library(car)
## Loading required package: carData
library(stargazer)
##
## Please cite as:
## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.3. 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 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ stringr 1.4.1
## ✔ tidyr 1.2.0 ✔ forcats 0.5.2
## ✔ readr 2.1.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ tidyr::expand() masks Matrix::expand()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ tidyr::pack() masks Matrix::pack()
## ✖ dplyr::recode() masks car::recode()
## ✖ purrr::some() masks car::some()
## ✖ 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)
library(psych)
##
## Attaching package: 'psych'
##
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
##
## The following object is masked from 'package:questionr':
##
## describe
##
## The following object is masked from 'package:car':
##
## logit
library(gmodels)
library(lmtest)
## Loading required package: zoo
##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
library(ResourceSelection)
## ResourceSelection 0.3-6 2023-06-27
x38304_0001_Data <- read_sav("WorkingDirectoryFall2020StatsDem1/38304-0001-Data.sav")
View(x38304_0001_Data)
x38304_0001_Data$sui_th<-Recode(x38304_0001_Data$SBQ_1, recodes= "1=0; 2:4=1; else=NA", as.numeric=T)
summary(x38304_0001_Data$sui_th)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.3884 1.0000 1.0000 84
x38304_0001_Data$ho_suith<-Recode(x38304_0001_Data$SBQ_2, recodes="1=1; 2:3=2; 4:5=3; else=NA", as.factor = T)
x38304_0001_Data$ho_suith<-relevel(x38304_0001_Data$ho_suith, ref ="1")
summary(x38304_0001_Data$ho_suith)
## 1 2 3 NA's
## 1086 311 87 11
x38304_0001_Data$sex<-Recode(x38304_0001_Data$GENDER, recodes="1=1; 2=0; else=NA", as.factor = T)
summary(x38304_0001_Data$sex)
## 0 1 NA's
## 483 1004 8
x38304_0001_Data$ce<-Recode(x38304_0001_Data$CES_1, recodes="1=1; 2=2; 3=3; 4=4; 5=5; else=NA", as.factor=T)
summary(x38304_0001_Data$ce)
## 1 2 3 4 5
## 752 298 210 134 101
x38304_0001_Data$RACE <-as.numeric(x38304_0001_Data$RACE)
x38304_0001_Data$race2<-Recode(x38304_0001_Data$RACE, recodes="1='white'; 2='black'; 3:6='other';
else=NA", as.factor=T)
x38304_0001_Data$race2<-relevel(x38304_0001_Data$race2, ref='white')
summary(x38304_0001_Data$race2)
## white black other
## 1129 215 151
x38304_0001_Data$SOC_INTEGRATION_1 <-as.numeric(x38304_0001_Data$SOC_INTEGRATION_1)
x38304_0001_Data$bel_com<-Recode(x38304_0001_Data$SOC_INTEGRATION_1, recodes="1:3='disagree'; 4='neutral'; 5:7='agree'; else=NA", as.factor =T)
x38304_0001_Data$ bel_com <-relevel(x38304_0001_Data$ bel_com, ref='agree')
summary(x38304_0001_Data$bel_com)
## agree disagree neutral
## 411 807 277
x38304_0001_Data$SOC_INTEGRATION_2 <-as.numeric(x38304_0001_Data$SOC_INTEGRATION_2)
x38304_0001_Data$clo_peo<-Recode(x38304_0001_Data$SOC_INTEGRATION_2, recodes="1:3='disagree'; 4='neutral'; 5:7='agree'; else=NA", as.factor =T)
x38304_0001_Data$clo_peo <-relevel(x38304_0001_Data$clo_peo, ref='agree')
summary(x38304_0001_Data$clo_peo)
## agree disagree neutral
## 758 426 311
x38304_0001_Data$SOC_INTEGRATION_3 <-as.numeric(x38304_0001_Data$SOC_INTEGRATION_3)
x38304_0001_Data$com_comf<-Recode(x38304_0001_Data$SOC_INTEGRATION_3, recodes="1:3='disagree'; 4='neutral'; 5:7='agree'; else=NA", as.factor =T)
x38304_0001_Data$com_comf <-relevel(x38304_0001_Data$com_comf, ref='agree')
summary(x38304_0001_Data$com_comf)
## agree disagree neutral
## 736 385 374
x38304_0001_Data$WELLNESS_8 <-as.numeric(x38304_0001_Data$WELLNESS_8)
x38304_0001_Data$fam_help<-Recode(x38304_0001_Data$WELLNESS_8, recodes="1:3='disagree'; 4='neutral'; 5:7='agree'; else=NA", as.factor =T)
x38304_0001_Data$fam_help <-relevel(x38304_0001_Data$fam_help, ref='agree')
summary(x38304_0001_Data$fam_help)
## agree disagree neutral
## 131 1053 311
x38304_0001_Data$WELLNESS_28 <-as.numeric(x38304_0001_Data$WELLNESS_28)
x38304_0001_Data$fri_help<-Recode(x38304_0001_Data$WELLNESS_28, recodes="1:3='disagree'; 4='neutral'; 5:7='agree'; else=NA", as.factor =T)
x38304_0001_Data$ fri_help <-relevel(x38304_0001_Data$fri_help, ref='agree')
summary(x38304_0001_Data$fri_help)
## agree disagree neutral
## 317 610 568
x38304_0001_Data$rel_stat<-Recode(x38304_0001_Data$MARITAL, recodes="1=1; 2=0; 3=0; 4=1; 5=1; else=NA", as.factor=T)
summary(x38304_0001_Data$rel_stat)
## 0 1
## 959 536
sub2<-x38304_0001_Data%>%
select(sui_th, sex, ce, race2, bel_com, clo_peo, com_comf, fam_help, fri_help, rel_stat, AGE)%>%
filter(complete.cases(.))
sub3<-x38304_0001_Data%>%
select(ho_suith, sex, ce, race2, bel_com, clo_peo, com_comf, fam_help, fri_help, rel_stat, AGE)%>%
filter(complete.cases(.))
