MYCGSS
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library(haven)
library(sjlabelled)##
## Attaching package: 'sjlabelled'## The following objects are masked from 'package:haven':
##
## as_factor, read_sas, read_spss, read_stata, write_sas, zap_labelslibrary(tidyverse)## -- Attaching packages ------------------------------------------ tidyverse 1.3.0 --## √ ggplot2 3.3.2 √ purrr 0.3.4
## √ tibble 3.0.3 √ dplyr 1.0.2
## √ tidyr 1.1.2 √ stringr 1.4.0
## √ readr 1.3.1 √ forcats 0.5.0## -- Conflicts --------------------------------------------- tidyverse_conflicts() --
## x forcats::as_factor() masks sjlabelled::as_factor(), haven::as_factor()
## x dplyr::as_label() masks sjlabelled::as_label()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x sjlabelled::read_sas() masks haven::read_sas()
## x sjlabelled::read_spss() masks haven::read_spss()
## x sjlabelled::read_stata() masks haven::read_stata()
## x sjlabelled::write_sas() masks haven::write_sas()
## x sjlabelled::zap_labels() masks haven::zap_labels()library(sjmisc)##
## Attaching package: 'sjmisc'## The following object is masked from 'package:purrr':
##
## is_empty## The following object is masked from 'package:tidyr':
##
## replace_na## The following object is masked from 'package:tibble':
##
## add_case## The following objects are masked from 'package:sjlabelled':
##
## to_character, to_factor, to_label, to_numericlibrary(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## somelibrary(gtsummary)
library(VIM)## Loading required package: colorspace## Loading required package: grid## VIM is ready to use.## Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues##
## Attaching package: 'VIM'## The following object is masked from 'package:datasets':
##
## sleeplibrary(table1)##
## Attaching package: 'table1'## The following objects are masked from 'package:base':
##
## units, units<-library(ggplot2)
library(emmeans)
library(effects)## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car## lattice theme set by effectsTheme()
## See ?effectsTheme for details.library(ggeffects)
library(ggstatsplot)## Registered S3 method overwritten by 'broom.mixed':
## method from
## tidy.gamlss broom## In case you would like cite this package, cite it as:
## Patil, I. (2018). ggstatsplot: "ggplot2" Based Plots with Statistical Details. CRAN.
## Retrieved from https://cran.r-project.org/web/packages/ggstatsplot/index.htmllibrary(modelsummary)
library(ordinal)##
## Attaching package: 'ordinal'## The following object is masked from 'package:VIM':
##
## wine## The following object is masked from 'package:dplyr':
##
## slicecgss2017 <-read_dta("C:/Users/huawei/Desktop/R/cgss/cgss2017.dta")cgss2017$age<-2017-cgss2017$a31
cgss2017[cgss2017 == 98] <- NA
cgss2017[cgss2017 == 9999999] <- NA
