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)
GSS2018 <- read_sav("GSS2018.sav")
View(GSS2018)
nams<-names(GSS2018)
head(nams, n=10)
##  [1] "ABANY"     "ABDEFECT"  "ABFELEGL"  "ABHELP1"   "ABHELP2"   "ABHELP3"  
##  [7] "ABHELP4"   "ABHLTH"    "ABINSPAY"  "ABMEDGOV1"
newnames<-tolower(gsub(pattern = "_",replacement =  "",x =  nams))
names(GSS2018)<-newnames
GSS2018$sup_dp<-Recode(GSS2018$cappun, recodes="1=1; 2=0;else=NA")
summary(GSS2018$sup_dp, na.rm = TRUE)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  1.0000  0.6315  1.0000  1.0000     155
GSS2018$polhitok2<-Recode(GSS2018$polhitok, recodes="1=1; 2=0;else=NA")
summary(GSS2018$polhitok2, na.rm = TRUE)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  1.0000  0.6361  1.0000  1.0000     867
GSS2018$polabuse2<-Recode(GSS2018$polabuse, recodes="1=1; 2=0;else=NA")
summary(GSS2018$polabuse2, na.rm = TRUE)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.0984  0.0000  1.0000     824
GSS2018$polmurdr2<-Recode(GSS2018$polmurdr, recodes="1=1; 2=0;else=NA")
summary(GSS2018$polmurdr2, na.rm = TRUE)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  0.0000  0.1672  0.0000  1.0000     835
GSS2018$polescap2<-Recode(GSS2018$polescap, recodes="1=1; 2=0;else=NA")
summary(GSS2018$polescap2, na.rm = TRUE)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  0.0000  1.0000  0.6855  1.0000  1.0000     879
GSS2018$polattak2<-Recode(GSS2018$polattak, recodes="1=1; 2=0;else=NA")
summary(GSS2018$polattak2, na.rm = TRUE)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.0000  1.0000  1.0000  0.8574  1.0000  1.0000     819
GSS2018$sex <- as.numeric(GSS2018$sex)
GSS2018$geniden<-Recode (GSS2018$sex, recodes="1='male'; 2='female'; else=NA", as.factor=T)
GSS2018geniden<-relevel(GSS2018$geniden, ref='female')
summary(GSS2018$geniden, na.rm = TRUE)
## female   male 
##   1296   1052
GSS2018$race <- as.numeric(GSS2018$race)
GSS2018$race_eth2<-Recode(GSS2018$race, recodes="1='white'; 2='black'; 3='other'; else=NA", as.factor=T)
GSS2018race_eth2<-relevel(GSS2018$race_eth2, ref='white')
summary(GSS2018$race_eth2, na.rm = TRUE)
## black other white 
##   385   270  1693
GSS2018$fund <- as.numeric(GSS2018$fund)
GSS2018$fund2<-Recode(GSS2018$fund, recodes="1='fundamentalist'; 2='moderate'; 3='liberal'; else=NA", as.factor=T)
GSS2018fund2<-relevel(GSS2018$fund2, ref='fundamentalist')
summary(GSS2018$fund2, na.rm = TRUE)
## fundamentalist        liberal       moderate           NA's 
##            544            797            882            125
GSS2018$partyid <- as.numeric(GSS2018$partyid)
GSS2018$polaff<-Recode(GSS2018$partyid, recodes="0:2='dem'; 4:6='repub'; 3='independent'; else=NA", as.factor=T)
GSS2018polaff<-relevel(GSS2018$polaff, ref='dem')
summary(GSS2018$polaff, na.rm = TRUE)
##         dem independent       repub        NA's 
##        1038         414         786         110
GSS2018$degree <- as.numeric(GSS2018$degree)
GSS2018$educ_new<-Recode(GSS2018$degree, recodes="0='lsshgh'; 1='hghsch'; 2='smecol'; 3:4='col'; else=NA", as.factor=T)
GSS2018educ_new<-relevel(GSS2018$educ_new, ref='col')
summary(GSS2018$educ_new, na.rm = TRUE)
##    col hghsch lsshgh smecol 
##    712   1178    262    196
sub<-GSS2018%>%
  select(sup_dp, geniden, race_eth2, polaff, educ_new, fund2, polhitok2, polabuse2, polmurdr2, polescap2, polattak2, vstrat, wtssall) %>%
  filter( complete.cases( . ))
table1(~ race_eth2 + geniden + educ_new + polaff + fund2| polhitok2,  data=sub, overall="Total")