table1(~ sex + rel_stat + AGE + race2 + ce + bel_com + clo_peo + com_comf + fam_help + fri_help| sui_th, data=sub2, overall="Total")
## Warning in table1.formula(~sex + rel_stat + AGE + race2 + ce + bel_com + : Terms
## to the right of '|' in formula 'x' define table columns and are expected to be
## factors with meaningful labels.
|
0
(N=863) |
1
(N=540) |
Total
(N=1403) |
| sex |
|
|
|
| 0 |
240 (27.8%) |
198 (36.7%) |
438 (31.2%) |
| 1 |
623 (72.2%) |
342 (63.3%) |
965 (68.8%) |
| rel_stat |
|
|
|
| 0 |
577 (66.9%) |
332 (61.5%) |
909 (64.8%) |
| 1 |
286 (33.1%) |
208 (38.5%) |
494 (35.2%) |
| What is your age in years? |
|
|
|
| Mean (SD) |
52.1 (12.5) |
47.8 (13.8) |
50.5 (13.2) |
| Median [Min, Max] |
56.0 [18.0, 86.0] |
51.5 [18.0, 78.0] |
55.0 [18.0, 86.0] |
| race2 |
|
|
|
| white |
670 (77.6%) |
397 (73.5%) |
1067 (76.1%) |
| black |
121 (14.0%) |
78 (14.4%) |
199 (14.2%) |
| other |
72 (8.3%) |
65 (12.0%) |
137 (9.8%) |
| ce |
|
|
|
| 1 |
487 (56.4%) |
228 (42.2%) |
715 (51.0%) |
| 2 |
163 (18.9%) |
115 (21.3%) |
278 (19.8%) |
| 3 |
99 (11.5%) |
90 (16.7%) |
189 (13.5%) |
| 4 |
60 (7.0%) |
66 (12.2%) |
126 (9.0%) |
| 5 |
54 (6.3%) |
41 (7.6%) |
95 (6.8%) |
| bel_com |
|
|
|
| agree |
152 (17.6%) |
213 (39.4%) |
365 (26.0%) |
| disagree |
557 (64.5%) |
223 (41.3%) |
780 (55.6%) |
| neutral |
154 (17.8%) |
104 (19.3%) |
258 (18.4%) |
| clo_peo |
|
|
|
| agree |
507 (58.7%) |
227 (42.0%) |
734 (52.3%) |
| disagree |
161 (18.7%) |
215 (39.8%) |
376 (26.8%) |
| neutral |
195 (22.6%) |
98 (18.1%) |
293 (20.9%) |
| com_comf |
|
|
|
| agree |
487 (56.4%) |
220 (40.7%) |
707 (50.4%) |
| disagree |
147 (17.0%) |
193 (35.7%) |
340 (24.2%) |
| neutral |
229 (26.5%) |
127 (23.5%) |
356 (25.4%) |
| fam_help |
|
|
|
| agree |
46 (5.3%) |
58 (10.7%) |
104 (7.4%) |
| disagree |
672 (77.9%) |
340 (63.0%) |
1012 (72.1%) |
| neutral |
145 (16.8%) |
142 (26.3%) |
287 (20.5%) |
| fri_help |
|
|
|
| agree |
233 (27.0%) |
69 (12.8%) |
302 (21.5%) |
| disagree |
290 (33.6%) |
262 (48.5%) |
552 (39.3%) |
| neutral |
340 (39.4%) |
209 (38.7%) |
549 (39.1%) |
cs1<-chisq.test(sub2$sui_th, sub2$sex, correct=FALSE)
cs2<-chisq.test(sub2$sui_th, sub2$rel_stat, correct=FALSE)
cs3<-chisq.test(sub2$sui_th, sub2$AGE, correct=FALSE)
## Warning in chisq.test(sub2$sui_th, sub2$AGE, correct = FALSE): Chi-squared
## approximation may be incorrect
cs4<-chisq.test(sub2$sui_th, sub2$race2, correct=FALSE)
cs5<-chisq.test(sub2$sui_th, sub2$ce, correct=FALSE)
cs6<-chisq.test(sub2$sui_th, sub2$bel_com, correct=FALSE)
cs7<-chisq.test(sub2$sui_th, sub2$clo_peo, correct=FALSE)
cs8<-chisq.test(sub2$sui_th, sub2$com_comf, correct=FALSE)
cs9<-chisq.test(sub2$sui_th, sub2$fam_help, correct=FALSE)
cs10<-chisq.test(sub2$sui_th, sub2$fri_help, correct=FALSE)
library(MASS)
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
summary(m1<-glm.nb(sui_th ~ ce, data=sub2))
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
##
## Call:
## glm.nb(formula = sui_th ~ ce, data = sub2, init.theta = 11909.94017,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.0235 -0.9096 -0.7986 0.7374 0.9610
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.14294 0.06623 -17.258 < 2e-16 ***
## ce2 0.26025 0.11438 2.275 0.022885 *
## ce3 0.40100 0.12449 3.221 0.001277 **
## ce4 0.49631 0.13978 3.551 0.000384 ***
## ce5 0.30263 0.16964 1.784 0.074426 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(11909.94) family taken to be 1)
##
## Null deviance: 1031.2 on 1402 degrees of freedom
## Residual deviance: 1012.0 on 1398 degrees of freedom
## AIC: 2104
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 11910
## Std. Err.: 48207
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2092.024
summary(m2<-glm.nb(sui_th ~ ce + sex + AGE + rel_stat + race2, data=sub2))
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
##
## Call:
## glm.nb(formula = sui_th ~ ce + sex + AGE + rel_stat + race2,
## data = sub2, init.theta = 11430.84889, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.2625 -0.8617 -0.7431 0.7165 1.1506
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.546498 0.204900 -2.667 0.00765 **