cgss2017[cgss2017 == 9999998] <- NA
cgss2017[cgss2017 == 9999997] <- NA
cgss2017[cgss2017 == 99] <- NA
cgss2017 <- sjlabelled::drop_labels(cgss2017)
table(cgss2017$c10)##
## 1 2 3 4 8
## 101 708 2588 646 176MY_CGSS <-
cgss2017 %>% select(
Pre_Sex = a38,
gender = a2,
nation = a4,
age = age,
edu = a7a,
fair = c10,
trust = v934,
son = a68a1,
daugther = a68a2,
)
MY_CGSS[!complete.cases(MY_CGSS),]## # A tibble: 8,486 x 9
## Pre_Sex gender nation age edu fair trust son daugther
## <dbl+lbl> <dbl+l> <dbl+l> <dbl> <dbl+lbl> <dbl+> <dbl+> <dbl> <dbl>
## 1 2 [大多数情况下是不~ 2 [女] 1 [汉] 80 9 [大学专科（成人高~ NA NA 2 0
## 2 5 [完全是对的]~ 1 [男] 1 [汉] 29 9 [大学专科（成人高~ NA NA 0 0
## 3 3 [说不上对与不对]~ 2 [女] 1 [汉] 59 4 [初中] NA NA 1 0
## 4 4 [有时是对的]~ 2 [女] 1 [汉] 48 12 [大学本科（正规高~ NA NA 0 0
## 5 3 [说不上对与不对]~ 2 [女] 1 [汉] 60 4 [初中] NA NA 1 0
## 6 4 [有时是对的]~ 1 [男] 1 [汉] 38 10 [大学专科（正规高~ NA NA 0 1
## 7 3 [说不上对与不对]~ 1 [男] 1 [汉] 25 11 [大学本科（成人高~ NA NA 0 0
## 8 1 [总是不对的]~ 2 [女] 1 [汉] 55 11 [大学本科（成人高~ NA NA 1 0
## 9 2 [大多数情况下是不~ 1 [男] 4 [回] 58 6 [普通高中] NA NA 1 0
## 10 4 [有时是对的]~ 1 [男] 1 [汉] 20 10 [大学专科（正规高~ NA NA NA NA
## # ... with 8,476 more rowssum(is.na(MY_CGSS))## [1] 17256aggr(MY_CGSS,prop=F,numbers=T)
MY_CGSS$age <- as.numeric(MY_CGSS$age)
MY_CGSS$age <-
car::recode(MY_CGSS$age,"18:30='1';31:44='2';45:59='3';60:103='4'")
table(MY_CGSS$age)##
## 1 2 3 4
## 1848 2607 3755 4371MY_CGSS$edu <- as.numeric(MY_CGSS$edu)
MY_CGSS$edu <-
car::recode(MY_CGSS$edu, "9:13='1';5:8='2';1:4='3';'14'=NA")
table(MY_CGSS$edu)##
## 1 2 3
## 2470 2235 7856MY_CGSS$edu <-
factor(MY_CGSS$edu,
levels = c(1, 2, 3),
labels = c("高", "中", "低"))
table(MY_CGSS$edu)##
## 高 中 低
## 2470 2235 7856MY_CGSS$Pre_Sex <- as.numeric(MY_CGSS$Pre_Sex)
MY_CGSS$Pre_Sex <-
car::recode(MY_CGSS$Pre_Sex,"4:5='1';3='2';1:2='3'")
MY_CGSS$Pre_Sex <-
factor(MY_CGSS$Pre_Sex,
levels = c(1, 2, 3),
labels = c("同意", "中立", "不同意"))
MY_CGSS$nation <- as.numeric(MY_CGSS$nation)
MY_CGSS$nation <-
car::recode(MY_CGSS$nation,"1='1';2:8='2'")
MY_CGSS<-na.omit(MY_CGSS)
MY_CGSS<-filter(MY_CGSS,MY_CGSS$nation<2)
MY_CGSS$fair <- as.numeric(MY_CGSS$fair)
MY_CGSS$fair <-
car::recode(MY_CGSS$fair,"1:2='1';3:4='2';8='3'")
MY_CGSS$fair <-
factor(MY_CGSS$fair,
levels = c(1, 2, 3),
labels = c("占便宜", "公平", "矛盾"))
MY_CGSS$trust <- as.numeric(MY_CGSS$trust)
MY_CGSS$trust <-
car::recode(MY_CGSS$trust,"1:2='1';3:4='2';8='3'")
MY_CGSS$trust <-
factor(MY_CGSS$trust,
levels = c(1, 2, 3),
labels = c("信任", "谨慎", "矛盾"))
MY_CGSS$gender <-
factor(MY_CGSS$gender,
levels = c(1, 2),
labels = c("男", "女"))tbl_summary(MY_CGSS)| Characteristic | N = 3,7811 |
|---|---|
| Pre_Sex | |
| 同意 | 525 (14%) |
| 中立 | 910 (24%) |
| 不同意 | 2,346 (62%) |
| gender | |
| 男 | 1,820 (48%) |
| 女 | 1,961 (52%) |
| nation | 3,781 (100%) |
| age | |
| 1 | 521 (14%) |
| 2 | 810 (21%) |