## Warning in table1.formula(~race_eth2 + geniden + educ_new + polaff + fund2 | :
## Terms to the right of '|' in formula 'x' define table columns and are expected
## to be factors with meaningful labels.
0
(N=413)		 1
(N=757)		 Total
(N=1170)
race_eth2
black 128 (31.0%) 67 (8.9%) 195 (16.7%)
other 69 (16.7%) 45 (5.9%) 114 (9.7%)
white 216 (52.3%) 645 (85.2%) 861 (73.6%)
geniden
female 259 (62.7%) 348 (46.0%) 607 (51.9%)
male 154 (37.3%) 409 (54.0%) 563 (48.1%)
educ_new
col 68 (16.5%) 297 (39.2%) 365 (31.2%)
hghsch 224 (54.2%) 358 (47.3%) 582 (49.7%)
lsshgh 83 (20.1%) 40 (5.3%) 123 (10.5%)
smecol 38 (9.2%) 62 (8.2%) 100 (8.5%)
polaff
dem 231 (55.9%) 314 (41.5%) 545 (46.6%)
independent 81 (19.6%) 96 (12.7%) 177 (15.1%)
repub 101 (24.5%) 347 (45.8%) 448 (38.3%)
fund2
fundamentalist 112 (27.1%) 174 (23.0%) 286 (24.4%)
liberal 129 (31.2%) 289 (38.2%) 418 (35.7%)
moderate 172 (41.6%) 294 (38.8%) 466 (39.8%) 
table1(~ race_eth2 + geniden + educ_new + polaff + fund2| polabuse2,  data=sub, overall="Total")
## Warning in table1.formula(~race_eth2 + geniden + educ_new + polaff + fund2 | :
## Terms to the right of '|' in formula 'x' define table columns and are expected
## to be factors with meaningful labels.
0
(N=1052)		 1
(N=118)		 Total
(N=1170)
race_eth2
black 188 (17.9%) 7 (5.9%) 195 (16.7%)
other 97 (9.2%) 17 (14.4%) 114 (9.7%)
white 767 (72.9%) 94 (79.7%) 861 (73.6%)
geniden
female 548 (52.1%) 59 (50.0%) 607 (51.9%)
male 504 (47.9%) 59 (50.0%) 563 (48.1%)
educ_new
col 336 (31.9%) 29 (24.6%) 365 (31.2%)
hghsch 515 (49.0%) 67 (56.8%) 582 (49.7%)
lsshgh 106 (10.1%) 17 (14.4%) 123 (10.5%)
smecol 95 (9.0%) 5 (4.2%) 100 (8.5%)
polaff
dem 510 (48.5%) 35 (29.7%) 545 (46.6%)
independent 153 (14.5%) 24 (20.3%) 177 (15.1%)
repub 389 (37.0%) 59 (50.0%) 448 (38.3%)
fund2
fundamentalist 259 (24.6%) 27 (22.9%) 286 (24.4%)
liberal 388 (36.9%) 30 (25.4%) 418 (35.7%)
moderate 405 (38.5%) 61 (51.7%) 466 (39.8%) 
table1(~ race_eth2 + geniden + educ_new + polaff + fund2| polmurdr2,  data=sub, overall="Total")
## Warning in table1.formula(~race_eth2 + geniden + educ_new + polaff + fund2 | :
## Terms to the right of '|' in formula 'x' define table columns and are expected
## to be factors with meaningful labels.
0
(N=973)		 1
(N=197)		 Total
(N=1170)
race_eth2
black 159 (16.3%) 36 (18.3%) 195 (16.7%)
other 83 (8.5%) 31 (15.7%) 114 (9.7%)
white 731 (75.1%) 130 (66.0%) 861 (73.6%)
geniden
female 514 (52.8%) 93 (47.2%) 607 (51.9%)
male 459 (47.2%) 104 (52.8%) 563 (48.1%)
educ_new
col 332 (34.1%) 33 (16.8%) 365 (31.2%)
hghsch 462 (47.5%) 120 (60.9%) 582 (49.7%)
lsshgh 92 (9.5%) 31 (15.7%) 123 (10.5%)
smecol 87 (8.9%) 13 (6.6%) 100 (8.5%)
polaff
dem 467 (48.0%) 78 (39.6%) 545 (46.6%)
independent 140 (14.4%) 37 (18.8%) 177 (15.1%)
repub 366 (37.6%) 82 (41.6%) 448 (38.3%)
fund2
fundamentalist 235 (24.2%) 51 (25.9%) 286 (24.4%)
liberal 361 (37.1%) 57 (28.9%) 418 (35.7%)
moderate 377 (38.7%) 89 (45.2%) 466 (39.8%) 
table1(~ race_eth2 + geniden + educ_new + polaff + fund2| polescap2,  data=sub, overall="Total")
