library(car)
## Loading required package: carData
library(ggplot2)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:car':
##
## recode
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(car)
library(stargazer)
##
## Please cite as:
## Hlavac, Marek (2018). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.2. https://CRAN.R-project.org/package=stargazer
library(survey)
## Loading required package: grid
## Loading required package: Matrix
## Loading required package: survival
##
## Attaching package: 'survey'
## The following object is masked from 'package:graphics':
##
## dotchart
library(questionr)
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v tibble 3.1.6 v purrr 0.3.4
## v tidyr 1.1.4 v stringr 1.4.0
## v readr 2.1.1 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x tidyr::expand() masks Matrix::expand()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x tidyr::pack() masks Matrix::pack()
## x dplyr::recode() masks car::recode()
## x purrr::some() masks car::some()
## x tidyr::unpack() masks Matrix::unpack()
library(broom)
library(emmeans)
library(ipumsr)
## Warning: package 'ipumsr' was built under R version 4.1.3
library(table1)
##
## Attaching package: 'table1'
## The following objects are masked from 'package:base':
##
## units, units<-
library(haven)
X38199_0001_Data <- read_dta("38199-0001-Data.dta")
View(X38199_0001_Data)
nams<-names(X38199_0001_Data)
head(nams, n=10)
## [1] "SID" "STARTDATE" "ENDDATE"
## [4] "USERLANGUAGE" "CURRENT_RES_LEN_D12" "DETROIT_RES_LEN_D12"
## [7] "HOUSING_D12" "HOME_OWNER_D12" "HOME_TYPE_D12"
## [10] "HOME_TYPE_TEXT_D12"
newnames<-tolower(gsub(pattern = "_",replacement = "",x = nams))
names(X38199_0001_Data)<-newnames
## felt anxious
X38199_0001_Data$felt_anxious<-Recode(X38199_0001_Data$mhanxietyd12, recodes="1:2=0; 3:4=1;else=NA", as.factor=T)
summary(X38199_0001_Data$felt_anxious, na.rm = TRUE)
## 0 1 NA's
## 1634 514 90
## felt depressed
X38199_0001_Data$felt_depressed<-Recode(X38199_0001_Data$mhdepressd12, recodes="1:2=0; 3:4=1;else=NA", as.factor=T)
summary(X38199_0001_Data$felt_depressed, na.rm = TRUE)
## 0 1 NA's
## 1634 506 98
## felt worried
X38199_0001_Data$felt_worried<-Recode(X38199_0001_Data$mhworryd12, recodes="1:2=0; 3:4=1;else=NA", as.factor=T)
summary(X38199_0001_Data$felt_worried, na.rm = TRUE)
## 0 1 NA's
## 1672 464 102
## income less than 35,000
X38199_0001_Data$inclessthn35000<-Recode(X38199_0001_Data$income1d12, recodes="1=1; 2=0;else=NA", as.factor=T)
summary(X38199_0001_Data$inclessthn35000, na.rm = TRUE)
## 0 1 NA's
## 876 1256 106
## gender
X38199_0001_Data$gendercat <- as.numeric(X38199_0001_Data$gendercat)
X38199_0001_Data$gender5<-Recode(X38199_0001_Data$gendercat, recodes="1='male';2='female';3='other'; else=NA", as.factor=T)
X38199_0001_Data$gender5<-relevel(X38199_0001_Data$gender5, ref='male')
summary(X38199_0001_Data$gender5, na.rm = TRUE)
## male female other NA's
## 648 1553 11 26
## Race
X38199_0001_Data$racecat4 <- as.numeric(X38199_0001_Data$racecat4)
X38199_0001_Data$race_eth<-Recode(X38199_0001_Data$racecat4, recodes="1='white';2='black';3='other'; 4='hispanic'; else=NA", as.factor=T)
X38199_0001_Data$race_eth<-relevel(X38199_0001_Data$race_eth, ref='white')
summary(X38199_0001_Data$race_eth, na.rm = TRUE)
## white black hispanic other NA's
## 341 1487 171 165 74
##educ
X38199_0001_Data$educat4 <- as.numeric(X38199_0001_Data$educat4)
X38199_0001_Data$educa <-Recode(X38199_0001_Data$educat4, recodes="1='lsshgh';2='hghsch';3='somecol'; 4='col'; else=NA", as.factor=T)
X38199_0001_Data$educa <-relevel(X38199_0001_Data$educa, ref='col')
summary(X38199_0001_Data$educa , na.rm = TRUE)
## col hghsch lsshgh somecol NA's
## 722 863 173 425 55
##age
X38199_0001_Data$agecat4 <- as.numeric(X38199_0001_Data$agecat4)
X38199_0001_Data$new_age <-Recode(X38199_0001_Data$agecat4,recodes="1='youngeradult';2:3='middleadult';
4='old'; else=NA", as.factor=T)
X38199_0001_Data$new_age<-relevel(X38199_0001_Data$new_age, ref='old')
summary(X38199_0001_Data$new_age , na.rm = TRUE)
## old middleadult youngeradult NA's
## 468 1210 499 61
##qualityoflife
X38199_0001_Data$nbqold12 <- as.numeric(X38199_0001_Data$nbqold12)
X38199_0001_Data$qol <-Recode(X38199_0001_Data$nbqold12,recodes="1='improving';2='declining'; 3:4='unsure';
else=NA", as.factor=T)
X38199_0001_Data$qol<-relevel(X38199_0001_Data$qol, ref='improving')
summary(X38199_0001_Data$qol , na.rm = TRUE)
## improving declining unsure NA's
## 799 431 1002 6
##neighborhoodsafety
X38199_0001_Data$nbchngsafetyd12 <- as.numeric(X38199_0001_Data$nbchngsafetyd12)
X38199_0001_Data$nbsafe <-Recode(X38199_0001_Data$nbchngsafetyd12,recodes="1='improving';2='declining'; 3:4='unsure';
else=NA", as.factor=T)
X38199_0001_Data$nbsafe<-relevel(X38199_0001_Data$nbsafe, ref='improving')
summary(X38199_0001_Data$nbsafe , na.rm = TRUE)
## improving declining unsure NA's
## 324 466 1440 8
##infrastructure
X38199_0001_Data$satisinfra<-Recode(X38199_0001_Data$nbsatisinfrastd12, recodes="1:2=1; 4:5=0;else=NA", as.factor=T)
summary(X38199_0001_Data$satisinfra, na.rm = TRUE)
## 0 1 NA's
## 980 991 267
##crime
X38199_0001_Data$satiscrime<-Recode(X38199_0001_Data$nbsatiscrimed12, recodes="1:2=1; 4:5=0;else=NA", as.factor=T)
summary(X38199_0001_Data$satiscrime, na.rm = TRUE)
## 0 1 NA's
## 480 1167 591
##lots
X38199_0001_Data$satislot<-Recode(X38199_0001_Data$nbsatislotsd12, recodes="1:2=1; 4:5=0;else=NA", as.factor=T)
summary(X38199_0001_Data$satislot, na.rm = TRUE)
## 0 1 NA's
## 626 1106 506
##housequal
X38199_0001_Data$housequal<-Recode(X38199_0001_Data$nbsatishousequald12, recodes="1:2=1; 4:5=0;else=NA", as.factor=T)
summary(X38199_0001_Data$housequal, na.rm = TRUE)
## 0 1 NA's
## 946 923 369
##parkqual
X38199_0001_Data$parkqual<-Recode(X38199_0001_Data$parksqualityd12, recodes="1:2=1; 4:5=0;else=NA", as.factor=T)
summary(X38199_0001_Data$parkqual, na.rm = TRUE)
## 0 1 NA's
## 383 966 889
##safewalk
X38199_0001_Data$walksafetyd12 <- as.numeric(X38199_0001_Data$walksafetyd12)
X38199_0001_Data$safewalking <-Recode(X38199_0001_Data$walksafetyd12,recodes="1='notsafe';2:3='safe'; 3:4='unsure';
else=NA", as.factor=T)
X38199_0001_Data$safewalking<-relevel(X38199_0001_Data$safewalking, ref='safe')
summary(X38199_0001_Data$safewalking , na.rm = TRUE)
## safe notsafe unsure NA's
## 1660 426 145 7
##safehome
X38199_0001_Data$nbsafetyd12 <- as.numeric(X38199_0001_Data$nbsafetyd12)
X38199_0001_Data$safehome<-Recode(X38199_0001_Data$nbsafetyd12,recodes="1='notsafe';2:3='safe'; 3:4='unsure';
else=NA", as.factor=T)
X38199_0001_Data$safehome<-relevel(X38199_0001_Data$safehome, ref='safe')
summary(X38199_0001_Data$safehome , na.rm = TRUE)
## safe notsafe unsure NA's
## 2032 173 30 3
sub2<-X38199_0001_Data%>%
select(felt_depressed, felt_anxious, felt_worried, inclessthn35000, gender5, race_eth, educa, new_age, qol, nbsafe, satisinfra, satiscrime, satislot, housequal, parkqual, safewalking, safehome, weights) %>%
filter( complete.cases( . ))
table1(~ new_age + race_eth + educa + inclessthn35000 + gender5| felt_anxious, data=sub2, overall="Total")
|
0
(N=483) |
1
(N=165) |
Total
(N=648) |
| new_age |
|
|
|
| old |
103 (21.3%) |
17 (10.3%) |
120 (18.5%) |
| middleadult |
279 (57.8%) |
99 (60.0%) |
378 (58.3%) |
| youngeradult |
101 (20.9%) |
49 (29.7%) |
150 (23.1%) |
| race_eth |
|
|
|
| white |
71 (14.7%) |
31 (18.8%) |
102 (15.7%) |
| black |
350 (72.5%) |
103 (62.4%) |
453 (69.9%) |
| hispanic |
30 (6.2%) |
16 (9.7%) |
46 (7.1%) |
| other |
32 (6.6%) |
15 (9.1%) |
47 (7.3%) |
| educa |
|
|
|
| col |
155 (32.1%) |
52 (31.5%) |
207 (31.9%) |
| hghsch |
192 (39.8%) |
72 (43.6%) |
264 (40.7%) |
| lsshgh |
39 (8.1%) |
14 (8.5%) |
53 (8.2%) |
| somecol |
97 (20.1%) |
27 (16.4%) |
124 (19.1%) |
| inclessthn35000 |
|
|
|
| 0 |
192 (39.8%) |
57 (34.5%) |
249 (38.4%) |
| 1 |
291 (60.2%) |
108 (65.5%) |
399 (61.6%) |
| gender5 |
|
|
|
| male |
138 (28.6%) |
40 (24.2%) |
178 (27.5%) |
| female |
344 (71.2%) |
124 (75.2%) |
468 (72.2%) |
| other |
1 (0.2%) |
1 (0.6%) |
2 (0.3%) |
table1(~ new_age + race_eth + educa + inclessthn35000 + gender5| felt_worried, data=sub2, overall="Total")
|
0
(N=492) |
1
(N=156) |
Total
(N=648) |
| new_age |
|
|
|
| old |
102 (20.7%) |
18 (11.5%) |
120 (18.5%) |
| middleadult |
280 (56.9%) |
98 (62.8%) |
378 (58.3%) |
| youngeradult |
110 (22.4%) |
40 (25.6%) |
150 (23.1%) |
| race_eth |
|
|
|
| white |
76 (15.4%) |
26 (16.7%) |
102 (15.7%) |
| black |
351 (71.3%) |
102 (65.4%) |
453 (69.9%) |
| hispanic |
35 (7.1%) |
11 (7.1%) |
46 (7.1%) |
| other |
30 (6.1%) |
17 (10.9%) |
47 (7.3%) |
| educa |
|
|
|
| col |
164 (33.3%) |
43 (27.6%) |
207 (31.9%) |
| hghsch |
192 (39.0%) |
72 (46.2%) |
264 (40.7%) |
| lsshgh |
41 (8.3%) |
12 (7.7%) |
53 (8.2%) |
| somecol |
95 (19.3%) |
29 (18.6%) |
124 (19.1%) |
| inclessthn35000 |
|
|
|
| 0 |
202 (41.1%) |
47 (30.1%) |
249 (38.4%) |
| 1 |
290 (58.9%) |
109 (69.9%) |
399 (61.6%) |
| gender5 |
|
|
|
| male |
139 (28.3%) |
39 (25.0%) |
178 (27.5%) |
| female |
353 (71.7%) |
115 (73.7%) |
468 (72.2%) |
| other |
0 (0%) |
2 (1.3%) |
2 (0.3%) |
table1(~ new_age + race_eth + educa + inclessthn35000 + gender5| felt_depressed, data=sub2, overall="Total")
|
0
(N=483) |
1
(N=165) |
Total
(N=648) |
| new_age |
|
|
|
| old |
103 (21.3%) |
17 (10.3%) |
120 (18.5%) |
| middleadult |
273 (56.5%) |
105 (63.6%) |
378 (58.3%) |
| youngeradult |
107 (22.2%) |
43 (26.1%) |
150 (23.1%) |
| race_eth |
|
|
|
| white |
82 (17.0%) |
20 (12.1%) |
102 (15.7%) |
| black |
337 (69.8%) |
116 (70.3%) |
453 (69.9%) |
| hispanic |
34 (7.0%) |
12 (7.3%) |
46 (7.1%) |
| other |
30 (6.2%) |
17 (10.3%) |
47 (7.3%) |
| educa |
|
|
|
| col |
169 (35.0%) |
38 (23.0%) |
207 (31.9%) |
| hghsch |
185 (38.3%) |
79 (47.9%) |
264 (40.7%) |
| lsshgh |
35 (7.2%) |
18 (10.9%) |
53 (8.2%) |
| somecol |
94 (19.5%) |
30 (18.2%) |
124 (19.1%) |
| inclessthn35000 |
|
|
|
| 0 |
197 (40.8%) |
52 (31.5%) |
249 (38.4%) |
| 1 |
286 (59.2%) |
113 (68.5%) |
399 (61.6%) |
| gender5 |
|
|
|
| male |
144 (29.8%) |
34 (20.6%) |
178 (27.5%) |
| female |
338 (70.0%) |
130 (78.8%) |
468 (72.2%) |
| other |
1 (0.2%) |
1 (0.6%) |
2 (0.3%) |
options(survey.lonely.psu = "adjust")
des<-svydesign(ids= ~1,
weights= ~weights
, data = sub2 )