| 3 | 1,122 (30%) |
| 4 | 1,328 (35%) |
| edu | |
| 高 | 731 (19%) |
| 中 | 707 (19%) |
| 低 | 2,343 (62%) |
| fair | |
| 占便宜 | 724 (19%) |
| 公平 | 2,914 (77%) |
| 矛盾 | 143 (3.8%) |
| trust | |
| 信任 | 2,038 (54%) |
| 谨慎 | 1,698 (45%) |
| 矛盾 | 45 (1.2%) |
| [儿子] 请问您有几个亲生子女 (包括已去世子女)？ | |
| 0 | 1,261 (33%) |
| 1 | 1,772 (47%) |
| 2 | 593 (16%) |
| 3 | 110 (2.9%) |
| 4 | 33 (0.9%) |
| 5 | 11 (0.3%) |
| 11 | 1 (<0.1%) |
| [女儿] 请问您有几个亲生子女 (包括已去世子女)？ | |
| 0 | 1,749 (46%) |
| 1 | 1,398 (37%) |
| 2 | 465 (12%) |
| 3 | 120 (3.2%) |
| 4 | 38 (1.0%) |
| 5 | 9 (0.2%) |
| 26 | 1 (<0.1%) |
| 60 | 1 (<0.1%) |
|
1
Statistics presented: n (%)
|
|
MY_CGSS %>%
select(gender,Pre_Sex,edu,age,fair,trust,son,daugther) %>% tbl_summary(
by = Pre_Sex,
)| Characteristic | 同意, N = 5251 | 中立, N = 9101 | 不同意, N = 2,3461 |
|---|---|---|---|
| gender | |||
| 男 | 295 (56%) | 464 (51%) | 1,061 (45%) |
| 女 | 230 (44%) | 446 (49%) | 1,285 (55%) |
| edu | |||
| 高 | 180 (34%) | 268 (29%) | 283 (12%) |
| 中 | 111 (21%) | 203 (22%) | 393 (17%) |
| 低 | 234 (45%) | 439 (48%) | 1,670 (71%) |
| age | |||
| 1 | 169 (32%) | 199 (22%) | 153 (6.5%) |
| 2 | 156 (30%) | 259 (28%) | 395 (17%) |
| 3 | 115 (22%) | 245 (27%) | 762 (32%) |
| 4 | 85 (16%) | 207 (23%) | 1,036 (44%) |
| fair | |||
| 占便宜 | 96 (18%) | 168 (18%) | 460 (20%) |
| 公平 | 410 (78%) | 703 (77%) | 1,801 (77%) |
| 矛盾 | 19 (3.6%) | 39 (4.3%) | 85 (3.6%) |
| trust | |||
| 信任 | 279 (53%) | 471 (52%) | 1,288 (55%) |
| 谨慎 | 238 (45%) | 429 (47%) | 1,031 (44%) |
| 矛盾 | 8 (1.5%) | 10 (1.1%) | 27 (1.2%) |
| [儿子] 请问您有几个亲生子女 (包括已去世子女)？ | |||
| 0 | 247 (47%) | 359 (39%) | 655 (28%) |
| 1 | 218 (42%) | 423 (46%) | 1,131 (48%) |
| 2 | 49 (9.3%) | 102 (11%) | 442 (19%) |
| 3 | 7 (1.3%) | 19 (2.1%) | 84 (3.6%) |
| 4 | 2 (0.4%) | 5 (0.5%) | 26 (1.1%) |
| 5 | 2 (0.4%) | 2 (0.2%) | 7 (0.3%) |
| 11 | 0 (0%) | 0 (0%) | 1 (<0.1%) |
| [女儿] 请问您有几个亲生子女 (包括已去世子女)？ | |||
| 0 | 314 (60%) | 491 (54%) | 944 (40%) |
| 1 | 162 (31%) | 326 (36%) | 910 (39%) |
| 2 | 35 (6.7%) | 77 (8.5%) | 353 (15%) |
| 3 | 12 (2.3%) | 12 (1.3%) | 96 (4.1%) |
| 4 | 1 (0.2%) | 3 (0.3%) | 34 (1.4%) |
| 5 | 1 (0.2%) | 1 (0.1%) | 7 (0.3%) |
| 26 | 0 (0%) | 0 (0%) | 1 (<0.1%) |
| 60 | 0 (0%) | 0 (0%) | 1 (<0.1%) |
|
1
Statistics presented: n (%)
|
|||
table1(~ fair+trust+son+daugther+age+edu|gender, data=MY_CGSS)| 男 (N=1820) |
女 (N=1961) |
Overall (N=3781) |
|
|---|---|---|---|
| fair | |||
| 占便宜 | 377 (20.7%) | 347 (17.7%) | 724 (19.1%) |
| 公平 | 1377 (75.7%) | 1537 (78.4%) | 2914 (77.1%) |
| 矛盾 | 66 (3.6%) | 77 (3.9%) | 143 (3.8%) |
| trust | |||
| 信任 | 992 (54.5%) | 1046 (53.3%) | 2038 (53.9%) |
| 谨慎 | 808 (44.4%) | 890 (45.4%) | 1698 (44.9%) |
| 矛盾 | 20 (1.1%) | 25 (1.3%) | 45 (1.2%) |
| [儿子] 请问您有几个亲生子女 (包括已去世子女)？ | |||
| Mean (SD) | 0.880 (0.876) | 0.961 (0.857) | 0.922 (0.867) |
