library(car)
## Loading required package: carData
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(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(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v stringr 1.4.0
## v tidyr 1.1.4 v forcats 0.5.1
## v readr 2.1.1
## -- 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(table1)
##
## Attaching package: 'table1'
## The following objects are masked from 'package:base':
##
## units, units<-
library(ggplot2)
library(haven)
X37166_0005_Data <- read_sav("37166-0005-Data.sav")
View(X37166_0005_Data)
nams<-names(X37166_0005_Data)
head(nams, n=10)
## [1] "STUDYID" "WAVE3_WEIGHT" "W3Q01" "W3Q02" "W3Q03"
## [6] "W3Q04" "W3Q05" "W3Q06" "W3Q07" "W3Q08"
newnames<-tolower(gsub(pattern = "_",replacement = "",x = nams))
names(X37166_0005_Data)<-newnames
X37166_0005_Data$wish_death<-Recode(X37166_0005_Data$w3q82, recodes="1=0; 2=1;else=NA")
summary(X37166_0005_Data$wish_death, na.rm = TRUE)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00000 0.00000 0.00000 0.09855 0.00000 1.00000 17
X37166_0005_Data$plan_death<-Recode(X37166_0005_Data$w3q83, recodes="1=0; 2=1;else=NA")
summary(X37166_0005_Data$plan_death, na.rm = TRUE)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.2656 1.0000 1.0000 18
X37166_0005_Data$attempt_suicide<-Recode(X37166_0005_Data$w3q84, recodes="1=0; 2=1;else=NA")
summary(X37166_0005_Data$attempt_suicide, na.rm = TRUE)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00000 0.00000 0.00000 0.02032 0.00000 1.00000 18
##educ
X37166_0005_Data$educ<-Recode(X37166_0005_Data$geduc2, recodes="1=1; 2=0;else=NA")
summary(X37166_0005_Data$educ, na.rm = TRUE)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1556 0.0000 1.0000
X37166_0005_Data$grace <- as.numeric(X37166_0005_Data$grace)
X37166_0005_Data$race<-Recode(X37166_0005_Data$grace, recodes="1='white'; 3='black'; 5='hispanic'; else=NA", as.factor=T)
X37166_0005_Data$race<-relevel(X37166_0005_Data$race, ref='white')
summary(X37166_0005_Data$race, na.rm = TRUE)
## white black hispanic NA's
## 540 70 86 11
X37166_0005_Data$w3q20 <- as.numeric(X37166_0005_Data$w3q20)
X37166_0005_Data$geniden<-Recode(X37166_0005_Data$w3q20, recodes="1='female'; 2='male'; 3:5='nonbin/trans'; else=NA", as.factor=T)
X37166_0005_Data$geniden<-relevel(X37166_0005_Data$geniden, ref='female')
summary(X37166_0005_Data$geniden, na.rm = TRUE)
## female male nonbin/trans NA's
## 317 325 53 12
X37166_0005_Data$w3sexminid <- as.numeric(X37166_0005_Data$w3sexminid)
X37166_0005_Data$sexuality<-Recode(X37166_0005_Data$w3sexminid, recodes="1='les/gay'; 2='bisexual'; 3='other'; else=NA", as.factor=T)
X37166_0005_Data$sexuality<-relevel(X37166_0005_Data$sexuality, ref='bisexual')
summary(X37166_0005_Data$sexuality, na.rm = TRUE)
## bisexual les/gay other NA's
## 189 419 93 6
X37166_0005_Data$w3cohort <- as.numeric(X37166_0005_Data$w3cohort)
X37166_0005_Data$agecohort<-Recode(X37166_0005_Data$w3cohort, recodes="1='younger'; 2='middle'; 3='older'; else=NA", as.factor=T)
X37166_0005_Data$agecohort<-relevel(X37166_0005_Data$agecohort, ref='older')
summary(X37166_0005_Data$agecohort, na.rm = TRUE)
## older middle younger
## 274 157 276
X37166_0005_Data$soimp<-Recode(X37166_0005_Data$w3q25, recodes="1:3=1; 4:6=0;else=NA")
X37166_0005_Data$lgbimp<-Recode(X37166_0005_Data$w3q27, recodes="1:3=1; 4:6=0;else=NA")
X37166_0005_Data$parlgbcom<-Recode(X37166_0005_Data$w3q30, recodes="1:2=1; 3:4=0;else=NA")
X37166_0005_Data$poslgbcom<-Recode(X37166_0005_Data$w3q31, recodes="1:2=1; 3:4=0;else=NA")
X37166_0005_Data$bondlgbcom<-Recode(X37166_0005_Data$w3q32, recodes="1:2=1; 3:4=0;else=NA")
X37166_0005_Data$proudlgbcom<-Recode(X37166_0005_Data$w3q33, recodes="1:2=1; 3:4=0;else=NA")
X37166_0005_Data$imppolactlgbcom<-Recode(X37166_0005_Data$w3q34, recodes="1:2=1; 3:4=0;else=NA")
sub<-X37166_0005_Data%>%
select(attempt_suicide, wish_death, plan_death, geniden, educ, sexuality, race, agecohort, soimp, lgbimp, parlgbcom, poslgbcom, bondlgbcom, proudlgbcom, imppolactlgbcom, wave3weight) %>%
filter( complete.cases( . ))
table1(~ sexuality + geniden + educ + race + agecohort + sexuality + soimp + lgbimp + parlgbcom + poslgbcom + bondlgbcom
+ proudlgbcom + imppolactlgbcom| attempt_suicide, data=sub, overall="Total")
## Warning in table1.formula(~sexuality + geniden + educ + race + agecohort + :
## Terms to the right of '|' in formula 'x' define table columns and are expected
## to be factors with meaningful labels.
|
0
(N=632) |
1
(N=14) |
Total
(N=646) |
| sexuality |
|
|
|
| bisexual |
174 (27.5%) |
6 (42.9%) |
180 (27.9%) |
| les/gay |
377 (59.7%) |
4 (28.6%) |
381 (59.0%) |
| other |
81 (12.8%) |
4 (28.6%) |
85 (13.2%) |
| geniden |
|
|
|
| female |
281 (44.5%) |
6 (42.9%) |
287 (44.4%) |
| male |
305 (48.3%) |
3 (21.4%) |
308 (47.7%) |
| nonbin/trans |
46 (7.3%) |
5 (35.7%) |
51 (7.9%) |
| High school or less |
|
|
|
| Mean (SD) |
0.152 (0.359) |
0.357 (0.497) |
0.156 (0.363) |
| Median [Min, Max] |
0 [0, 1.00] |
0 [0, 1.00] |
0 [0, 1.00] |
| race |
|
|
|
| white |
493 (78.0%) |
12 (85.7%) |
505 (78.2%) |
| black |
61 (9.7%) |
1 (7.1%) |
62 (9.6%) |
| hispanic |
78 (12.3%) |
1 (7.1%) |
79 (12.2%) |
| agecohort |
|
|
|
| older |
247 (39.1%) |
1 (7.1%) |
248 (38.4%) |
| middle |
136 (21.5%) |
3 (21.4%) |
139 (21.5%) |
| younger |
249 (39.4%) |
10 (71.4%) |
259 (40.1%) |
| My sexual orientation is a central part of my identity. |
|
|
|
| Mean (SD) |
0.411 (0.492) |
0.357 (0.497) |
0.410 (0.492) |
| Median [Min, Max] |
0 [0, 1.00] |
0 [0, 1.00] |
0 [0, 1.00] |
| Being an LGB person is a very important aspect of my life. |
|
|
|
| Mean (SD) |
0.328 (0.470) |
0.143 (0.363) |
0.324 (0.468) |
| Median [Min, Max] |
0 [0, 1.00] |
0 [0, 1.00] |
0 [0, 1.00] |
| You feel you're a part of the LGBT community. |
|
|
|
| Mean (SD) |
0.608 (0.489) |
0.857 (0.363) |
0.613 (0.487) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
| Participating in the LGBT community is a positive thing for you. |
|
|
|
| Mean (SD) |
0.742 (0.438) |
0.857 (0.363) |
0.745 (0.436) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
| You feel a bond with the LGBT community. |
|
|
|
| Mean (SD) |
0.633 (0.482) |
0.929 (0.267) |
0.639 (0.481) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
| You are proud of the LGBT community. |
|
|
|
| Mean (SD) |
0.862 (0.345) |
0.857 (0.363) |
0.862 (0.345) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
| It is important for you to be politically active in the LGBT community. |
|
|
|
| Mean (SD) |
0.568 (0.496) |
0.786 (0.426) |
0.573 (0.495) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
table1(~ sexuality + geniden + educ + race + agecohort + sexuality + soimp + lgbimp + parlgbcom + poslgbcom + bondlgbcom
+ proudlgbcom + imppolactlgbcom| plan_death, data=sub, overall="Total")
## Warning in table1.formula(~sexuality + geniden + educ + race + agecohort + :
## Terms to the right of '|' in formula 'x' define table columns and are expected
## to be factors with meaningful labels.
|
0
(N=480) |
1
(N=166) |
Total
(N=646) |
| sexuality |
|
|
|
| bisexual |
121 (25.2%) |
59 (35.5%) |
180 (27.9%) |
| les/gay |
316 (65.8%) |
65 (39.2%) |
381 (59.0%) |
| other |
43 (9.0%) |
42 (25.3%) |
85 (13.2%) |
| geniden |
|
|
|
| female |
212 (44.2%) |
75 (45.2%) |
287 (44.4%) |
| male |
243 (50.6%) |
65 (39.2%) |
308 (47.7%) |
| nonbin/trans |
25 (5.2%) |
26 (15.7%) |
51 (7.9%) |
| High school or less |
|
|
|
| Mean (SD) |
0.135 (0.343) |
0.217 (0.413) |
0.156 (0.363) |
| Median [Min, Max] |
0 [0, 1.00] |
0 [0, 1.00] |
0 [0, 1.00] |
| race |
|
|
|
| white |
381 (79.4%) |
124 (74.7%) |
505 (78.2%) |
| black |
45 (9.4%) |
17 (10.2%) |
62 (9.6%) |
| hispanic |
54 (11.3%) |
25 (15.1%) |
79 (12.2%) |
| agecohort |
|
|
|
| older |
197 (41.0%) |
51 (30.7%) |
248 (38.4%) |
| middle |
116 (24.2%) |
23 (13.9%) |
139 (21.5%) |
| younger |
167 (34.8%) |
92 (55.4%) |
259 (40.1%) |
| My sexual orientation is a central part of my identity. |
|
|
|
| Mean (SD) |
0.417 (0.494) |
0.392 (0.490) |
0.410 (0.492) |
| Median [Min, Max] |
0 [0, 1.00] |
0 [0, 1.00] |
0 [0, 1.00] |
| Being an LGB person is a very important aspect of my life. |
|
|
|
| Mean (SD) |
0.333 (0.472) |
0.295 (0.458) |
0.324 (0.468) |
| Median [Min, Max] |
0 [0, 1.00] |
0 [0, 1.00] |
0 [0, 1.00] |
| You feel you're a part of the LGBT community. |
|
|
|
| Mean (SD) |
0.642 (0.480) |
0.530 (0.501) |
0.613 (0.487) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
| Participating in the LGBT community is a positive thing for you. |
|
|
|
| Mean (SD) |
0.758 (0.429) |
0.705 (0.458) |
0.745 (0.436) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
| You feel a bond with the LGBT community. |
|
|
|
| Mean (SD) |
0.640 (0.481) |
0.639 (0.482) |
0.639 (0.481) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
| You are proud of the LGBT community. |
|
|
|
| Mean (SD) |
0.865 (0.343) |
0.855 (0.353) |
0.862 (0.345) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
| It is important for you to be politically active in the LGBT community. |
|
|
|
| Mean (SD) |
0.575 (0.495) |
0.566 (0.497) |
0.573 (0.495) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
table1(~ sexuality + geniden + educ + race + agecohort + sexuality + soimp + lgbimp + parlgbcom + poslgbcom + bondlgbcom
+ proudlgbcom + imppolactlgbcom| wish_death, data=sub, overall="Total")