fit.logit1<-svyglm(felt_anxious ~ new_age + race_eth + educa + inclessthn35000 + gender5,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit2<-svyglm(felt_anxious ~ new_age + race_eth + educa + inclessthn35000 + gender5 + qol,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit3<-svyglm(felt_anxious ~ new_age + race_eth + educa + inclessthn35000 + gender5 + nbsafe,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit4<-svyglm(felt_anxious ~ new_age + race_eth + educa + inclessthn35000 + gender5 + satisinfra,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit5<-svyglm(felt_anxious ~ new_age + race_eth + educa + inclessthn35000 + gender5 + satiscrime,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit6<-svyglm(felt_anxious ~ new_age + race_eth + educa + inclessthn35000 + gender5 + satislot,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit7<-svyglm(felt_anxious ~ new_age + race_eth + educa + inclessthn35000 + gender5 + housequal,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit8<-svyglm(felt_anxious ~ new_age + race_eth + educa + inclessthn35000 + gender5 + parkqual,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit9<-svyglm(felt_anxious ~ new_age + race_eth + educa + inclessthn35000 + gender5 + safewalking,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit9<-svyglm(felt_anxious ~ new_age + race_eth + educa + inclessthn35000 + gender5 + safehome,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit10<-svyglm(felt_anxious ~ new_age + race_eth + educa + inclessthn35000 + gender5 + safewalking,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit11<-svyglm(felt_anxious ~ new_age + race_eth + educa + inclessthn35000 + gender5 + qol + satisinfra +
satislot + housequal + parkqual,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit12<-svyglm(felt_anxious ~ new_age + race_eth + educa + inclessthn35000 + gender5 + nbsafe + satiscrime +
safehome + safewalking,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit13<-svyglm(felt_anxious ~ new_age + race_eth + educa + inclessthn35000 + gender5 + + qol + satisinfra +
satislot + housequal + parkqual + nbsafe + satiscrime +
safehome + safewalking,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit1%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-1.381 |
0.448 |
-3.086 |
0.002 |
0.251 |
0.104 |
0.604 |
| new_agemiddleadult |
0.445 |
0.397 |
1.122 |
0.262 |
1.561 |
0.717 |
3.397 |
| new_ageyoungeradult |
0.861 |
0.440 |
1.959 |
0.051 |
2.367 |
0.999 |
5.603 |
| race_ethblack |
-0.601 |
0.387 |
-1.554 |
0.121 |
0.548 |
0.257 |
1.170 |
| race_ethhispanic |
-0.433 |
0.535 |
-0.809 |
0.419 |
0.649 |
0.227 |
1.851 |
| race_ethother |
-0.419 |
0.518 |
-0.809 |
0.419 |
0.658 |
0.239 |
1.814 |
| educahghsch |
-0.067 |
0.354 |
-0.191 |
0.849 |
0.935 |
0.467 |
1.871 |
| educalsshgh |
-0.260 |
0.469 |
-0.554 |
0.580 |
0.771 |
0.308 |
1.933 |
| educasomecol |
-0.359 |
0.405 |
-0.887 |
0.376 |
0.698 |
0.315 |
1.545 |
| inclessthn350001 |
0.389 |
0.318 |
1.223 |
0.222 |
1.475 |
0.791 |
2.749 |
| gender5female |
0.173 |
0.292 |
0.591 |
0.555 |
1.189 |
0.670 |
2.108 |
| gender5other |
1.757 |
1.251 |
1.405 |
0.161 |
5.797 |
0.499 |
67.273 |
fit.logit2%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-1.711 |
0.487 |
-3.516 |
0.000 |
0.181 |
0.070 |
0.469 |
| new_agemiddleadult |
0.486 |
0.402 |
1.208 |
0.228 |
1.625 |
0.739 |
3.574 |
| new_ageyoungeradult |
0.919 |
0.449 |
2.047 |
0.041 |
2.506 |
1.040 |
6.039 |
| race_ethblack |
-0.586 |
0.368 |
-1.592 |
0.112 |
0.556 |
0.270 |
1.145 |
| race_ethhispanic |
-0.393 |
0.503 |
-0.782 |
0.435 |
0.675 |
0.252 |
1.808 |
| race_ethother |
-0.326 |
0.502 |
-0.648 |
0.517 |
0.722 |
0.270 |
1.933 |
| educahghsch |
-0.061 |
0.342 |
-0.178 |
0.859 |
0.941 |
0.482 |
1.838 |
| educalsshgh |
-0.177 |
0.465 |
-0.381 |
0.703 |
0.838 |
0.337 |
2.083 |
| educasomecol |
-0.321 |
0.403 |
-0.795 |
0.427 |
0.726 |
0.329 |
1.600 |
| inclessthn350001 |
0.378 |
0.313 |
1.209 |
0.227 |
1.460 |
0.791 |
2.696 |
| gender5female |
0.136 |
0.289 |
0.469 |
0.639 |
1.145 |
0.650 |
2.018 |
| gender5other |
1.995 |
1.280 |
1.558 |
0.120 |
7.351 |
0.598 |
90.393 |
| qoldeclining |
0.752 |
0.340 |
2.210 |
0.027 |
2.121 |
1.089 |
4.133 |
| qolunsure |
0.169 |
0.324 |
0.522 |
0.602 |
1.184 |
0.628 |
2.233 |
fit.logit3%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-1.513 |
0.492 |
-3.075 |
0.002 |
0.220 |
0.084 |
0.578 |
| new_agemiddleadult |
0.385 |
0.402 |
0.958 |
0.338 |
1.470 |
0.668 |
3.232 |
| new_ageyoungeradult |
0.759 |
0.451 |
1.684 |
0.093 |
2.135 |
0.883 |
5.164 |
| race_ethblack |
-0.651 |
0.360 |
-1.806 |
0.071 |
0.522 |
0.257 |
1.057 |
| race_ethhispanic |
-0.442 |
0.506 |
-0.872 |
0.384 |
0.643 |
0.238 |
1.735 |
| race_ethother |
-0.446 |
0.511 |
-0.872 |
0.383 |
0.640 |
0.235 |
1.743 |
| educahghsch |
-0.107 |
0.337 |
-0.319 |
0.750 |
0.898 |
0.464 |
1.740 |
| educalsshgh |
-0.246 |
0.473 |
-0.521 |
0.603 |
0.782 |
0.309 |
1.975 |
| educasomecol |
-0.381 |
0.396 |
-0.963 |
0.336 |
0.683 |
0.315 |
1.484 |
| inclessthn350001 |
0.411 |
0.303 |
1.358 |
0.175 |
1.508 |
0.833 |
2.730 |
| gender5female |
0.150 |
0.285 |
0.526 |
0.599 |
1.161 |
0.665 |
2.030 |
| gender5other |
1.941 |
1.240 |
1.565 |
0.118 |
6.968 |
0.613 |
79.234 |
| nbsafedeclining |
0.662 |
0.477 |
1.387 |
0.166 |
1.939 |
0.761 |
4.939 |
| nbsafeunsure |
0.068 |
0.438 |
0.156 |
0.876 |
1.071 |
0.454 |
2.527 |
fit.logit4%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-1.442 |
0.453 |
-3.185 |
0.002 |
0.236 |
0.097 |
0.574 |
| new_agemiddleadult |
0.427 |
0.401 |
1.065 |
0.287 |
1.533 |
0.698 |
3.366 |
| new_ageyoungeradult |
0.829 |
0.452 |
1.835 |
0.067 |
2.292 |
0.945 |
5.558 |
| race_ethblack |
-0.603 |
0.390 |
-1.546 |
0.123 |
0.547 |
0.255 |
1.175 |
| race_ethhispanic |
-0.470 |
0.531 |
-0.885 |
0.377 |
0.625 |
0.221 |
1.769 |
| race_ethother |
-0.424 |
0.525 |
-0.808 |
0.420 |
0.654 |
0.234 |
1.831 |
| educahghsch |
-0.067 |
0.352 |
-0.189 |
0.850 |
0.936 |
0.470 |
1.865 |
| educalsshgh |
-0.254 |
0.467 |
-0.544 |
0.587 |
0.776 |
0.311 |
1.937 |
| educasomecol |
-0.367 |
0.409 |
-0.898 |
0.369 |
0.693 |
0.311 |
1.543 |
| inclessthn350001 |
0.381 |
0.321 |
1.188 |
0.235 |
1.464 |
0.780 |
2.748 |
| gender5female |
0.141 |
0.297 |
0.476 |
0.634 |
1.152 |
0.644 |
2.061 |
| gender5other |
1.794 |
1.296 |
1.385 |
0.167 |
6.016 |
0.475 |
76.239 |
| satisinfra1 |
0.175 |
0.296 |
0.593 |
0.553 |
1.192 |
0.668 |
2.127 |
fit.logit5%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-1.489 |
0.444 |
-3.351 |
0.001 |
0.226 |
0.095 |
0.539 |
| new_agemiddleadult |
0.395 |
0.406 |
0.972 |
0.331 |
1.484 |
0.670 |
3.288 |
| new_ageyoungeradult |
0.816 |
0.447 |
1.825 |
0.068 |
2.261 |
0.941 |
5.431 |
| race_ethblack |
-0.624 |
0.383 |
-1.630 |
0.104 |
0.536 |
0.253 |
1.135 |
| race_ethhispanic |
-0.485 |
0.532 |
-0.912 |
0.362 |
0.615 |
0.217 |
1.747 |
| race_ethother |
-0.429 |
0.521 |
-0.824 |
0.410 |
0.651 |
0.235 |
1.807 |
| educahghsch |
-0.110 |
0.344 |
-0.321 |
0.749 |
0.895 |
0.456 |
1.759 |
| educalsshgh |
-0.259 |
0.468 |
-0.555 |
0.579 |
0.772 |
0.309 |
1.929 |
| educasomecol |
-0.387 |
0.406 |
-0.953 |
0.341 |
0.679 |
0.307 |
1.505 |
| inclessthn350001 |
0.402 |
0.314 |
1.278 |
0.202 |
1.494 |
0.807 |
2.766 |
| gender5female |
0.113 |
0.299 |
0.377 |
0.706 |
1.120 |
0.623 |
2.013 |
| gender5other |
1.822 |
1.316 |
1.384 |
0.167 |
6.185 |
0.469 |
81.635 |
| satiscrime1 |
0.299 |
0.338 |
0.882 |
0.378 |
1.348 |
0.694 |
2.617 |
fit.logit6%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-1.752 |
0.445 |
-3.941 |
0.000 |
0.173 |
0.073 |
0.414 |
| new_agemiddleadult |
0.320 |
0.409 |
0.781 |
0.435 |
1.377 |
0.617 |
3.071 |
| new_ageyoungeradult |
0.703 |
0.458 |
1.536 |
0.125 |
2.019 |
0.824 |
4.950 |
| race_ethblack |
-0.534 |
0.375 |
-1.423 |
0.155 |
0.586 |
0.281 |
1.223 |
| race_ethhispanic |
-0.402 |
0.531 |
-0.758 |
0.449 |
0.669 |
0.236 |
1.893 |
| race_ethother |
-0.346 |
0.506 |
-0.683 |
0.495 |
0.708 |
0.263 |
1.909 |
| educahghsch |
-0.096 |
0.344 |
-0.277 |
0.782 |
0.909 |
0.463 |
1.785 |
| educalsshgh |
-0.152 |
0.467 |
-0.326 |
0.744 |
0.859 |
0.344 |
2.145 |
| educasomecol |
-0.372 |
0.408 |
-0.911 |
0.363 |
0.689 |
0.310 |
1.535 |
| inclessthn350001 |
0.400 |
0.317 |
1.262 |
0.207 |
1.492 |
0.802 |
2.776 |
| gender5female |
0.072 |
0.298 |
0.240 |
0.810 |
1.074 |
0.599 |
1.929 |
| gender5other |
2.048 |
1.457 |
1.406 |
0.160 |
7.753 |
0.446 |
134.732 |
| satislot1 |
0.676 |
0.344 |
1.963 |
0.050 |
1.966 |
1.001 |
3.860 |
fit.logit7%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-1.405 |
0.438 |
-3.205 |
0.001 |
0.245 |
0.104 |
0.579 |
| new_agemiddleadult |
0.437 |
0.402 |
1.088 |
0.277 |
1.549 |
0.704 |
3.406 |
| new_ageyoungeradult |
0.845 |
0.453 |
1.866 |
0.062 |
2.328 |
0.958 |
5.656 |
| race_ethblack |
-0.607 |
0.387 |
-1.569 |
0.117 |
0.545 |
0.255 |
1.163 |
| race_ethhispanic |
-0.444 |
0.536 |
-0.829 |
0.408 |
0.641 |
0.224 |
1.833 |
| race_ethother |
-0.417 |
0.517 |
-0.806 |
0.420 |
0.659 |
0.239 |
1.815 |
| educahghsch |
-0.082 |
0.349 |
-0.234 |
0.815 |
0.922 |
0.465 |
1.826 |
| educalsshgh |
-0.266 |
0.469 |
-0.566 |
0.572 |
0.767 |
0.306 |
1.924 |
| educasomecol |
-0.369 |
0.407 |
-0.908 |
0.364 |
0.691 |
0.312 |
1.534 |
| inclessthn350001 |
0.391 |
0.316 |
1.236 |
0.217 |
1.478 |
0.795 |
2.746 |
| gender5female |
0.169 |
0.293 |
0.577 |
0.564 |
1.184 |
0.667 |
2.102 |
| gender5other |
1.770 |
1.270 |
1.394 |
0.164 |
5.874 |
0.487 |
70.825 |
| housequal1 |
0.078 |
0.285 |
0.273 |
0.785 |
1.081 |
0.618 |
1.889 |
fit.logit8%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-1.573 |
0.461 |
-3.408 |
0.001 |
0.207 |
0.084 |
0.513 |
| new_agemiddleadult |
0.391 |
0.402 |
0.973 |
0.331 |
1.479 |
0.673 |
3.250 |
| new_ageyoungeradult |
0.753 |
0.446 |
1.688 |
0.092 |
2.124 |
0.886 |
5.094 |
| race_ethblack |
-0.640 |
0.381 |
-1.681 |
0.093 |
0.527 |
0.250 |
1.112 |
| race_ethhispanic |
-0.507 |
0.535 |
-0.949 |
0.343 |
0.602 |
0.211 |
1.717 |
| race_ethother |
-0.424 |
0.519 |
-0.815 |
0.415 |
0.655 |
0.237 |
1.812 |
| educahghsch |
-0.094 |
0.352 |
-0.268 |
0.789 |
0.910 |
0.457 |
1.813 |
| educalsshgh |
-0.299 |
0.465 |
-0.643 |
0.521 |
0.742 |
0.298 |
1.845 |
| educasomecol |
-0.400 |
0.407 |
-0.983 |
0.326 |
0.671 |
0.302 |
1.488 |
| inclessthn350001 |
0.363 |
0.319 |
1.138 |
0.256 |
1.437 |
0.769 |
2.685 |
| gender5female |
0.160 |
0.295 |
0.542 |
0.588 |
1.173 |
0.658 |
2.091 |
| gender5other |
1.924 |
1.355 |
1.421 |
0.156 |
6.850 |
0.482 |
97.430 |
| parkqual1 |
0.429 |
0.307 |
1.400 |
0.162 |
1.536 |
0.842 |
2.803 |