| Median [Min, Max] | 1.00 [0, 11.0] | 1.00 [0, 5.00] | 1.00 [0, 11.0] |
| [女儿] 请问您有几个亲生子女 (包括已去世子女)？ | |||
| Mean (SD) | 0.740 (0.882) | 0.828 (1.70) | 0.786 (1.37) |
| Median [Min, Max] | 1.00 [0, 5.00] | 1.00 [0, 60.0] | 1.00 [0, 60.0] |
| age | |||
| Mean (SD) | 2.86 (1.05) | 2.86 (1.04) | 2.86 (1.05) |
| Median [Min, Max] | 3.00 [1.00, 4.00] | 3.00 [1.00, 4.00] | 3.00 [1.00, 4.00] |
| edu | |||
| 高 | 394 (21.6%) | 337 (17.2%) | 731 (19.3%) |
| 中 | 366 (20.1%) | 341 (17.4%) | 707 (18.7%) |
| 低 | 1060 (58.2%) | 1283 (65.4%) | 2343 (62.0%) |
table1(~ Pre_Sex|son*daugther, data=MY_CGSS)## Warning in table1.formula(~Pre_Sex | son * daugther, data = MY_CGSS): Terms
## to the right of '|' in formula 'x' define table columns and are expected to be
## factors with meaningful labels.## Warning in .table1.internal(x = x, labels = labels, groupspan = groupspan, :
## Table has 43 columns. Are you sure this is what you want?0 |
1 |
2 |
3 |
4 |
5 |
11 |
Overall |
||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 (N=476) |
1 (N=573) |
2 (N=170) |
3 (N=30) |
4 (N=8) |
5 (N=3) |
26 (N=1) |
0 (N=884) |
1 (N=616) |
2 (N=192) |
3 (N=60) |
4 (N=15) |
5 (N=5) |
0 (N=332) |
1 (N=151) |
2 (N=80) |
3 (N=21) |
4 (N=9) |
0 (N=45) |
1 (N=38) |
2 (N=19) |
3 (N=4) |
4 (N=3) |
5 (N=1) |
0 (N=9) |
1 (N=14) |
2 (N=3) |
3 (N=4) |
4 (N=3) |
0 (N=3) |
1 (N=5) |
2 (N=1) |
3 (N=1) |
60 (N=1) |
1 (N=1) |
0 (N=1749) |
1 (N=1398) |
2 (N=465) |
3 (N=120) |
4 (N=38) |
5 (N=9) |
26 (N=1) |
60 (N=1) |
|
| Pre_Sex | |||||||||||||||||||||||||||||||||||||||||||
| 同意 | 151 (31.7%) | 76 (13.3%) | 17 (10.0%) | 2 (6.7%) | 0 (0%) | 1 (33.3%) | 0 (0%) | 129 (14.6%) | 68 (11.0%) | 13 (6.8%) | 7 (11.7%) | 1 (6.7%) | 0 (0%) | 30 (9.0%) | 13 (8.6%) | 4 (5.0%) | 2 (9.5%) | 0 (0%) | 3 (6.7%) | 2 (5.3%) | 1 (5.3%) | 1 (25.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (14.3%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (33.3%) | 1 (20.0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 314 (18.0%) | 162 (11.6%) | 35 (7.5%) | 12 (10.0%) | 1 (2.6%) | 1 (11.1%) | 0 (0%) | 0 (0%) |
| 中立 | 156 (32.8%) | 173 (30.2%) | 26 (15.3%) | 4 (13.3%) | 0 (0%) | 0 (0%) | 0 (0%) | 259 (29.3%) | 121 (19.6%) | 36 (18.8%) | 4 (6.7%) | 2 (13.3%) | 1 (20.0%) | 65 (19.6%) | 23 (15.2%) | 11 (13.8%) | 3 (14.3%) | 0 (0%) | 9 (20.0%) | 8 (21.1%) | 2 (10.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (22.2%) | 0 (0%) | 1 (33.3%) | 1 (25.0%) | 1 (33.3%) | 0 (0%) | 1 (20.0%) | 1 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 491 (28.1%) | 326 (23.3%) | 77 (16.6%) | 12 (10.0%) | 3 (7.9%) | 1 (11.1%) | 0 (0%) | 0 (0%) |