## Warning in table1.formula(~sexuality + geniden + educ + race + agecohort + :
## Terms to the right of '|' in formula 'x' define table columns and are expected
## to be factors with meaningful labels.
|
0
(N=583) |
1
(N=63) |
Total
(N=646) |
| sexuality |
|
|
|
| bisexual |
156 (26.8%) |
24 (38.1%) |
180 (27.9%) |
| les/gay |
357 (61.2%) |
24 (38.1%) |
381 (59.0%) |
| other |
70 (12.0%) |
15 (23.8%) |
85 (13.2%) |
| geniden |
|
|
|
| female |
264 (45.3%) |
23 (36.5%) |
287 (44.4%) |
| male |
282 (48.4%) |
26 (41.3%) |
308 (47.7%) |
| nonbin/trans |
37 (6.3%) |
14 (22.2%) |
51 (7.9%) |
| High school or less |
|
|
|
| Mean (SD) |
0.132 (0.339) |
0.381 (0.490) |
0.156 (0.363) |
| Median [Min, Max] |
0 [0, 1.00] |
0 [0, 1.00] |
0 [0, 1.00] |
| race |
|
|
|
| white |
464 (79.6%) |
41 (65.1%) |
505 (78.2%) |
| black |
53 (9.1%) |
9 (14.3%) |
62 (9.6%) |
| hispanic |
66 (11.3%) |
13 (20.6%) |
79 (12.2%) |
| agecohort |
|
|
|
| older |
234 (40.1%) |
14 (22.2%) |
248 (38.4%) |
| middle |
132 (22.6%) |
7 (11.1%) |
139 (21.5%) |
| younger |
217 (37.2%) |
42 (66.7%) |
259 (40.1%) |
| My sexual orientation is a central part of my identity. |
|
|
|
| Mean (SD) |
0.410 (0.492) |
0.413 (0.496) |
0.410 (0.492) |
| Median [Min, Max] |
0 [0, 1.00] |
0 [0, 1.00] |
0 [0, 1.00] |
| Being an LGB person is a very important aspect of my life. |
|
|
|
| Mean (SD) |
0.324 (0.468) |
0.317 (0.469) |
0.324 (0.468) |
| Median [Min, Max] |
0 [0, 1.00] |
0 [0, 1.00] |
0 [0, 1.00] |
| You feel you're a part of the LGBT community. |
|
|
|
| Mean (SD) |
0.617 (0.486) |
0.571 (0.499) |
0.613 (0.487) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
| Participating in the LGBT community is a positive thing for you. |
|
|
|
| Mean (SD) |
0.746 (0.436) |
0.730 (0.447) |
0.745 (0.436) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
| You feel a bond with the LGBT community. |
|
|
|
| Mean (SD) |
0.640 (0.480) |
0.635 (0.485) |
0.639 (0.481) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
| You are proud of the LGBT community. |
|
|
|
| Mean (SD) |
0.868 (0.339) |
0.810 (0.396) |
0.862 (0.345) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
| It is important for you to be politically active in the LGBT community. |
|
|
|
| Mean (SD) |
0.573 (0.495) |
0.571 (0.499) |
0.573 (0.495) |
| Median [Min, Max] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
1.00 [0, 1.00] |
options(survey.lonely.psu = "adjust")
des<-svydesign(ids= ~1,
weights= ~wave3weight
, data = sub )
fit.logit1<-svyglm(attempt_suicide ~ race + agecohort + educ + sexuality,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit2<-svyglm(attempt_suicide ~ race + agecohort + educ + geniden,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit3<-svyglm(attempt_suicide ~ race + agecohort + educ + sexuality + geniden,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit4<-svyglm(attempt_suicide ~ race + agecohort + educ + sexuality + geniden + soimp,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit5<-svyglm(attempt_suicide ~ race + agecohort + educ + sexuality + geniden + lgbimp,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit6<-svyglm(attempt_suicide ~ race + agecohort + educ + sexuality + geniden + parlgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit7<-svyglm(attempt_suicide ~ race + agecohort + educ + sexuality + geniden + poslgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit8<-svyglm(attempt_suicide ~ race + agecohort + educ + sexuality + geniden + bondlgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit9<-svyglm(attempt_suicide ~ race + agecohort + educ + sexuality + geniden + proudlgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit10<-svyglm(attempt_suicide ~ race + agecohort + educ + sexuality + geniden + imppolactlgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit11<-svyglm(attempt_suicide ~ race + agecohort + educ + sexuality + geniden + soimp + lgbimp + parlgbcom + poslgbcom + bondlgbcom + proudlgbcom + imppolactlgbcom,
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) |
-5.203 |
1.071 |
-4.859 |
0.000 |
0.005 |
0.001 |
0.045 |
| raceblack |
-1.649 |
1.246 |
-1.323 |
0.186 |
0.192 |
0.017 |
2.211 |
| racehispanic |
-1.255 |
1.050 |
-1.195 |
0.233 |
0.285 |
0.036 |
2.233 |
| agecohortmiddle |
2.426 |
1.285 |
1.887 |
0.060 |
11.310 |
0.911 |
140.491 |
| agecohortyounger |
2.842 |
1.094 |
2.596 |
0.010 |
17.142 |
2.006 |
146.460 |
| educ |
0.750 |
0.713 |
1.052 |
0.293 |
2.117 |
0.523 |
8.563 |
| sexualityles/gay |
-1.619 |
0.866 |
-1.870 |
0.062 |
0.198 |
0.036 |
1.081 |
| sexualityother |
-0.689 |
0.796 |
-0.865 |
0.387 |
0.502 |
0.106 |
2.389 |
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) |
-5.991 |
1.098 |
-5.455 |
0.000 |
0.003 |
0.000 |
0.022 |
| raceblack |
-1.708 |
1.271 |
-1.343 |
0.180 |
0.181 |
0.015 |
2.191 |
| racehispanic |
-1.337 |
1.062 |
-1.259 |
0.208 |
0.263 |
0.033 |
2.105 |
| agecohortmiddle |
2.958 |
1.361 |
2.173 |
0.030 |
19.261 |
1.336 |
277.713 |
| agecohortyounger |
3.053 |
1.074 |
2.843 |
0.005 |
21.180 |
2.582 |
173.754 |
| educ |
0.818 |
0.760 |
1.076 |
0.282 |
2.265 |
0.511 |
10.037 |
| genidenmale |
-1.677 |
0.906 |
-1.851 |
0.065 |
0.187 |
0.032 |
1.104 |
| genidennonbin/trans |
1.266 |
0.741 |
1.708 |
0.088 |
3.547 |
0.830 |
15.160 |
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) |
-5.502 |
1.150 |
-4.784 |
0.000 |
0.004 |
0.000 |
0.039 |
| raceblack |
-1.710 |
1.205 |
-1.419 |
0.156 |
0.181 |
0.017 |
1.919 |
| racehispanic |
-1.190 |
1.030 |
-1.155 |
0.249 |
0.304 |
0.040 |
2.293 |
| agecohortmiddle |
2.839 |
1.386 |
2.049 |
0.041 |
17.097 |
1.130 |
258.578 |
| agecohortyounger |
2.993 |
1.128 |
2.653 |
0.008 |
19.949 |
2.186 |
182.030 |
| educ |
0.820 |
0.727 |
1.128 |
0.260 |
2.270 |
0.546 |
9.433 |
| sexualityles/gay |
-1.139 |
0.821 |
-1.387 |
0.166 |
0.320 |
0.064 |
1.600 |
| sexualityother |
-1.263 |
0.686 |
-1.841 |
0.066 |
0.283 |
0.074 |
1.085 |
| genidenmale |
-1.345 |
0.841 |
-1.599 |
0.110 |
0.261 |
0.050 |
1.354 |
| genidennonbin/trans |
1.629 |
0.693 |
2.350 |
0.019 |
5.101 |
1.311 |
19.848 |
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) |
-5.336 |
1.152 |
-4.634 |
0.000 |
0.005 |
0.001 |
0.046 |
| raceblack |
-1.742 |
1.230 |
-1.416 |
0.157 |
0.175 |
0.016 |
1.953 |
| racehispanic |
-1.219 |
1.027 |
-1.186 |
0.236 |
0.296 |
0.039 |
2.215 |
| agecohortmiddle |
2.894 |
1.403 |
2.062 |
0.040 |
18.061 |
1.155 |
282.537 |
| agecohortyounger |
3.020 |
1.134 |
2.664 |
0.008 |
20.493 |
2.221 |
189.075 |
| educ |
0.821 |
0.722 |
1.137 |
0.256 |
2.272 |
0.552 |
9.352 |
| sexualityles/gay |
-1.204 |
0.858 |
-1.404 |
0.161 |
0.300 |
0.056 |
1.612 |
| sexualityother |
-1.316 |
0.660 |
-1.996 |
0.046 |
0.268 |
0.074 |
0.977 |
| genidenmale |
-1.307 |
0.854 |
-1.530 |
0.127 |
0.271 |
0.051 |
1.444 |
| genidennonbin/trans |
1.591 |
0.666 |
2.387 |
0.017 |
4.909 |
1.329 |
18.126 |
| soimp |
-0.449 |
0.744 |
-0.603 |
0.547 |
0.639 |
0.149 |
2.744 |
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) |
-5.202 |
1.170 |
-4.447 |
0.000 |
0.006 |
0.001 |
0.055 |
| raceblack |
-1.812 |
1.273 |
-1.423 |
0.155 |
0.163 |
0.013 |
1.980 |
| racehispanic |
-1.202 |
1.025 |
-1.173 |
0.241 |
0.300 |
0.040 |
2.240 |
| agecohortmiddle |
2.972 |
1.391 |
2.137 |
0.033 |
19.537 |
1.279 |
298.396 |
| agecohortyounger |
2.998 |
1.152 |
2.601 |
0.010 |
20.039 |
2.094 |
191.784 |
| educ |
0.881 |
0.734 |
1.200 |
0.231 |
2.414 |
0.572 |
10.176 |
| sexualityles/gay |
-1.250 |
0.866 |
-1.445 |
0.149 |
0.286 |
0.053 |
1.562 |