fit.logit9%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-1.427 |
0.437 |
-3.270 |
0.001 |
0.240 |
0.102 |
0.564 |
| new_agemiddleadult |
0.416 |
0.388 |
1.072 |
0.284 |
1.516 |
0.709 |
3.242 |
| new_ageyoungeradult |
0.847 |
0.442 |
1.918 |
0.056 |
2.332 |
0.982 |
5.542 |
| race_ethblack |
-0.591 |
0.401 |
-1.476 |
0.140 |
0.554 |
0.253 |
1.214 |
| race_ethhispanic |
-0.530 |
0.529 |
-1.002 |
0.317 |
0.589 |
0.209 |
1.660 |
| race_ethother |
-0.487 |
0.529 |
-0.921 |
0.357 |
0.614 |
0.218 |
1.732 |
| educahghsch |
-0.089 |
0.347 |
-0.255 |
0.799 |
0.915 |
0.463 |
1.808 |
| educalsshgh |
-0.224 |
0.482 |
-0.464 |
0.643 |
0.800 |
0.311 |
2.056 |
| educasomecol |
-0.332 |
0.407 |
-0.816 |
0.415 |
0.718 |
0.323 |
1.592 |
| inclessthn350001 |
0.427 |
0.315 |
1.359 |
0.175 |
1.533 |
0.828 |
2.841 |
| gender5female |
0.072 |
0.301 |
0.241 |
0.810 |
1.075 |
0.596 |
1.939 |
| gender5other |
1.824 |
1.249 |
1.460 |
0.145 |
6.195 |
0.536 |
71.638 |
| safehomenotsafe |
0.849 |
0.371 |
2.286 |
0.023 |
2.337 |
1.129 |
4.841 |
| safehomeunsure |
-1.850 |
1.371 |
-1.349 |
0.178 |
0.157 |
0.011 |
2.310 |
fit.logit10%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-1.420 |
0.435 |
-3.263 |
0.001 |
0.242 |
0.103 |
0.567 |
| new_agemiddleadult |
0.441 |
0.389 |
1.134 |
0.257 |
1.554 |
0.725 |
3.331 |
| new_ageyoungeradult |
0.810 |
0.433 |
1.869 |
0.062 |
2.247 |
0.961 |
5.254 |
| race_ethblack |
-0.617 |
0.369 |
-1.674 |
0.095 |
0.539 |
0.262 |
1.111 |
| race_ethhispanic |
-0.545 |
0.549 |
-0.992 |
0.321 |
0.580 |
0.198 |
1.702 |
| race_ethother |
-0.369 |
0.505 |
-0.730 |
0.466 |
0.692 |
0.257 |
1.860 |
| educahghsch |
-0.183 |
0.333 |
-0.551 |
0.582 |
0.832 |
0.433 |
1.599 |
| educalsshgh |
-0.360 |
0.468 |
-0.770 |
0.442 |
0.697 |
0.279 |
1.746 |
| educasomecol |
-0.370 |
0.403 |
-0.919 |
0.359 |
0.691 |
0.313 |
1.521 |
| inclessthn350001 |
0.403 |
0.310 |
1.299 |
0.194 |
1.496 |
0.815 |
2.748 |
| gender5female |
0.078 |
0.299 |
0.262 |
0.793 |
1.082 |
0.602 |
1.943 |
| gender5other |
1.814 |
1.271 |
1.427 |
0.154 |
6.132 |
0.508 |
74.068 |
| safewalkingnotsafe |
0.582 |
0.283 |
2.055 |
0.040 |
1.789 |
1.027 |
3.117 |
| safewalkingunsure |
-0.108 |
0.940 |
-0.115 |
0.909 |
0.898 |
0.142 |
5.667 |
fit.logit11%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.009 |
0.495 |
-4.056 |
0.000 |
0.134 |
0.051 |
0.354 |
| new_agemiddleadult |
0.385 |
0.418 |
0.921 |
0.358 |
1.469 |
0.648 |
3.332 |
| new_ageyoungeradult |
0.816 |
0.477 |
1.710 |
0.088 |
2.261 |
0.887 |
5.763 |
| race_ethblack |
-0.479 |
0.358 |
-1.339 |
0.181 |
0.619 |
0.307 |
1.249 |
| race_ethhispanic |
-0.277 |
0.508 |
-0.546 |
0.585 |
0.758 |
0.280 |
2.050 |
| race_ethother |
-0.248 |
0.485 |
-0.512 |
0.609 |
0.780 |
0.302 |
2.018 |
| educahghsch |
-0.014 |
0.345 |
-0.040 |
0.968 |
0.986 |
0.502 |
1.938 |
| educalsshgh |
-0.033 |
0.467 |
-0.072 |
0.943 |
0.967 |
0.387 |
2.417 |
| educasomecol |
-0.291 |
0.410 |
-0.711 |
0.477 |
0.747 |
0.335 |
1.668 |
| inclessthn350001 |
0.370 |
0.314 |
1.178 |
0.239 |
1.448 |
0.782 |
2.679 |
| gender5female |
0.072 |
0.295 |
0.244 |
0.807 |
1.075 |
0.603 |
1.915 |
| gender5other |
2.265 |
1.387 |
1.633 |
0.103 |
9.632 |
0.636 |
145.906 |
| qoldeclining |
0.724 |
0.355 |
2.040 |
0.042 |
2.062 |
1.029 |
4.133 |
| qolunsure |
0.144 |
0.319 |
0.452 |
0.652 |
1.155 |
0.618 |
2.156 |
| satisinfra1 |
-0.177 |
0.285 |
-0.621 |
0.535 |
0.838 |
0.479 |
1.465 |
| satislot1 |
0.793 |
0.335 |
2.367 |
0.018 |
2.209 |
1.146 |
4.259 |
| housequal1 |
-0.519 |
0.297 |
-1.745 |
0.082 |
0.595 |
0.332 |
1.066 |
| parkqual1 |
0.220 |
0.377 |
0.583 |
0.560 |
1.246 |
0.595 |
2.607 |
fit.logit12%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-1.521 |
0.508 |
-2.996 |
0.003 |
0.218 |
0.081 |
0.591 |
| new_agemiddleadult |
0.382 |
0.390 |
0.978 |
0.329 |
1.465 |
0.681 |
3.148 |
| new_ageyoungeradult |
0.781 |
0.444 |
1.761 |
0.079 |
2.185 |
0.916 |
5.213 |
| race_ethblack |
-0.622 |
0.367 |
-1.696 |
0.090 |
0.537 |
0.261 |
1.102 |
| race_ethhispanic |
-0.554 |
0.519 |
-1.068 |
0.286 |
0.575 |
0.208 |
1.588 |
| race_ethother |
-0.480 |
0.519 |
-0.924 |
0.356 |
0.619 |
0.224 |
1.712 |
| educahghsch |
-0.155 |
0.329 |
-0.470 |
0.638 |
0.857 |
0.450 |
1.632 |
| educalsshgh |
-0.220 |
0.481 |
-0.457 |
0.648 |
0.802 |
0.312 |
2.061 |
| educasomecol |
-0.370 |
0.399 |
-0.928 |
0.354 |
0.691 |
0.316 |
1.509 |
| inclessthn350001 |
0.438 |
0.302 |
1.452 |
0.147 |
1.549 |
0.858 |
2.799 |
| gender5female |
0.029 |
0.301 |
0.096 |
0.924 |
1.029 |
0.571 |
1.856 |
| gender5other |
1.958 |
1.258 |
1.557 |
0.120 |
7.087 |
0.602 |
83.435 |
| nbsafedeclining |
0.479 |
0.509 |
0.940 |
0.347 |
1.614 |
0.595 |
4.375 |
| nbsafeunsure |
0.012 |
0.423 |
0.029 |
0.977 |
1.012 |
0.442 |
2.320 |
| satiscrime1 |
0.037 |
0.339 |
0.110 |
0.912 |
1.038 |
0.534 |
2.018 |
| safehomenotsafe |
0.563 |
0.397 |
1.419 |
0.156 |
1.756 |
0.807 |
3.824 |
| safehomeunsure |
-2.171 |
1.405 |
-1.545 |
0.123 |
0.114 |
0.007 |
1.790 |
| safewalkingnotsafe |
0.192 |
0.327 |
0.588 |
0.557 |
1.212 |
0.639 |
2.298 |
| safewalkingunsure |
-0.086 |
0.934 |
-0.092 |
0.927 |
0.918 |
0.147 |
5.731 |
fit.logit13%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-1.857 |
0.528 |
-3.515 |
0.000 |
0.156 |
0.055 |
0.440 |
| new_agemiddleadult |
0.356 |
0.405 |
0.881 |
0.379 |
1.428 |
0.646 |
3.156 |
| new_ageyoungeradult |
0.792 |
0.476 |
1.666 |
0.096 |
2.209 |
0.869 |
5.611 |
| race_ethblack |
-0.504 |
0.358 |
-1.408 |
0.160 |
0.604 |
0.300 |
1.218 |
| race_ethhispanic |
-0.376 |
0.509 |
-0.738 |
0.461 |
0.687 |
0.253 |
1.862 |
| race_ethother |
-0.354 |
0.493 |
-0.720 |
0.472 |
0.702 |
0.267 |
1.842 |
| educahghsch |
-0.048 |
0.336 |
-0.144 |
0.885 |
0.953 |
0.494 |
1.839 |
| educalsshgh |
-0.056 |
0.477 |
-0.117 |
0.907 |
0.946 |
0.371 |
2.410 |
| educasomecol |
-0.283 |
0.401 |
-0.705 |
0.481 |
0.753 |
0.343 |
1.655 |
| inclessthn350001 |
0.410 |
0.306 |
1.340 |
0.181 |
1.507 |
0.827 |
2.744 |
| gender5female |
0.012 |
0.304 |
0.039 |
0.969 |
1.012 |
0.557 |
1.838 |
| gender5other |
2.298 |
1.341 |
1.714 |
0.087 |
9.950 |
0.719 |
137.776 |
| qoldeclining |
0.547 |
0.391 |
1.400 |
0.162 |
1.729 |
0.803 |
3.720 |
| qolunsure |
0.152 |
0.339 |
0.448 |
0.654 |
1.164 |
0.599 |
2.263 |
| satisinfra1 |
-0.290 |
0.288 |
-1.005 |
0.315 |
0.748 |
0.425 |
1.317 |
| satislot1 |
0.731 |
0.329 |
2.220 |
0.027 |
2.078 |
1.089 |
3.962 |
| housequal1 |
-0.591 |
0.305 |
-1.935 |
0.053 |
0.554 |
0.304 |
1.008 |
| parkqual1 |
0.226 |
0.386 |
0.586 |
0.558 |
1.254 |
0.588 |
2.673 |
| nbsafedeclining |
0.207 |
0.521 |
0.396 |
0.692 |
1.229 |
0.443 |
3.416 |
| nbsafeunsure |
-0.108 |
0.423 |
-0.255 |
0.799 |
0.898 |
0.391 |
2.059 |
| satiscrime1 |
0.021 |
0.380 |
0.056 |
0.955 |
1.022 |
0.485 |
2.153 |
| safehomenotsafe |
0.674 |
0.400 |
1.684 |
0.093 |
1.962 |
0.896 |
4.301 |
| safehomeunsure |
-1.766 |
1.361 |
-1.298 |
0.195 |
0.171 |
0.012 |
2.464 |
| safewalkingnotsafe |
0.077 |
0.327 |
0.234 |
0.815 |
1.080 |
0.569 |
2.049 |
| safewalkingunsure |
-0.298 |
0.853 |
-0.349 |
0.727 |
0.743 |
0.140 |
3.953 |
exp(coefficients(fit.logit1))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.2512502 1.5609262 2.3665002 0.5480958
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.6488585 0.6578133 0.9347622 0.7711981
## educasomecol inclessthn350001 gender5female gender5other
## 0.6980997 1.4748127 1.1887244 5.7965109
exp(coefficients(fit.logit2))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.1806727 1.6252682 2.5060380 0.5564308
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.6750252 0.7221551 0.9411307 0.8377765
## educasomecol inclessthn350001 gender5female gender5other
## 0.7255176 1.4598477 1.1451903 7.3510683
## qoldeclining qolunsure
## 2.1212742 1.1839489
exp(coefficients(fit.logit3))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.2201421 1.4698508 2.1352620 0.5216320
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.6429845 0.6404733 0.8980847 0.7817945
## educasomecol inclessthn350001 gender5female gender5other
## 0.6832359 1.5084762 1.1614725 6.9684590
## nbsafedeclining nbsafeunsure
## 1.9386005 1.0705641
exp(coefficients(fit.logit4))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.2363600 1.5331229 2.2920266 0.5472816
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.6252601 0.6543989 0.9356591 0.7759412
## educasomecol inclessthn350001 gender5female gender5other
## 0.6927381 1.4643937 1.1517052 6.0155000
## satisinfra1
## 1.1916678
exp(coefficients(fit.logit5))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.2257087 1.4838529 2.2611137 0.5359653
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.6154636 0.6509793 0.8954433 0.7715731
## educasomecol inclessthn350001 gender5female gender5other
## 0.6793745 1.4942267 1.1196089 6.1848735
## satiscrime1
## 1.3480463
exp(coefficients(fit.logit6))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.1733416 1.3768201 2.0190682 0.5864728
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.6686858 0.7078468 0.9088648 0.8586663
## educasomecol inclessthn350001 gender5female gender5other
## 0.6894960 1.4916685 1.0744139 7.7532991
## satislot1
## 1.9658145
exp(coefficients(fit.logit7))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.2453226 1.5487306 2.3282419 0.5447175
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.6413921 0.6591600 0.9215776 0.7668126
## educasomecol inclessthn350001 gender5female gender5other
## 0.6914452 1.4777772 1.1842031 5.8737475
## housequal1
## 1.0808437
exp(coefficients(fit.logit8))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.2074696 1.4785135 2.1239763 0.5270970
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.6020451 0.6547062 0.9100700 0.7416181
## educasomecol inclessthn350001 gender5female gender5other
## 0.6706227 1.4374380 1.1731770 6.8502730
## parkqual1
## 1.5363996
exp(coefficients(fit.logit9))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.2399081 1.5156385 2.3323780 0.5536778
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.5888834 0.6143016 0.9152690 0.7996578
## educasomecol inclessthn350001 gender5female gender5other
## 0.7175859 1.5333076 1.0750671 6.1946483
## safehomenotsafe safehomeunsure
## 2.3373317 0.1573136
exp(coefficients(fit.logit10))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.2418269 1.5541541 2.2472509 0.5394072
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.5797814 0.6916719 0.8323991 0.6974646