## Warning in table1.formula(~race_eth2 + geniden + educ_new + polaff + fund2 | :
## Terms to the right of '|' in formula 'x' define table columns and are expected
## to be factors with meaningful labels.
0
(N=356)		 1
(N=814)		 Total
(N=1170)
race_eth2
black 102 (28.7%) 93 (11.4%) 195 (16.7%)
other 49 (13.8%) 65 (8.0%) 114 (9.7%)
white 205 (57.6%) 656 (80.6%) 861 (73.6%)
geniden
female 212 (59.6%) 395 (48.5%) 607 (51.9%)
male 144 (40.4%) 419 (51.5%) 563 (48.1%)
educ_new
col 111 (31.2%) 254 (31.2%) 365 (31.2%)
hghsch 170 (47.8%) 412 (50.6%) 582 (49.7%)
lsshgh 45 (12.6%) 78 (9.6%) 123 (10.5%)
smecol 30 (8.4%) 70 (8.6%) 100 (8.5%)
polaff
dem 205 (57.6%) 340 (41.8%) 545 (46.6%)
independent 63 (17.7%) 114 (14.0%) 177 (15.1%)
repub 88 (24.7%) 360 (44.2%) 448 (38.3%)
fund2
fundamentalist 89 (25.0%) 197 (24.2%) 286 (24.4%)
liberal 140 (39.3%) 278 (34.2%) 418 (35.7%)
moderate 127 (35.7%) 339 (41.6%) 466 (39.8%) 
table1(~ race_eth2 + geniden + educ_new + polaff + fund2| polattak2,  data=sub, overall="Total")
## Warning in table1.formula(~race_eth2 + geniden + educ_new + polaff + fund2 | :
## Terms to the right of '|' in formula 'x' define table columns and are expected
## to be factors with meaningful labels.
0
(N=152)		 1
(N=1018)		 Total
(N=1170)
race_eth2
black 49 (32.2%) 146 (14.3%) 195 (16.7%)
other 21 (13.8%) 93 (9.1%) 114 (9.7%)
white 82 (53.9%) 779 (76.5%) 861 (73.6%)
geniden
female 91 (59.9%) 516 (50.7%) 607 (51.9%)
male 61 (40.1%) 502 (49.3%) 563 (48.1%)
educ_new
col 32 (21.1%) 333 (32.7%) 365 (31.2%)
hghsch 74 (48.7%) 508 (49.9%) 582 (49.7%)
lsshgh 32 (21.1%) 91 (8.9%) 123 (10.5%)
smecol 14 (9.2%) 86 (8.4%) 100 (8.5%)
polaff
dem 88 (57.9%) 457 (44.9%) 545 (46.6%)
independent 29 (19.1%) 148 (14.5%) 177 (15.1%)
repub 35 (23.0%) 413 (40.6%) 448 (38.3%)
fund2
fundamentalist 39 (25.7%) 247 (24.3%) 286 (24.4%)
liberal 45 (29.6%) 373 (36.6%) 418 (35.7%)
moderate 68 (44.7%) 398 (39.1%) 466 (39.8%) 
options(survey.lonely.psu = "adjust")
des<-svydesign(ids= ~1,
               strata= ~vstrat,
               weights= ~wtssall
               , data = sub )


fit.logit1<-svyglm(polhitok2 ~ race_eth2,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit2<-svyglm(polhitok2 ~ race_eth2 + geniden,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit3<-svyglm(polhitok2 ~ race_eth2 + geniden + educ_new,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit4<-svyglm(polhitok2 ~ race_eth2 + geniden + educ_new + polaff,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit5<-svyglm(polhitok2 ~ race_eth2 + geniden + educ_new + polaff + fund2,
                  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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -0.413 0.177 -2.330 0.020 0.662 0.468 0.937
race_eth2other -0.062 0.285 -0.220 0.826 0.939 0.538 1.641
race_eth2white 1.468 0.200 7.328 0.000 4.342 2.932 6.430 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -0.801 0.193 -4.147 0.000 0.449 0.307 0.655
race_eth2other -0.061 0.281 -0.218 0.828 0.941 0.542 1.633
race_eth2white 1.507 0.202 7.464 0.000 4.513 3.038 6.704
genidenmale 0.780 0.160 4.871 0.000 2.181 1.594 2.985 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -0.075 0.244 -0.309 0.757 0.927 0.575 1.495
race_eth2other -0.005 0.301 -0.017 0.987 0.995 0.552 1.794
race_eth2white 1.460 0.212 6.875 0.000 4.307 2.840 6.530
genidenmale 0.847 0.167 5.073 0.000 2.332 1.681 3.235
educ_newhghsch -0.828 0.210 -3.949 0.000 0.437 0.290 0.659
educ_newlsshgh -1.917 0.294 -6.519 0.000 0.147 0.083 0.262
educ_newsmecol -1.020 0.326 -3.134 0.002 0.360 0.190 0.682 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -0.093 0.249 -0.373 0.709 0.912 0.560 1.484
race_eth2other -0.104 0.302 -0.345 0.730 0.901 0.498 1.630