| sexualityother |
-1.215 |
0.643 |
-1.889 |
0.059 |
0.297 |
0.084 |
1.047 |
| genidenmale |
-1.345 |
0.850 |
-1.582 |
0.114 |
0.261 |
0.049 |
1.379 |
| genidennonbin/trans |
1.484 |
0.685 |
2.165 |
0.031 |
4.410 |
1.151 |
16.897 |
| lgbimp |
-1.285 |
0.947 |
-1.357 |
0.175 |
0.277 |
0.043 |
1.770 |
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) |
-6.938 |
1.633 |
-4.248 |
0.000 |
0.001 |
0.000 |
0.024 |
| raceblack |
-1.776 |
1.160 |
-1.531 |
0.126 |
0.169 |
0.017 |
1.646 |
| racehispanic |
-1.281 |
1.012 |
-1.266 |
0.206 |
0.278 |
0.038 |
2.017 |
| agecohortmiddle |
3.077 |
1.383 |
2.226 |
0.026 |
21.700 |
1.444 |
326.201 |
| agecohortyounger |
2.978 |
1.145 |
2.600 |
0.010 |
19.642 |
2.081 |
185.444 |
| educ |
0.714 |
0.694 |
1.029 |
0.304 |
2.042 |
0.524 |
7.957 |
| sexualityles/gay |
-1.216 |
0.817 |
-1.487 |
0.137 |
0.296 |
0.060 |
1.472 |
| sexualityother |
-1.355 |
0.695 |
-1.950 |
0.052 |
0.258 |
0.066 |
1.007 |
| genidenmale |
-1.452 |
0.884 |
-1.643 |
0.101 |
0.234 |
0.041 |
1.324 |
| genidennonbin/trans |
1.373 |
0.708 |
1.940 |
0.053 |
3.948 |
0.986 |
15.811 |
| parlgbcom |
1.985 |
1.005 |
1.975 |
0.049 |
7.282 |
1.015 |
52.246 |
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) |
-5.918 |
1.347 |
-4.394 |
0.000 |
0.003 |
0.000 |
0.038 |
| raceblack |
-1.731 |
1.212 |
-1.429 |
0.154 |
0.177 |
0.016 |
1.904 |
| racehispanic |
-1.153 |
1.016 |
-1.135 |
0.257 |
0.316 |
0.043 |
2.312 |
| agecohortmiddle |
2.873 |
1.394 |
2.061 |
0.040 |
17.698 |
1.152 |
271.911 |
| agecohortyounger |
2.928 |
1.125 |
2.603 |
0.009 |
18.694 |
2.061 |
169.546 |
| educ |
0.809 |
0.740 |
1.093 |
0.275 |
2.246 |
0.526 |
9.587 |
| sexualityles/gay |
-1.224 |
0.829 |
-1.476 |
0.140 |
0.294 |
0.058 |
1.494 |
| sexualityother |
-1.331 |
0.678 |
-1.963 |
0.050 |
0.264 |
0.070 |
0.998 |
| genidenmale |
-1.290 |
0.852 |
-1.513 |
0.131 |
0.275 |
0.052 |
1.463 |
| genidennonbin/trans |
1.627 |
0.702 |
2.319 |
0.021 |
5.088 |
1.286 |
20.127 |
| poslgbcom |
0.593 |
0.913 |
0.649 |
0.516 |
1.809 |
0.302 |
10.828 |
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) |
-9.123 |
2.112 |
-4.319 |
0.000 |
0.000 |
0.000 |
0.007 |
| raceblack |
-1.737 |
1.158 |
-1.500 |
0.134 |
0.176 |
0.018 |
1.705 |
| racehispanic |
-1.217 |
1.021 |
-1.192 |
0.234 |
0.296 |
0.040 |
2.190 |
| agecohortmiddle |
2.994 |
1.346 |
2.224 |
0.026 |
19.972 |
1.427 |
279.520 |
| agecohortyounger |
3.047 |
1.145 |
2.662 |
0.008 |
21.043 |
2.233 |
198.318 |
| educ |
0.727 |
0.679 |
1.070 |
0.285 |
2.068 |
0.546 |
7.828 |
| sexualityles/gay |
-1.083 |
0.839 |
-1.291 |
0.197 |
0.339 |
0.065 |
1.752 |
| sexualityother |
-1.322 |
0.698 |
-1.892 |
0.059 |
0.267 |
0.068 |
1.048 |
| genidenmale |
-1.182 |
0.867 |
-1.364 |
0.173 |
0.307 |
0.056 |
1.677 |
| genidennonbin/trans |
1.339 |
0.723 |
1.851 |
0.065 |
3.816 |
0.924 |
15.750 |
| bondlgbcom |
4.083 |
1.077 |
3.791 |
0.000 |
59.325 |
7.186 |
489.741 |
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) |
-5.364 |
1.578 |
-3.400 |
0.001 |
0.005 |
0.000 |
0.103 |
| raceblack |
-1.684 |
1.334 |
-1.262 |
0.207 |
0.186 |
0.014 |
2.537 |
| racehispanic |
-1.177 |
1.035 |
-1.137 |
0.256 |
0.308 |
0.041 |
2.344 |
| agecohortmiddle |
2.811 |
1.311 |
2.145 |
0.032 |
16.629 |
1.274 |
217.095 |
| agecohortyounger |
2.977 |
1.134 |
2.624 |
0.009 |
19.619 |
2.123 |
181.263 |
| educ |
0.810 |
0.734 |
1.105 |
0.270 |
2.249 |
0.534 |
9.472 |
| sexualityles/gay |
-1.124 |
0.871 |
-1.291 |
0.197 |
0.325 |
0.059 |
1.792 |
| sexualityother |
-1.247 |
0.696 |
-1.792 |
0.074 |
0.287 |
0.073 |
1.124 |
| genidenmale |
-1.366 |
0.926 |
-1.476 |
0.141 |
0.255 |
0.042 |
1.566 |
| genidennonbin/trans |
1.613 |
0.729 |
2.211 |
0.027 |
5.017 |
1.201 |
20.956 |
| proudlgbcom |
-0.138 |
1.149 |
-0.120 |
0.904 |
0.871 |
0.092 |
8.282 |
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) |
-6.297 |
1.408 |
-4.471 |
0.000 |
0.002 |
0.000 |
0.029 |
| raceblack |
-1.809 |
1.256 |
-1.440 |
0.150 |
0.164 |
0.014 |
1.920 |
| racehispanic |
-1.097 |
1.035 |
-1.060 |
0.289 |
0.334 |
0.044 |
2.538 |
| agecohortmiddle |
3.019 |
1.469 |
2.056 |
0.040 |
20.469 |
1.151 |
364.134 |
| agecohortyounger |
2.854 |
1.171 |
2.438 |
0.015 |
17.359 |
1.750 |
172.152 |
| educ |
0.903 |
0.754 |
1.198 |
0.231 |
2.468 |
0.563 |
10.821 |
| sexualityles/gay |
-1.298 |
0.912 |
-1.423 |
0.155 |
0.273 |
0.046 |
1.633 |
| sexualityother |
-1.325 |
0.636 |
-2.084 |
0.038 |
0.266 |
0.076 |
0.924 |
| genidenmale |
-1.315 |
0.845 |
-1.557 |
0.120 |
0.268 |
0.051 |
1.406 |
| genidennonbin/trans |
1.654 |
0.686 |
2.412 |
0.016 |
5.226 |
1.364 |
20.033 |
| imppolactlgbcom |
1.230 |
0.833 |
1.477 |
0.140 |
3.421 |
0.669 |
17.494 |
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) |
-8.509 |
2.368 |
-3.594 |
0.000 |
0.000 |
0.000 |
0.021 |
| raceblack |
-1.698 |
1.235 |
-1.375 |
0.170 |
0.183 |
0.016 |
2.060 |
| racehispanic |
-1.419 |
1.266 |
-1.120 |
0.263 |
0.242 |
0.020 |
2.896 |
| agecohortmiddle |
2.746 |
1.220 |
2.250 |
0.025 |
15.575 |
1.424 |
170.333 |
| agecohortyounger |
2.858 |
1.153 |
2.479 |
0.013 |
17.429 |
1.819 |
167.034 |
| educ |
0.616 |
0.825 |
0.747 |
0.455 |
1.852 |
0.368 |
9.324 |
| sexualityles/gay |
-1.169 |
1.022 |
-1.144 |
0.253 |
0.311 |
0.042 |
2.303 |
| sexualityother |
-1.182 |
0.739 |
-1.599 |
0.110 |
0.307 |
0.072 |
1.306 |
| genidenmale |
-1.144 |
0.912 |
-1.255 |
0.210 |
0.318 |
0.053 |
1.902 |
| genidennonbin/trans |
1.318 |
0.860 |
1.533 |
0.126 |
3.735 |
0.693 |
20.143 |
| soimp |
0.467 |
0.871 |
0.536 |
0.592 |
1.595 |
0.289 |
8.801 |
| lgbimp |
-0.307 |
1.208 |
-0.254 |
0.799 |
0.735 |
0.069 |
7.853 |
| parlgbcom |
0.854 |
1.189 |
0.718 |
0.473 |
2.350 |
0.228 |
24.165 |
| poslgbcom |
-1.275 |
1.018 |
-1.252 |
0.211 |
0.279 |
0.038 |
2.057 |
| bondlgbcom |
4.324 |
1.624 |
2.663 |
0.008 |
75.519 |
3.132 |
1820.985 |
| proudlgbcom |
-1.989 |
1.316 |
-1.511 |
0.131 |
0.137 |
0.010 |
1.806 |
| imppolactlgbcom |
1.881 |
1.079 |
1.742 |
0.082 |
6.557 |
0.791 |
54.388 |
exp(coefficients(fit.logit1))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.005499169 0.192289755 0.285124090 11.310297876
## agecohortyounger educ sexualityles/gay sexualityother
## 17.142151428 2.116898452 0.198178805 0.502328423
exp(coefficients(fit.logit2))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.002500245 0.181276826 0.262612652 19.261376692
## agecohortyounger educ genidenmale genidennonbin/trans
## 21.180147970 2.265038506 0.186944196 3.546880950
exp(coefficients(fit.logit3))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.004077586 0.180884165 0.304335824 17.097423999
## agecohortyounger educ sexualityles/gay sexualityother
## 19.948820804 2.270341945 0.320247934 0.282810915
## genidenmale genidennonbin/trans
## 0.260612630 5.100582827
exp(coefficients(fit.logit4))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.004812983 0.175094643 0.295633263 18.060819923
## agecohortyounger educ sexualityles/gay sexualityother
## 20.493133508 2.271953637 0.299895626 0.268080160
## genidenmale genidennonbin/trans soimp
## 0.270580416 4.908989491 0.638560570
exp(coefficients(fit.logit5))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.005504497 0.163253493 0.300470595 19.537105739
## agecohortyounger educ sexualityles/gay sexualityother
## 20.039239830 2.413646903 0.286385280 0.296779162
## genidenmale genidennonbin/trans lgbimp
## 0.260652944 4.409571045 0.276671038
exp(coefficients(fit.logit6))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.000970381 0.169291533 0.277770880 21.700166928
