_parent, ds_selected$adhd_risk,
prop.r = F, #row proportions
prop.c = F, #column proportion
total.r = F, #new column with totals
total.c = F,
prop.t = F,
expected = F,
mcnemar = T) #Data is pareid. shoudl be mcnemar
Cell Contents
|-------------------------|
| N |
| Chi-square contribution |
|-------------------------|
===========================================
ds_selected$adhd_risk
ds_selected$adhd_parent no yes
-------------------------------------------
no 6446 163
1.401 34.583
-------------------------------------------
yes 391 114
18.337 452.589
===========================================
McNemar's Chi-squared test
------------------------------------------------------------
Chi^2 = 93.83394 d.f. = 1 p <2e-16
McNemar's Chi-squared test with continuity correction
------------------------------------------------------------
Chi^2 = 93.01264 d.f. = 1 p <2e-16 #select specitic dataset
ds_model_adhd <- ds_selected %>%
dplyr::select(-id, -psychostimulant, -adhd_risk, -age_group) %>%
mutate(married = relevel(married, ref = "married"))mod_adhd_complete <- glm(adhd_parent ~ ., family = binomial(link = "logit"), ds_model_adhd)epiDisplay::logistic.display(mod_adhd_complete) %>%
knitr::kable() %>%
kableExtra::kable_styling(., latex_options = "striped", full_width = T)
Warning in kableExtra::kable_styling(., latex_options = "striped", full_width = T) :
Please specify format in kable. kableExtra can customize either HTML or LaTeX outputs. See https://haozhu233.github.io/kableExtra/ for details.| x |
|---|
| Logistic regression predicting adhd_parent : yes vs no |
| crude OR(95%CI) | adj. OR(95%CI) | P(Wald’s test) | P(LR-test) | |
|---|---|---|---|---|
| public_School: private vs public | 1.14 (0.88,1.47) | 1.73 (1.24,2.39) | 0.001 | 0.001 |
| city_size: ref.=small | 0.059 | |||
| medium | 1.04 (0.84,1.29) | 1.16 (0.9,1.49) | 0.258 | |
| big | 0.83 (0.61,1.12) | 0.78 (0.56,1.08) | 0.138 | |
| region: ref.=CO | 0.041 | |||
| NE | 0.85 (0.52,1.39) | 0.94 (0.55,1.62) | 0.821 | |
| NO | 1.08 (0.55,2.1) | 0.83 (0.41,1.68) | 0.611 | |
| SE | 1.36 (0.92,2.03) | 1.54 (0.96,2.47) | 0.072 | |
| SU | 1.32 (0.89,1.95) | 1.53 (0.97,2.43) | 0.069 | |
| age (cont. var.) | 0.9919 (0.9542,1.031) | 0.9948 (0.9532,1.0382) | 0.812 | 0.811 |
| sex_male: male vs female | 1.9 (1.54,2.33) | 1.71 (1.38,2.11) | < 0.001 | < 0.001 |
| race_white: white vs other | 0.9 (0.73,1.11) | 0.88 (0.7,1.11) | 0.275 | 0.277 |
| married: divorced vs married | 1.6 (1.3,1.97) | 1.47 (1.18,1.84) | < 0.001 | < 0.001 |
| schooling: ref.=illiteracy | 0.389 | |||
| primary | 1.18 (0.65,2.14) | 1.43 (0.76,2.68) | 0.27 | |
| high_or_above | 1.2 (0.66,2.19) | 1.55 (0.8,3) | 0.192 | |
| economic_status: ref.=AB | 0.034 | |||
| C | 1.02 (0.82,1.25) | 0.88 (0.69,1.13) | 0.326 | |
| DE | 0.75 (0.53,1.08) | 0.57 (0.37,0.88) | 0.012 | |
| smoking: yes vs no | 1.66 (1.28,2.16) | 1.29 (0.95,1.74) | 0.103 | 0.108 |