## educasomecol inclessthn350001 gender5female gender5other
## 0.6905416 1.4960726 1.0815751 6.1321648
## safewalkingnotsafe safewalkingunsure
## 1.7892973 0.8976225
exp(coefficients(fit.logit11))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.1341599 1.4690177 2.2614747 0.6192075
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.7580706 0.7802046 0.9864338 0.9670953
## educasomecol inclessthn350001 gender5female gender5other
## 0.7471954 1.4475829 1.0745468 9.6317593
## qoldeclining qolunsure satisinfra1 satislot1
## 2.0619831 1.1548486 0.8377000 2.2093843
## housequal1 parkqual1
## 0.5951105 1.2455343
exp(coefficients(fit.logit12))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.2184903 1.4646977 2.1845547 0.5366772
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.5746254 0.6188060 0.8567313 0.8023701
## educasomecol inclessthn350001 gender5female gender5other
## 0.6905376 1.5494339 1.0292452 7.0874495
## nbsafedeclining nbsafeunsure satiscrime1 safehomenotsafe
## 1.6137156 1.0123794 1.0381490 1.7563186
## safehomeunsure safewalkingnotsafe safewalkingunsure
## 0.1140435 1.2116800 0.9178618
exp(coefficients(fit.logit13))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.1560877 1.4280547 2.2086074 0.6043636
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.6869306 0.7015273 0.9526982 0.9455406
## educasomecol inclessthn350001 gender5female gender5other
## 0.7534004 1.5065741 1.0120072 9.9500405
## qoldeclining qolunsure satisinfra1 satislot1
## 1.7286436 1.1640820 0.7484558 2.0776305
## housequal1 parkqual1 nbsafedeclining nbsafeunsure
## 0.5537729 1.2539692 1.2294520 0.8977384
## satiscrime1 safehomenotsafe safehomeunsure safewalkingnotsafe
## 1.0215596 1.9624895 0.1709593 1.0795541
## safewalkingunsure
## 0.7426085
fit.logit14<-svyglm(felt_worried ~ new_age + race_eth + educa + inclessthn35000 + gender5,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit15<-svyglm(felt_worried ~ new_age + race_eth + educa + inclessthn35000 + gender5 + qol,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit16<-svyglm(felt_worried ~ new_age + race_eth + educa + inclessthn35000 + gender5 + nbsafe,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit17<-svyglm(felt_worried ~ new_age + race_eth + educa + inclessthn35000 + gender5 + satisinfra,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit18<-svyglm(felt_worried ~ new_age + race_eth + educa + inclessthn35000 + gender5 + satiscrime,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit19<-svyglm(felt_worried ~ new_age + race_eth + educa + inclessthn35000 + gender5 + satislot,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit20<-svyglm(felt_worried ~ new_age + race_eth + educa + inclessthn35000 + gender5 + housequal,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit21<-svyglm(felt_worried ~ new_age + race_eth + educa + inclessthn35000 + gender5 + parkqual,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit22<-svyglm(felt_worried ~ new_age + race_eth + educa + inclessthn35000 + gender5 + safewalking,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit23<-svyglm(felt_worried ~ new_age + race_eth + educa + inclessthn35000 + gender5 + safehome,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit24<-svyglm(felt_worried ~ new_age + race_eth + educa + inclessthn35000 + gender5 + safewalking,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit25<-svyglm(felt_worried ~ new_age + race_eth + educa + inclessthn35000 + gender5 + qol + satisinfra +
satislot + housequal + parkqual,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit26<-svyglm(felt_worried ~ new_age + race_eth + educa + inclessthn35000 + gender5 + nbsafe + satiscrime +
safehome + safewalking,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit27<-svyglm(felt_worried ~ new_age + race_eth + educa + inclessthn35000 + gender5 + + qol + satisinfra +
satislot + housequal + parkqual + nbsafe + satiscrime +
safehome + safewalking,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit14%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.201 |
0.464 |
-4.743 |
0.000 |
0.111 |
0.045 |
0.275 |
| new_agemiddleadult |
0.499 |
0.402 |
1.242 |
0.215 |
1.647 |
0.749 |
3.619 |
| new_ageyoungeradult |
0.616 |
0.454 |
1.358 |
0.175 |
1.852 |
0.761 |
4.506 |
| race_ethblack |
-0.071 |
0.375 |
-0.191 |
0.849 |
0.931 |
0.447 |
1.940 |
| race_ethhispanic |
-0.015 |
0.557 |
-0.026 |
0.979 |
0.985 |
0.331 |
2.937 |
| race_ethother |
0.314 |
0.480 |
0.654 |
0.513 |
1.369 |
0.534 |
3.506 |
| educahghsch |
0.314 |
0.358 |
0.878 |
0.380 |
1.369 |
0.679 |
2.759 |
| educalsshgh |
-0.035 |
0.487 |
-0.072 |
0.942 |
0.965 |
0.372 |
2.506 |
| educasomecol |
0.033 |
0.414 |
0.080 |
0.936 |
1.034 |
0.459 |
2.329 |
| inclessthn350001 |
0.355 |
0.332 |
1.069 |
0.285 |
1.426 |
0.744 |
2.730 |
| gender5female |
0.215 |
0.311 |
0.691 |
0.490 |
1.240 |
0.674 |
2.282 |
| gender5other |
14.830 |
0.906 |
16.373 |
0.000 |
2758297.473 |
467331.857 |
16280090.565 |
fit.logit15%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.308 |
0.510 |
-4.530 |
0.000 |
0.099 |
0.037 |
0.270 |
| new_agemiddleadult |
0.509 |
0.400 |
1.273 |
0.204 |
1.664 |
0.760 |
3.644 |
| new_ageyoungeradult |
0.632 |
0.458 |
1.380 |
0.168 |
1.881 |
0.767 |
4.618 |
| race_ethblack |
-0.063 |
0.380 |
-0.165 |
0.869 |
0.939 |
0.446 |
1.978 |
| race_ethhispanic |
0.001 |
0.553 |
0.002 |
0.999 |
1.001 |
0.338 |
2.961 |
| race_ethother |
0.345 |
0.484 |
0.712 |
0.476 |
1.412 |
0.547 |
3.648 |
| educahghsch |
0.317 |
0.353 |
0.899 |
0.369 |
1.373 |
0.688 |
2.743 |
| educalsshgh |
-0.009 |
0.485 |
-0.018 |
0.986 |
0.991 |
0.383 |
2.566 |
| educasomecol |
0.047 |
0.413 |
0.115 |
0.909 |
1.049 |
0.467 |
2.356 |
| inclessthn350001 |
0.350 |
0.332 |
1.055 |
0.292 |
1.419 |
0.741 |
2.718 |
| gender5female |
0.201 |
0.310 |
0.647 |
0.518 |
1.223 |
0.665 |
2.246 |
| gender5other |
14.914 |
0.922 |
16.176 |
0.000 |
2999256.052 |
492261.789 |
18273888.138 |
| qoldeclining |
0.256 |
0.351 |
0.729 |
0.466 |
1.292 |
0.649 |
2.570 |
| qolunsure |
0.059 |
0.325 |
0.181 |
0.856 |
1.061 |
0.561 |
2.007 |
fit.logit16%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.501 |
0.558 |
-4.481 |
0.000 |
0.082 |
0.027 |
0.245 |
| new_agemiddleadult |
0.432 |
0.401 |
1.076 |
0.282 |
1.540 |
0.701 |
3.380 |
| new_ageyoungeradult |
0.507 |
0.468 |
1.084 |
0.279 |
1.660 |
0.664 |
4.150 |
| race_ethblack |
-0.089 |
0.376 |
-0.238 |
0.812 |
0.915 |
0.438 |
1.911 |
| race_ethhispanic |
-0.018 |
0.551 |
-0.033 |
0.974 |
0.982 |
0.333 |
2.892 |
| race_ethother |
0.321 |
0.484 |
0.664 |
0.507 |
1.379 |
0.534 |
3.560 |
| educahghsch |
0.269 |
0.344 |
0.784 |
0.434 |
1.309 |
0.668 |
2.567 |
| educalsshgh |
-0.003 |
0.494 |
-0.006 |
0.995 |
0.997 |
0.379 |
2.624 |
| educasomecol |
0.016 |
0.411 |
0.039 |
0.969 |
1.016 |
0.454 |
2.275 |
| inclessthn350001 |
0.386 |
0.314 |
1.227 |
0.220 |
1.471 |
0.794 |
2.723 |
| gender5female |
0.169 |
0.301 |
0.560 |
0.576 |
1.184 |
0.656 |
2.137 |
| gender5other |
14.998 |
0.882 |
16.998 |
0.000 |
3261241.711 |
578529.659 |
18384014.260 |
| nbsafedeclining |
0.759 |
0.526 |
1.443 |
0.150 |
2.137 |
0.762 |
5.995 |
| nbsafeunsure |
0.304 |
0.482 |
0.630 |
0.529 |
1.355 |
0.526 |
3.489 |
fit.logit17%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.248 |
0.449 |
-5.005 |
0.000 |
0.106 |
0.044 |
0.255 |
| new_agemiddleadult |
0.486 |
0.411 |
1.180 |
0.238 |
1.625 |
0.725 |
3.640 |
| new_ageyoungeradult |
0.591 |
0.468 |
1.263 |
0.207 |
1.806 |
0.721 |
4.519 |
| race_ethblack |
-0.070 |
0.371 |
-0.189 |
0.850 |
0.932 |
0.450 |
1.930 |
| race_ethhispanic |
-0.039 |
0.558 |
-0.070 |
0.944 |
0.962 |
0.322 |
2.872 |
| race_ethother |
0.314 |
0.479 |
0.655 |
0.512 |
1.369 |
0.535 |
3.503 |
| educahghsch |
0.316 |
0.357 |
0.886 |
0.376 |
1.372 |
0.682 |
2.760 |
| educalsshgh |
-0.030 |
0.486 |
-0.061 |
0.951 |
0.971 |
0.374 |
2.517 |
| educasomecol |
0.030 |
0.419 |
0.070 |
0.944 |
1.030 |
0.453 |
2.342 |
| inclessthn350001 |
0.347 |
0.333 |
1.042 |
0.298 |
1.415 |
0.736 |
2.720 |
| gender5female |
0.191 |
0.306 |
0.625 |
0.532 |
1.211 |
0.664 |
2.207 |
| gender5other |
14.866 |
0.929 |
15.993 |
0.000 |
2858049.677 |
462249.246 |
17671089.856 |
| satisinfra1 |
0.130 |
0.303 |
0.429 |
0.668 |
1.139 |
0.629 |
2.063 |
fit.logit18%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.385 |
0.469 |
-5.091 |
0.000 |
0.092 |
0.037 |
2.31000e-01 |
| new_agemiddleadult |
0.412 |
0.408 |
1.010 |
0.313 |
1.510 |
0.678 |
3.36300e+00 |
| new_ageyoungeradult |
0.537 |
0.465 |
1.155 |
0.248 |
1.711 |
0.688 |
4.25700e+00 |
| race_ethblack |
-0.098 |
0.381 |
-0.258 |
0.796 |
0.906 |
0.430 |
1.91100e+00 |
| race_ethhispanic |
-0.081 |
0.554 |
-0.146 |
0.884 |
0.922 |
0.311 |
2.73100e+00 |
| race_ethother |
0.310 |
0.490 |
0.631 |
0.528 |
1.363 |
0.521 |
3.56300e+00 |
| educahghsch |
0.254 |
0.346 |
0.734 |
0.463 |
1.289 |
0.654 |
2.54100e+00 |
| educalsshgh |
-0.029 |
0.489 |
-0.060 |
0.952 |
0.971 |
0.373 |
2.53100e+00 |
| educasomecol |
-0.002 |
0.415 |
-0.006 |
0.996 |
0.998 |
0.442 |
2.25100e+00 |
| inclessthn350001 |
0.376 |
0.325 |
1.157 |
0.248 |
1.456 |
0.770 |
2.75300e+00 |
| gender5female |
0.125 |
0.317 |
0.396 |
0.693 |
1.133 |
0.609 |
2.10800e+00 |
| gender5other |
14.995 |
0.974 |
15.396 |
0.000 |
3253130.837 |
482218.453 |
2.19462e+07 |
| satiscrime1 |
0.469 |
0.361 |
1.300 |
0.194 |
1.599 |
0.788 |
3.24500e+00 |
fit.logit19%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.384 |
0.477 |
-5.000 |
0.000 |
0.092 |
0.036 |
2.35000e-01 |
| new_agemiddleadult |
0.431 |
0.409 |
1.054 |
0.292 |
1.539 |
0.690 |
3.42900e+00 |
| new_ageyoungeradult |
0.527 |
0.471 |
1.117 |
0.264 |
1.693 |
0.672 |
4.26500e+00 |
| race_ethblack |
-0.030 |
0.378 |
-0.080 |
0.936 |
0.970 |
0.462 |
2.03500e+00 |
| race_ethhispanic |
0.012 |
0.557 |
0.021 |
0.983 |
1.012 |
0.340 |
3.01400e+00 |
| race_ethother |
0.361 |
0.477 |
0.757 |
0.450 |
1.434 |
0.564 |
3.65000e+00 |
| educahghsch |
0.300 |
0.352 |
0.852 |
0.395 |
1.350 |
0.677 |
2.69300e+00 |
| educalsshgh |
0.018 |
0.492 |
0.036 |
0.972 |
1.018 |
0.388 |
2.66800e+00 |
| educasomecol |
0.031 |
0.414 |
0.076 |
0.940 |
1.032 |
0.458 |
2.32500e+00 |
| inclessthn350001 |
0.360 |
0.328 |
1.098 |
0.273 |
1.434 |
0.754 |
2.72700e+00 |
| gender5female |
0.163 |
0.316 |
0.516 |
0.606 |
1.177 |
0.633 |
2.18900e+00 |
| gender5other |
15.011 |
0.977 |
15.367 |
0.000 |
3306786.076 |
