inputter |
The inputter object should contain a list of all target values for the raking procedure. Each list element in inputter should be a vector corresponding to the weighting targets for a single variable. Hence, the vector enumerating the weighting targets for a variable with 2 levels should be of length 2, while a vector enumerating the weighting targets for a variable with 5 levels should be of length 5. List elements in inputter should be named according to the variable that they will match in the corresponding dataset. Hence, a list element enumerating the proportion of the sample that should be of each gender should be labeled "female" if the variable in dataframe is also titled "female."
inputter elements must be vectors and can be of class numeric, or factor and must match the class of the corresponding variable in dataframe. Logical variables in dataframe can be matched to a numeric vector of length 2 and ordered with the TRUE target as the first element and the FALSE target as the second element. Targets for factors must be labeled to match every level present in the dataframe (e.g. a variable with 2 age groups "under40" and "over40" should have elements named "under40" and "over40" respectively). anesrake attempts to conform any unrecognized types of vectors to class(numeric). Weighting targets can be entered either as an N to be reached or as a percent for any given variable. Targets can be either proportions (ideal) or the number of individuals in the population in each target category (N). Totals of greater than 1.5 for any given list element are treated as Ns, while values of less than 1.5 are treated as percentages.
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dataframe |
The dataframe command identifies a data.frame object of the data to be weighted. The data.frame must contain all of the variables that will be used in the weighting process and those variables must have the same names as are present in the inputter list element.
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caseid |
The caseid command identifies a unique case identifier for each individual in the dataset. If filters are to be used, the resulting list of weights will be a different length from the overall dataframe. caseid is included in the output so that weights can be matched to the dataset of relevance. caseid must be of a length matching the number of cases in dataframe.
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weightvec |
weightvec is an optional input if some kind of base weights, stratification correction, or other sampling probability of note that should be accounted for before weighting is conducted. If defined, weightvec must be of a length equivalent to the number of cases in the dataframe. If undefined, weightvec will be automatically seeded with a vector of 1s.
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cap |
cap defines the maximum weight to be used. cap can be defined by the user with the command cap=x, where x is any value above 1 at which the algorithm will cap weights. If cap is set below 1, the function will return an error. If cap is set between 1 and 1.5, the function will return a warning that the low cap may substantially increase the amount of time required for weighting. In the absence of a user-defined cap, the algorithm defaults to a starting value of 5 in line with DeBell and Krosnick, 2009. For no cap, cap simply needs to be set to an arbitrarily high number. (Note: Capping using the cap command caps at each iteration.)
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verbose |
Users interested in seeing the progress of the algorithm can set verbose to equal TRUE. The algorithm will then inform the user of the progress of each raking and capping iteration.
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maxit |
Users can set a maximum number of iterations for the function should it fail to converge using maxit=X, where X is the maximum number of iterations. The default is set to 1000.
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convcrit |
convcrit is the criterion for convergence. The raking algorithm is determined to have converged when the most recent iteration represents less than a convcrit percentage improvement over the prior iteration.
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weightvec |
Vector of weights From raking algorithm
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caseid |
Case IDs for final weights – helpful for matching weightvec to cases if a filter is used
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iterations |
Number of iterations required for convergence (or non-convergence) of final model
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nonconvergence |
Measure of remaining discrepancy from benchmarks if convergence was not achieved
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converge |
Notes whether full convergence was achieved, algorithm failed to converge because convergence was not possible, or maximum iterations were reached
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varsused |
List of variables selected for weighting
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targets |
inputter from above, a list of the targets used for weighting
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dataframe |
Copy of the original dataframe used for weighting (filter variable applied if specified)
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Josh Pasek, Assistant Professor of Communication Studies at the University of Michigan (www.joshpasek.com).