| 不同意 | 169 (35.5%) | 324 (56.5%) | 127 (74.7%) | 24 (80.0%) | 8 (100%) | 2 (66.7%) | 1 (100%) | 496 (56.1%) | 427 (69.3%) | 143 (74.5%) | 49 (81.7%) | 12 (80.0%) | 4 (80.0%) | 237 (71.4%) | 115 (76.2%) | 65 (81.2%) | 16 (76.2%) | 9 (100%) | 33 (73.3%) | 28 (73.7%) | 16 (84.2%) | 3 (75.0%) | 3 (100%) | 1 (100%) | 7 (77.8%) | 12 (85.7%) | 2 (66.7%) | 3 (75.0%) | 2 (66.7%) | 2 (66.7%) | 3 (60.0%) | 0 (0%) | 1 (100%) | 1 (100%) | 1 (100%) | 944 (54.0%) | 910 (65.1%) | 353 (75.9%) | 96 (80.0%) | 34 (89.5%) | 7 (77.8%) | 1 (100%) | 1 (100%) |
table1(~ Pre_Sex|fair, data=MY_CGSS)| 占便宜 (N=724) |
公平 (N=2914) |
矛盾 (N=143) |
Overall (N=3781) |
|
|---|---|---|---|---|
| Pre_Sex | ||||
| 同意 | 96 (13.3%) | 410 (14.1%) | 19 (13.3%) | 525 (13.9%) |
| 中立 | 168 (23.2%) | 703 (24.1%) | 39 (27.3%) | 910 (24.1%) |
| 不同意 | 460 (63.5%) | 1801 (61.8%) | 85 (59.4%) | 2346 (62.0%) |
table1(~ Pre_Sex|trust, data=MY_CGSS)| 信任 (N=2038) |
谨慎 (N=1698) |
矛盾 (N=45) |
Overall (N=3781) |
|
|---|---|---|---|---|
| Pre_Sex | ||||
| 同意 | 279 (13.7%) | 238 (14.0%) | 8 (17.8%) | 525 (13.9%) |
| 中立 | 471 (23.1%) | 429 (25.3%) | 10 (22.2%) | 910 (24.1%) |
| 不同意 | 1288 (63.2%) | 1031 (60.7%) | 27 (60.0%) | 2346 (62.0%) |
ggstatsplot::ggpiestats(
data = MY_CGSS,
x = edu,
y = gender,
title = "教育与性别", # title for the plot
legend.title = "性别", # title for the legend
caption = substitute(paste(italic("Source"), ": 1974 Motor Trend US magazine"))
)
ggstatsplot::ggpiestats(
data = MY_CGSS,
x = edu,
y = Pre_Sex,
title = "教育与态度", # title for the plot
legend.title = "态度", # title for the legend
caption = substitute(paste(italic("Source"), ": 1974 Motor Trend US magazine"))
)
ggstatsplot::ggpiestats(
data = MY_CGSS,
x = trust,
y = Pre_Sex,
title = "信任程度与态度", # title for the plot
legend.title = "态度", # title for the legend
caption = substitute(paste(italic("Source"), ": 1974 Motor Trend US magazine"))
)
ggstatsplot::ggpiestats(
data = MY_CGSS,
x = fair,
y = Pre_Sex,
title = "公平与态度", # title for the plot
legend.title = "态度", # title for the legend
caption = substitute(paste(italic("Source"), ": 1974 Motor Trend US magazine"))
)
ggstatsplot::ggpiestats(
data = MY_CGSS,
x = age,
y = Pre_Sex,
title = "年龄与态度", # title for the plot
legend.title = "态度", # title for the legend
caption = substitute(paste(italic("Source"), ": 1974 Motor Trend US magazine"))
)
ggstatsplot::ggpiestats(
data = MY_CGSS,
x = son,
y = Pre_Sex,
title = "是否有儿子与态度", # title for the plot
legend.title = "态度", # title for the legend
caption = substitute(paste(italic("Source"), ": 1974 Motor Trend US magazine"))
)## Warning in stats::chisq.test(x = x_arg, correct = FALSE): Chi-squared
## approximation may be incorrect
ggstatsplot::ggpiestats(
data = MY_CGSS,
x = daugther,
y = Pre_Sex,
title = "是否有女儿与态度", # title for the plot
legend.title = "态度", # title for the legend
caption = substitute(paste(italic("Source"), ": 1974 Motor Trend US magazine"))