487374.621 |
2.24362e+07 |
| satislot1 |
0.341 |
0.330 |
1.035 |
0.301 |
1.407 |
0.737 |
2.68500e+00 |
fit.logit20%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.234 |
0.457 |
-4.890 |
0.000 |
0.107 |
0.044 |
0.262 |
| new_agemiddleadult |
0.488 |
0.406 |
1.202 |
0.230 |
1.629 |
0.735 |
3.608 |
| new_ageyoungeradult |
0.594 |
0.469 |
1.265 |
0.206 |
1.811 |
0.722 |
4.545 |
| race_ethblack |
-0.077 |
0.375 |
-0.206 |
0.837 |
0.926 |
0.444 |
1.932 |
| race_ethhispanic |
-0.026 |
0.557 |
-0.047 |
0.962 |
0.974 |
0.327 |
2.902 |
| race_ethother |
0.319 |
0.481 |
0.664 |
0.507 |
1.376 |
0.536 |
3.532 |
| educahghsch |
0.297 |
0.351 |
0.846 |
0.398 |
1.346 |
0.676 |
2.679 |
| educalsshgh |
-0.042 |
0.488 |
-0.086 |
0.931 |
0.959 |
0.368 |
2.497 |
| educasomecol |
0.023 |
0.414 |
0.056 |
0.955 |
1.024 |
0.454 |
2.306 |
| inclessthn350001 |
0.357 |
0.329 |
1.087 |
0.277 |
1.430 |
0.751 |
2.723 |
| gender5female |
0.211 |
0.312 |
0.674 |
0.500 |
1.234 |
0.669 |
2.276 |
| gender5other |
14.857 |
0.926 |
16.051 |
0.000 |
2834082.806 |
461849.957 |
17390984.294 |
| housequal1 |
0.099 |
0.296 |
0.334 |
0.738 |
1.104 |
0.618 |
1.972 |
fit.logit21%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.636 |
0.501 |
-5.260 |
0.000 |
0.072 |
0.027 |
0.191 |
| new_agemiddleadult |
0.399 |
0.404 |
0.987 |
0.324 |
1.490 |
0.675 |
3.288 |
| new_ageyoungeradult |
0.416 |
0.456 |
0.911 |
0.362 |
1.516 |
0.620 |
3.708 |
| race_ethblack |
-0.128 |
0.373 |
-0.342 |
0.732 |
0.880 |
0.424 |
1.827 |
| race_ethhispanic |
-0.137 |
0.561 |
-0.244 |
0.807 |
0.872 |
0.290 |
2.620 |
| race_ethother |
0.326 |
0.485 |
0.672 |
0.502 |
1.386 |
0.536 |
3.585 |
| educahghsch |
0.276 |
0.358 |
0.771 |
0.441 |
1.318 |
0.653 |
2.660 |
| educalsshgh |
-0.100 |
0.489 |
-0.205 |
0.838 |
0.905 |
0.347 |
2.361 |
| educasomecol |
-0.023 |
0.416 |
-0.054 |
0.957 |
0.978 |
0.432 |
2.212 |
| inclessthn350001 |
0.312 |
0.334 |
0.933 |
0.351 |
1.366 |
0.709 |
2.629 |
| gender5female |
0.190 |
0.314 |
0.605 |
0.546 |
1.209 |
0.653 |
2.240 |
| gender5other |
15.301 |
1.009 |
15.157 |
0.000 |
4416155.378 |
610593.376 |
31940124.317 |
| parkqual1 |
0.841 |
0.324 |
2.597 |
0.010 |
2.318 |
1.229 |
4.371 |
fit.logit22%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.249 |
0.463 |
-4.855 |
0.000 |
0.105 |
0.043 |
2.62000e-01 |
| new_agemiddleadult |
0.494 |
0.397 |
1.247 |
0.213 |
1.640 |
0.754 |
3.56700e+00 |
| new_ageyoungeradult |
0.571 |
0.453 |
1.259 |
0.209 |
1.770 |
0.728 |
4.30400e+00 |
| race_ethblack |
-0.074 |
0.389 |
-0.191 |
0.848 |
0.928 |
0.433 |
1.98900e+00 |
| race_ethhispanic |
-0.094 |
0.583 |
-0.162 |
0.871 |
0.910 |
0.290 |
2.85200e+00 |
| race_ethother |
0.364 |
0.490 |
0.743 |
0.457 |
1.440 |
0.551 |
3.76200e+00 |
| educahghsch |
0.230 |
0.339 |
0.679 |
0.497 |
1.259 |
0.648 |
2.44800e+00 |
| educalsshgh |
-0.110 |
0.489 |
-0.225 |
0.822 |
0.896 |
0.344 |
2.33500e+00 |
| educasomecol |
0.033 |
0.420 |
0.079 |
0.937 |
1.034 |
0.454 |
2.35300e+00 |
| inclessthn350001 |
0.367 |
0.327 |
1.123 |
0.262 |
1.443 |
0.761 |
2.73800e+00 |
| gender5female |
0.140 |
0.318 |
0.441 |
0.659 |
1.151 |
0.617 |
2.14800e+00 |
| gender5other |
14.900 |
0.923 |
16.146 |
0.000 |
2958023.246 |
484708.197 |
1.80519e+07 |
| safewalkingnotsafe |
0.470 |
0.290 |
1.621 |
0.106 |
1.601 |
0.906 |
2.82800e+00 |
| safewalkingunsure |
0.012 |
0.859 |
0.014 |
0.989 |
1.012 |
0.188 |
5.45500e+00 |
fit.logit23%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.269 |
0.448 |
-5.067 |
0.000 |
0.103 |
0.043 |
0.249 |
| new_agemiddleadult |
0.459 |
0.390 |
1.177 |
0.240 |
1.583 |
0.737 |
3.398 |
| new_ageyoungeradult |
0.556 |
0.460 |
1.209 |
0.227 |
1.743 |
0.708 |
4.291 |
| race_ethblack |
-0.084 |
0.394 |
-0.212 |
0.832 |
0.920 |
0.425 |
1.992 |
| race_ethhispanic |
-0.118 |
0.544 |
-0.216 |
0.829 |
0.889 |
0.306 |
2.580 |
| race_ethother |
0.249 |
0.490 |
0.509 |
0.611 |
1.283 |
0.491 |
3.354 |
| educahghsch |
0.296 |
0.347 |
0.854 |
0.393 |
1.345 |
0.681 |
2.655 |
| educalsshgh |
-0.073 |
0.517 |
-0.142 |
0.887 |
0.929 |
0.337 |
2.562 |
| educasomecol |
0.080 |
0.414 |
0.193 |
0.847 |
1.083 |
0.481 |
2.436 |
| inclessthn350001 |
0.403 |
0.334 |
1.208 |
0.227 |
1.496 |
0.778 |
2.878 |
| gender5female |
0.128 |
0.325 |
0.395 |
0.693 |
1.137 |
0.601 |
2.149 |
| gender5other |
14.947 |
0.917 |
16.307 |
0.000 |
3101204.358 |
514389.904 |
18696845.302 |
| safehomenotsafe |
1.069 |
0.362 |
2.954 |
0.003 |
2.912 |
1.433 |
5.918 |
| safehomeunsure |
-0.178 |
1.164 |
-0.153 |
0.878 |
0.837 |
0.086 |
8.189 |
fit.logit24%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.249 |
0.463 |
-4.855 |
0.000 |
0.105 |
0.043 |
2.62000e-01 |
| new_agemiddleadult |
0.494 |
0.397 |
1.247 |
0.213 |
1.640 |
0.754 |
3.56700e+00 |
| new_ageyoungeradult |
0.571 |
0.453 |
1.259 |
0.209 |
1.770 |
0.728 |
4.30400e+00 |
| race_ethblack |
-0.074 |
0.389 |
-0.191 |
0.848 |
0.928 |
0.433 |
1.98900e+00 |
| race_ethhispanic |
-0.094 |
0.583 |
-0.162 |
0.871 |
0.910 |
0.290 |
2.85200e+00 |
| race_ethother |
0.364 |
0.490 |
0.743 |
0.457 |
1.440 |
0.551 |
3.76200e+00 |
| educahghsch |
0.230 |
0.339 |
0.679 |
0.497 |
1.259 |
0.648 |
2.44800e+00 |
| educalsshgh |
-0.110 |
0.489 |
-0.225 |
0.822 |
0.896 |
0.344 |
2.33500e+00 |
| educasomecol |
0.033 |
0.420 |
0.079 |
0.937 |
1.034 |
0.454 |
2.35300e+00 |
| inclessthn350001 |
0.367 |
0.327 |
1.123 |
0.262 |
1.443 |
0.761 |
2.73800e+00 |
| gender5female |
0.140 |
0.318 |
0.441 |
0.659 |
1.151 |
0.617 |
2.14800e+00 |
| gender5other |
14.900 |
0.923 |
16.146 |
0.000 |
2958023.246 |
484708.197 |
1.80519e+07 |
| safewalkingnotsafe |
0.470 |
0.290 |
1.621 |
0.106 |
1.601 |
0.906 |
2.82800e+00 |
| safewalkingunsure |
0.012 |
0.859 |
0.014 |
0.989 |
1.012 |
0.188 |
5.45500e+00 |
fit.logit25%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.685 |
0.531 |
-5.055 |
0.000 |
0.068 |
0.024 |
1.93000e-01 |
| new_agemiddleadult |
0.401 |
0.408 |
0.983 |
0.326 |
1.494 |
0.671 |
3.32600e+00 |
| new_ageyoungeradult |
0.434 |
0.480 |
0.904 |
0.366 |
1.544 |
0.602 |
3.95800e+00 |
| race_ethblack |
-0.091 |
0.379 |
-0.240 |
0.810 |
0.913 |
0.434 |
1.92000e+00 |
| race_ethhispanic |
-0.063 |
0.569 |
-0.112 |
0.911 |
0.938 |
0.308 |
2.86300e+00 |
| race_ethother |
0.359 |
0.488 |
0.735 |
0.462 |
1.431 |
0.550 |
3.72300e+00 |
| educahghsch |
0.319 |
0.361 |
0.881 |
0.379 |
1.375 |
0.677 |
2.79300e+00 |
| educalsshgh |
-0.029 |
0.500 |
-0.058 |
0.953 |
0.971 |
0.364 |
2.58800e+00 |
| educasomecol |
0.017 |
0.419 |
0.040 |
0.968 |
1.017 |
0.448 |
2.31100e+00 |
| inclessthn350001 |
0.307 |
0.328 |
0.935 |
0.350 |
1.359 |
0.714 |
2.58700e+00 |
| gender5female |
0.186 |
0.302 |
0.616 |
0.538 |
1.204 |
0.667 |
2.17600e+00 |
| gender5other |
15.337 |
1.016 |
15.098 |
0.000 |
4579707.169 |
625397.897 |
3.35366e+07 |
| qoldeclining |
0.137 |
0.368 |
0.371 |
0.710 |
1.146 |
0.557 |
2.35800e+00 |
| qolunsure |
-0.041 |
0.320 |
-0.130 |
0.897 |
0.959 |
0.513 |
1.79500e+00 |
| satisinfra1 |
-0.096 |
0.337 |
-0.286 |
0.775 |
0.908 |
0.469 |
1.75700e+00 |
| satislot1 |
0.204 |
0.349 |
0.586 |
0.558 |
1.226 |
0.619 |
2.42900e+00 |
| housequal1 |
-0.263 |
0.312 |
-0.842 |
0.400 |
0.769 |
0.417 |
1.41800e+00 |
| parkqual1 |
0.888 |
0.381 |
2.332 |
0.020 |
2.431 |
1.152 |
5.12700e+00 |
fit.logit26%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.514 |
0.571 |
-4.399 |
0.000 |
0.081 |
0.026 |
0.248 |
| new_agemiddleadult |
0.390 |
0.388 |
1.006 |
0.315 |
1.477 |
0.691 |
3.157 |
| new_ageyoungeradult |
0.479 |
0.468 |
1.024 |
0.306 |
1.614 |
0.646 |
4.036 |
| race_ethblack |
-0.096 |
0.392 |
-0.246 |
0.806 |
0.908 |
0.421 |
1.957 |
| race_ethhispanic |
-0.141 |
0.546 |
-0.258 |
0.796 |
0.868 |
0.298 |
2.533 |
| race_ethother |
0.254 |
0.499 |
0.509 |
0.611 |
1.289 |
0.485 |
3.431 |
| educahghsch |
0.249 |
0.333 |
0.747 |
0.455 |
1.282 |
0.668 |
2.463 |
| educalsshgh |
-0.025 |
0.529 |
-0.047 |
0.963 |
0.975 |
0.346 |
2.750 |
| educasomecol |
0.046 |
0.411 |
0.112 |
0.911 |
1.047 |
0.468 |
2.342 |
| inclessthn350001 |
0.425 |
0.319 |
1.333 |
0.183 |
1.529 |
0.819 |
2.857 |
| gender5female |
0.062 |
0.320 |
0.192 |
0.848 |
1.064 |
0.568 |
1.992 |
| gender5other |
15.099 |
0.937 |
16.121 |
0.000 |
3610561.871 |
575806.708 |
22639814.441 |
| nbsafedeclining |
0.417 |
0.558 |
0.749 |
0.454 |
1.518 |
0.509 |
4.528 |
| nbsafeunsure |
0.159 |
0.458 |
0.347 |
0.729 |
1.172 |
0.478 |
2.873 |
| satiscrime1 |
0.234 |
0.343 |
0.681 |
0.496 |
1.263 |
0.645 |
2.473 |
| safehomenotsafe |
0.908 |
0.398 |
2.284 |
0.023 |
2.481 |
1.137 |
5.410 |
| safehomeunsure |
-0.479 |
1.284 |
-0.373 |
0.709 |
0.620 |
0.050 |
7.678 |
| safewalkingnotsafe |
-0.013 |
0.360 |
-0.035 |
0.972 |
0.988 |
0.487 |
2.001 |
| safewalkingunsure |
-0.074 |
0.861 |
-0.086 |
0.931 |
0.928 |
0.172 |
5.016 |
fit.logit27%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.789 |
0.577 |
-4.834 |
0.000 |
0.061 |
0.020 |
0.190 |
| new_agemiddleadult |
0.341 |
0.379 |
0.901 |
0.368 |
1.407 |
0.669 |
2.957 |
| new_ageyoungeradult |
0.366 |
0.477 |
0.767 |
0.443 |
1.442 |
0.566 |
3.675 |
| race_ethblack |
-0.124 |
0.397 |
-0.312 |
0.755 |
0.884 |
0.406 |
1.923 |
| race_ethhispanic |
-0.178 |
0.570 |
-0.313 |
0.755 |
0.837 |
0.274 |
2.559 |
| race_ethother |
0.257 |
0.502 |
0.512 |
0.609 |
1.293 |
0.484 |
3.455 |
| educahghsch |
0.239 |
0.349 |
0.685 |
0.493 |
1.270 |
0.641 |
2.517 |
| educalsshgh |
-0.067 |
0.529 |
-0.127 |
0.899 |
0.935 |
0.332 |
2.637 |
| educasomecol |
0.024 |
0.405 |
0.060 |
0.952 |
1.025 |
0.463 |
2.266 |
| inclessthn350001 |
0.419 |
0.320 |
1.309 |
0.191 |
1.521 |
0.812 |
2.848 |
| gender5female |
0.073 |
0.311 |
0.236 |
0.813 |
1.076 |
0.585 |
1.980 |
| gender5other |
15.319 |
0.973 |
15.739 |
0.000 |
4498929.761 |
667691.778 |
30313940.743 |
| qoldeclining |
-0.195 |
0.391 |
-0.498 |
0.618 |
0.823 |
0.383 |
1.770 |
| qolunsure |
-0.168 |
0.329 |
-0.512 |
0.609 |
0.845 |
0.444 |
1.610 |
| satisinfra1 |
-0.305 |
0.346 |
-0.880 |
0.379 |
0.737 |
0.374 |
1.454 |
| satislot1 |
0.022 |
0.342 |
0.065 |
0.948 |
1.022 |
0.523 |
1.999 |
| housequal1 |
-0.448 |
0.317 |
-1.412 |
0.158 |
0.639 |
0.343 |
1.190 |
| parkqual1 |
0.874 |
0.395 |
2.215 |
0.027 |
2.398 |
1.106 |
5.197 |
| nbsafedeclining |
0.643 |
0.552 |
1.165 |
0.244 |
1.902 |
0.645 |
5.611 |
| nbsafeunsure |
0.305 |
0.431 |
0.708 |
0.479 |
1.357 |
0.583 |
3.158 |
| satiscrime1 |
0.314 |
0.370 |
0.847 |
0.397 |
1.369 |
0.662 |
2.829 |