race_eth2white 1.263 0.214 5.914 0.000 3.536 2.327 5.374
genidenmale 0.815 0.167 4.874 0.000 2.260 1.628 3.137
educ_newhghsch -0.860 0.212 -4.063 0.000 0.423 0.280 0.641
educ_newlsshgh -1.893 0.291 -6.496 0.000 0.151 0.085 0.267
educ_newsmecol -1.038 0.329 -3.157 0.002 0.354 0.186 0.675
polaffindependent -0.041 0.214 -0.190 0.849 0.960 0.631 1.461
polaffrepub 0.563 0.194 2.901 0.004 1.756 1.200 2.568 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) 0.022 0.274 0.081 0.935 1.023 0.597 1.751
race_eth2other -0.065 0.313 -0.208 0.835 0.937 0.507 1.730
race_eth2white 1.277 0.218 5.872 0.000 3.587 2.342 5.495
genidenmale 0.809 0.168 4.822 0.000 2.246 1.617 3.121
educ_newhghsch -0.861 0.212 -4.060 0.000 0.423 0.279 0.641
educ_newlsshgh -1.909 0.288 -6.624 0.000 0.148 0.084 0.261
educ_newsmecol -1.013 0.327 -3.102 0.002 0.363 0.191 0.689
polaffindependent -0.031 0.217 -0.145 0.885 0.969 0.633 1.483
polaffrepub 0.567 0.192 2.951 0.003 1.762 1.210 2.568
fund2liberal -0.008 0.218 -0.036 0.972 0.992 0.647 1.522
fund2moderate -0.298 0.213 -1.400 0.162 0.742 0.489 1.126 
exp(coefficients(fit.logit1))
##    (Intercept) race_eth2other race_eth2white 
##      0.6618501      0.9394464      4.3416400
exp(coefficients(fit.logit2))
##    (Intercept) race_eth2other race_eth2white    genidenmale 
##      0.4487809      0.9405793      4.5129209      2.1811022
exp(coefficients(fit.logit3))
##    (Intercept) race_eth2other race_eth2white    genidenmale educ_newhghsch 
##      0.9273756      0.9949748      4.3067443      2.3321090      0.4368479 
## educ_newlsshgh educ_newsmecol 
##      0.1469857      0.3604356
exp(coefficients(fit.logit4))
##       (Intercept)    race_eth2other    race_eth2white       genidenmale 
##         0.9115218         0.9009778         3.5359701         2.2602242 
##    educ_newhghsch    educ_newlsshgh    educ_newsmecol polaffindependent 
##         0.4232893         0.1506546         0.3541791         0.9600970 
##       polaffrepub 
##         1.7557843
exp(coefficients(fit.logit5))
##       (Intercept)    race_eth2other    race_eth2white       genidenmale 
##         1.0225020         0.9370084         3.5872484         2.2464328 
##    educ_newhghsch    educ_newlsshgh    educ_newsmecol polaffindependent 
##         0.4229387         0.1482482         0.3629581         0.9690664 
##       polaffrepub      fund2liberal     fund2moderate 
##         1.7624438         0.9922520         0.7424940
fit.logit6<-svyglm(polabuse2 ~ race_eth2,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit7<-svyglm(polabuse2 ~ race_eth2 + geniden,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit8<-svyglm(polabuse2 ~ race_eth2 + geniden + educ_new,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit9<-svyglm(polabuse2 ~ race_eth2 + geniden + educ_new + polaff,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit10<-svyglm(polabuse2 ~ race_eth2 + geniden + educ_new + polaff + fund2,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -3.766 0.411 -9.166 0 0.023 0.010 0.052
race_eth2other 2.094 0.508 4.125 0 8.117 3.001 21.953
race_eth2white 1.641 0.429 3.828 0 5.159 2.227 11.952 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -3.821 0.423 -9.032 0.00 0.022 0.010 0.050
race_eth2other 2.095 0.507 4.131 0.00 8.125 3.007 21.955
race_eth2white 1.639 0.429 3.824 0.00 5.151 2.223 11.935
genidenmale 0.111 0.223 0.496 0.62 1.117 0.721 1.730 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -4.264 0.464 -9.200 0.000 0.014 0.006 0.035
race_eth2other 2.088 0.506 4.127 0.000 8.066 2.993 21.737
race_eth2white 1.758 0.427 4.118 0.000 5.800 2.512 13.391
genidenmale 0.021 0.223 0.095 0.924 1.022 0.659 1.583
educ_newhghsch 0.630 0.266 2.374 0.018 1.878 1.116 3.160
