library (haven)library (descr)
Warning: package 'descr' was built under R version 4.4.3
library (MASS)#glm modeling library (rstatix) #Summary Tables
Attaching package: 'rstatix'
The following object is masked from 'package:MASS':
select
The following object is masked from 'package:stats':
filter
library (tidyverse) #General coding
Warning: package 'tidyverse' was built under R version 4.4.3
Warning: package 'ggplot2' was built under R version 4.4.3
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks rstatix::filter(), stats::filter()
✖ dplyr::lag() masks stats::lag()
✖ dplyr::select() masks rstatix::select(), MASS::select()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library (patchwork) #Merge GGPlots togeter
Attaching package: 'patchwork'
The following object is masked from 'package:MASS':
area
library (ggplot2) #Graphing library (stargazer) #Tabular Regression Results
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 (jtools) #Tabular Regression Results
Warning: package 'jtools' was built under R version 4.4.3
library (descr) #Easy Frequency Tables library (haven) #Imports survey data library (stats) #Various statistical calculations library (ggeffects) #Predicted Probabilities from Regressions
Warning: package 'ggeffects' was built under R version 4.4.3
library (pscl) #Zero Inflated Negative Binomial
Classes and Methods for R originally developed in the
Political Science Computational Laboratory
Department of Political Science
Stanford University (2002-2015),
by and under the direction of Simon Jackman.
hurdle and zeroinfl functions by Achim Zeileis.
Load-in data
<- read_dta ("C:/Users/Admin/Downloads/anes_2020_count.dta" )head (anes_2020_count)
# A tibble: 6 × 1,780
version V200001 V160001_orig V200002 V200003 V200004 V200005 V200006 V200007
<chr> <dbl> <dbl+lbl> <dbl+l> <dbl+l> <dbl+l> <dbl+l> <dbl+lb> <dbl+lb>
1 ANES20… 200015 401318 3 [3. … 2 [2. … 3 [3. … 0 [0. … -2 [-2.… -2 [-2.…
2 ANES20… 200039 400181 3 [3. … 2 [2. … 3 [3. … 0 [0. … -2 [-2.… -2 [-2.…
3 ANES20… 200053 405145 3 [3. … 2 [2. … 3 [3. … 1 [1. … -2 [-2.… -2 [-2.…
4 ANES20… 200114 402782 3 [3. … 2 [2. … 3 [3. … 1 [1. … 4 [4. … -1 [-1.…
5 ANES20… 200138 300209 3 [3. … 2 [2. … 3 [3. … 0 [0. … -2 [-2.… -2 [-2.…
6 ANES20… 200206 301799 3 [3. … 2 [2. … 3 [3. … 0 [0. … -2 [-2.… -2 [-2.…
# ℹ 1,771 more variables: V200008 <dbl+lbl>, V200009 <dbl+lbl>, V200010a <dbl>,
# V200010b <dbl>, V200010c <dbl>, V200010d <dbl>, V200011a <dbl>,
# V200011b <dbl>, V200011c <dbl>, V200011d <dbl>, V200012a <dbl>,
# V200012b <dbl>, V200012c <dbl>, V200012d <dbl>, V200013a <dbl>,
# V200013b <dbl>, V200013c <dbl>, V200013d <dbl>, V200014a <dbl>,
# V200014b <dbl>, V200014c <dbl>, V200014d <dbl>, V200015a <dbl>,
# V200015b <dbl>, V200015c <dbl>, V200015d <dbl>, V200016a <dbl>, …
<- anes_2020_count %>% select (rw_shows, libcon, pid_x, V201005, V202543, poc, education, female, V200010b)head (anes_data)
# A tibble: 6 × 9
rw_shows libcon pid_x V201005 V202543 poc education female V200010b
<dbl> <dbl+lbl> <dbl> <dbl+l> <dbl+lb> <dbl> <dbl+lbl> <dbl> <dbl>
1 3 6 [6. Conser… 7 2 [2. … 4 [4. … 1 3 [BA/BS] 0 1.01
2 0 2 [2. Libera… 3 1 [1. … 4 [4. … 0 1 [HS or… 1 0.769
3 1 5 [5. Slight… 4 2 [2. … NA 1 4 [Advan… 0 0.966
4 0 NA 2 2 [2. … 4 [4. … 1 2 [Some … 0 1.84
5 0 4 [4. Modera… 2 4 [4. … 5 [5. … 0 1 [HS or… 0 1.25
6 0 3 [3. Slight… 1 2 [2. … 5 [5. … 0 3 [BA/BS] 0 0.742