| safehomenotsafe |
0.994 |
0.405 |
2.455 |
0.014 |
2.701 |
1.222 |
5.970 |
| safehomeunsure |
-0.546 |
1.342 |
-0.407 |
0.684 |
0.579 |
0.042 |
8.040 |
| safewalkingnotsafe |
0.023 |
0.365 |
0.062 |
0.950 |
1.023 |
0.500 |
2.093 |
| safewalkingunsure |
-0.102 |
0.837 |
-0.121 |
0.904 |
0.903 |
0.175 |
4.660 |
exp(coefficients(fit.logit14))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 1.106417e-01 1.646804e+00 1.851672e+00 9.310517e-01
## race_ethhispanic race_ethother educahghsch educalsshgh
## 9.853604e-01 1.368963e+00 1.368781e+00 9.654298e-01
## educasomecol inclessthn350001 gender5female gender5other
## 1.033801e+00 1.425557e+00 1.239958e+00 2.758297e+06
exp(coefficients(fit.logit15))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 9.941235e-02 1.663694e+00 1.881477e+00 9.391576e-01
## race_ethhispanic race_ethother educahghsch educalsshgh
## 1.000835e+00 1.412052e+00 1.373336e+00 9.912289e-01
## educasomecol inclessthn350001 gender5female gender5other
## 1.048509e+00 1.418975e+00 1.222530e+00 2.999256e+06
## qoldeclining qolunsure
## 1.291684e+00 1.060719e+00
exp(coefficients(fit.logit16))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 8.203433e-02 1.539836e+00 1.659748e+00 9.145389e-01
## race_ethhispanic race_ethother educahghsch educalsshgh
## 9.820268e-01 1.378843e+00 1.308905e+00 9.971025e-01
## educasomecol inclessthn350001 gender5female gender5other
## 1.016358e+00 1.470757e+00 1.183804e+00 3.261242e+06
## nbsafedeclining nbsafeunsure
## 2.136756e+00 1.355365e+00
exp(coefficients(fit.logit17))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 1.056258e-01 1.624988e+00 1.805648e+00 9.322765e-01
## race_ethhispanic race_ethother educahghsch educalsshgh
## 9.617934e-01 1.369106e+00 1.371701e+00 9.706012e-01
## educasomecol inclessthn350001 gender5female gender5other
## 1.029948e+00 1.415231e+00 1.210886e+00 2.858050e+06
## satisinfra1
## 1.138808e+00
exp(coefficients(fit.logit18))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 9.205497e-02 1.510326e+00 1.711106e+00 9.062998e-01
## race_ethhispanic race_ethother educahghsch educalsshgh
## 9.223748e-01 1.362915e+00 1.289343e+00 9.712785e-01
## educasomecol inclessthn350001 gender5female gender5other
## 9.976982e-01 1.456313e+00 1.133381e+00 3.253131e+06
## satiscrime1
## 1.598988e+00
exp(coefficients(fit.logit19))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 9.213963e-02 1.538579e+00 1.693225e+00 9.700579e-01
## race_ethhispanic race_ethother educahghsch educalsshgh
## 1.011914e+00 1.434156e+00 1.350062e+00 1.017671e+00
## educasomecol inclessthn350001 gender5female gender5other
## 1.031917e+00 1.433750e+00 1.177337e+00 3.306786e+06
## satislot1
## 1.406598e+00
exp(coefficients(fit.logit20))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 1.070571e-01 1.628809e+00 1.811146e+00 9.256620e-01
## race_ethhispanic race_ethother educahghsch educalsshgh
## 9.741245e-01 1.376286e+00 1.345871e+00 9.588120e-01
## educasomecol inclessthn350001 gender5female gender5other
## 1.023528e+00 1.429576e+00 1.234335e+00 2.834083e+06
## housequal1
## 1.104022e+00
exp(coefficients(fit.logit21))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 7.162259e-02 1.489655e+00 1.515816e+00 8.802505e-01
## race_ethhispanic race_ethother educahghsch educalsshgh
## 8.717790e-01 1.385579e+00 1.317912e+00 9.046153e-01
## educasomecol inclessthn350001 gender5female gender5other
## 9.777232e-01 1.365785e+00 1.209500e+00 4.416155e+06
## parkqual1
## 2.317645e+00
exp(coefficients(fit.logit22))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 1.054994e-01 1.639567e+00 1.769696e+00 9.282679e-01
## race_ethhispanic race_ethother educahghsch educalsshgh
## 9.098948e-01 1.439612e+00 1.259226e+00 8.956877e-01
## educasomecol inclessthn350001 gender5female gender5other
## 1.033780e+00 1.443254e+00 1.150774e+00 2.958023e+06
## safewalkingnotsafe safewalkingunsure
## 1.600681e+00 1.012035e+00
exp(coefficients(fit.logit23))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 1.034287e-01 1.582550e+00 1.743276e+00 9.197766e-01
## race_ethhispanic race_ethother educahghsch educalsshgh
## 8.889699e-01 1.283372e+00 1.344982e+00 9.292175e-01
## educasomecol inclessthn350001 gender5female gender5other
## 1.083011e+00 1.496490e+00 1.136802e+00 3.101204e+06
## safehomenotsafe safehomeunsure
## 2.912097e+00 8.367860e-01
exp(coefficients(fit.logit24))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 1.054994e-01 1.639567e+00 1.769696e+00 9.282679e-01
## race_ethhispanic race_ethother educahghsch educalsshgh
## 9.098948e-01 1.439612e+00 1.259226e+00 8.956877e-01
## educasomecol inclessthn350001 gender5female gender5other
## 1.033780e+00 1.443254e+00 1.150774e+00 2.958023e+06
## safewalkingnotsafe safewalkingunsure
## 1.600681e+00 1.012035e+00
exp(coefficients(fit.logit25))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 6.821556e-02 1.493858e+00 1.543963e+00 9.130444e-01
## race_ethhispanic race_ethother educahghsch educalsshgh
## 9.384993e-01 1.431387e+00 1.375151e+00 9.712328e-01
## educasomecol inclessthn350001 gender5female gender5other
## 1.017074e+00 1.359214e+00 1.204442e+00 4.579707e+06
## qoldeclining qolunsure satisinfra1 satislot1
## 1.146462e+00 9.593564e-01 9.080150e-01 1.226447e+00
## housequal1 parkqual1
## 7.685867e-01 2.430502e+00
exp(coefficients(fit.logit26))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 8.097984e-02 1.476710e+00 1.614202e+00 9.080592e-01
## race_ethhispanic race_ethother educahghsch educalsshgh
## 8.683177e-01 1.289398e+00 1.282464e+00 9.754922e-01
## educasomecol inclessthn350001 gender5female gender5other
## 1.047214e+00 1.529490e+00 1.063514e+00 3.610562e+06
## nbsafedeclining nbsafeunsure satiscrime1 safehomenotsafe
## 1.518075e+00 1.172016e+00 1.263083e+00 2.480574e+00
## safehomeunsure safewalkingnotsafe safewalkingunsure
## 6.196187e-01 9.875063e-01 9.284481e-01
exp(coefficients(fit.logit27))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 6.146580e-02 1.406887e+00 1.442295e+00 8.836661e-01
## race_ethhispanic race_ethother educahghsch educalsshgh
## 8.367164e-01 1.292627e+00 1.270172e+00 9.349634e-01
## educasomecol inclessthn350001 gender5female gender5other
## 1.024521e+00 1.520631e+00 1.076178e+00 4.498930e+06
## qoldeclining qolunsure satisinfra1 satislot1
## 8.231769e-01 8.450431e-01 7.373850e-01 1.022471e+00
## housequal1 parkqual1 nbsafedeclining nbsafeunsure
## 6.389653e-01 2.397584e+00 1.902224e+00 1.356865e+00
## satiscrime1 safehomenotsafe safehomeunsure safewalkingnotsafe
## 1.368783e+00 2.700859e+00 5.791127e-01 1.022946e+00
## safewalkingunsure
## 9.034716e-01
fit.logit28<-svyglm(felt_depressed ~ new_age + race_eth + educa + inclessthn35000 + gender5,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit29<-svyglm(felt_depressed ~ new_age + race_eth + educa + inclessthn35000 + gender5 + qol,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit30<-svyglm(felt_depressed ~ new_age + race_eth + educa + inclessthn35000 + gender5 + nbsafe,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit31<-svyglm(felt_depressed ~ new_age + race_eth + educa + inclessthn35000 + gender5 + satisinfra,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit32<-svyglm(felt_depressed ~ new_age + race_eth + educa + inclessthn35000 + gender5 + satiscrime,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit33<-svyglm(felt_depressed ~ new_age + race_eth + educa + inclessthn35000 + gender5 + satislot,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit34<-svyglm(felt_depressed ~ new_age + race_eth + educa + inclessthn35000 + gender5 + housequal,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit35<-svyglm(felt_depressed ~ new_age + race_eth + educa + inclessthn35000 + gender5 + parkqual,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit36<-svyglm(felt_depressed ~ new_age + race_eth + educa + inclessthn35000 + gender5 + safewalking,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit37<-svyglm(felt_depressed ~ new_age + race_eth + educa + inclessthn35000 + gender5 + safehome,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit38<-svyglm(felt_depressed ~ new_age + race_eth + educa + inclessthn35000 + gender5 + safewalking,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit39<-svyglm(felt_depressed ~ new_age + race_eth + educa + inclessthn35000 + gender5 + qol + satisinfra +
satislot + housequal + parkqual,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit40<-svyglm(felt_depressed ~ new_age + race_eth + educa + inclessthn35000 + gender5 + nbsafe + satiscrime +
safehome + safewalking,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit41<-svyglm(felt_depressed ~ new_age + race_eth + educa + inclessthn35000 + gender5 + + qol + satisinfra +
satislot + housequal + parkqual + nbsafe + satiscrime +
safehome + safewalking,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit28%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.303 |
0.510 |
-4.515 |
0.000 |
0.100 |
0.037 |
0.272 |
| new_agemiddleadult |
0.544 |
0.390 |
1.392 |
0.164 |
1.722 |
0.801 |
3.701 |
| new_ageyoungeradult |
0.566 |
0.429 |
1.320 |
0.187 |
1.761 |
0.760 |
4.080 |
| race_ethblack |
-0.100 |
0.484 |
-0.206 |
0.837 |
0.905 |
0.351 |
2.336 |
| race_ethhispanic |
-0.342 |
0.674 |
-0.507 |
0.612 |
0.710 |
0.190 |
2.661 |
| race_ethother |
0.313 |
0.571 |
0.549 |
0.583 |
1.368 |
0.447 |
4.185 |
| educahghsch |
0.598 |
0.353 |
1.693 |
0.091 |
1.819 |
0.910 |
3.637 |
| educalsshgh |
0.602 |
0.467 |
1.287 |
0.198 |
1.825 |
0.730 |
4.563 |
| educasomecol |
0.207 |
0.423 |
0.489 |
0.625 |
1.230 |
0.537 |
2.818 |
| inclessthn350001 |
0.104 |
0.319 |
0.327 |
0.744 |
1.110 |
0.594 |
2.074 |
| gender5female |
0.477 |
0.298 |
1.602 |
0.110 |
1.612 |
0.899 |
2.891 |
| gender5other |
2.583 |
1.649 |
1.566 |
0.118 |
13.234 |
0.522 |
335.462 |
fit.logit29%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.723 |
0.543 |
-5.016 |
0.000 |
0.066 |
0.023 |
0.190 |
| new_agemiddleadult |
0.610 |
0.403 |
1.512 |
0.131 |
1.840 |
0.835 |
4.054 |
| new_ageyoungeradult |
0.645 |
0.443 |
1.457 |
0.145 |
1.907 |
0.800 |
4.541 |
| race_ethblack |
-0.077 |
0.449 |
-0.173 |
0.863 |
0.925 |
0.384 |
2.229 |
| race_ethhispanic |
-0.280 |
0.626 |
-0.448 |
0.655 |
0.756 |
0.221 |
2.579 |
| race_ethother |
0.426 |
0.547 |
0.779 |
0.436 |
1.531 |
0.524 |
4.473 |
| educahghsch |
0.629 |
0.347 |
1.814 |
0.070 |
1.875 |
