README.md file gives a quick overview of the code used to generate state level MRmP estimates (For further information on MRmP please refer to Leemann and Wasserfallen's 2014 paper: Extending the Use and Prediction Precision of Subnational Public Opinion Estimation).
This code can be used to generate unique MRmP estimates:
x <- get_margins(states = c("ALL"), vars = c('sex', 'age', 'race', 'education', 'religion', 'party')) joints <- get_joint_probs(x) individualvars <- c("age + stname + sex + education + race + party + religion") groupingvars <- c("obama12 + medianhhincome + percent_gdp_increase") test <- mrmp( survey_data = df, jointp_list = joints, individualvars = individualvars, groupingvars = groupingvars, response = 'y', survey_sample = 10000 ) %>% bind_rows()
survey_data, a (nxn) data frame / survey data, where each row is a survey respondent and columns serve as covariates
individualvars, variables used as individual level covariates - random intercepts
groupingvars, variables that serve as grouping level variables: state level covariates for example that do not vary in their slope or intercept
jointp_list, a single data frame or a list of data frames containing the synthetic joint distributuions by state from their respective state marginal distrbutions of each demograhpic variable, which can be calculated using get_joint_probs(X)
response, the response variable in the data set (currently needs to be binary, i.e. 0:1)
survey_sample, Take a random sample of the dataframe (without replacement)
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