educ_newlsshgh 0.743 0.372 1.994 0.046 2.102 1.013 4.362
educ_newsmecol -0.433 0.574 -0.753 0.452 0.649 0.211 2.000 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -4.429 0.466 -9.508 0.000 0.012 0.005 0.030
race_eth2other 1.951 0.501 3.893 0.000 7.033 2.634 18.781
race_eth2white 1.498 0.435 3.443 0.001 4.474 1.907 10.499
genidenmale -0.027 0.222 -0.123 0.902 0.973 0.630 1.502
educ_newhghsch 0.535 0.273 1.962 0.050 1.708 1.000 2.915
educ_newlsshgh 0.716 0.376 1.903 0.057 2.046 0.979 4.277
educ_newsmecol -0.480 0.584 -0.821 0.412 0.619 0.197 1.945
polaffindependent 0.703 0.326 2.155 0.031 2.019 1.066 3.825
polaffrepub 0.737 0.266 2.765 0.006 2.089 1.239 3.521 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -4.538 0.505 -8.981 0.000 0.011 0.004 0.029
race_eth2other 1.865 0.511 3.653 0.000 6.458 2.374 17.566
race_eth2white 1.489 0.443 3.359 0.001 4.431 1.859 10.564
genidenmale 0.000 0.223 0.000 1.000 1.000 0.646 1.549
educ_newhghsch 0.526 0.273 1.930 0.054 1.693 0.992 2.889
educ_newlsshgh 0.707 0.380 1.860 0.063 2.028 0.963 4.272
educ_newsmecol -0.532 0.582 -0.914 0.361 0.588 0.188 1.837
polaffindependent 0.649 0.325 1.996 0.046 1.914 1.012 3.620
polaffrepub 0.692 0.278 2.487 0.013 1.997 1.158 3.445
fund2liberal -0.304 0.339 -0.896 0.371 0.738 0.380 1.434
fund2moderate 0.492 0.276 1.780 0.075 1.636 0.952 2.812 
exp(coefficients(fit.logit6))
##    (Intercept) race_eth2other race_eth2white 
##     0.02313439     8.11742160     5.15949453
exp(coefficients(fit.logit7))
##    (Intercept) race_eth2other race_eth2white    genidenmale 
##     0.02190836     8.12473003     5.15096237     1.11706888
exp(coefficients(fit.logit8))
##    (Intercept) race_eth2other race_eth2white    genidenmale educ_newhghsch 
##     0.01406082     8.06579864     5.79985215     1.02154272     1.87807395 
## educ_newlsshgh educ_newsmecol 
##     2.10185429     0.64886419
exp(coefficients(fit.logit9))
##       (Intercept)    race_eth2other    race_eth2white       genidenmale 
##        0.01193024        7.03311505        4.47426367        0.97318552 
##    educ_newhghsch    educ_newlsshgh    educ_newsmecol polaffindependent 
##        1.70775410        2.04608682        0.61873157        2.01895102 
##       polaffrepub 
##        2.08907270
exp(coefficients(fit.logit10))
##       (Intercept)    race_eth2other    race_eth2white       genidenmale 
##        0.01068967        6.45767425        4.43122140        0.99999201 
##    educ_newhghsch    educ_newlsshgh    educ_newsmecol polaffindependent 
##        1.69286376        2.02805943        0.58766955        1.91401785 
##       polaffrepub      fund2liberal     fund2moderate 
##        1.99726269        0.73822810        1.63564557
fit.logit11<-svyglm(polmurdr2 ~ race_eth2,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit12<-svyglm(polmurdr2 ~ race_eth2 + geniden,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit13<-svyglm(polmurdr2 ~ race_eth2 + geniden + educ_new,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit14<-svyglm(polmurdr2 ~ race_eth2 + geniden + educ_new + polaff,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit15<-svyglm(polmurdr2 ~ race_eth2 + geniden + educ_new + polaff + fund2,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -1.493 0.205 -7.279 0.000 0.225 0.150 0.336
race_eth2other 0.513 0.312 1.640 0.101 1.669 0.905 3.080
race_eth2white -0.342 0.232 -1.475 0.140 0.711 0.451 1.119 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -1.583 0.217 -7.289 0.000 0.205 0.134 0.314
race_eth2other 0.514 0.311 1.654 0.099 1.672 0.909 3.075
race_eth2white -0.345 0.232 -1.488 0.137 0.708 0.450 1.116
genidenmale 0.181 0.179 1.015 0.310 1.199 0.845 1.702 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -2.347 0.300 -7.835 0.000 0.096 0.053 0.172