## ce2 0.199194 0.121009 1.646 0.09974 .
## ce3 0.296736 0.132352 2.242 0.02496 *
## ce4 0.459981 0.147599 3.116 0.00183 **
## ce5 0.321689 0.172882 1.861 0.06278 .
## sex1 -0.251559 0.093398 -2.693 0.00707 **
## AGE -0.008889 0.003431 -2.590 0.00959 **
## rel_stat1 0.107237 0.090308 1.187 0.23505
## race2black -0.078526 0.127198 -0.617 0.53700
## race2other 0.084418 0.137726 0.613 0.53991
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(11430.85) family taken to be 1)
##
## Null deviance: 1031.15 on 1402 degrees of freedom
## Residual deviance: 991.08 on 1393 degrees of freedom
## AIC: 2093.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 11431
## Std. Err.: 44357
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2071.123
summary(m3<-glm.nb(sui_th ~ ce + sex + AGE + rel_stat + race2 +bel_com +clo_peo + com_comf, data=sub2))
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
##
## Call:
## glm.nb(formula = sui_th ~ ce + sex + AGE + rel_stat + race2 +
## bel_com + clo_peo + com_comf, data = sub2, init.theta = 10309.38929,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4363 -0.7965 -0.6686 0.5896 1.3286
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.455392 0.222975 -2.042 0.04112 *
## ce2 0.181193 0.120130 1.508 0.13148
## ce3 0.344603 0.132596 2.599 0.00935 **
## ce4 0.488457 0.149023 3.278 0.00105 **
## ce5 0.326114 0.172848 1.887 0.05920 .
## sex1 -0.230371 0.093706 -2.458 0.01395 *
## AGE -0.008702 0.003514 -2.476 0.01328 *
## rel_stat1 0.019254 0.091185 0.211 0.83277
## race2black -0.027780 0.127767 -0.217 0.82787
## race2other 0.097285 0.138038 0.705 0.48095
## bel_comdisagree -0.448587 0.115170 -3.895 9.82e-05 ***
## bel_comneutral -0.212132 0.130387 -1.627 0.10375
## clo_peodisagree 0.253675 0.145435 1.744 0.08111 .
## clo_peoneutral -0.012207 0.144098 -0.085 0.93249
## com_comfdisagree 0.232755 0.147758 1.575 0.11520
## com_comfneutral 0.035990 0.136741 0.263 0.79240
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(10309.39) family taken to be 1)
##
## Null deviance: 1031.15 on 1402 degrees of freedom
## Residual deviance: 925.47 on 1387 degrees of freedom
## AIC: 2039.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 10309
## Std. Err.: 36010
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2005.52
summary(m4<-glm.nb(sui_th ~ ce + sex + AGE + rel_stat + race2 +fam_help + fri_help, data=sub2))
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
##
## Call:
## glm.nb(formula = sui_th ~ ce + sex + AGE + rel_stat + race2 +
## fam_help + fri_help, data = sub2, init.theta = 10264.19352,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4834 -0.8401 -0.6795 0.6416 1.4978
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.851814 0.253336 -3.362 0.000773 ***
## ce2 0.192521 0.120704 1.595 0.110716
## ce3 0.271314 0.131878 2.057 0.039657 *
## ce4 0.414396 0.149051 2.780 0.005432 **
## ce5 0.363034 0.173448 2.093 0.036345 *
## sex1 -0.232166 0.093553 -2.482 0.013077 *
## AGE -0.008194 0.003477 -2.357 0.018427 *
## rel_stat1 0.075912 0.090497 0.839 0.401561
## race2black -0.068052 0.127405 -0.534 0.593245
## race2other 0.062883 0.137922 0.456 0.648442
## fam_helpdisagree -0.326678 0.146417 -2.231 0.025672 *
## fam_helpneutral -0.111858 0.157855 -0.709 0.478565
## fri_helpdisagree 0.721861 0.136623 5.284 1.27e-07 ***
## fri_helpneutral 0.529151 0.140288 3.772 0.000162 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(10264.19) family taken to be 1)
##
## Null deviance: 1031.2 on 1402 degrees of freedom
## Residual deviance: 949.1 on 1389 degrees of freedom
## AIC: 2059.2
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 10264
## Std. Err.: 36523
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2029.157
summary(m5<-glm.nb(sui_th ~ ce + sex + AGE + rel_stat + race2 +bel_com +clo_peo + com_comf +fam_help + fri_help, data=sub2))
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
##
## Call:
## glm.nb(formula = sui_th ~ ce + sex + AGE + rel_stat + race2 +
## bel_com + clo_peo + com_comf + fam_help + fri_help, data = sub2,
## init.theta = 9661.53051, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4448 -0.7953 -0.6422 0.5402 1.5353
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.680346 0.263599 -2.581 0.00985 **
## ce2 0.177553 0.120207 1.477 0.13966
## ce3 0.316409 0.132506 2.388 0.01695 *
## ce4 0.440428 0.150022 2.936 0.00333 **
## ce5 0.340735 0.173486 1.964 0.04952 *
## sex1 -0.221431 0.093702 -2.363 0.01812 *
## AGE -0.008532 0.003544 -2.407 0.01606 *
## rel_stat1 0.012690 0.091476 0.139 0.88967
## race2black -0.018353 0.128003 -0.143 0.88599
## race2other 0.094403 0.138296 0.683 0.49485
## bel_comdisagree -0.366784 0.116651 -3.144 0.00166 **
## bel_comneutral -0.188558 0.130181 -1.448 0.14750
## clo_peodisagree 0.237762 0.146660 1.621 0.10498
## clo_peoneutral -0.013488 0.144635 -0.093 0.92570
## com_comfdisagree 0.179243 0.148949 1.203 0.22883
## com_comfneutral -0.005482 0.137698 -0.040 0.96824
## fam_helpdisagree -0.222312 0.148241 -1.500 0.13370
## fam_helpneutral -0.074363 0.158512 -0.469 0.63898
## fri_helpdisagree 0.454431 0.147139 3.088 0.00201 **
## fri_helpneutral 0.448788 0.141814 3.165 0.00155 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(9661.531) family taken to be 1)
##
## Null deviance: 1031.15 on 1402 degrees of freedom
## Residual deviance: 909.69 on 1383 degrees of freedom
## AIC: 2031.7
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 9662
## Std. Err.: 32278
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -1989.743
(est1<-cbind(Estimate = coef(m1), confint(m1)))
