library(Zelig)
## Loading required package: MASS
## Loading required package: boot
## ## 
## ##  Zelig (Version 3.5.3, built: 2011-11-29)
## ##  Please refer to http://gking.harvard.edu/zelig for full
## ##  documentation or help.zelig() for help with commands and
## ##  models supported by Zelig.
## ##
## 
## ##  Zelig project citations:
## ##    Kosuke Imai, Gary King, and Olivia Lau. (2009).
## ##    ``Zelig: Everyone's Statistical Software,''
## ##    http://gking.harvard.edu/zelig.
## ##  and
## ##    Kosuke Imai, Gary King, and Olivia Lau. (2008).
## ##    ``Toward A Common Framework for Statistical Analysis
## ##    and Development,'' Journal of Computational and
## ##    Graphical Statistics, Vol. 17, No. 4 (December)
## ##    pp. 892-913. 
## 
## ##  To cite individual Zelig models, please use the citation format printed with
## ##  each model run and in the documentation.
## ##
library(DescTools)
library(stargazer)
## 
## Please cite as: 
## 
##  Hlavac, Marek (2014). stargazer: LaTeX code and ASCII text for well-formatted regression and summary statistics tables.
##  R package version 5.1. http://CRAN.R-project.org/package=stargazer
library(dplyr)
## 
## Attaching package: 'dplyr'
## 
## The following object is masked from 'package:MASS':
## 
##     select
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyr)
library(memisc)
## Loading required package: lattice
## 
## Attaching package: 'lattice'
## 
## The following object is masked from 'package:boot':
## 
##     melanoma
## 
## 
## Attaching package: 'memisc'
## 
## The following objects are masked from 'package:dplyr':
## 
##     collect, query, rename
## 
## The following object is masked from 'package:DescTools':
## 
##     %nin%
## 
## The following objects are masked from 'package:stats':
## 
##     contr.sum, contr.treatment, contrasts
## 
## The following object is masked from 'package:base':
## 
##     as.array
library(pander)
library(foreign)
library(gmodels)
library(car)
## 
## Attaching package: 'car'
## 
## The following object is masked from 'package:memisc':
## 
##     recode
## 
## The following object is masked from 'package:DescTools':
## 
##     Recode
## 
## The following object is masked from 'package:boot':
## 
##     logit
library(visreg)
library(aod)
library(erer)
## Loading required package: lmtest
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## 
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
GSS = read.spss("C:\\Users\\Robert Johnson\\RSquared\\GSS2014.sav", to.data.frame=TRUE)
## Warning in read.spss("C:\\Users\\Robert Johnson\\RSquared\\GSS2014.sav", :
## C:\Users\Robert Johnson\RSquared\GSS2014.sav: Unrecognized record type 7,
## subtype 18 encountered in system file
## Warning in `levels<-`(`*tmp*`, value = if (nl == nL) as.character(labels)
## else paste0(labels, : duplicated levels in factors are deprecated
## Warning in `levels<-`(`*tmp*`, value = if (nl == nL) as.character(labels)
## else paste0(labels, : duplicated levels in factors are deprecated
## Warning in `levels<-`(`*tmp*`, value = if (nl == nL) as.character(labels)
## else paste0(labels, : duplicated levels in factors are deprecated
## Warning in `levels<-`(`*tmp*`, value = if (nl == nL) as.character(labels)
## else paste0(labels, : duplicated levels in factors are deprecated
## How to cite this model in Zelig:
## Kosuke Imai, Gary King, and Oliva Lau. 2008. "logit: Logistic Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software," http://gking.harvard.edu/zelig
## 
##   Model: logit 
##   Number of simulations: 1000 
## 
## Values of X 
##   (Intercept) raceBLACK raceOTHER      age
## 1           1         0         0 49.43758
## 
## Expected Values: E(Y|X)
##        mean         sd      2.5%     97.5%
## 1 0.5925005 0.01442349 0.5636585 0.6196563
## 
## Predicted Values: Y|X
##       0     1
## 1 0.432 0.568
## How to cite this model in Zelig:
## Kosuke Imai, Gary King, and Oliva Lau. 2008. "logit: Logistic Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software," http://gking.harvard.edu/zelig
## 
##   Model: logit 
##   Number of simulations: 1000 
## 
## Values of X 
##   (Intercept) raceBLACK raceOTHER      age sexFEMALE
## 1           1         0         0 49.43758         1
## 
## Expected Values: E(Y|X)
##        mean         sd      2.5%     97.5%
## 1 0.5924156 0.01886343 0.5546601 0.6284884
## 
## Predicted Values: Y|X
##       0     1
## 1 0.378 0.622

The below two logistical regressions look at the relationship between race sex and political views, whether or not race or sex is a factor in political views. 1 looks at the relationship between sex and political views. 2 does the same thing but uses age instead of race. 1 has as a lower Akaike so is better to interpret over model 2. According to the chart sex is least likely to be a factor in voting and this is not statistically significant. When race is considered the likelihood of it having an effect on someone’s political views it is 49.9% and is statistically significant. According to the logistical regression race plays a bigger factor in political views than sex.

lmod1<- zelig(sex~ polviews + race, model="logit", data= GSS)
## How to cite this model in Zelig:
## Kosuke Imai, Gary King, and Oliva Lau. 2008. "logit: Logistic Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software," http://gking.harvard.edu/zelig
lmod2<- zelig(sex~ polviews + age, model="logit", data= GSS)
## How to cite this model in Zelig:
## Kosuke Imai, Gary King, and Oliva Lau. 2008. "logit: Logistic Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software," http://gking.harvard.edu/zelig
stargazer(lmod1, lmod2, type="text")
## 
## ==============================================
##                       Dependent variable:     
##                   ----------------------------
##                               sex             
##                        (1)            (2)     
## ----------------------------------------------
## polviews              0.001          0.001    
##                      (0.003)        (0.003)   
##                                               
## raceBLACK            0.499***                 
##                      (0.117)                  
##                                               
## raceOTHER             -0.041                  
##                      (0.134)                  
##                                               
## age                                  0.001    
##                                     (0.002)   
##                                               
## Constant             0.124**         0.121    
##                      (0.049)        (0.122)   
##                                               
## ----------------------------------------------
## Observations          2,514          2,505    
## Log Likelihood      -1,719.986    -1,723.633  
## Akaike Inf. Crit.   3,447.973      3,453.265  
## ==============================================
## Note:              *p<0.1; **p<0.05; ***p<0.01
GSS2 <- zelig(polviewsbinary ~ agebinary + educbinary + agebinary:educbinary, data = GSS, model = "logit")
## How to cite this model in Zelig:
## Kosuke Imai, Gary King, and Oliva Lau. 2008. "logit: Logistic Regression for Dichotomous Dependent Variables" in Kosuke Imai, Gary King, and Olivia Lau, "Zelig: Everyone's Statistical Software," http://gking.harvard.edu/zelig
xh1 <- setx(GSS2, agebinary = 1, sex = "yes")
xl1 <- setx(GSS2, agebinary = 0, sex = "yes")
xh0 <- setx(GSS2, agebinary = 1, sex = "no")
xl0 <- setx(GSS2, agebinary = 0, sex = "no")

zh1 <- sim(GSS2, x=xh1)
zl1 <- sim(GSS2, x=xl1)
zh0 <- sim(GSS2, x=xh0)
zl0 <- sim(GSS2, x=xl0)

eff <- (zh1$qi$ev - zl1$qi$ev) - (zh0$qi$ev - zl0$qi$ev)
quantile(eff, c(.025,.975))
##        2.5%       97.5% 
## -0.09966472  0.09312542
hist(eff)