race_eth2other 0.511 0.314 1.627 0.104 1.666 0.901 3.082
race_eth2white -0.227 0.241 -0.943 0.346 0.797 0.497 1.278
genidenmale 0.120 0.182 0.658 0.511 1.127 0.789 1.610
educ_newhghsch 0.985 0.236 4.180 0.000 2.677 1.687 4.248
educ_newlsshgh 1.131 0.313 3.613 0.000 3.099 1.678 5.724
educ_newsmecol 0.470 0.397 1.184 0.237 1.601 0.735 3.487 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -2.434 0.292 -8.326 0.000 0.088 0.049 0.155
race_eth2other 0.427 0.316 1.350 0.177 1.533 0.825 2.849
race_eth2white -0.373 0.259 -1.439 0.150 0.689 0.414 1.145
genidenmale 0.099 0.183 0.542 0.588 1.104 0.771 1.582
educ_newhghsch 0.922 0.239 3.864 0.000 2.514 1.575 4.012
educ_newlsshgh 1.090 0.319 3.415 0.001 2.973 1.591 5.556
educ_newsmecol 0.446 0.395 1.128 0.259 1.562 0.720 3.390
polaffindependent 0.522 0.261 1.998 0.046 1.685 1.010 2.811
polaffrepub 0.393 0.211 1.866 0.062 1.482 0.980 2.240 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -2.418 0.321 -7.531 0.000 0.089 0.047 0.167
race_eth2other 0.402 0.320 1.256 0.210 1.494 0.798 2.796
race_eth2white -0.366 0.258 -1.417 0.157 0.694 0.418 1.151
genidenmale 0.128 0.186 0.685 0.493 1.136 0.789 1.637
educ_newhghsch 0.907 0.238 3.814 0.000 2.476 1.554 3.946
educ_newlsshgh 1.074 0.318 3.380 0.001 2.927 1.570 5.455
educ_newsmecol 0.415 0.392 1.058 0.290 1.514 0.702 3.266
polaffindependent 0.515 0.261 1.971 0.049 1.673 1.003 2.791
polaffrepub 0.361 0.216 1.673 0.095 1.435 0.940 2.191
fund2liberal -0.288 0.243 -1.186 0.236 0.750 0.466 1.206
fund2moderate 0.184 0.216 0.851 0.395 1.202 0.787 1.836 
exp(coefficients(fit.logit11))
##    (Intercept) race_eth2other race_eth2white 
##      0.2246056      1.6694990      0.7106853
exp(coefficients(fit.logit12))
##    (Intercept) race_eth2other race_eth2white    genidenmale 
##      0.2053967      1.6720545      0.7081947      1.1988357
exp(coefficients(fit.logit13))
##    (Intercept) race_eth2other race_eth2white    genidenmale educ_newhghsch 
##     0.09565191     1.66624525     0.79669561     1.12715724     2.67705932 
## educ_newlsshgh educ_newsmecol 
##     3.09918208     1.60060726
exp(coefficients(fit.logit14))
##       (Intercept)    race_eth2other    race_eth2white       genidenmale 
##        0.08766611        1.53273986        0.68859810        1.10444154 
##    educ_newhghsch    educ_newlsshgh    educ_newsmecol polaffindependent 
##        2.51389718        2.97280885        1.56221574        1.68496536 
##       polaffrepub 
##        1.48172817
exp(coefficients(fit.logit15))
##       (Intercept)    race_eth2other    race_eth2white       genidenmale 
##        0.08908622        1.49411970        0.69360512        1.13606587 
##    educ_newhghsch    educ_newlsshgh    educ_newsmecol polaffindependent 
##        2.47628552        2.92657820        1.51434503        1.67296789 
##       polaffrepub      fund2liberal     fund2moderate 
##        1.43495920        0.75001820        1.20198698
fit.logit16<-svyglm(polescap2 ~ race_eth2,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit17<-svyglm(polescap2 ~ race_eth2 + geniden,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit18<-svyglm(polescap2~ race_eth2 + geniden + educ_new,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit19<-svyglm(polescap2 ~ race_eth2 + geniden + educ_new + polaff,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit20<-svyglm(polescap2 ~ race_eth2 + geniden + educ_new + polaff + fund2,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -0.116 0.165 -0.703 0.482 0.891 0.645 1.230
race_eth2other 0.435 0.275 1.581 0.114 1.544 0.901 2.647
race_eth2white 1.241 0.190 6.521 0.000 3.458 2.381 5.020 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -0.318 0.187 -1.703 0.089 0.728 0.505 1.049