## agecohortyounger educ sexualityles/gay sexualityother
## 19.642257009 2.041780434 0.296437890 0.257885502
## genidenmale genidennonbin/trans parlgbcom
## 0.234129512 3.947975613 7.281507831
exp(coefficients(fit.logit7))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.002690346 0.177101280 0.315836538 17.697988602
## agecohortyounger educ sexualityles/gay sexualityother
## 18.693643944 2.245994161 0.293956509 0.264283737
## genidenmale genidennonbin/trans poslgbcom
## 0.275351838 5.088024700 1.809231533
exp(coefficients(fit.logit8))
## (Intercept) raceblack racehispanic agecohortmiddle
## 1.091779e-04 1.760524e-01 2.961870e-01 1.997194e+01
## agecohortyounger educ sexualityles/gay sexualityother
## 2.104324e+01 2.068135e+00 3.386045e-01 2.667314e-01
## genidenmale genidennonbin/trans bondlgbcom
## 3.066091e-01 3.815597e+00 5.932451e+01
exp(coefficients(fit.logit9))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.004681283 0.185586649 0.308158587 16.629342125
## agecohortyounger educ sexualityles/gay sexualityother
## 19.619108889 2.248699426 0.324845296 0.287277070
## genidenmale genidennonbin/trans proudlgbcom
## 0.255126349 5.017393261 0.870795335
exp(coefficients(fit.logit10))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.001842338 0.163895259 0.333758794 20.468955801
## agecohortyounger educ sexualityles/gay sexualityother
## 17.359035097 2.467869801 0.273066040 0.265820864
## genidenmale genidennonbin/trans imppolactlgbcom
## 0.268352424 5.226476067 3.421302359
exp(coefficients(fit.logit11))
## (Intercept) raceblack racehispanic agecohortmiddle
## 2.015875e-04 1.830522e-01 2.420100e-01 1.557490e+01
## agecohortyounger educ sexualityles/gay sexualityother
## 1.742860e+01 1.851619e+00 3.105643e-01 3.066897e-01
## genidenmale genidennonbin/trans soimp lgbimp
## 3.184028e-01 3.734985e+00 1.594762e+00 7.353544e-01
## parlgbcom poslgbcom bondlgbcom proudlgbcom
## 2.349675e+00 2.794277e-01 7.551870e+01 1.368683e-01
## imppolactlgbcom
## 6.557226e+00
## With survey design "interesting cases" Race and Sexual Identity
rg<-ref_grid(fit.logit3)
marg_logit<-emmeans(object = rg,
specs = c( "race", "sexuality"),
type="response" ,
data=sub)
knitr::kable(marg_logit, digits = 4)
| white |
bisexual |
0.0451 |
0.0254 |
Inf |
0.0146 |
0.1306 |
| black |
bisexual |
0.0085 |
0.0084 |
Inf |
0.0012 |
0.0567 |
| hispanic |
bisexual |
0.0142 |
0.0152 |
Inf |
0.0017 |
0.1089 |
| white |
les/gay |
0.0149 |
0.0107 |
Inf |
0.0036 |
0.0592 |
| black |
les/gay |
0.0027 |
0.0045 |
Inf |
0.0001 |
0.0657 |
| hispanic |
les/gay |
0.0046 |
0.0052 |
Inf |
0.0005 |
0.0409 |
| white |
other |
0.0132 |
0.0095 |
Inf |
0.0032 |
0.0529 |
| black |
other |
0.0024 |
0.0030 |
Inf |
0.0002 |
0.0269 |
| hispanic |
other |
0.0040 |
0.0058 |
Inf |
0.0002 |
0.0629 |
## With survey design "interesting cases" Race and Gender Identity
rg<-ref_grid(fit.logit3)
marg_logit<-emmeans(object = rg,
specs = c( "race", "geniden"),
type="response" ,
data=sub)
knitr::kable(marg_logit, digits = 4)
| white |
female |
0.0189 |
0.0099 |
Inf |
0.0067 |
0.0520 |
| black |
female |
0.0035 |
0.0048 |
Inf |
0.0002 |
0.0498 |
| hispanic |
female |
0.0058 |
0.0064 |
Inf |
0.0007 |
0.0480 |
| white |
male |
0.0050 |
0.0044 |
Inf |
0.0009 |
0.0272 |
| black |
male |
0.0009 |
0.0012 |
Inf |
0.0001 |
0.0127 |
| hispanic |
male |
0.0015 |
0.0020 |
Inf |
0.0001 |
0.0194 |
| white |
nonbin/trans |
0.0895 |
0.0516 |
Inf |
0.0277 |
0.2537 |
| black |
nonbin/trans |
0.0175 |
0.0217 |
Inf |
0.0015 |
0.1742 |
| hispanic |
nonbin/trans |
0.0291 |
0.0364 |
Inf |
0.0024 |
0.2729 |
## With survey design "interesting cases" Race and Age Cohort
rg<-ref_grid(fit.logit3)
marg_logit<-emmeans(object = rg,
specs = c( "race", "agecohort"),
type="response" ,
data=sub)
knitr::kable(marg_logit, digits = 4)
| white |
older |
0.0030 |
0.0034 |
Inf |
0.0003 |
0.0266 |
| black |
older |
0.0005 |
0.0009 |
Inf |
0.0000 |
0.0133 |
| hispanic |
older |
0.0009 |
0.0014 |
Inf |
0.0000 |
0.0186 |
| white |
middle |
0.0493 |
0.0390 |
Inf |
0.0101 |
0.2092 |
| black |
middle |
0.0093 |
0.0121 |
Inf |
0.0007 |
0.1088 |
| hispanic |
middle |
0.0155 |
0.0202 |
Inf |
0.0012 |
0.1733 |
| white |
younger |
0.0571 |
0.0238 |
Inf |
0.0248 |
0.1259 |
| black |
younger |
0.0108 |
0.0137 |
Inf |
0.0009 |
0.1186 |
| hispanic |
younger |
0.0181 |
0.0190 |
Inf |
0.0023 |
0.1306 |
options(survey.lonely.psu = "adjust")
des<-svydesign(ids= ~1,
weights= ~wave3weight
, data = sub )
fit.logit12<-svyglm(plan_death ~ race + agecohort + educ + sexuality,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit13<-svyglm(plan_death ~ race + agecohort + educ + geniden,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit14<-svyglm(plan_death ~ race + agecohort + educ + sexuality + geniden,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit15<-svyglm(plan_death ~ race + agecohort + educ + sexuality + geniden + soimp,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit16<-svyglm(plan_death ~ race + agecohort + educ + sexuality + geniden + lgbimp,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit17<-svyglm(plan_death ~ race + agecohort + educ + sexuality + geniden + parlgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit18<-svyglm(plan_death ~ race + agecohort + educ + sexuality + geniden + poslgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit19<-svyglm(plan_death ~ race + agecohort + educ + sexuality + geniden + bondlgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit20<-svyglm(plan_death ~ race + agecohort + educ + sexuality + geniden + proudlgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit21<-svyglm(plan_death ~ race + agecohort + educ + sexuality + geniden + imppolactlgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit22<-svyglm(plan_death ~ race + agecohort + educ + sexuality + geniden + soimp + lgbimp + parlgbcom + poslgbcom + bondlgbcom + proudlgbcom + imppolactlgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
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) |
-0.646 |
0.319 |
-2.027 |
0.043 |
0.524 |
0.281 |
0.979 |
| raceblack |
-0.246 |
0.406 |
-0.606 |
0.545 |
0.782 |
0.353 |
1.732 |
| racehispanic |
0.411 |
0.339 |
1.211 |
0.226 |
1.508 |
0.776 |
2.931 |
| agecohortmiddle |
-0.734 |
0.369 |
-1.988 |
0.047 |
0.480 |
0.233 |
0.990 |
| agecohortyounger |
0.049 |
0.320 |
0.155 |
0.877 |
1.051 |
0.561 |
1.966 |
| educ |
0.433 |
0.283 |
1.530 |
0.127 |
1.542 |
0.885 |
2.685 |
| sexualityles/gay |
-1.039 |
0.313 |
-3.324 |
0.001 |
0.354 |
0.192 |
0.653 |
| sexualityother |
0.616 |
0.369 |
1.668 |
0.096 |
1.852 |
0.898 |
3.818 |
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) |
-1.364 |
0.243 |
-5.624 |
0.000 |
0.256 |
0.159 |
0.411 |
| raceblack |
-0.293 |
0.426 |
-0.689 |
0.491 |
0.746 |
0.323 |
1.719 |
| racehispanic |
0.278 |
0.327 |
0.848 |
0.397 |
1.320 |
0.695 |
2.508 |
| agecohortmiddle |
-0.194 |
0.364 |
-0.534 |
0.593 |
0.823 |
0.404 |
1.680 |
| agecohortyounger |
0.602 |
0.258 |
2.330 |
0.020 |
1.826 |
1.100 |
3.030 |
| educ |
0.372 |
0.277 |
1.341 |
0.180 |
1.450 |
0.842 |
2.498 |
| genidenmale |
-0.301 |
0.276 |
-1.094 |
0.275 |
0.740 |
0.431 |
1.270 |
| genidennonbin/trans |
1.125 |
0.405 |
2.774 |
0.006 |
3.080 |
1.391 |
6.819 |
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) |
-0.751 |
0.329 |
-2.282 |
0.023 |
0.472 |
0.248 |
0.899 |
| raceblack |
-0.307 |
0.428 |
-0.717 |
0.474 |
0.736 |
0.318 |
1.702 |
| racehispanic |
0.399 |
0.332 |
1.201 |
0.230 |
1.490 |
0.777 |
2.856 |
| agecohortmiddle |
-0.688 |
0.363 |
-1.895 |