## Waiting for profiling to be done...
## Estimate 2.5 % 97.5 %
## (Intercept) -1.1429369 -1.27561426 -1.0158855
## ce2 0.2602479 0.03306252 0.4818995
## ce3 0.4009996 0.15236357 0.6409991
## ce4 0.4963097 0.21496066 0.7638317
## ce5 0.3026321 -0.04359179 0.6231589
(est2<-cbind(Estimate = coef(m2), confint(m2)))
## Waiting for profiling to be done...
## Estimate 2.5 % 97.5 %
## (Intercept) -0.546498308 -0.95159139 -0.148294213
## ce2 0.199194227 -0.04077630 0.433987774
## ce3 0.296736464 0.03299213 0.552340897
## ce4 0.459981051 0.16403227 0.743476342
## ce5 0.321689122 -0.03036533 0.649088323
## sex1 -0.251559147 -0.43358175 -0.067255313
## AGE -0.008888995 -0.01558878 -0.002134211
## rel_stat1 0.107236827 -0.07099545 0.283212024
## race2black -0.078525841 -0.33447153 0.164797778
## race2other 0.084418451 -0.19378156 0.346947246
(est3<-cbind(Estimate = coef(m3), confint(m3)))
## Waiting for profiling to be done...
## Estimate 2.5 % 97.5 %
## (Intercept) -0.45539212 -0.89647342 -0.02228130
## ce2 0.18119339 -0.05707396 0.41425777
## ce3 0.34460261 0.08038562 0.60068972
## ce4 0.48845745 0.18982633 0.77483573
## ce5 0.32611403 -0.02587924 0.65343849
## sex1 -0.23037103 -0.41299136 -0.04545908
## AGE -0.00870180 -0.01556123 -0.00178230
## rel_stat1 0.01925395 -0.16065918 0.19698291
## race2black -0.02778014 -0.28478097 0.21671710
## race2other 0.09728541 -0.18152071 0.36042598
## bel_comdisagree -0.44858690 -0.67430691 -0.22271013
## bel_comneutral -0.21213206 -0.47075846 0.04078194
## clo_peodisagree 0.25367495 -0.03111591 0.53883567
## clo_peoneutral -0.01220654 -0.29764157 0.26747376
## com_comfdisagree 0.23275481 -0.05747843 0.52158012
## com_comfneutral 0.03598966 -0.23402178 0.30209894
(est4<-cbind(Estimate = coef(m4), confint(m4)))
## Waiting for profiling to be done...
## Estimate 2.5 % 97.5 %
## (Intercept) -0.851813946 -1.355338122 -0.361843425
## ce2 0.192521314 -0.046853862 0.426720447
## ce3 0.271313892 0.008490799 0.525992473
## ce4 0.414395834 0.115626281 0.700733797
## ce5 0.363034358 0.009933408 0.691592383
## sex1 -0.232166312 -0.414480566 -0.047547832
## AGE -0.008193967 -0.014981525 -0.001350445
## rel_stat1 0.075911904 -0.102696829 0.252251556
## race2black -0.068052129 -0.324376322 0.175704662
## race2other 0.062882722 -0.215678271 0.325821401
## fam_helpdisagree -0.326677831 -0.605560769 -0.030578901
## fam_helpneutral -0.111858025 -0.415468434 0.204539311
## fri_helpdisagree 0.721861413 0.460353994 0.996768498
## fri_helpneutral 0.529150991 0.259863027 0.810678136
(est5<-cbind(Estimate = coef(m5), confint(m5)))
## Waiting for profiling to be done...
## Estimate 2.5 % 97.5 %
## (Intercept) -0.680345951 -1.20394265 -0.170230530
## ce2 0.177552726 -0.06085467 0.410780171
## ce3 0.316409053 0.05238659 0.572345712
## ce4 0.440428447 0.13984661 0.728753777
## ce5 0.340734563 -0.01242520 0.669383366
## sex1 -0.221431170 -0.40403448 -0.036519916
## AGE -0.008531673 -0.01544919 -0.001553911
## rel_stat1 0.012689648 -0.16781017 0.190971723
## race2black -0.018353484 -0.27578616 0.226638335
## race2other 0.094402718 -0.18488481 0.358070692
## bel_comdisagree -0.366784101 -0.59567350 -0.138255744
## bel_comneutral -0.188557876 -0.44679532 0.063940307
## clo_peodisagree 0.237762368 -0.04935095 0.525434583
## clo_peoneutral -0.013488382 -0.29983708 0.267413025
## com_comfdisagree 0.179243348 -0.11327999 0.470471223
## com_comfneutral -0.005481941 -0.27717795 0.262720624
## fam_helpdisagree -0.222311569 -0.50499627 0.077128059
## fam_helpneutral -0.074362997 -0.37929751 0.243275141
## fri_helpdisagree 0.454430531 0.17151795 0.749042830
## fri_helpneutral 0.448787621 0.17634619 0.733132679
exp(est1)
## Estimate 2.5 % 97.5 %
## (Intercept) 0.3188811 0.2792594 0.3620817
## ce2 1.2972517 1.0336152 1.6191471
## ce3 1.4933166 1.1645836 1.8983766
## ce4 1.6426483 1.2398131 2.1464852
## ce5 1.3534164 0.9573447 1.8648095
exp(est2)
## Estimate 2.5 % 97.5 %
## (Intercept) 0.5789737 0.3861261 0.8621774
## ce2 1.2204190 0.9600439 1.5434000
## ce3 1.3454607 1.0335424 1.7373151
## ce4 1.5840440 1.1782523 2.1032344
## ce5 1.3794559 0.9700911 1.9137953
## sex1 0.7775875 0.6481833 0.9349565
## AGE 0.9911504 0.9845321 0.9978681
## rel_stat1 1.1131979 0.9314661 1.3273866
## race2black 0.9244782 0.7157162 1.1791546
## race2other 1.0880841 0.8238378 1.4147421
exp(est3)