| alcohol: yes vs no | 1.64 (1.17,2.29) | 1.24 (0.85,1.82) | 0.267 | 0.274 |
| scholar_achievement: ref.=average | < 0.001 | |||
| above | 0.51 (0.37,0.7) | 0.55 (0.4,0.77) | < 0.001 | |
| below | 2.76 (2.21,3.44) | 3.1 (2.44,3.94) | < 0.001 | |
| snap_parents_only: yes vs no | 3.61 (2.84,4.6) | 3.45 (2.65,4.48) | < 0.001 | < 0.001 |
| snap_teachers_only: yes vs no | 1.29 (0.97,1.71) | 0.77 (0.56,1.05) | 0.104 | 0.098 |
| x |
|---|
| Log-likelihood = -1361.0522 |
No. of observations = 5954 AIC value = 2766.1044 |
Second output
ds_selected %>%
select(-id, -psychostimulant, -age_group, -adhd_parent, -snap_parents_only , -snap_teachers_only)
Error in select(., -id, -psychostimulant, -age_group, -adhd_parent, -snap_parents_only, :
unused arguments (-id, -psychostimulant, -age_group, -adhd_parent, -snap_parents_only, -snap_teachers_only)epiDisplay::logistic.display(mod_adhd_risk_complete)
Logistic regression predicting adhd_risk : yes vs no
crude OR(95%CI) adj. OR(95%CI) P(Wald's test) P(LR-test)
public_School: private vs public 0.51 (0.32,0.8) 1.05 (0.62,1.78) 0.861 0.862
city_size: ref.=small 0.168
medium 0.64 (0.48,0.86) 0.75 (0.53,1.05) 0.095
big 0.75 (0.51,1.09) 0.74 (0.49,1.13) 0.163
region: ref.=CO 0.099
NE 2.05 (0.91,4.65) 1.41 (0.59,3.41) 0.44
NO 0.96 (0.25,3.64) 0.76 (0.19,3.02) 0.701
SE 3.39 (1.64,7.01) 2.02 (0.9,4.55) 0.088
SU 2.66 (1.29,5.5) 2.18 (0.98,4.85) 0.057
age (cont. var.) 0.96 (0.91,1.01) 0.94 (0.89,1) 0.046 0.043
sex_male: male vs female 2.84 (2.11,3.82) 2.32 (1.7,3.17) < 0.001 < 0.001
race_white: white vs other 0.75 (0.57,0.98) 0.89 (0.66,1.21) 0.467 0.468
married: divorced vs married 2.07 (1.58,2.7) 1.65 (1.23,2.21) < 0.001 0.001
schooling: ref.=illiteracy 0.339
primary 0.5 (0.29,0.86) 0.83 (0.46,1.49) 0.531
high_or_above 0.36 (0.2,0.62) 1.06 (0.56,2.03) 0.854
economic_status: ref.=AB 0.183
C 1.83 (1.34,2.52) 1.38 (0.95,2) 0.091
DE 2.6 (1.74,3.91) 1.53 (0.91,2.59) 0.109
smoking: yes vs no 1.98 (1.42,2.75) 1.1 (0.75,1.63) 0.62 0.622
alcohol: yes vs no 2.25 (1.51,3.35) 1.62 (1.01,2.58) 0.045 0.052
scholar_achievement: ref.=average < 0.001
above 0.85 (0.43,1.67) 0.88 (0.45,1.75) 0.722
below 15.28 (9.69,24.08) 13.74 (8.67,21.78) < 0.001
Log-likelihood = -758.8407
No. of observations = 5954
AIC value = 1557.6813epiDisplay::logistic.display(mod_psychostimulant_complete)
Logistic regression predicting psychostimulant : yes vs no
crude OR(95%CI) adj. OR(95%CI) P(Wald's test) P(LR-test)
public_School: private vs public 1.02 (0.62,1.67) 1.97 (1.05,3.68) 0.034 0.04
city_size: ref.=small 0.07
medium 1.48 (0.98,2.23) 1.63 (1.02,2.6) 0.041
big 0.81 (0.44,1.51) 0.93 (0.48,1.79) 0.825
region: ref.=CO < 0.001
NE 0.27 (0.07,1.03) 0.38 (0.1,1.52) 0.173
NO 0.63 (0.13,3.01) 0.53 (0.11,2.57) 0.43
SE 1.18 (0.54,2.55) 1.72 (0.7,4.21) 0.234
SU 1.8 (0.86,3.77) 2.57 (1.1,6.02) 0.029
age (cont. var.) 1 (0.94,1.08) 1.03 (0.95,1.11) 0.455 0.458
sex_male: male vs female 2.21 (1.49,3.3) 1.96 (1.31,2.94) 0.001 < 0.001