0.059 |
0.503 |
0.247 |
1.024 |
| agecohortyounger |
0.036 |
0.314 |
0.115 |
0.909 |
1.037 |
0.560 |
1.919 |
| educ |
0.436 |
0.281 |
1.552 |
0.121 |
1.547 |
0.891 |
2.685 |
| sexualityles/gay |
-1.138 |
0.316 |
-3.604 |
0.000 |
0.320 |
0.173 |
0.595 |
| sexualityother |
0.398 |
0.393 |
1.013 |
0.312 |
1.489 |
0.689 |
3.219 |
| genidenmale |
0.242 |
0.292 |
0.829 |
0.407 |
1.274 |
0.719 |
2.256 |
| genidennonbin/trans |
0.990 |
0.413 |
2.400 |
0.017 |
2.692 |
1.199 |
6.043 |
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) |
-0.730 |
0.361 |
-2.022 |
0.044 |
0.482 |
0.238 |
0.978 |
| raceblack |
-0.308 |
0.428 |
-0.719 |
0.472 |
0.735 |
0.318 |
1.701 |
| racehispanic |
0.395 |
0.330 |
1.196 |
0.232 |
1.484 |
0.777 |
2.836 |
| agecohortmiddle |
-0.689 |
0.362 |
-1.901 |
0.058 |
0.502 |
0.247 |
1.022 |
| agecohortyounger |
0.035 |
0.314 |
0.113 |
0.910 |
1.036 |
0.559 |
1.919 |
| educ |
0.437 |
0.281 |
1.556 |
0.120 |
1.549 |
0.893 |
2.686 |
| sexualityles/gay |
-1.144 |
0.321 |
-3.558 |
0.000 |
0.319 |
0.170 |
0.598 |
| sexualityother |
0.398 |
0.393 |
1.012 |
0.312 |
1.488 |
0.689 |
3.213 |
| genidenmale |
0.247 |
0.295 |
0.838 |
0.402 |
1.281 |
0.718 |
2.283 |
| genidennonbin/trans |
0.982 |
0.414 |
2.374 |
0.018 |
2.671 |
1.187 |
6.011 |
| soimp |
-0.044 |
0.276 |
-0.160 |
0.873 |
0.957 |
0.557 |
1.643 |
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) |
-0.697 |
0.365 |
-1.910 |
0.057 |
0.498 |
0.243 |
1.019 |
| raceblack |
-0.314 |
0.433 |
-0.726 |
0.468 |
0.730 |
0.313 |
1.707 |
| racehispanic |
0.399 |
0.331 |
1.207 |
0.228 |
1.490 |
0.780 |
2.849 |
| agecohortmiddle |
-0.684 |
0.361 |
-1.892 |
0.059 |
0.505 |
0.248 |
1.025 |
| agecohortyounger |
0.035 |
0.315 |
0.111 |
0.911 |
1.036 |
0.558 |
1.922 |
| educ |
0.441 |
0.280 |
1.577 |
0.115 |
1.554 |
0.898 |
2.688 |
| sexualityles/gay |
-1.165 |
0.333 |
-3.502 |
0.000 |
0.312 |
0.163 |
0.599 |
| sexualityother |
0.394 |
0.389 |
1.013 |
0.312 |
1.483 |
0.692 |
3.180 |
| genidenmale |
0.249 |
0.294 |
0.848 |
0.397 |
1.283 |
0.721 |
2.281 |
| genidennonbin/trans |
0.965 |
0.412 |
2.342 |
0.020 |
2.626 |
1.170 |
5.891 |
| lgbimp |
-0.132 |
0.311 |
-0.426 |
0.670 |
0.876 |
0.476 |
1.611 |
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) |
-0.563 |
0.340 |
-1.655 |
0.098 |
0.570 |
0.292 |
1.109 |
| raceblack |
-0.284 |
0.436 |
-0.650 |
0.516 |
0.753 |
0.320 |
1.770 |
| racehispanic |
0.448 |
0.332 |
1.347 |
0.179 |
1.565 |
0.816 |
3.001 |
| agecohortmiddle |
-0.720 |
0.367 |
-1.961 |
0.050 |
0.487 |
0.237 |
1.000 |
| agecohortyounger |
0.032 |
0.309 |
0.104 |
0.918 |
1.033 |
0.563 |
1.893 |
| educ |
0.461 |
0.287 |
1.604 |
0.109 |
1.586 |
0.903 |
2.785 |
| sexualityles/gay |
-1.119 |
0.318 |
-3.520 |
0.000 |
0.327 |
0.175 |
0.609 |
| sexualityother |
0.436 |
0.405 |
1.076 |
0.282 |
1.546 |
0.699 |
3.418 |
| genidenmale |
0.227 |
0.294 |
0.773 |
0.440 |
1.255 |
0.705 |
2.234 |
| genidennonbin/trans |
1.051 |
0.421 |
2.498 |
0.013 |
2.861 |
1.254 |
6.528 |
| parlgbcom |
-0.348 |
0.288 |
-1.208 |
0.227 |
0.706 |
0.401 |
1.242 |
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) |
-0.308 |
0.386 |
-0.798 |
0.425 |
0.735 |
0.345 |
1.565 |
| raceblack |
-0.295 |
0.431 |
-0.683 |
0.495 |
0.745 |
0.320 |
1.734 |
| racehispanic |
0.481 |
0.333 |
1.443 |
0.150 |
1.617 |
0.842 |
3.107 |
| agecohortmiddle |
-0.726 |
0.354 |
-2.052 |
0.041 |
0.484 |
0.242 |
0.968 |
| agecohortyounger |
0.099 |
0.313 |
0.315 |
0.753 |
1.104 |
0.598 |
2.037 |
| educ |
0.426 |
0.283 |
1.507 |
0.132 |
1.531 |
0.880 |
2.664 |
| sexualityles/gay |
-1.110 |
0.317 |
-3.497 |
0.001 |
0.330 |
0.177 |
0.614 |
| sexualityother |
0.464 |
0.403 |
1.151 |
0.250 |
1.591 |
0.722 |
3.507 |
| genidenmale |
0.190 |
0.294 |
0.645 |
0.519 |
1.209 |
0.679 |
2.151 |
| genidennonbin/trans |
0.990 |
0.420 |
2.358 |
0.019 |
2.690 |
1.182 |
6.125 |
| poslgbcom |
-0.649 |
0.321 |
-2.023 |
0.043 |
0.523 |
0.279 |
0.980 |
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) |
-0.674 |
0.353 |
-1.909 |
0.057 |
0.510 |
0.255 |
1.018 |
| raceblack |
-0.297 |
0.429 |
-0.692 |
0.489 |
0.743 |
0.320 |
1.723 |
| racehispanic |
0.409 |
0.332 |
1.232 |
0.219 |
1.505 |
0.785 |
2.884 |
| agecohortmiddle |
-0.706 |
0.364 |
-1.938 |
0.053 |
0.494 |
0.242 |
1.008 |
| agecohortyounger |
0.022 |
0.310 |
0.071 |
0.943 |
1.022 |
0.556 |
1.879 |
| educ |
0.436 |
0.282 |
1.548 |
0.122 |
1.547 |
0.890 |
2.689 |
| sexualityles/gay |
-1.143 |
0.313 |
-3.653 |
0.000 |
0.319 |
0.173 |
0.589 |
| sexualityother |
0.406 |
0.395 |
1.027 |
0.305 |
1.500 |
0.692 |
3.254 |
| genidenmale |
0.233 |
0.290 |
0.802 |
0.423 |
1.262 |
0.715 |
2.228 |
| genidennonbin/trans |
1.011 |
0.418 |
2.416 |
0.016 |
2.748 |
1.210 |
6.241 |
| bondlgbcom |
-0.105 |
0.282 |
-0.372 |
0.710 |
0.900 |
0.518 |
1.565 |
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) |
-0.199 |
0.479 |
-0.416 |
0.677 |
0.819 |
0.321 |
2.093 |
| raceblack |
-0.218 |
0.427 |
-0.509 |
0.611 |
0.804 |
0.348 |
1.859 |
| racehispanic |
0.451 |
0.331 |
1.361 |
0.174 |
1.569 |
0.820 |
3.004 |
| agecohortmiddle |
-0.747 |
0.355 |
-2.104 |
0.036 |
0.474 |
0.236 |
0.950 |
| agecohortyounger |
-0.016 |
0.313 |
-0.050 |
0.960 |
0.985 |
0.533 |
1.818 |
| educ |
0.427 |
0.281 |
1.521 |
0.129 |
1.533 |
0.884 |
2.657 |
| sexualityles/gay |
-1.175 |
0.314 |
-3.744 |
0.000 |
0.309 |
0.167 |
0.571 |
| sexualityother |
0.394 |
0.400 |
0.986 |
0.325 |
1.483 |
0.677 |
3.249 |
| genidenmale |
0.191 |
0.289 |
0.662 |
0.509 |
1.211 |
0.687 |
2.134 |
| genidennonbin/trans |
0.982 |
0.416 |
2.362 |
0.018 |
2.670 |
1.182 |
6.031 |
| proudlgbcom |
-0.579 |
0.399 |
-1.451 |
0.147 |
0.561 |
0.257 |
1.225 |
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) |
-0.696 |
0.348 |
-1.999 |
0.046 |
0.498 |
0.252 |
0.987 |
| raceblack |
-0.300 |
0.430 |
-0.699 |
0.485 |
0.741 |
0.319 |
1.719 |
| racehispanic |
0.411 |
0.330 |
1.244 |
0.214 |
1.508 |
0.790 |
2.878 |
| agecohortmiddle |
-0.694 |
0.361 |
-1.921 |
0.055 |
0.500 |
0.246 |
1.014 |
| agecohortyounger |
0.042 |
0.315 |
0.133 |
0.894 |
1.043 |
0.562 |
1.934 |
| educ |
0.433 |
0.281 |
1.539 |
0.124 |
1.541 |
0.888 |
2.674 |
| sexualityles/gay |
-1.133 |
0.316 |
-3.590 |
0.000 |
0.322 |
0.174 |
0.598 |
| sexualityother |
0.397 |
0.395 |
1.006 |
0.315 |
1.488 |
0.686 |
3.226 |
| genidenmale |
0.234 |
0.292 |
0.802 |
0.423 |
1.264 |
0.713 |
2.241 |
| genidennonbin/trans |
0.992 |
0.413 |
2.404 |
0.017 |
2.697 |
1.201 |
6.057 |
| imppolactlgbcom |
-0.100 |
0.273 |
-0.367 |
0.714 |
0.905 |
0.530 |
1.544 |
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) |
0.179 |
0.595 |
0.301 |
0.764 |
1.196 |
0.372 |
3.842 |
| raceblack |
-0.247 |
0.441 |
-0.561 |
0.575 |
0.781 |
0.329 |
1.852 |
| racehispanic |
0.540 |
0.327 |
1.650 |
0.099 |
1.716 |
0.903 |
3.261 |
| agecohortmiddle |
-0.709 |
0.351 |
-2.020 |
0.044 |
0.492 |
0.247 |
0.979 |
| agecohortyounger |
0.115 |
0.314 |
0.366 |
0.715 |
1.122 |
0.606 |
2.075 |
| educ |
0.473 |
0.281 |
1.686 |
0.092 |
1.605 |
0.926 |
2.782 |
| sexualityles/gay |
-1.174 |
0.341 |
-3.448 |
0.001 |
0.309 |
0.159 |
0.602 |
| sexualityother |
0.469 |
0.398 |
1.178 |
0.239 |
1.598 |
0.732 |
3.487 |
| genidenmale |
0.200 |
0.298 |
0.671 |
0.502 |
1.221 |
0.681 |
2.188 |
| genidennonbin/trans |
0.899 |
0.432 |
2.080 |
0.038 |
2.458 |
1.053 |
5.736 |
| soimp |
-0.032 |
0.318 |
-0.101 |
0.919 |
0.968 |
0.520 |
1.804 |
| lgbimp |
-0.379 |
0.360 |
-1.052 |
0.293 |
0.685 |
0.338 |
1.387 |
| parlgbcom |
-0.534 |
0.371 |
-1.440 |
0.150 |
0.586 |
0.283 |
1.213 |
| poslgbcom |
-0.594 |
0.402 |