0.951 |
3.698 |
| educalsshgh |
0.728 |
0.470 |
1.547 |
0.122 |
2.070 |
0.823 |
5.206 |
| educasomecol |
0.282 |
0.422 |
0.667 |
0.505 |
1.326 |
0.579 |
3.034 |
| inclessthn350001 |
0.083 |
0.316 |
0.263 |
0.793 |
1.086 |
0.585 |
2.017 |
| gender5female |
0.441 |
0.298 |
1.479 |
0.140 |
1.555 |
0.866 |
2.789 |
| gender5other |
2.892 |
1.687 |
1.714 |
0.087 |
18.027 |
0.660 |
492.103 |
| qoldeclining |
0.902 |
0.334 |
2.701 |
0.007 |
2.466 |
1.281 |
4.746 |
| qolunsure |
0.155 |
0.316 |
0.489 |
0.625 |
1.167 |
0.628 |
2.170 |
fit.logit30%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.955 |
0.587 |
-5.031 |
0.000 |
0.052 |
0.016 |
0.165 |
| new_agemiddleadult |
0.426 |
0.394 |
1.080 |
0.280 |
1.531 |
0.707 |
3.315 |
| new_ageyoungeradult |
0.364 |
0.431 |
0.845 |
0.398 |
1.439 |
0.619 |
3.346 |
| race_ethblack |
-0.148 |
0.439 |
-0.338 |
0.735 |
0.862 |
0.365 |
2.037 |
| race_ethhispanic |
-0.370 |
0.629 |
-0.588 |
0.557 |
0.691 |
0.201 |
2.371 |
| race_ethother |
0.342 |
0.551 |
0.621 |
0.535 |
1.408 |
0.478 |
4.144 |
| educahghsch |
0.536 |
0.342 |
1.565 |
0.118 |
1.709 |
0.874 |
3.345 |
| educalsshgh |
0.702 |
0.486 |
1.445 |
0.149 |
2.017 |
0.779 |
5.227 |
| educasomecol |
0.189 |
0.406 |
0.465 |
0.642 |
1.208 |
0.545 |
2.680 |
| inclessthn350001 |
0.155 |
0.303 |
0.512 |
0.609 |
1.168 |
0.645 |
2.115 |
| gender5female |
0.407 |
0.297 |
1.371 |
0.171 |
1.502 |
0.840 |
2.687 |
| gender5other |
2.907 |
1.493 |
1.948 |
0.052 |
18.307 |
0.982 |
341.370 |
| nbsafedeclining |
1.451 |
0.466 |
3.111 |
0.002 |
4.267 |
1.711 |
10.642 |
| nbsafeunsure |
0.650 |
0.434 |
1.498 |
0.135 |
1.915 |
0.818 |
4.481 |
fit.logit31%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.403 |
0.535 |
-4.488 |
0.000 |
0.090 |
0.032 |
0.258 |
| new_agemiddleadult |
0.521 |
0.397 |
1.313 |
0.190 |
1.683 |
0.774 |
3.663 |
| new_ageyoungeradult |
0.516 |
0.437 |
1.179 |
0.239 |
1.675 |
0.711 |
3.946 |
| race_ethblack |
-0.095 |
0.493 |
-0.192 |
0.848 |
0.910 |
0.346 |
2.391 |
| race_ethhispanic |
-0.387 |
0.669 |
-0.579 |
0.563 |
0.679 |
0.183 |
2.519 |
| race_ethother |
0.314 |
0.584 |
0.538 |
0.591 |
1.369 |
0.436 |
4.297 |
| educahghsch |
0.600 |
0.351 |
1.711 |
0.087 |
1.823 |
0.916 |
3.625 |
| educalsshgh |
0.611 |
0.464 |
1.317 |
0.188 |
1.843 |
0.742 |
4.576 |
| educasomecol |
0.198 |
0.432 |
0.458 |
0.647 |
1.219 |
0.523 |
2.841 |
| inclessthn350001 |
0.089 |
0.321 |
0.276 |
0.782 |
1.093 |
0.582 |
2.052 |
| gender5female |
0.427 |
0.295 |
1.447 |
0.148 |
1.533 |
0.859 |
2.736 |
| gender5other |
2.679 |
1.745 |
1.535 |
0.125 |
14.563 |
0.476 |
445.241 |
| satisinfra1 |
0.268 |
0.287 |
0.933 |
0.351 |
1.307 |
0.745 |
2.295 |
fit.logit32%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.516 |
0.499 |
-5.039 |
0.000 |
0.081 |
0.030 |
0.215 |
| new_agemiddleadult |
0.443 |
0.398 |
1.113 |
0.266 |
1.558 |
0.714 |
3.399 |
| new_ageyoungeradult |
0.467 |
0.424 |
1.101 |
0.271 |
1.595 |
0.695 |
3.662 |
| race_ethblack |
-0.140 |
0.468 |
-0.299 |
0.765 |
0.869 |
0.348 |
2.174 |
| race_ethhispanic |
-0.431 |
0.664 |
-0.650 |
0.516 |
0.650 |
0.177 |
2.387 |
| race_ethother |
0.307 |
0.570 |
0.538 |
0.591 |
1.359 |
0.444 |
4.157 |
| educahghsch |
0.531 |
0.343 |
1.547 |
0.122 |
1.701 |
0.868 |
3.333 |
| educalsshgh |
0.615 |
0.467 |
1.317 |
0.188 |
1.850 |
0.740 |
4.626 |
| educasomecol |
0.161 |
0.414 |
0.390 |
0.697 |
1.175 |
0.522 |
2.646 |
| inclessthn350001 |
0.127 |
0.314 |
0.405 |
0.686 |
1.136 |
0.614 |
2.102 |
| gender5female |
0.371 |
0.316 |
1.174 |
0.241 |
1.449 |
0.780 |
2.690 |
| gender5other |
2.795 |
1.807 |
1.547 |
0.122 |
16.365 |
0.474 |
565.118 |
| satiscrime1 |
0.558 |
0.322 |
1.733 |
0.084 |
1.748 |
0.930 |
3.287 |
fit.logit33%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.787 |
0.513 |
-5.432 |
0.000 |
0.062 |
0.023 |
0.168 |
| new_agemiddleadult |
0.388 |
0.400 |
0.971 |
0.332 |
1.475 |
0.673 |
3.230 |
| new_ageyoungeradult |
0.354 |
0.427 |
0.831 |
0.407 |
1.425 |
0.618 |
3.289 |
| race_ethblack |
-0.018 |
0.465 |
-0.038 |
0.970 |
0.983 |
0.395 |
2.445 |
| race_ethhispanic |
-0.294 |
0.667 |
-0.441 |
0.660 |
0.746 |
0.202 |
2.753 |
| race_ethother |
0.413 |
0.555 |
0.743 |
0.458 |
1.511 |
0.509 |
4.488 |
| educahghsch |
0.569 |
0.346 |
1.645 |
0.100 |
1.767 |
0.897 |
3.483 |
| educalsshgh |
0.740 |
0.471 |
1.572 |
0.117 |
2.095 |
0.833 |
5.272 |
| educasomecol |
0.206 |
0.421 |
0.488 |
0.626 |
1.228 |
0.538 |
2.805 |
| inclessthn350001 |
0.113 |
0.317 |
0.358 |
0.721 |
1.120 |
0.602 |
2.084 |
| gender5female |
0.354 |
0.309 |
1.145 |
0.253 |
1.425 |
0.777 |
2.613 |
| gender5other |
3.108 |
1.950 |
1.594 |
0.111 |
22.384 |
0.490 |
1022.552 |
| satislot1 |
0.862 |
0.297 |
2.902 |
0.004 |
2.368 |
1.323 |
4.240 |
fit.logit34%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.495 |
0.509 |
-4.900 |
0.000 |
0.082 |
0.030 |
0.224 |
| new_agemiddleadult |
0.496 |
0.398 |
1.248 |
0.213 |
1.642 |
0.753 |
3.579 |
| new_ageyoungeradult |
0.462 |
0.430 |
1.074 |
0.283 |
1.588 |
0.683 |
3.692 |
| race_ethblack |
-0.133 |
0.472 |
-0.282 |
0.778 |
0.875 |
0.347 |
2.208 |
| race_ethhispanic |
-0.410 |
0.666 |
-0.615 |
0.539 |
0.664 |
0.180 |
2.448 |
| race_ethother |
0.338 |
0.571 |
0.591 |
0.555 |
1.402 |
0.458 |
4.292 |
| educahghsch |
0.513 |
0.346 |
1.482 |
0.139 |
1.671 |
0.847 |
3.295 |
| educalsshgh |
0.571 |
0.469 |
1.220 |
0.223 |
1.771 |
0.707 |
4.437 |
| educasomecol |
0.161 |
0.412 |
0.391 |
0.696 |
1.175 |
0.524 |
2.636 |
| inclessthn350001 |
0.113 |
0.317 |
0.356 |
0.722 |
1.119 |
0.601 |
2.084 |
| gender5female |
0.459 |
0.300 |
1.531 |
0.126 |
1.582 |
0.879 |
2.847 |
| gender5other |
2.774 |
1.815 |
1.528 |
0.127 |
16.022 |
0.457 |
562.155 |
| housequal1 |
0.515 |
0.267 |
1.931 |
0.054 |
1.673 |
0.992 |
2.821 |
fit.logit35%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.538 |
0.520 |
-4.882 |
0.000 |
0.079 |
0.029 |
0.219 |
| new_agemiddleadult |
0.477 |
0.395 |
1.208 |
0.228 |
1.611 |
0.743 |
3.493 |
| new_ageyoungeradult |
0.432 |
0.435 |
0.994 |
0.320 |
1.541 |
0.657 |
3.611 |
| race_ethblack |
-0.146 |
0.473 |
-0.307 |
0.759 |
0.865 |
0.342 |
2.187 |
| race_ethhispanic |
-0.434 |
0.671 |
-0.647 |
0.518 |
0.648 |
0.174 |
2.413 |
| race_ethother |
0.312 |
0.570 |
0.546 |
0.585 |
1.366 |
0.446 |
4.178 |
| educahghsch |
0.580 |
0.352 |
1.648 |
0.100 |
1.785 |
0.896 |
3.556 |
| educalsshgh |
0.573 |
0.464 |
1.235 |
0.217 |
1.773 |
0.714 |
4.402 |
| educasomecol |
0.176 |
0.421 |
0.419 |
0.676 |
1.193 |
0.522 |
2.723 |
| inclessthn350001 |
0.070 |
0.322 |
0.217 |
0.828 |
1.072 |
0.571 |
2.014 |
| gender5female |
0.465 |
0.299 |
1.553 |
0.121 |
1.592 |
0.885 |
2.864 |
| gender5other |
2.855 |
1.811 |
1.577 |
0.115 |
17.379 |
0.500 |
604.629 |
| parkqual1 |
0.506 |
0.312 |
1.623 |
0.105 |
1.658 |
0.900 |
3.054 |
fit.logit36%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.419 |
0.483 |
-5.011 |
0.000 |
0.089 |
0.035 |
0.229 |
| new_agemiddleadult |
0.538 |
0.382 |
1.409 |
0.159 |
1.713 |
0.810 |
3.621 |
| new_ageyoungeradult |
0.474 |
0.414 |
1.147 |
0.252 |
1.607 |
0.714 |
3.614 |
| race_ethblack |
-0.128 |
0.439 |
-0.292 |
0.771 |
0.880 |
0.372 |
2.082 |
| race_ethhispanic |
-0.547 |
0.678 |
-0.807 |
0.420 |
0.579 |
0.153 |
2.185 |
| race_ethother |
0.411 |
0.545 |
0.753 |
0.451 |
1.508 |
0.518 |
4.386 |
| educahghsch |
0.442 |
0.332 |
1.333 |
0.183 |
1.556 |
0.812 |
2.982 |
| educalsshgh |
0.468 |
0.471 |
0.995 |
0.320 |
1.597 |
0.635 |
4.020 |
| educasomecol |
0.227 |
0.421 |
0.541 |
0.589 |
1.255 |
0.550 |
2.864 |
| inclessthn350001 |
0.130 |
0.310 |
0.419 |
0.675 |
1.139 |
0.620 |
2.092 |
| gender5female |
0.327 |
0.303 |
1.079 |
0.281 |
1.386 |
0.766 |
2.509 |
| gender5other |
2.743 |
1.710 |
1.604 |
0.109 |
15.539 |
0.544 |
443.889 |
| safewalkingnotsafe |
0.966 |
0.284 |
3.404 |
0.001 |
2.627 |
1.506 |
4.580 |
| safewalkingunsure |
0.079 |
0.825 |
0.096 |
0.924 |
1.082 |
0.215 |
5.454 |
fit.logit37%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.417 |
0.505 |
-4.789 |
0.000 |
0.089 |
0.033 |
0.240 |
| new_agemiddleadult |
0.508 |
0.373 |
1.361 |
0.174 |
1.662 |
0.800 |
3.455 |
| new_ageyoungeradult |
0.521 |
0.433 |
1.204 |
0.229 |
1.684 |
0.721 |
3.932 |
| race_ethblack |
-0.101 |
0.509 |
-0.199 |
0.842 |
0.904 |
0.333 |
2.452 |
| race_ethhispanic |
-0.490 |
0.658 |
-0.745 |
0.457 |
0.613 |
0.169 |
2.224 |
| race_ethother |
0.228 |
0.597 |
0.381 |
0.703 |
1.255 |
0.390 |
4.046 |
| educahghsch |
0.588 |
0.346 |
1.697 |
0.090 |
1.800 |
0.913 |
3.548 |
| educalsshgh |
0.617 |
0.478 |
1.290 |
0.198 |
1.853 |
0.726 |
4.735 |
| educasomecol |
0.278 |
0.426 |
0.652 |
0.514 |
1.320 |
0.573 |
3.042 |
| inclessthn350001 |
0.170 |
0.320 |
0.530 |
0.596 |
1.185 |
0.632 |
2.221 |
| gender5female |
0.347 |
0.307 |
1.131 |
0.258 |
1.415 |
0.775 |
2.584 |
| gender5other |
2.728 |
1.673 |
1.631 |
0.103 |
15.309 |
0.576 |
406.666 |
| safehomenotsafe |
1.382 |
0.369 |
3.746 |
0.000 |
3.982 |
1.933 |
8.205 |
| safehomeunsure |
-1.752 |
1.284 |
-1.364 |
0.173 |
0.173 |
0.014 |
2.148 |
fit.logit38%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.419 |
0.483 |
-5.011 |
0.000 |
0.089 |
0.035 |
0.229 |
| new_agemiddleadult |
0.538 |
0.382 |
1.409 |
0.159 |
1.713 |
0.810 |
3.621 |
| new_ageyoungeradult |
0.474 |
0.414 |
1.147 |
0.252 |
1.607 |
0.714 |
3.614 |
| race_ethblack |
-0.128 |
0.439 |
-0.292 |
0.771 |
0.880 |
0.372 |
2.082 |
| race_ethhispanic |
-0.547 |
0.678 |
-0.807 |
0.420 |
0.579 |
0.153 |
2.185 |
| race_ethother |
0.411 |
0.545 |
0.753 |
0.451 |
1.508 |
0.518 |
4.386 |
| educahghsch |
0.442 |
0.332 |
1.333 |
0.183 |
1.556 |
0.812 |
2.982 |
| educalsshgh |
0.468 |
0.471 |
0.995 |
0.320 |
1.597 |
0.635 |
4.020 |
| educasomecol |
0.227 |
0.421 |
0.541 |
0.589 |
1.255 |
0.550 |
2.864 |
| inclessthn350001 |
0.130 |
0.310 |
0.419 |
0.675 |
1.139 |
0.620 |
2.092 |
| gender5female |
0.327 |
0.303 |
1.079 |
0.281 |
1.386 |
0.766 |
2.509 |
| gender5other |
2.743 |
1.710 |
1.604 |
0.109 |
15.539 |
0.544 |
443.889 |
| safewalkingnotsafe |
0.966 |
0.284 |
3.404 |
0.001 |
2.627 |
1.506 |
4.580 |
| safewalkingunsure |
0.079 |
0.825 |
0.096 |
0.924 |
1.082 |
0.215 |
5.454 |
fit.logit39%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-3.007 |
0.547 |
-5.501 |
0.000 |
0.049 |
0.017 |
0.144 |
| new_agemiddleadult |
0.484 |
0.412 |
1.175 |
0.240 |
1.623 |
0.723 |
3.642 |
| new_ageyoungeradult |
0.476 |
0.459 |
1.038 |
0.300 |
1.609 |
0.655 |
3.953 |
| race_ethblack |