)## Warning in stats::chisq.test(x = x_arg, correct = FALSE): Chi-squared
## approximation may be incorrect
ggplot(MY_CGSS[is.na(MY_CGSS$Pre_Sex) == F,], aes(x=Pre_Sex,fill=edu)) +geom_bar(stat="count",position = "dodge")+geom_text(stat='count',aes(label=..count..),vjust=-1,position=position_dodge(width=0.9))
ggplot(MY_CGSS[is.na(MY_CGSS$Pre_Sex) == F,], aes(x=Pre_Sex,fill=fair)) +geom_bar(stat="count",position = "dodge")+geom_text(stat='count',aes(label=..count..),vjust=-1,position=position_dodge(width=0.9))
ggplot(MY_CGSS[is.na(MY_CGSS$Pre_Sex) == F,], aes(x=Pre_Sex,fill=trust)) +geom_bar(stat="count",position = "dodge")+geom_text(stat='count',aes(label=..count..),vjust=-1,position=position_dodge(width=0.9))
sat_mod1 <- lm(Pre_Sex ~ fair+edu, # regression formula
data = MY_CGSS) # data ## Warning in model.response(mf, "numeric"): using type = "numeric" with a factor
## response will be ignored## Warning in Ops.factor(y, z$residuals): '-' not meaningful for factorssat_mod2<-lm(Pre_Sex ~ fair+edu + gender, data = MY_CGSS) ## Warning in model.response(mf, "numeric"): using type = "numeric" with a factor
## response will be ignored
## Warning in model.response(mf, "numeric"): '-' not meaningful for factorssat_mod3<-lm(Pre_Sex ~ fair+edu + gender+age*edu*gender, data = MY_CGSS) ## Warning in model.response(mf, "numeric"): using type = "numeric" with a factor
## response will be ignored
## Warning in model.response(mf, "numeric"): '-' not meaningful for factors# model marginsplot ggeffects
mydf<-ggpredict(sat_mod3, terms = c("gender", "edu"),data = MY_CGSS)## Warning in Ops.factor(r, 2): '^' not meaningful for factorsggplot(mydf, aes(x = x, y = predicted, colour = group)) +
geom_point() +
stat_smooth(method = "lm", se = FALSE)## `geom_smooth()` using formula 'y ~ x'
m3 <- clm(Pre_Sex ~ age+edu + gender+fair,
data = MY_CGSS,na.action=na.omit)
m4 <- clm(Pre_Sex ~ age+edu + gender+fair+fair*edu ,
data = MY_CGSS,na.action=na.omit)
modelsc <- list( m3,m4)
modelsummary(modelsc,stars = TRUE, coef_omit = "公平",stars_note = TRUE)| Model 1 | Model 2 | |
|---|---|---|
| 同意|中立 | 0.259** | 0.329 |
| (0.128) | (0.201) | |
| 中立|不同意 | 1.766*** | 1.837*** |
| (0.131) | (0.203) | |
| age | 0.595*** | 0.595*** |
| (0.036) | (0.036) | |
| edu中 | 0.351*** | 0.267 |
| (0.103) | (0.253) | |
| edu低 | 0.660*** | 0.802*** |
| (0.091) | (0.210) | |
| gender女 | 0.300*** | 0.302*** |
| (0.069) | (0.069) | |
| fair矛盾 | -0.239 | 0.320 |
| (0.191) | (0.429) | |
| edu中 × fair矛盾 | -0.796 | |
| (0.562) | ||
| edu低 × fair矛盾 | -0.605 | |
| (0.504) | ||
| Num.Obs. | 3781 | 3781 |
| AIC | 6363.7 | 6366.5 |
| BIC | 6413.6 | 6441.3 |
| Log.Lik. | -3173.86128197866 | -3171.2355492606 |
| edf | 8 | 12 |
| * p < 0.1, ** p < 0.05, *** p < 0.01 |
??clm## starting httpd help server ...## done