race_eth2other 0.441 0.280 1.573 0.116 1.555 0.897 2.694
race_eth2white 1.246 0.197 6.339 0.000 3.476 2.364 5.109
genidenmale 0.420 0.156 2.687 0.007 1.521 1.120 2.066 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -0.409 0.224 -1.822 0.069 0.665 0.428 1.031
race_eth2other 0.451 0.283 1.591 0.112 1.570 0.901 2.735
race_eth2white 1.274 0.197 6.460 0.000 3.576 2.429 5.264
genidenmale 0.390 0.155 2.513 0.012 1.476 1.089 2.001
educ_newhghsch 0.204 0.179 1.137 0.256 1.226 0.863 1.741
educ_newlsshgh 0.012 0.271 0.044 0.965 1.012 0.595 1.722
educ_newsmecol -0.160 0.309 -0.519 0.604 0.852 0.465 1.560 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -0.439 0.226 -1.940 0.053 0.645 0.414 1.005
race_eth2other 0.366 0.285 1.285 0.199 1.442 0.825 2.519
race_eth2white 1.070 0.201 5.321 0.000 2.915 1.965 4.323
genidenmale 0.352 0.156 2.250 0.025 1.421 1.046 1.931
educ_newhghsch 0.181 0.183 0.991 0.322 1.199 0.837 1.716
educ_newlsshgh 0.055 0.272 0.201 0.840 1.056 0.619 1.801
educ_newsmecol -0.168 0.318 -0.531 0.596 0.845 0.453 1.575
polaffindependent -0.035 0.213 -0.163 0.870 0.966 0.637 1.465
polaffrepub 0.605 0.188 3.212 0.001 1.832 1.266 2.651 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) -0.487 0.247 -1.971 0.049 0.614 0.378 0.997
race_eth2other 0.342 0.297 1.150 0.250 1.407 0.786 2.519
race_eth2white 1.077 0.208 5.184 0.000 2.937 1.954 4.413
genidenmale 0.366 0.156 2.345 0.019 1.443 1.062 1.960
educ_newhghsch 0.173 0.181 0.956 0.339 1.189 0.834 1.696
educ_newlsshgh 0.049 0.273 0.181 0.857 1.051 0.615 1.794
educ_newsmecol -0.195 0.319 -0.611 0.541 0.823 0.440 1.538
polaffindependent -0.046 0.214 -0.216 0.829 0.955 0.628 1.452
polaffrepub 0.586 0.192 3.045 0.002 1.796 1.232 2.619
fund2liberal -0.074 0.216 -0.342 0.732 0.929 0.608 1.419
fund2moderate 0.198 0.209 0.946 0.344 1.218 0.809 1.835 
exp(coefficients(fit.logit16))
##    (Intercept) race_eth2other race_eth2white 
##      0.8907585      1.5444004      3.4575499
exp(coefficients(fit.logit17))
##    (Intercept) race_eth2other race_eth2white    genidenmale 
##      0.7277883      1.5546247      3.4755599      1.5213948
exp(coefficients(fit.logit18))
##    (Intercept) race_eth2other race_eth2white    genidenmale educ_newhghsch 
##      0.6646000      1.5695601      3.5762761      1.4764999      1.2257727 
## educ_newlsshgh educ_newsmecol 
##      1.0118723      0.8520505
exp(coefficients(fit.logit19))
##       (Intercept)    race_eth2other    race_eth2white       genidenmale 
##         0.6446743         1.4415989         2.9147538         1.4213781 
##    educ_newhghsch    educ_newlsshgh    educ_newsmecol polaffindependent 
##         1.1987337         1.0563774         0.8449382         0.9658730 
##       polaffrepub 
##         1.8320953
exp(coefficients(fit.logit20))
##       (Intercept)    race_eth2other    race_eth2white       genidenmale 
##         0.6142428         1.4071341         2.9367824         1.4426139 
##    educ_newhghsch    educ_newlsshgh    educ_newsmecol polaffindependent 
##         1.1892307         1.0505912         0.8226156         0.9547823 
##       polaffrepub      fund2liberal     fund2moderate 
##         1.7964887         0.9286652         1.2184614
fit.logit21<-svyglm(polattak2 ~ race_eth2,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit22<-svyglm(polattak2 ~ race_eth2 + geniden,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit23<-svyglm(polattak2 ~ race_eth2 + geniden + educ_new,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit24<-svyglm(polattak2 ~ race_eth2 + geniden + educ_new + polaff,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