-1.476 |
0.140 |
0.552 |
0.251 |
1.215 |
| bondlgbcom |
0.497 |
0.375 |
1.323 |
0.186 |
1.643 |
0.787 |
3.429 |
| proudlgbcom |
-0.488 |
0.453 |
-1.078 |
0.282 |
0.614 |
0.253 |
1.491 |
| imppolactlgbcom |
0.031 |
0.317 |
0.097 |
0.923 |
1.031 |
0.554 |
1.920 |
exp(coefficients(fit.logit12))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.5242510 0.7821790 1.5078522 0.4799200
## agecohortyounger educ sexualityles/gay sexualityother
## 1.0506355 1.5417733 0.3538735 1.8516040
exp(coefficients(fit.logit13))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.2556736 0.7456632 1.3200158 0.8233836
## agecohortyounger educ genidenmale genidennonbin/trans
## 1.8258448 1.4503990 0.7397830 3.0800862
exp(coefficients(fit.logit14))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.4718941 0.7359515 1.4899319 0.5027265
## agecohortyounger educ sexualityles/gay sexualityother
## 1.0367100 1.5471783 0.3204104 1.4892836
## genidenmale genidennonbin/trans
## 1.2735672 2.6921217
exp(coefficients(fit.logit15))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.4818757 0.7349503 1.4843132 0.5021612
## agecohortyounger educ sexualityles/gay sexualityother
## 1.0360167 1.5485247 0.3186505 1.4881666
## genidenmale genidennonbin/trans soimp
## 1.2806169 2.6711200 0.9568336
exp(coefficients(fit.logit16))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.4979958 0.7304185 1.4902361 0.5046742
## agecohortyounger educ sexualityles/gay sexualityother
## 1.0357094 1.5538818 0.3120761 1.4829223
## genidenmale genidennonbin/trans lgbimp
## 1.2827745 2.6258824 0.8759820
exp(coefficients(fit.logit17))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.5695215 0.7530911 1.5645426 0.4867161
## agecohortyounger educ sexualityles/gay sexualityother
## 1.0325527 1.5855236 0.3267290 1.5459633
## genidenmale genidennonbin/trans parlgbcom
## 1.2554279 2.8614471 0.7059730
exp(coefficients(fit.logit18))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.7348701 0.7447347 1.6170974 0.4838065
## agecohortyounger educ sexualityles/gay sexualityother
## 1.1035219 1.5310584 0.3296387 1.5909399
## genidenmale genidennonbin/trans poslgbcom
## 1.2088617 2.6902937 0.5226908
exp(coefficients(fit.logit19))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.5097530 0.7428186 1.5049816 0.4938386
## agecohortyounger educ sexualityles/gay sexualityother
## 1.0224169 1.5472490 0.3189972 1.5001056
## genidenmale genidennonbin/trans bondlgbcom
## 1.2620109 2.7483685 0.9003813
exp(coefficients(fit.logit20))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.8193711 0.8044505 1.5694415 0.4737265
## agecohortyounger educ sexualityles/gay sexualityother
## 0.9845538 1.5326392 0.3087767 1.4832254
## genidenmale genidennonbin/trans proudlgbcom
## 1.2107340 2.6697213 0.5605741
exp(coefficients(fit.logit21))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.4984092 0.7405435 1.5076435 0.4996785
## agecohortyounger educ sexualityles/gay sexualityother
## 1.0429468 1.5413294 0.3221427 1.4878516
## genidenmale genidennonbin/trans imppolactlgbcom
## 1.2641041 2.6972851 0.9047796
exp(coefficients(fit.logit22))
## (Intercept) raceblack racehispanic agecohortmiddle
## 1.1959627 0.7809377 1.7164385 0.4920133
## agecohortyounger educ sexualityles/gay sexualityother
## 1.1216699 1.6051639 0.3090498 1.5981123
## genidenmale genidennonbin/trans soimp lgbimp
## 1.2210942 2.4579355 0.9683607 0.6846082
## parlgbcom poslgbcom bondlgbcom proudlgbcom
## 0.5859641 0.5520720 1.6430004 0.6138251
## imppolactlgbcom
## 1.0312187
## With survey design "interesting cases" Race and Sexual Identity
rg<-ref_grid(fit.logit14)
marg_logit<-emmeans(object = rg,
specs = c( "race", "sexuality"),
type="response" ,
data=sub)
knitr::kable(marg_logit, digits = 4)
| white |
bisexual |
0.4160 |
0.0662 |
Inf |
0.2946 |
0.5485 |
| black |
bisexual |
0.3439 |
0.0998 |
Inf |
0.1805 |
0.5551 |
| hispanic |
bisexual |
0.5149 |
0.0962 |
Inf |
0.3328 |
0.6930 |
| white |
les/gay |
0.1858 |
0.0386 |
Inf |
0.1215 |
0.2735 |
| black |
les/gay |
0.1438 |
0.0518 |
Inf |
0.0686 |
0.2769 |
| hispanic |
les/gay |
0.2538 |
0.0621 |
Inf |
0.1517 |
0.3927 |
| white |
other |
0.5147 |
0.0842 |
Inf |
0.3540 |
0.6725 |
| black |
other |
0.4384 |
0.1243 |
Inf |
0.2249 |
0.6774 |
| hispanic |
other |
0.6125 |
0.1095 |
Inf |
0.3902 |
0.7961 |
## With survey design "interesting cases" Race and Gender Identity
rg<-ref_grid(fit.logit14)
marg_logit<-emmeans(object = rg,
specs = c( "race", "geniden"),
type="response" ,
data=sub)
knitr::kable(marg_logit, digits = 4)
| white |
female |
0.2696 |
0.0440 |
Inf |
0.1924 |
0.3639 |
| black |
female |
0.2136 |
0.0702 |
Inf |
0.1069 |
0.3813 |
| hispanic |
female |
0.3548 |
0.0809 |
Inf |
0.2158 |
0.5236 |
| white |
male |
0.3198 |
0.0571 |
Inf |
0.2194 |
0.4401 |
| black |
male |
0.2570 |
0.0818 |
Inf |
0.1300 |
0.4447 |
| hispanic |
male |
0.4119 |
0.0907 |
Inf |
0.2517 |
0.5933 |
| white |
nonbin/trans |
0.4984 |
0.0933 |
Inf |
0.3235 |
0.6738 |
| black |
nonbin/trans |
0.4224 |
0.1278 |
Inf |
0.2076 |
0.6712 |
| hispanic |
nonbin/trans |
0.5969 |
0.1100 |
Inf |
0.3767 |
0.7839 |
## With survey design "interesting cases" Race and Age Cohort
rg<-ref_grid(fit.logit14)
marg_logit<-emmeans(object = rg,
specs = c( "race", "agecohort"),
type="response" ,
data=sub)
knitr::kable(marg_logit, digits = 4)
| white |
older |
0.4089 |
0.0644 |
Inf |
0.2909 |
0.5383 |
| black |
older |
0.3373 |
0.1053 |
Inf |
0.1682 |
0.5617 |
| hispanic |
older |
0.5075 |
0.1055 |
Inf |
0.3107 |
0.7020 |
| white |
middle |
0.2580 |
0.0628 |
Inf |
0.1545 |
0.3982 |
| black |
middle |
0.2038 |
0.0739 |
Inf |
0.0948 |
0.3847 |
| hispanic |
middle |
0.3413 |
0.0939 |
Inf |
0.1860 |
0.5402 |
| white |
younger |
0.4176 |
0.0517 |
Inf |
0.3209 |
0.5211 |
| black |
younger |
0.3454 |
0.0929 |
Inf |
0.1909 |
0.5414 |
| hispanic |
younger |
0.5165 |
0.0738 |
Inf |
0.3744 |
0.6560 |
options(survey.lonely.psu = "adjust")
des<-svydesign(ids= ~1,
weights= ~wave3weight
, data = sub )
fit.logit23<-svyglm(wish_death ~ race + agecohort + educ + sexuality,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit24<-svyglm(wish_death ~ race + agecohort + educ + geniden,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit25<-svyglm(wish_death ~ race + agecohort + educ + sexuality + geniden,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit26<-svyglm(wish_death ~ race + agecohort + educ + sexuality + geniden + soimp,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit27<-svyglm(wish_death ~ race + agecohort + educ + sexuality + geniden + lgbimp,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit28<-svyglm(wish_death ~ race + agecohort + educ + sexuality + geniden + parlgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit29<-svyglm(wish_death ~ race + agecohort + educ + sexuality + geniden + poslgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit30<-svyglm(wish_death ~ race + agecohort + educ + sexuality + geniden + bondlgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit31<-svyglm(wish_death ~ race + agecohort + educ + sexuality + geniden + proudlgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit32<-svyglm(wish_death ~ race + agecohort + educ + sexuality + geniden + imppolactlgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit33<-svyglm(wish_death ~ race + agecohort + educ + sexuality + geniden + soimp + lgbimp + parlgbcom + poslgbcom + bondlgbcom + proudlgbcom + imppolactlgbcom,
design = des,
family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