## Estimate 2.5 % 97.5 %
## (Intercept) 0.6341992 0.4080060 0.9779651
## ce2 1.1986470 0.9445242 1.5132471
## ce3 1.4114289 1.0837049 1.8233760
## ce4 1.6298002 1.2090396 2.1702356
## ce5 1.3855734 0.9744528 1.9221387
## sex1 0.7942389 0.6616680 0.9555587
## AGE 0.9913360 0.9845592 0.9982193
## rel_stat1 1.0194405 0.8515823 1.2177232
## race2black 0.9726022 0.7521790 1.2419927
## race2other 1.1021749 0.8340010 1.4339401
## bel_comdisagree 0.6385298 0.5095094 0.8003468
## bel_comneutral 0.8088579 0.6245284 1.0416249
## clo_peodisagree 1.2887528 0.9693632 1.7140100
## clo_peoneutral 0.9878677 0.7425674 1.3066593
## com_comfdisagree 1.2620720 0.9441423 1.6846876
## com_comfneutral 1.0366451 0.7913446 1.3526951
exp(est4)
## Estimate 2.5 % 97.5 %
## (Intercept) 0.4266403 0.2578601 0.6963914
## ce2 1.2123023 0.9542268 1.5322243
## ce3 1.3116867 1.0085269 1.6921374
## ce4 1.5134561 1.1225763 2.0152309
## ce5 1.4376853 1.0099829 1.9968928
## sex1 0.7928143 0.6606834 0.9535649
## AGE 0.9918395 0.9851301 0.9986505
## rel_stat1 1.0788675 0.9024005 1.2869197
## race2black 0.9342118 0.7229781 1.1920859
## race2other 1.0649019 0.8059946 1.3851680
## fam_helpdisagree 0.7213161 0.5457683 0.9698839
## fam_helpneutral 0.8941712 0.6600310 1.2269597
## fri_helpdisagree 2.0582609 1.5846348 2.7095119
## fri_helpneutral 1.6974905 1.2967525 2.2494329
exp(est5)
## Estimate 2.5 % 97.5 %
## (Intercept) 0.5064418 0.3000090 0.8434703
## ce2 1.1942910 0.9409600 1.5079938
## ce3 1.3721914 1.0537831 1.7724198
## ce4 1.5533726 1.1500974 2.0724962
## ce5 1.4059800 0.9876517 1.9530326
## sex1 0.8013711 0.6676211 0.9641389
## AGE 0.9915046 0.9846695 0.9984473
## rel_stat1 1.0127705 0.8455143 1.2104252
## race2black 0.9818139 0.7589752 1.2543761
## race2other 1.0990022 0.8312000 1.4305667
## bel_comdisagree 0.6929592 0.5511912 0.8708759
## bel_comneutral 0.8281526 0.6396748 1.0660288
## clo_peodisagree 1.2684077 0.9518470 1.6911937
## clo_peoneutral 0.9866022 0.7409389 1.3065800
## com_comfdisagree 1.1963118 0.8929006 1.6007483
## com_comfneutral 0.9945331 0.7579196 1.3004633
## fam_helpdisagree 0.8006659 0.6035078 1.0801804
## fam_helpneutral 0.9283347 0.6843420 1.2754195
## fri_helpdisagree 1.5752761 1.1871055 2.1149747
## fri_helpneutral 1.5664119 1.1928509 2.0815914
des<-svydesign(ids= ~1, data=sub2)
## Warning in svydesign.default(ids = ~1, data = sub2): No weights or probabilities
## supplied, assuming equal probability
fit.logit1<-svyglm(sui_th ~ ce,
design = des,
family=binomial)
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) |
-0.759 |
0.080 |
-9.454 |
0.000 |
0.468 |
0.400 |
0.548 |
| ce2 |
0.410 |
0.146 |
2.811 |
0.005 |
1.507 |
1.132 |
2.006 |
| ce3 |
0.664 |
0.166 |
3.989 |
0.000 |
1.942 |
1.402 |
2.690 |
| ce4 |
0.854 |
0.196 |
4.366 |
0.000 |
2.350 |
1.601 |
3.448 |
| ce5 |
0.484 |
0.222 |
2.176 |
0.030 |
1.622 |
1.049 |
2.507 |
fit.logit2<-svyglm(sui_th ~ ce + sex + rel_stat + AGE + race2,
design = des,
family=binomial)
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) |
0.338 |
0.274 |
1.234 |
0.217 |
1.402 |
0.820 |
2.397 |
| ce2 |
0.314 |
0.157 |
1.994 |
0.046 |
1.369 |
1.005 |
1.864 |
| ce3 |
0.498 |
0.174 |
2.869 |
0.004 |
1.645 |
1.171 |
2.312 |
| ce4 |
0.811 |
0.204 |
3.970 |
0.000 |
2.249 |
1.507 |
3.356 |
| ce5 |
0.520 |
0.227 |
2.290 |
0.022 |
1.683 |
1.078 |
2.627 |
| sex1 |
-0.438 |
0.123 |
-3.557 |
0.000 |
0.646 |
0.507 |
0.822 |
| rel_stat1 |
0.183 |
0.119 |
1.536 |
0.125 |
1.200 |
0.951 |
1.516 |
| AGE |
-0.016 |
0.005 |
-3.592 |
0.000 |
0.984 |
0.975 |
0.993 |
| race2black |
-0.126 |
0.162 |
-0.778 |
0.437 |
0.882 |
0.642 |
1.211 |
| race2other |
0.167 |
0.190 |
0.880 |
0.379 |
1.182 |
0.814 |
1.715 |
fit.logit3<-svyglm(sui_th ~ ce + sex + rel_stat + AGE + race2 + bel_com,
design = des,
family=binomial)
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) |
1.026 |
0.294 |
3.488 |
0.001 |
2.791 |
1.568 |
4.968 |
| ce2 |
0.288 |
0.160 |
1.801 |
0.072 |
1.334 |
0.975 |
1.825 |
| ce3 |
0.561 |
0.176 |
3.181 |
0.002 |
1.753 |
1.240 |
2.476 |
| ce4 |
0.894 |
0.213 |
4.199 |