fit.logit25<-svyglm(polattak2 ~ race_eth2 + geniden + educ_new + polaff + fund2,
                  design = des,
                  family = binomial)
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) 1.065 0.185 5.749 0.000 2.902 2.018 4.173
race_eth2other 0.466 0.322 1.446 0.149 1.593 0.847 2.995
race_eth2white 1.180 0.231 5.101 0.000 3.254 2.068 5.121 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) 0.923 0.204 4.520 0.000 2.516 1.686 3.754
race_eth2other 0.469 0.323 1.449 0.148 1.598 0.848 3.012
race_eth2white 1.178 0.232 5.079 0.000 3.249 2.062 5.120
genidenmale 0.310 0.210 1.478 0.140 1.364 0.904 2.059 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) 1.349 0.301 4.483 0.000 3.854 2.137 6.952
race_eth2other 0.547 0.332 1.645 0.100 1.728 0.901 3.315
race_eth2white 1.124 0.235 4.773 0.000 3.076 1.939 4.879
genidenmale 0.289 0.206 1.404 0.161 1.336 0.892 2.001
educ_newhghsch -0.284 0.258 -1.100 0.272 0.753 0.454 1.248
educ_newlsshgh -1.208 0.314 -3.849 0.000 0.299 0.161 0.553
educ_newsmecol -0.796 0.432 -1.843 0.066 0.451 0.194 1.052 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) 1.325 0.303 4.373 0.000 3.762 2.077 6.814
race_eth2other 0.492 0.340 1.448 0.148 1.635 0.840 3.182
race_eth2white 0.960 0.245 3.924 0.000 2.612 1.617 4.220
genidenmale 0.260 0.208 1.253 0.210 1.297 0.863 1.949
educ_newhghsch -0.303 0.259 -1.169 0.243 0.738 0.444 1.228
educ_newlsshgh -1.182 0.316 -3.739 0.000 0.307 0.165 0.570
educ_newsmecol -0.803 0.439 -1.828 0.068 0.448 0.189 1.060
polaffindependent -0.029 0.286 -0.101 0.920 0.972 0.554 1.703
polaffrepub 0.502 0.267 1.881 0.060 1.653 0.979 2.789 
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)
term estimate std.error statistic p.value OR LowerOR_Ci UpperOR_Ci
(Intercept) 1.292 0.314 4.115 0.000 3.639 1.967 6.732
race_eth2other 0.509 0.364 1.398 0.162 1.663 0.815 3.392
race_eth2white 0.938 0.258 3.632 0.000 2.556 1.540 4.241
genidenmale 0.235 0.211 1.114 0.265 1.266 0.836 1.915
educ_newhghsch -0.281 0.253 -1.110 0.267 0.755 0.460 1.240
educ_newlsshgh -1.161 0.315 -3.681 0.000 0.313 0.169 0.581
educ_newsmecol -0.768 0.436 -1.762 0.078 0.464 0.198 1.090
polaffindependent -0.034 0.282 -0.120 0.904 0.967 0.556 1.681
polaffrepub 0.541 0.270 2.004 0.045 1.717 1.012 2.913
fund2liberal 0.279 0.295 0.947 0.344 1.322 0.742 2.355
fund2moderate -0.131 0.283 -0.464 0.643 0.877 0.503 1.528 
exp(coefficients(fit.logit21))
##    (Intercept) race_eth2other race_eth2white 
##       2.902028       1.592924       3.254086
exp(coefficients(fit.logit22))
##    (Intercept) race_eth2other race_eth2white    genidenmale 
##       2.515996       1.598052       3.249156       1.364091
exp(coefficients(fit.logit23))
##    (Intercept) race_eth2other race_eth2white    genidenmale educ_newhghsch 
##      3.8542543      1.7278606      3.0758941      1.3356643      0.7529232 
## educ_newlsshgh educ_newsmecol 
##      0.2987259      0.4513491
exp(coefficients(fit.logit24))
##       (Intercept)    race_eth2other    race_eth2white       genidenmale 
##         3.7623368         1.6352366         2.6122019         1.2973922 
##    educ_newhghsch    educ_newlsshgh    educ_newsmecol polaffindependent 
##         0.7383418         0.3065829         0.4479648         0.9715862 
##       polaffrepub 
##         1.6525716
exp(coefficients(fit.logit25))
##       (Intercept)    race_eth2other    race_eth2white       genidenmale 
##         3.6388381         1.6629629         2.5556833         1.2655237 
##    educ_newhghsch    educ_newlsshgh    educ_newsmecol polaffindependent 
##         0.7551509         0.3132332         0.4641247         0.9666251 
##       polaffrepub      fund2liberal     fund2moderate 
##         1.7169574         1.3217200         0.8768025