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) |
-2.239 |
0.475 |
-4.717 |
0.000 |
0.107 |
0.042 |
0.270 |
| raceblack |
0.316 |
0.515 |
0.613 |
0.540 |
1.371 |
0.500 |
3.763 |
| racehispanic |
0.261 |
0.435 |
0.600 |
0.549 |
1.298 |
0.553 |
3.047 |
| agecohortmiddle |
-0.628 |
0.667 |
-0.942 |
0.347 |
0.534 |
0.144 |
1.972 |
| agecohortyounger |
0.494 |
0.479 |
1.032 |
0.303 |
1.639 |
0.641 |
4.189 |
| educ |
1.161 |
0.352 |
3.297 |
0.001 |
3.192 |
1.601 |
6.365 |
| sexualityles/gay |
-0.906 |
0.434 |
-2.088 |
0.037 |
0.404 |
0.173 |
0.946 |
| sexualityother |
-0.193 |
0.492 |
-0.393 |
0.694 |
0.824 |
0.314 |
2.162 |
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) |
-2.927 |
0.407 |
-7.185 |
0.000 |
0.054 |
0.024 |
0.119 |
| raceblack |
0.341 |
0.513 |
0.664 |
0.507 |
1.406 |
0.514 |
3.845 |
| racehispanic |
0.170 |
0.449 |
0.379 |
0.705 |
1.186 |
0.491 |
2.861 |
| agecohortmiddle |
-0.229 |
0.659 |
-0.347 |
0.728 |
0.795 |
0.219 |
2.893 |
| agecohortyounger |
0.842 |
0.409 |
2.056 |
0.040 |
2.320 |
1.040 |
5.176 |
| educ |
1.090 |
0.343 |
3.183 |
0.002 |
2.975 |
1.520 |
5.822 |
| genidenmale |
-0.166 |
0.389 |
-0.428 |
0.669 |
0.847 |
0.395 |
1.814 |
| genidennonbin/trans |
0.808 |
0.542 |
1.490 |
0.137 |
2.243 |
0.775 |
6.491 |
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) |
-2.353 |
0.471 |
-5.000 |
0.000 |
0.095 |
0.038 |
0.239 |
| raceblack |
0.239 |
0.531 |
0.449 |
0.653 |
1.270 |
0.448 |
3.596 |
| racehispanic |
0.242 |
0.441 |
0.548 |
0.584 |
1.273 |
0.536 |
3.023 |
| agecohortmiddle |
-0.540 |
0.643 |
-0.840 |
0.401 |
0.583 |
0.165 |
2.055 |
| agecohortyounger |
0.499 |
0.457 |
1.093 |
0.275 |
1.647 |
0.673 |
4.031 |
| educ |
1.171 |
0.357 |
3.278 |
0.001 |
3.225 |
1.601 |
6.493 |
| sexualityles/gay |
-0.983 |
0.468 |
-2.101 |
0.036 |
0.374 |
0.150 |
0.936 |
| sexualityother |
-0.454 |
0.526 |
-0.864 |
0.388 |
0.635 |
0.227 |
1.780 |
| genidenmale |
0.173 |
0.422 |
0.409 |
0.682 |
1.188 |
0.520 |
2.716 |
| genidennonbin/trans |
0.961 |
0.557 |
1.726 |
0.085 |
2.615 |
0.878 |
7.792 |
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) |
-2.266 |
0.525 |
-4.316 |
0.000 |
0.104 |
0.037 |
0.290 |
| raceblack |
0.234 |
0.523 |
0.449 |
0.654 |
1.264 |
0.454 |
3.521 |
| racehispanic |
0.224 |
0.444 |
0.505 |
0.614 |
1.251 |
0.524 |
2.988 |
| agecohortmiddle |
-0.555 |
0.646 |
-0.858 |
0.391 |
0.574 |
0.162 |
2.039 |
| agecohortyounger |
0.490 |
0.459 |
1.066 |
0.287 |
1.632 |
0.663 |
4.014 |
| educ |
1.173 |
0.356 |
3.291 |
0.001 |
3.230 |
1.607 |
6.495 |
| sexualityles/gay |
-1.007 |
0.487 |
-2.069 |
0.039 |
0.365 |
0.141 |
0.948 |
| sexualityother |
-0.453 |
0.526 |
-0.861 |
0.389 |
0.635 |
0.226 |
1.783 |
| genidenmale |
0.192 |
0.432 |
0.445 |
0.656 |
1.212 |
0.520 |
2.825 |
| genidennonbin/trans |
0.923 |
0.558 |
1.654 |
0.099 |
2.517 |
0.843 |
7.518 |
| soimp |
-0.164 |
0.392 |
-0.418 |
0.676 |
0.849 |
0.393 |
1.832 |
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) |
-2.326 |
0.519 |
-4.479 |
0.000 |
0.098 |
0.035 |
0.270 |
| raceblack |
0.235 |
0.532 |
0.443 |
0.658 |
1.265 |
0.446 |
3.586 |
| racehispanic |
0.241 |
0.443 |
0.544 |
0.587 |
1.273 |
0.534 |
3.032 |
| agecohortmiddle |
-0.540 |
0.642 |
-0.841 |
0.400 |
0.583 |
0.166 |
2.050 |
| agecohortyounger |
0.499 |
0.457 |
1.093 |
0.275 |
1.648 |
0.673 |
4.034 |
| educ |
1.174 |
0.359 |
3.269 |
0.001 |
3.234 |
1.600 |
6.536 |
| sexualityles/gay |
-0.997 |
0.501 |
-1.989 |
0.047 |
0.369 |
0.138 |
0.986 |
| sexualityother |
-0.452 |
0.525 |
-0.862 |
0.389 |
0.636 |
0.228 |
1.779 |
| genidenmale |
0.176 |
0.426 |
0.413 |
0.680 |
1.192 |
0.517 |
2.747 |
| genidennonbin/trans |
0.944 |
0.563 |
1.678 |
0.094 |
2.571 |
0.853 |
7.747 |
| lgbimp |
-0.068 |
0.423 |
-0.161 |
0.872 |
0.934 |
0.407 |
2.142 |
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) |
-2.335 |
0.478 |
-4.886 |
0.000 |
0.097 |
0.038 |
0.247 |
| raceblack |
0.240 |
0.534 |
0.449 |
0.654 |
1.271 |
0.446 |
3.623 |
| racehispanic |
0.246 |
0.447 |
0.549 |
0.583 |
1.278 |
0.532 |
3.073 |
| agecohortmiddle |
-0.541 |
0.645 |
-0.839 |
0.402 |
0.582 |
0.164 |
2.060 |
| agecohortyounger |
0.498 |
0.454 |
1.096 |
0.273 |
1.646 |
0.675 |
4.011 |
| educ |
1.173 |
0.356 |
3.295 |
0.001 |
3.231 |
1.608 |
6.489 |
| sexualityles/gay |
-0.982 |
0.473 |
-2.074 |
0.038 |
0.375 |
0.148 |
0.947 |
| sexualityother |
-0.451 |
0.526 |
-0.857 |
0.392 |
0.637 |
0.227 |
1.786 |
| genidenmale |
0.172 |
0.422 |
0.407 |
0.684 |
1.187 |
0.520 |
2.713 |
| genidennonbin/trans |
0.968 |
0.566 |
1.711 |
0.088 |
2.632 |
0.868 |
7.979 |
| parlgbcom |
-0.032 |
0.392 |
-0.081 |
0.936 |
0.969 |
0.449 |
2.090 |
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) |
-2.070 |
0.514 |
-4.030 |
0.000 |
0.126 |
0.046 |
0.345 |
| raceblack |
0.246 |
0.536 |
0.460 |
0.646 |
1.279 |
0.448 |
3.655 |
| racehispanic |
0.276 |
0.442 |
0.623 |
0.533 |
1.317 |
0.554 |
3.134 |
| agecohortmiddle |
-0.535 |
0.620 |
-0.863 |
0.388 |
0.586 |
0.174 |
1.973 |
| agecohortyounger |
0.538 |
0.448 |
1.200 |
0.230 |
1.712 |
0.712 |
4.121 |
| educ |
1.164 |
0.357 |
3.258 |
0.001 |
3.201 |
1.590 |
6.447 |
| sexualityles/gay |
-0.953 |
0.471 |
-2.022 |
0.044 |
0.386 |
0.153 |
0.971 |
| sexualityother |
-0.395 |
0.526 |
-0.750 |
0.454 |
0.674 |
0.240 |
1.891 |
| genidenmale |
0.133 |
0.425 |
0.314 |
0.754 |
1.143 |
0.497 |
2.628 |
| genidennonbin/trans |
0.955 |
0.556 |
1.719 |
0.086 |
2.599 |
0.874 |
7.728 |
| poslgbcom |
-0.421 |
0.436 |
-0.965 |
0.335 |
0.657 |
0.279 |
1.543 |
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) |
-2.443 |
0.514 |
-4.755 |
0.000 |
0.087 |
0.032 |
0.238 |
| raceblack |
0.232 |
0.528 |
0.439 |
0.661 |
1.261 |
0.448 |
3.550 |
| racehispanic |
0.232 |
0.444 |
0.523 |
0.601 |
1.261 |
0.528 |
3.013 |
| agecohortmiddle |
-0.528 |
0.642 |
-0.823 |
0.411 |
0.590 |
0.168 |
2.074 |
| agecohortyounger |
0.516 |
0.456 |
1.133 |
0.258 |
1.676 |
0.686 |
4.095 |
| educ |
1.173 |
0.356 |
3.294 |
0.001 |
3.232 |
1.608 |
6.496 |
| sexualityles/gay |
-0.977 |
0.467 |
-2.094 |
0.037 |
0.376 |
0.151 |
0.939 |
| sexualityother |
-0.464 |
0.523 |
-0.887 |
0.375 |
0.629 |
0.225 |
1.754 |
| genidenmale |
0.184 |
0.419 |
0.438 |
0.662 |
1.202 |
0.528 |
2.732 |
| genidennonbin/trans |
0.935 |
0.573 |
1.630 |
0.104 |
2.547 |
0.828 |
7.836 |
| bondlgbcom |
0.117 |
0.388 |
0.301 |
0.763 |
1.124 |
0.526 |
2.402 |
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) |
-1.742 |
0.683 |
-2.551 |
0.011 |
0.175 |
0.046 |
0.668 |
| raceblack |
0.349 |
0.533 |
0.656 |
0.512 |
1.418 |
0.499 |
4.028 |
| racehispanic |
0.294 |
0.442 |
0.664 |
0.507 |
1.341 |
0.564 |
3.191 |
| agecohortmiddle |
-0.629 |
0.596 |
-1.056 |
0.291 |
0.533 |
0.166 |
1.714 |
| agecohortyounger |
0.426 |
0.460 |
0.927 |
0.354 |
1.531 |
0.622 |
3.768 |
| educ |
1.161 |
0.356 |
3.261 |
0.001 |
3.194 |
1.589 |
6.420 |
| sexualityles/gay |
-1.020 |
0.469 |
-2.177 |
0.030 |
0.360 |
0.144 |
0.903 |
| sexualityother |
-0.460 |
0.529 |
-0.869 |
0.385 |
0.631 |
0.224 |
1.781 |
| genidenmale |
0.115 |
0.429 |
0.268 |
0.788 |
1.122 |
0.484 |
2.599 |
| genidennonbin/trans |
0.958 |
0.566 |
1.692 |
0.091 |
2.605 |
0.859 |
7.899 |
| proudlgbcom |
-0.635 |
0.537 |
-1.183 |
0.237 |
0.530 |
0.185 |
1.517 |
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) |
-2.302 |
0.478 |
-4.821 |
0.000 |
0.100 |
0.039 |
0.255 |
| raceblack |
0.241 |
0.534 |
0.452 |
0.651 |
1.273 |
0.447 |
3.622 |
| racehispanic |
0.248 |
0.443 |
0.560 |
0.576 |
1.281 |
0.538 |
3.054 |
| agecohortmiddle |
-0.543 |
0.638 |
-0.851 |
0.395 |