0.000 |
2.445 |
1.611 |
3.711 |
| ce5 |
0.594 |
0.241 |
2.461 |
0.014 |
1.811 |
1.129 |
2.906 |
| sex1 |
-0.444 |
0.127 |
-3.486 |
0.001 |
0.641 |
0.500 |
0.823 |
| rel_stat1 |
0.079 |
0.122 |
0.645 |
0.519 |
1.082 |
0.851 |
1.375 |
| AGE |
-0.014 |
0.005 |
-2.906 |
0.004 |
0.986 |
0.977 |
0.996 |
| race2black |
-0.027 |
0.169 |
-0.162 |
0.871 |
0.973 |
0.698 |
1.355 |
| race2other |
0.256 |
0.198 |
1.293 |
0.196 |
1.292 |
0.876 |
1.905 |
| bel_comdisagree |
-1.260 |
0.137 |
-9.184 |
0.000 |
0.284 |
0.217 |
0.371 |
| bel_comneutral |
-0.748 |
0.171 |
-4.364 |
0.000 |
0.473 |
0.338 |
0.662 |
fit.logit4<-svyglm(sui_th ~ ce + sex + rel_stat + AGE + race2 + clo_peo ,
design = des,
family=binomial)
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) |
0.026 |
0.279 |
0.092 |
0.927 |
1.026 |
0.594 |
1.772 |
| ce2 |
0.323 |
0.160 |
2.018 |
0.044 |
1.381 |
1.009 |
1.890 |
| ce3 |
0.592 |
0.174 |
3.402 |
0.001 |
1.808 |
1.285 |
2.542 |
| ce4 |
0.956 |
0.212 |
4.507 |
0.000 |
2.601 |
1.716 |
3.941 |
| ce5 |
0.560 |
0.240 |
2.336 |
0.020 |
1.750 |
1.094 |
2.799 |
| sex1 |
-0.456 |
0.128 |
-3.568 |
0.000 |
0.634 |
0.493 |
0.814 |
| rel_stat1 |
0.072 |
0.123 |
0.584 |
0.559 |
1.075 |
0.844 |
1.369 |
| AGE |
-0.018 |
0.005 |
-3.789 |
0.000 |
0.983 |
0.974 |
0.992 |
| race2black |
-0.088 |
0.167 |
-0.528 |
0.598 |
0.916 |
0.660 |
1.270 |
| race2other |
0.158 |
0.195 |
0.811 |
0.418 |
1.171 |
0.799 |
1.715 |
| clo_peodisagree |
1.194 |
0.139 |
8.594 |
0.000 |
3.300 |
2.513 |
4.333 |
| clo_peoneutral |
0.224 |
0.152 |
1.479 |
0.139 |
1.251 |
0.930 |
1.684 |
fit.logit5<-svyglm(sui_th ~ ce + sex + rel_stat + AGE + race2 + com_comf ,
design = des,
family=binomial)
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) |
0.124 |
0.282 |
0.440 |
0.660 |
1.132 |
0.652 |
1.967 |
| ce2 |
0.298 |
0.159 |
1.879 |
0.060 |
1.347 |
0.987 |
1.839 |
| ce3 |
0.603 |
0.179 |
3.377 |
0.001 |
1.827 |
1.288 |
2.593 |
| ce4 |
0.869 |
0.211 |
4.116 |
0.000 |
2.384 |
1.576 |
3.606 |
| ce5 |
0.528 |
0.236 |
2.239 |
0.025 |
1.696 |
1.068 |
2.693 |
| sex1 |
-0.456 |
0.127 |
-3.584 |
0.000 |
0.634 |
0.494 |
0.813 |
| rel_stat1 |
0.081 |
0.123 |
0.663 |
0.507 |
1.085 |
0.853 |
1.380 |
| AGE |
-0.019 |
0.005 |
-4.182 |
0.000 |
0.981 |
0.972 |
0.990 |
| race2black |
-0.093 |
0.170 |
-0.548 |
0.584 |
0.911 |
0.653 |
1.271 |
| race2other |
0.171 |
0.192 |
0.890 |
0.374 |
1.186 |
0.815 |
1.727 |
| com_comfdisagree |
1.193 |
0.144 |
8.305 |
0.000 |
3.298 |
2.489 |
4.371 |
| com_comfneutral |
0.327 |
0.143 |
2.282 |
0.023 |
1.387 |
1.047 |
1.836 |
fit.logit6<-svyglm(sui_th ~ ce + sex + rel_stat + AGE + race2 + fam_help ,
design = des,
family=binomial)
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) |
0.666 |
0.327 |
2.037 |
0.042 |
1.946 |
1.026 |
3.692 |
| ce2 |
0.294 |
0.160 |
1.845 |
0.065 |
1.342 |
0.982 |
1.835 |
| ce3 |
0.459 |
0.174 |
2.640 |
0.008 |
1.583 |
1.126 |
2.226 |
| ce4 |
0.748 |
0.208 |
3.597 |
0.000 |
2.113 |
1.406 |
3.177 |
| ce5 |
0.507 |
0.227 |
2.230 |
0.026 |
1.660 |
1.063 |
2.592 |
| sex1 |
-0.412 |
0.124 |
-3.320 |
0.001 |
0.662 |
0.519 |
0.845 |
| rel_stat1 |
0.163 |
0.120 |
1.361 |
0.174 |
1.177 |
0.931 |
1.490 |
| AGE |
-0.013 |
0.005 |
-2.845 |
0.005 |
0.987 |
0.978 |
0.996 |
| race2black |
-0.100 |
0.164 |
-0.611 |
0.541 |
0.905 |
0.656 |
1.247 |
| race2other |
0.118 |
0.195 |
0.606 |
0.545 |
1.126 |
0.768 |
1.651 |
| fam_helpdisagree |
-0.638 |
0.214 |
-2.980 |
0.003 |
0.528 |
0.347 |
0.804 |
| fam_helpneutral |
-0.125 |
0.236 |
-0.530 |
0.596 |
0.883 |
0.556 |
1.401 |
fit.logit7<-svyglm(sui_th ~ ce + sex + rel_stat + AGE + race2 + fri_help ,
design = des,
family=binomial)
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) |
-0.362 |
0.298 |
-1.215 |
0.225 |
0.696 |
0.389 |
1.249 |
| ce2 |
0.328 |
0.158 |
2.070 |
0.039 |
1.388 |
1.018 |
1.894 |
| ce3 |
0.503 |
0.175 |
2.869 |
0.004 |
1.654 |
1.173 |
2.333 |
| ce4 |
0.836 |
0.206 |
4.059 |
0.000 |
2.308 |
1.541 |
3.456 |
| ce5 |
0.652 |
0.239 |
2.728 |
0.006 |
1.920 |
1.201 |
3.068 |
| sex1 |
-0.457 |
0.126 |
-3.628 |
0.000 |
0.633 |
0.495 |
0.811 |
| rel_stat1 |
0.149 |
0.121 |
1.235 |
0.217 |
1.161 |
0.916 |
1.470 |
| AGE |
-0.018 |
0.005 |
-3.976 |
0.000 |
0.982 |