%>% summarize (var_rw_shows = var (rw_shows, na.rm = TRUE ),mean_rw_shows = mean (rw_shows, na.rm = TRUE ),ratio = var_rw_shows / mean_rw_shows)
# A tibble: 1 × 3
var_rw_shows mean_rw_shows ratio
<dbl> <dbl> <dbl>
1 1.50 0.536 2.81
<- glm (rw_shows ~ libcon+ pid_x+ V201005+ V202543+ poc+ education+ female, family = quasipoisson, data = anes_data, weights = anes_data$ V200010b)summary (quasi_all)
Call:
glm(formula = rw_shows ~ libcon + pid_x + V201005 + V202543 +
poc + education + female, family = quasipoisson, data = anes_data,
weights = anes_data$V200010b)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.405738 0.339126 -10.043 < 2e-16 ***
libcon 0.542642 0.050346 10.778 < 2e-16 ***
pid_x 0.192327 0.037853 5.081 4.07e-07 ***
V201005 -0.573236 0.049605 -11.556 < 2e-16 ***
V202543 0.026170 0.046521 0.563 0.574
poc -0.148317 0.125122 -1.185 0.236
education 0.003585 0.039537 0.091 0.928
female -0.048341 0.078526 -0.616 0.538
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for quasipoisson family taken to be 1.565885)
Null deviance: 3562.2 on 2216 degrees of freedom
Residual deviance: 2059.3 on 2209 degrees of freedom
(1284 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 6
<- glm.nb (rw_shows ~ libcon+ pid_x+ V201005+ V202543+ poc+ education+ female, data= anes_data, weights = anes_data$ V200010b)summary (nb_all)
Call:
glm.nb(formula = rw_shows ~ libcon + pid_x + V201005 + V202543 +
poc + education + female, data = anes_data, weights = anes_data$V200010b,
init.theta = 0.7877996507, link = log)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.56722 0.37922 -9.407 < 2e-16 ***
libcon 0.57969 0.05612 10.330 < 2e-16 ***
pid_x 0.20718 0.04008 5.169 2.35e-07 ***
V201005 -0.52377 0.05439 -9.629 < 2e-16 ***
V202543 0.01078 0.05772 0.187 0.8519
poc -0.29017 0.14091 -2.059 0.0395 *
education -0.04527 0.04860 -0.931 0.3516
female -0.06987 0.09595 -0.728 0.4665
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Negative Binomial(0.7878) family taken to be 1)
Null deviance: 2091.8 on 2216 degrees of freedom
Residual deviance: 1176.7 on 2209 degrees of freedom
(1284 observations deleted due to missingness)
AIC: 2992.8
Number of Fisher Scoring iterations: 1
Theta: 0.7878
Std. Err.: 0.0907
2 x log-likelihood: -2974.7880
stargazer (quasi_all, nb_all, type= "text" , diits= 3 )
====================================================
Dependent variable:
----------------------------------
rw_shows
glm: quasipoisson negative
link = log binomial
(1) (2)
----------------------------------------------------
libcon 0.543*** 0.580***
(0.050) (0.056)
pid_x 0.192*** 0.207***
(0.038) (0.040)
V201005 -0.573*** -0.524***
(0.050) (0.054)
V202543 0.026 0.011
(0.047) (0.058)
poc -0.148 -0.290**
(0.125) (0.141)
education 0.004 -0.045
(0.040) (0.049)
female -0.048 -0.070
(0.079) (0.096)
Constant -3.406*** -3.567***
(0.339) (0.379)
----------------------------------------------------
Observations 2,217 2,217
Log Likelihood -1,488.394
theta 0.788*** (0.091)
Akaike Inf. Crit. 2,992.788
====================================================
Note: *p<0.1; **p<0.05; ***p<0.01
=
3
-
According to the result, “Dispersion parameter for quasipoisson family taken to be 1.565885,” which is higher than 1, so the negative binomial will be more suitable.
According to the result, for both model, 1) people who are conservative are more likely to consume right-wing media resource, 2) people who lean to the republican are more likely to consume the right-wing media resource.