0.581 |
0.166 |
2.029 |
| agecohortyounger |
0.506 |
0.460 |
1.102 |
0.271 |
1.659 |
0.674 |
4.084 |
| educ |
1.166 |
0.358 |
3.257 |
0.001 |
3.208 |
1.591 |
6.468 |
| sexualityles/gay |
-0.977 |
0.473 |
-2.064 |
0.039 |
0.377 |
0.149 |
0.952 |
| sexualityother |
-0.456 |
0.527 |
-0.866 |
0.387 |
0.634 |
0.226 |
1.779 |
| genidenmale |
0.163 |
0.427 |
0.382 |
0.703 |
1.177 |
0.510 |
2.715 |
| genidennonbin/trans |
0.961 |
0.559 |
1.718 |
0.086 |
2.614 |
0.873 |
7.827 |
| imppolactlgbcom |
-0.089 |
0.381 |
-0.235 |
0.815 |
0.914 |
0.433 |
1.931 |
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) |
-1.569 |
0.729 |
-2.151 |
0.032 |
0.208 |
0.050 |
0.870 |
| raceblack |
0.345 |
0.517 |
0.668 |
0.505 |
1.412 |
0.513 |
3.890 |
| racehispanic |
0.301 |
0.443 |
0.681 |
0.496 |
1.352 |
0.568 |
3.218 |
| agecohortmiddle |
-0.590 |
0.584 |
-1.010 |
0.313 |
0.554 |
0.177 |
1.741 |
| agecohortyounger |
0.555 |
0.473 |
1.174 |
0.241 |
1.742 |
0.690 |
4.401 |
| educ |
1.180 |
0.349 |
3.385 |
0.001 |
3.253 |
1.643 |
6.441 |
| sexualityles/gay |
-0.986 |
0.514 |
-1.916 |
0.056 |
0.373 |
0.136 |
1.023 |
| sexualityother |
-0.421 |
0.530 |
-0.795 |
0.427 |
0.656 |
0.232 |
1.854 |
| genidenmale |
0.154 |
0.435 |
0.353 |
0.724 |
1.166 |
0.497 |
2.737 |
| genidennonbin/trans |
0.798 |
0.590 |
1.351 |
0.177 |
2.220 |
0.698 |
7.063 |
| soimp |
-0.218 |
0.462 |
-0.472 |
0.637 |
0.804 |
0.325 |
1.988 |
| lgbimp |
-0.071 |
0.495 |
-0.143 |
0.886 |
0.932 |
0.353 |
2.458 |
| parlgbcom |
-0.237 |
0.505 |
-0.469 |
0.639 |
0.789 |
0.293 |
2.123 |
| poslgbcom |
-0.460 |
0.549 |
-0.837 |
0.403 |
0.631 |
0.215 |
1.853 |
| bondlgbcom |
0.644 |
0.532 |
1.210 |
0.227 |
1.905 |
0.671 |
5.408 |
| proudlgbcom |
-0.731 |
0.636 |
-1.151 |
0.250 |
0.481 |
0.138 |
1.672 |
| imppolactlgbcom |
-0.035 |
0.449 |
-0.078 |
0.937 |
0.965 |
0.400 |
2.328 |
exp(coefficients(fit.logit23))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.1066083 1.3711815 1.2983644 0.5335280
## agecohortyounger educ sexualityles/gay sexualityother
## 1.6389247 3.1923544 0.4041753 0.8242058
exp(coefficients(fit.logit24))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.05354379 1.40631782 1.18564840 0.79544579
## agecohortyounger educ genidenmale genidennonbin/trans
## 2.32014516 2.97495178 0.84674200 2.24267307
exp(coefficients(fit.logit25))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.09510079 1.26958480 1.27345408 0.58279005
## agecohortyounger educ sexualityles/gay sexualityother
## 1.64743925 3.22459195 0.37415620 0.63499856
## genidenmale genidennonbin/trans
## 1.18842601 2.61494712
exp(coefficients(fit.logit26))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.1036971 1.2642398 1.2512736 0.5742966
## agecohortyounger educ sexualityles/gay sexualityother
## 1.6318721 3.2304031 0.3651996 0.6354213
## genidenmale genidennonbin/trans soimp
## 1.2118452 2.5174742 0.8487625
exp(coefficients(fit.logit27))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.09772084 1.26522242 1.27255652 0.58278562
## agecohortyounger educ sexualityles/gay sexualityother
## 1.64764325 3.23375962 0.36912092 0.63624006
## genidenmale genidennonbin/trans lgbimp
## 1.19209672 2.57066180 0.93425324
exp(coefficients(fit.logit28))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.09684085 1.27105832 1.27833342 0.58196018
## agecohortyounger educ sexualityles/gay sexualityother
## 1.64570708 3.23053852 0.37468236 0.63697731
## genidenmale genidennonbin/trans parlgbcom
## 1.18744573 2.63225354 0.96889421
exp(coefficients(fit.logit29))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.1262421 1.2791412 1.3174422 0.5856883
## agecohortyounger educ sexualityles/gay sexualityother
## 1.7124114 3.2013414 0.3856404 0.6738684
## genidenmale genidennonbin/trans poslgbcom
## 1.1425669 2.5994633 0.6565795
exp(coefficients(fit.logit30))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.08691664 1.26095952 1.26144744 0.58977584
## agecohortyounger educ sexualityles/gay sexualityother
## 1.67608690 3.23222321 0.37647451 0.62850721
## genidenmale genidennonbin/trans bondlgbcom
## 1.20153022 2.54697625 1.12380152
exp(coefficients(fit.logit31))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.1752223 1.4178877 1.3414009 0.5330439
## agecohortyounger educ sexualityles/gay sexualityother
## 1.5309927 3.1943235 0.3604531 0.6313805
## genidenmale genidennonbin/trans proudlgbcom
## 1.1219336 2.6053608 0.5299168
exp(coefficients(fit.logit32))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.1000428 1.2727199 1.2813740 0.5811388
## agecohortyounger educ sexualityles/gay sexualityother
## 1.6593353 3.2076015 0.3765643 0.6336171
## genidenmale genidennonbin/trans imppolactlgbcom
## 1.1768996 2.6142976 0.9144296
exp(coefficients(fit.logit33))
## (Intercept) raceblack racehispanic agecohortmiddle
## 0.2082381 1.4121339 1.3517810 0.5543825
## agecohortyounger educ sexualityles/gay sexualityother
## 1.7422447 3.2532038 0.3732416 0.6561397
## genidenmale genidennonbin/trans soimp lgbimp
## 1.1662693 2.2199877 0.8040028 0.9317544
## parlgbcom poslgbcom bondlgbcom proudlgbcom
## 0.7892645 0.6313818 1.9045017 0.4812440
## imppolactlgbcom
## 0.9653622
## With survey design "interesting cases" Race and Sexual Identity
rg<-ref_grid(fit.logit25)
marg_logit<-emmeans(object = rg,
specs = c( "race", "sexuality"),
type="response" ,
data=sub)
knitr::kable(marg_logit, digits = 4)
| white |
bisexual |
0.1973 |
0.0665 |
Inf |
0.0974 |
0.3589 |
| black |
bisexual |
0.2379 |
0.0899 |
Inf |
0.1056 |
0.4521 |
| hispanic |
bisexual |
0.2384 |
0.0998 |
Inf |
0.0963 |
0.4790 |
| white |
les/gay |
0.0842 |
0.0292 |
Inf |
0.0419 |
0.1621 |
| black |
les/gay |
0.1046 |
0.0523 |
Inf |
0.0376 |
0.2585 |
| hispanic |
les/gay |
0.1049 |
0.0432 |
Inf |
0.0454 |
0.2239 |
| white |
other |
0.1350 |
0.0559 |
Inf |
0.0576 |
0.2852 |
| black |
other |
0.1654 |
0.0853 |
Inf |
0.0558 |
0.3994 |
| hispanic |
other |
0.1658 |
0.0850 |
Inf |
0.0563 |
0.3986 |
## With survey design "interesting cases" Race and Gender Identity
rg<-ref_grid(fit.logit25)
marg_logit<-emmeans(object = rg,
specs = c( "race", "geniden"),
type="response" ,
data=sub)
knitr::kable(marg_logit, digits = 4)
| white |
female |
0.0945 |
0.0283 |
Inf |
0.0517 |
0.1664 |
| black |
female |
0.1170 |
0.0508 |
Inf |
0.0481 |
0.2577 |
| hispanic |
female |
0.1173 |
0.0467 |
Inf |
0.0521 |
0.2433 |
| white |
male |
0.1103 |
0.0376 |
Inf |
0.0553 |
0.2082 |
| black |
male |
0.1360 |
0.0660 |
Inf |
0.0498 |
0.3212 |
| hispanic |
male |
0.1364 |
0.0602 |
Inf |
0.0548 |
0.3006 |
| white |
nonbin/trans |
0.2144 |
0.0921 |
Inf |
0.0854 |
0.4435 |
| black |
nonbin/trans |
0.2573 |
0.1191 |
Inf |
0.0927 |
0.5403 |
| hispanic |
nonbin/trans |
0.2579 |
0.1260 |
Inf |
0.0873 |
0.5580 |
## With survey design "interesting cases" Race and Age Cohort
rg<-ref_grid(fit.logit25)
marg_logit<-emmeans(object = rg,
specs = c( "race", "agecohort"),
type="response" ,
data=sub)
knitr::kable(marg_logit, digits = 4)
| white |
older |
0.1337 |
0.0474 |
Inf |
0.0647 |
0.2561 |
| black |
older |
0.1639 |
0.0803 |
Inf |
0.0585 |
0.3819 |
| hispanic |
older |
0.1643 |
0.0847 |
Inf |
0.0554 |
0.3972 |
| white |
middle |
0.0825 |
0.0450 |
Inf |
0.0273 |
0.2238 |
| black |
middle |
0.1025 |
0.0560 |
Inf |
0.0335 |
0.2736 |
| hispanic |
middle |
0.1028 |
0.0578 |
Inf |
0.0324 |
0.2814 |
| white |
younger |
0.2027 |
0.0413 |
Inf |
0.1336 |
0.2955 |
| black |
younger |
0.2440 |
0.0943 |
Inf |
0.1060 |
0.4679 |
| hispanic |
younger |
0.2446 |
0.0724 |
Inf |
0.1306 |
0.4111 |