-0.002 |
0.429 |
-0.006 |
0.995 |
0.998 |
0.431 |
2.311 |
| race_ethhispanic |
-0.185 |
0.622 |
-0.297 |
0.766 |
0.831 |
0.245 |
2.814 |
| race_ethother |
0.513 |
0.526 |
0.976 |
0.330 |
1.671 |
0.596 |
4.688 |
| educahghsch |
0.598 |
0.346 |
1.728 |
0.085 |
1.818 |
0.923 |
3.580 |
| educalsshgh |
0.843 |
0.481 |
1.754 |
0.080 |
2.323 |
0.906 |
5.959 |
| educasomecol |
0.277 |
0.414 |
0.669 |
0.503 |
1.319 |
0.586 |
2.967 |
| inclessthn350001 |
0.097 |
0.313 |
0.310 |
0.756 |
1.102 |
0.597 |
2.035 |
| gender5female |
0.399 |
0.292 |
1.368 |
0.172 |
1.491 |
0.841 |
2.641 |
| gender5other |
3.223 |
1.913 |
1.685 |
0.093 |
25.092 |
0.591 |
1066.000 |
| qoldeclining |
0.698 |
0.369 |
1.891 |
0.059 |
2.009 |
0.975 |
4.142 |
| qolunsure |
-0.005 |
0.317 |
-0.014 |
0.989 |
0.995 |
0.534 |
1.854 |
| satisinfra1 |
-0.279 |
0.349 |
-0.800 |
0.424 |
0.756 |
0.382 |
1.499 |
| satislot1 |
0.791 |
0.337 |
2.345 |
0.019 |
2.205 |
1.139 |
4.268 |
| housequal1 |
0.027 |
0.310 |
0.087 |
0.931 |
1.027 |
0.559 |
1.887 |
| parkqual1 |
0.067 |
0.375 |
0.180 |
0.857 |
1.070 |
0.513 |
2.230 |
fit.logit40%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-2.966 |
0.601 |
-4.936 |
0.000 |
0.051 |
0.016 |
0.167 |
| new_agemiddleadult |
0.454 |
0.385 |
1.177 |
0.239 |
1.574 |
0.740 |
3.349 |
| new_ageyoungeradult |
0.409 |
0.420 |
0.973 |
0.331 |
1.505 |
0.661 |
3.429 |
| race_ethblack |
-0.114 |
0.444 |
-0.257 |
0.797 |
0.892 |
0.374 |
2.131 |
| race_ethhispanic |
-0.540 |
0.625 |
-0.864 |
0.388 |
0.583 |
0.171 |
1.984 |
| race_ethother |
0.325 |
0.559 |
0.581 |
0.561 |
1.384 |
0.463 |
4.138 |
| educahghsch |
0.483 |
0.328 |
1.470 |
0.142 |
1.620 |
0.851 |
3.084 |
| educalsshgh |
0.685 |
0.501 |
1.369 |
0.172 |
1.984 |
0.744 |
5.293 |
| educasomecol |
0.239 |
0.403 |
0.592 |
0.554 |
1.269 |
0.576 |
2.796 |
| inclessthn350001 |
0.199 |
0.302 |
0.658 |
0.511 |
1.220 |
0.675 |
2.204 |
| gender5female |
0.241 |
0.314 |
0.768 |
0.443 |
1.273 |
0.687 |
2.357 |
| gender5other |
2.940 |
1.544 |
1.904 |
0.057 |
18.918 |
0.917 |
390.447 |
| nbsafedeclining |
1.109 |
0.529 |
2.096 |
0.036 |
3.033 |
1.075 |
8.557 |
| nbsafeunsure |
0.541 |
0.438 |
1.234 |
0.218 |
1.717 |
0.728 |
4.053 |
| satiscrime1 |
-0.001 |
0.373 |
-0.003 |
0.997 |
0.999 |
0.481 |
2.075 |
| safehomenotsafe |
0.924 |
0.413 |
2.236 |
0.026 |
2.520 |
1.121 |
5.664 |
| safehomeunsure |
-2.266 |
1.329 |
-1.705 |
0.089 |
0.104 |
0.008 |
1.403 |
| safewalkingnotsafe |
0.349 |
0.334 |
1.046 |
0.296 |
1.418 |
0.737 |
2.726 |
| safewalkingunsure |
0.055 |
0.807 |
0.068 |
0.945 |
1.057 |
0.217 |
5.139 |
fit.logit41%>%
tidy()%>%
mutate(OR = exp(estimate),
LowerOR_Ci = exp(estimate - 1.96*std.error),
UpperOR_Ci = exp(estimate + 1.96*std.error))%>%
knitr::kable(digits = 3)
| (Intercept) |
-3.185 |
0.609 |
-5.233 |
0.000 |
0.041 |
0.013 |
0.136 |
| new_agemiddleadult |
0.453 |
0.385 |
1.179 |
0.239 |
1.574 |
0.740 |
3.345 |
| new_ageyoungeradult |
0.416 |
0.440 |
0.947 |
0.344 |
1.517 |
0.641 |
3.590 |
| race_ethblack |
-0.018 |
0.415 |
-0.043 |
0.966 |
0.982 |
0.435 |
2.217 |
| race_ethhispanic |
-0.324 |
0.602 |
-0.538 |
0.591 |
0.723 |
0.222 |
2.354 |
| race_ethother |
0.456 |
0.528 |
0.863 |
0.388 |
1.577 |
0.560 |
4.438 |
| educahghsch |
0.514 |
0.333 |
1.543 |
0.123 |
1.672 |
0.870 |
3.213 |
| educalsshgh |
0.841 |
0.493 |
1.705 |
0.089 |
2.318 |
0.882 |
6.091 |
| educasomecol |
0.296 |
0.390 |
0.760 |
0.447 |
1.345 |
0.627 |
2.887 |
| inclessthn350001 |
0.215 |
0.298 |
0.720 |
0.472 |
1.240 |
0.691 |
2.224 |
| gender5female |
0.300 |
0.301 |
0.995 |
0.320 |
1.350 |
0.748 |
2.436 |
| gender5other |
3.080 |
1.582 |
1.947 |
0.052 |
21.767 |
0.980 |
483.551 |
| qoldeclining |
0.214 |
0.390 |
0.548 |
0.584 |
1.238 |
0.577 |
2.658 |
| qolunsure |
-0.204 |
0.339 |
-0.601 |
0.548 |
0.816 |
0.419 |
1.586 |
| satisinfra1 |
-0.558 |
0.361 |
-1.542 |
0.123 |
0.573 |
0.282 |
1.163 |
| satislot1 |
0.578 |
0.333 |
1.735 |
0.083 |
1.782 |
0.928 |
3.423 |
| housequal1 |
-0.099 |
0.326 |
-0.305 |
0.761 |
0.905 |
0.478 |
1.716 |
| parkqual1 |
0.074 |
0.367 |
0.202 |
0.840 |
1.077 |
0.524 |
2.212 |
| nbsafedeclining |
1.062 |
0.569 |
1.869 |
0.062 |
2.894 |
0.949 |
8.818 |
| nbsafeunsure |
0.584 |
0.449 |
1.300 |
0.194 |
1.793 |
0.743 |
4.327 |
| satiscrime1 |
-0.036 |
0.415 |
-0.088 |
0.930 |
0.964 |
0.428 |
2.174 |
| safehomenotsafe |
1.019 |
0.400 |
2.547 |
0.011 |
2.772 |
1.265 |
6.073 |
| safehomeunsure |
-2.120 |
1.346 |
-1.576 |
0.116 |
0.120 |
0.009 |
1.677 |
| safewalkingnotsafe |
0.289 |
0.332 |
0.871 |
0.384 |
1.336 |
0.696 |
2.561 |
| safewalkingunsure |
0.115 |
0.763 |
0.150 |
0.881 |
1.121 |
0.251 |
5.003 |
exp(coefficients(fit.logit28))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.09995664 1.72215488 1.76100496 0.90519062
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.71041306 1.36773749 1.81906563 1.82536157
## educasomecol inclessthn350001 gender5female gender5other
## 1.22986228 1.10976613 1.61183092 13.23406240
exp(coefficients(fit.logit29))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.06566894 1.83951349 1.90656214 0.92546102
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.75552537 1.53123970 1.87495766 2.07041287
## educasomecol inclessthn350001 gender5female gender5other
## 1.32568831 1.08643300 1.55454065 18.02654621
## qoldeclining qolunsure
## 2.46573132 1.16731002
exp(coefficients(fit.logit30))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.05206308 1.53084855 1.43874788 0.86216499
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.69102132 1.40790462 1.70938183 2.01723509
## educasomecol inclessthn350001 gender5female gender5other
## 1.20817796 1.16775358 1.50209822 18.30659935
## nbsafedeclining nbsafeunsure
## 4.26681171 1.91484845
exp(coefficients(fit.logit31))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.0904449 1.6831595 1.6746543 0.9097512
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.6788566 1.3687624 1.8227030 1.8427130
## educasomecol inclessthn350001 gender5female gender5other
## 1.2186498 1.0928774 1.5333621 14.5634878
## satisinfra1
## 1.3073034
exp(coefficients(fit.logit32))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.08077398 1.55755121 1.59513042 0.86945837
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.64958203 1.35915382 1.70068194 1.85048512
## educasomecol inclessthn350001 gender5female gender5other
## 1.17524793 1.13566789 1.44887479 16.36496157
## satiscrime1
## 1.74802503
exp(coefficients(fit.logit33))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.06159472 1.47459386 1.42521497 0.98260081
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.74555422 1.51085669 1.76730902 2.09546425
## educasomecol inclessthn350001 gender5female gender5other
## 1.22836034 1.12008592 1.42497061 22.38389237
## satislot1
## 2.36820218
exp(coefficients(fit.logit34))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.08248573 1.64222136 1.58772074 0.87548198
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.66393557 1.40158957 1.67083802 1.77085939
## educasomecol inclessthn350001 gender5female gender5other
## 1.17483402 1.11947786 1.58211075 16.02203755
## housequal1
## 1.67332155
exp(coefficients(fit.logit35))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.07903864 1.61095489 1.54064875 0.86452531
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.64781062 1.36572174 1.78529790 1.77313906
## educasomecol inclessthn350001 gender5female gender5other
## 1.19284019 1.07225644 1.59229690 17.37855676
## parkqual1
## 1.65831369
exp(coefficients(fit.logit36))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.08904104 1.71293421 1.60687436 0.87966758
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.57879209 1.50758499 1.55638576 1.59740083
## educasomecol inclessthn350001 gender5female gender5other
## 1.25540039 1.13894924 1.38619564 15.53905529
## safewalkingnotsafe safewalkingunsure
## 2.62662982 1.08219757
exp(coefficients(fit.logit37))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.08922774 1.66216176 1.68380950 0.90369636
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.61262680 1.25547717 1.79993090 1.85344374
## educasomecol inclessthn350001 gender5female gender5other
## 1.32019009 1.18515248 1.41535078 15.30893433
## safehomenotsafe safehomeunsure
## 3.98206083 0.17347657
exp(coefficients(fit.logit38))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.08904104 1.71293421 1.60687436 0.87966758
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.57879209 1.50758499 1.55638576 1.59740083
## educasomecol inclessthn350001 gender5female gender5other
## 1.25540039 1.13894924 1.38619564 15.53905529
## safewalkingnotsafe safewalkingunsure
## 2.62662982 1.08219757
exp(coefficients(fit.logit39))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.04944271 1.62325406 1.60927315 0.99756205
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.83100463 1.67109399 1.81769395 2.32300157
## educasomecol inclessthn350001 gender5female gender5other
## 1.31897002 1.10195400 1.49073087 25.09171350
## qoldeclining qolunsure satisinfra1 satislot1
## 2.00943535 0.99544298 0.75624205 2.20453969
## housequal1 parkqual1
## 1.02720710 1.06980742
exp(coefficients(fit.logit40))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.05149221 1.57390050 1.50485710 0.89220929
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.58250675 1.38374638 1.62045781 1.98396781
## educasomecol inclessthn350001 gender5female gender5other
## 1.26937771 1.21965501 1.27289131 18.91786673
## nbsafedeclining nbsafeunsure satiscrime1 safehomenotsafe
## 3.03261876 1.71726158 0.99877996 2.51973979
## safehomeunsure safewalkingnotsafe safewalkingunsure
## 0.10369576 1.41760342 1.05677521
exp(coefficients(fit.logit41))
## (Intercept) new_agemiddleadult new_ageyoungeradult race_ethblack
## 0.04137849 1.57375362 1.51653424 0.98245586
## race_ethhispanic race_ethother educahghsch educalsshgh
## 0.72320829 1.57724070 1.67214800 2.31760187
## educasomecol inclessthn350001 gender5female gender5other
## 1.34499244 1.23950626 1.34953290 21.76699256
## qoldeclining qolunsure satisinfra1 satislot1
## 1.23826094 0.81552880 0.57259163 1.78219846
## housequal1 parkqual1 nbsafedeclining nbsafeunsure
## 0.90539992 1.07697797 2.89354529 1.79325054
## satiscrime1 safehomenotsafe safehomeunsure safewalkingnotsafe
## 0.96425273 2.77163756 0.12000695 1.33566217
## safewalkingunsure
## 1.12144022