0.973 |
0.991 |
| race2black |
-0.144 |
0.163 |
-0.884 |
0.377 |
0.866 |
0.629 |
1.191 |
| race2other |
0.177 |
0.194 |
0.911 |
0.363 |
1.194 |
0.816 |
1.747 |
| fri_helpdisagree |
1.212 |
0.169 |
7.157 |
0.000 |
3.361 |
2.411 |
4.684 |
| fri_helpneutral |
0.802 |
0.170 |
4.732 |
0.000 |
2.231 |
1.600 |
3.110 |
fit.logit8<-svyglm(sui_th ~ ce + sex + rel_stat + AGE + race2 + bel_com + clo_peo + com_comf + fam_help + fri_help ,
design = des,
family=binomial)
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) |
0.303 |
0.362 |
0.837 |
0.403 |
1.354 |
0.666 |
2.753 |
| ce2 |
0.282 |
0.162 |
1.739 |
0.082 |
1.326 |
0.965 |
1.823 |
| ce3 |
0.568 |
0.178 |
3.201 |
0.001 |
1.766 |
1.247 |
2.501 |
| ce4 |
0.861 |
0.216 |
3.979 |
0.000 |
2.366 |
1.548 |
3.616 |
| ce5 |
0.633 |
0.258 |
2.451 |
0.014 |
1.884 |
1.135 |
3.126 |
| sex1 |
-0.435 |
0.131 |
-3.319 |
0.001 |
0.647 |
0.501 |
0.837 |
| rel_stat1 |
0.025 |
0.126 |
0.202 |
0.840 |
1.026 |
0.802 |
1.312 |
| AGE |
-0.016 |
0.005 |
-3.247 |
0.001 |
0.985 |
0.975 |
0.994 |
| race2black |
-0.028 |
0.170 |
-0.165 |
0.869 |
0.972 |
0.697 |
1.357 |
| race2other |
0.200 |
0.204 |
0.984 |
0.325 |
1.222 |
0.820 |
1.822 |
| bel_comdisagree |
-0.686 |
0.163 |
-4.200 |
0.000 |
0.504 |
0.366 |
0.694 |
| bel_comneutral |
-0.408 |
0.185 |
-2.212 |
0.027 |
0.665 |
0.463 |
0.955 |
| clo_peodisagree |
0.456 |
0.206 |
2.209 |
0.027 |
1.578 |
1.053 |
2.365 |
| clo_peoneutral |
-0.031 |
0.187 |
-0.166 |
0.868 |
0.969 |
0.672 |
1.399 |
| com_comfdisagree |
0.352 |
0.208 |
1.692 |
0.091 |
1.422 |
0.946 |
2.139 |
| com_comfneutral |
-0.019 |
0.184 |
-0.103 |
0.918 |
0.981 |
0.684 |
1.407 |
| fam_helpdisagree |
-0.404 |
0.225 |
-1.798 |
0.072 |
0.668 |
0.430 |
1.037 |
| fam_helpneutral |
-0.132 |
0.243 |
-0.543 |
0.587 |
0.876 |
0.544 |
1.412 |
| fri_helpdisagree |
0.701 |
0.189 |
3.712 |
0.000 |
2.015 |
1.392 |
2.917 |
| fri_helpneutral |
0.689 |
0.180 |
3.828 |
0.000 |
1.991 |
1.399 |
2.832 |
exp(coefficients(fit.logit1))
## (Intercept) ce2 ce3 ce4 ce5
## 0.4681725 1.5069691 1.9417863 2.3495614 1.6217511
exp(coefficients(fit.logit2))
## (Intercept) ce2 ce3 ce4 ce5 sex1
## 1.4018622 1.3689274 1.6452707 2.2493582 1.6827294 0.6456318
## rel_stat1 AGE race2black race2other
## 1.2004409 0.9837617 0.8817692 1.1818669
exp(coefficients(fit.logit3))
## (Intercept) ce2 ce3 ce4 ce5
## 2.7907205 1.3338974 1.7525508 2.4447243 1.8111557
## sex1 rel_stat1 AGE race2black race2other
## 0.6413603 1.0820270 0.9862399 0.9729408 1.2919641
## bel_comdisagree bel_comneutral
## 0.2837639 0.4732238
exp(coefficients(fit.logit4))
## (Intercept) ce2 ce3 ce4 ce5
## 1.0260139 1.3810412 1.8075242 2.6006460 1.7499073
## sex1 rel_stat1 AGE race2black race2other
## 0.6337559 1.0747493 0.9826166 0.9156004 1.1710239
## clo_peodisagree clo_peoneutral
## 3.3000008 1.2513142
exp(coefficients(fit.logit5))
## (Intercept) ce2 ce3 ce4
## 1.1320288 1.3474273 1.8272466 2.3842717
## ce5 sex1 rel_stat1 AGE
## 1.6961503 0.6336929 1.0848659 0.9808189
## race2black race2other com_comfdisagree com_comfneutral
## 0.9112508 1.1860376 3.2982910 1.3867000
exp(coefficients(fit.logit6))
## (Intercept) ce2 ce3 ce4
## 1.9458011 1.3421685 1.5830597 2.1133527
## ce5 sex1 rel_stat1 AGE
## 1.6600399 0.6623609 1.1773877 0.9868721
## race2black race2other fam_helpdisagree fam_helpneutral
## 0.9047587 1.1257086 0.5284897 0.8825007
exp(coefficients(fit.logit7))
## (Intercept) ce2 ce3 ce4
## 0.6964861 1.3881761 1.6542774 2.3079251
## ce5 sex1 rel_stat1 AGE
## 1.9198682 0.6333058 1.1606444 0.9817046
## race2black race2other fri_helpdisagree fri_helpneutral
## 0.8659415 1.1935727 3.3608391 2.2308313
exp(coefficients(fit.logit8))
## (Intercept) ce2 ce3 ce4
## 1.3539488 1.3262324 1.7655231 2.3656526
## ce5 sex1 rel_stat1 AGE
## 1.8838856 0.6474940 1.0256776 0.9845090
## race2black race2other bel_comdisagree bel_comneutral
## 0.9724453 1.2219277 0.5036831 0.6648530
## clo_peodisagree clo_peoneutral com_comfdisagree com_comfneutral
## 1.5776886 0.9693194 1.4222967 0.9811431
## fam_helpdisagree fam_helpneutral fri_helpdisagree fri_helpneutral
## 0.6678743 0.8760653 2.0152414 1.9908295
library(ResourceSelection)
hl <- hoslem.test(sub2$sui_th, fitted(fit.logit1), g=10)
