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Accessing Raw judgeit.object Data

All data are stored in an object of class judgeit . If desired, the user can access each component within. Here is a list of components and their attributes:

• covars is a list of data frames comprising the predictors for each election in the system. So judgeit.object$covars[[25]] is a data frame with the covariates from the 25th election.

• voteshare is a list of vectors comprising the vote shares for each election in the system. So judgeit.object$voteshare[[25]] is a vector of the results of the 25th election.

• turnout,elgvotes,seats are lists of vectors comprising the actual turnout, the number of eligible voters, and the seats per district in the system for each election.

• fullrow is a list of vectors containing those rows whose primary elements (covariates, vote shares, eligible and actual voters and seats) contain complete data.

• uncL,uncLR,uncU,uncUR are the uncontested election detection thresholds and imputations as listed above.

• svexpected.value.only is the value of expected.value.only as given above.

• simulations is the number of simulations conducted by JudgeIt during each analysis.

• weight is the option selected by the user to indicate what weights should be used in the linear model, as described above.

• distweights is a list of the actual values of these weights.

• covarsnew is a list of data frames of counterfactual or future predictors as manipulated by the option new.covariates. It must have the same data type in each column as covars though not necessarily the same number of rows.

• same.dists is a vector indicating whether the previous election's district map is identical to the current one, as described above.

• output contains the output of the last analytical routine, and is displayed with the command print(JudgeIt.object).

• outputyear, outputclass indicate the year and type of the last analysis conducted. These are used mainly in plot(judgeit.object).

• beta,vc are the estimates given by the linear model for the system for the coefficients of the covariates and their covariance matrix.

• sigma,lambda,sind,lind are, respectively, the mean and year-by-year estimates of the standard error and systematic error fraction of the system.

• years is a vector of the names of each election variable in the inputted data frame list. In the case of house6311, this is a vector of the election years between 1896 and 1992.