race_white: white vs other 1.35 (0.89,2.05) 1.18 (0.75,1.85) 0.471 0.467
married: divorced vs married 1.5 (1.02,2.2) 1.68 (1.12,2.53) 0.013 0.015
schooling: ref.=illiteracy 0.578
primary 0.99 (0.36,2.75) 0.93 (0.32,2.67) 0.888
high_or_above 0.89 (0.32,2.5) 0.73 (0.24,2.24) 0.583
economic_status: ref.=AB 0.068
C 0.76 (0.52,1.12) 0.62 (0.4,0.98) 0.04
DE 0.55 (0.27,1.11) 0.46 (0.2,1.05) 0.064
smoking: yes vs no 1.03 (0.59,1.81) 0.99 (0.54,1.81) 0.97 0.97
alcohol: yes vs no 0.51 (0.19,1.4) 0.43 (0.15,1.22) 0.112 0.076
scholar_achievement: ref.=average < 0.001
above 0.63 (0.34,1.18) 0.62 (0.33,1.17) 0.142
below 3.56 (2.31,5.48) 3.95 (2.5,6.24) < 0.001
snap_parents_only: yes vs no 2.1 (1.29,3.43) 1.85 (1.1,3.1) 0.02 0.027
snap_teachers_only: yes vs no 1.34 (0.79,2.25) 0.66 (0.38,1.16) 0.152 0.139
Log-likelihood = -503.7951
No. of observations = 5938
AIC value = 1051.5903| Overall (N=7114) | |
|---|---|
| age | |
| Mean (SD) | 9.273 (2.564) |
| Range | 5.000 - 18.000 |
arsenal::tableby(~age, data = ds_selected) %>% summary()
glm(ds_selected$adhd_parent ~ ds_selected$age, family = binomial) %>% summary()
Call:
glm(formula = ds_selected$adhd_parent ~ ds_selected$age, family = binomial)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3938 -0.3846 -0.3834 -0.3811 2.3108
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.628758 0.172938 -15.201 <2e-16 ***
ds_selected$age 0.006149 0.017905 0.343 0.731
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3645.0 on 7113 degrees of freedom
Residual deviance: 3644.9 on 7112 degrees of freedom
AIC: 3648.9
Number of Fisher Scoring iterations: 5glm(ds_selected$adhd_risk ~ ds_selected$age, family = binomial) %>% summary()
Call:
glm(formula = ds_selected$adhd_risk ~ ds_selected$age, family = binomial)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.3089 -0.2955 -0.2827 -0.2704 2.6630
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.79248 0.23383 -11.942 <2e-16 ***
ds_selected$age -0.04525 0.02504 -1.807 0.0708 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2341.2 on 7113 degrees of freedom
Residual deviance: 2337.9 on 7112 degrees of freedom
AIC: 2341.9
Number of Fisher Scoring iterations: 6glm(ds_selected$psychostimulant ~ ds_selected$age, family = binomial) %>% summary()
Call:
glm(formula = ds_selected$psychostimulant ~ ds_selected$age,
family = binomial)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.2099 -0.1986 -0.1955 -0.1924 2.8390
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.09172 0.32428 -12.618 <2e-16 ***
ds_selected$age 0.01596 0.03332 0.479 0.632
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1337.2 on 7096 degrees of freedom
Residual deviance: 1337.0 on 7095 degrees of freedom
(17 observations deleted due to missingness)
AIC: 1341
Number of Fisher Scoring iterations: 6! done
If you use this markdown, please cite it.
Contact me at luisfca@puc-rio.br
Done on January 4, 2022.