## Warning in hoslem.test(sub2$sui_th, fitted(fit.logit1), g = 10): The data did
## not allow for the requested number of bins.
hl
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: sub2$sui_th, fitted(fit.logit1)
## X-squared = 2.3388e-23, df = 1, p-value = 1
h2 <- hoslem.test(sub2$sui_th, fitted(fit.logit2), g=10)
h2
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: sub2$sui_th, fitted(fit.logit2)
## X-squared = 15.239, df = 8, p-value = 0.05466
h3 <- hoslem.test(sub2$sui_th, fitted(fit.logit3), g=10)
h3
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: sub2$sui_th, fitted(fit.logit3)
## X-squared = 11.996, df = 8, p-value = 0.1514
h4 <- hoslem.test(sub2$sui_th, fitted(fit.logit4), g=10)
h4
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: sub2$sui_th, fitted(fit.logit4)
## X-squared = 22.31, df = 8, p-value = 0.004374
h5 <- hoslem.test(sub2$sui_th, fitted(fit.logit5), g=10)
h5
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: sub2$sui_th, fitted(fit.logit5)
## X-squared = 15.677, df = 8, p-value = 0.04725
h6 <- hoslem.test(sub2$sui_th, fitted(fit.logit6), g=10)
h6
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: sub2$sui_th, fitted(fit.logit6)
## X-squared = 4.7126, df = 8, p-value = 0.7878
h7 <- hoslem.test(sub2$sui_th, fitted(fit.logit7), g=10)
h7
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: sub2$sui_th, fitted(fit.logit7)
## X-squared = 13.948, df = 8, p-value = 0.08314
h8 <- hoslem.test(sub2$sui_th, fitted(fit.logit8), g=10)
h8
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: sub2$sui_th, fitted(fit.logit8)
## X-squared = 8.0808, df = 8, p-value = 0.4256
rg<-ref_grid(fit.logit7)
marg_logit<-emmeans(object = rg,
specs = c( "ce", "sex"),
type="response" ,
data=sub2)
knitr::kable(marg_logit, digits = 4)
| 1 |
0 |
0.3691 |
0.0299 |
Inf |
0.3127 |
0.4293 |
| 2 |
0 |
0.4481 |
0.0413 |
Inf |
0.3693 |
0.5297 |
| 3 |
0 |
0.4918 |
0.0456 |
Inf |
0.4036 |
0.5805 |
| 4 |
0 |
0.5745 |
0.0536 |
Inf |
0.4677 |
0.6747 |
| 5 |
0 |
0.5290 |
0.0623 |
Inf |
0.4075 |
0.6471 |
| 1 |
1 |
0.2703 |
0.0247 |
Inf |
0.2247 |
0.3214 |
| 2 |
1 |
0.3396 |
0.0322 |
Inf |
0.2795 |
0.4053 |
| 3 |
1 |
0.3800 |
0.0397 |
Inf |
0.3058 |
0.4602 |
| 4 |
1 |
0.4609 |
0.0484 |
Inf |
0.3685 |
0.5561 |
| 5 |
1 |
0.4156 |
0.0553 |
Inf |
0.3128 |
0.5263 |
rg<-ref_grid(fit.logit7)
marg_logit<-emmeans(object = rg,
specs = c( "ce", "fri_help"),
type="response" ,
data=sub2)
knitr::kable(marg_logit, digits = 4)
| 1 |
agree |
0.1921 |
0.0270 |
Inf |
0.1447 |
0.2506 |
| 2 |
agree |
0.2482 |
0.0353 |
Inf |
0.1856 |
0.3235 |
| 3 |
agree |
0.2824 |
0.0422 |
Inf |
0.2073 |
0.3718 |
| 4 |
agree |
0.3544 |
0.0540 |
Inf |
0.2569 |
0.4656 |
| 5 |
agree |
0.3135 |
0.0558 |
Inf |
0.2155 |
0.4315 |
| 1 |
disagree |
0.4442 |
0.0301 |
Inf |
0.3863 |
0.5037 |
| 2 |
disagree |
0.5260 |
0.0391 |
Inf |
0.4493 |
0.6015 |
| 3 |
disagree |
0.5694 |
0.0431 |
Inf |
0.4837 |
0.6511 |
| 4 |
disagree |
0.6485 |
0.0481 |
Inf |
0.5496 |
0.7361 |
| 5 |
disagree |
0.6055 |
0.0585 |
Inf |
0.4870 |
0.7127 |
| 1 |
neutral |
0.3466 |
0.0284 |
Inf |
0.2932 |
0.4042 |
| 2 |
neutral |
0.4241 |
0.0386 |
Inf |
0.3508 |
0.5010 |
| 3 |
neutral |
0.4674 |
0.0436 |
Inf |
0.3837 |
0.5530 |
| 4 |
neutral |
0.5505 |
0.0504 |
Inf |
0.4511 |
0.6460 |
| 5 |
neutral |
0.5046 |
0.0596 |
Inf |
0.3896 |
0.6191 |