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Inputs

formula

An optional formula without a dependent variable that is of class ``formula'' and that follows standard R conventions for formulas, e.g. ~ x1 + x2. Allows you to transform or otherwise re-specify combinations of the variables in both data and cfact. To use this parameter, both data and cfact must be coercable to data frames; the variables of both data and cfact must be labeled; and all variables appearing in formula must also appear in both data and cfact. Otherwise, errors are returned. The intercept is automatically dropped. Default is NULL.

data

May take one of the following forms:

1. A R model output object, such as the output from calls to lm, glm, and zelig. Such an output object must be a list. It must additionally have either a formula or terms component and either a data or model component; if it does not, an error is returned. Of the latter, whatif first looks for data, which should contain either the original data set supplied as part of the model call (as in glm) or the name of this data set (as in zelig), which is assumed to reside in the global environment. If data does not exist, whatif then looks for model, which should contain the model frame (as in lm). The intercept is automatically dropped from the extracted observed covariate data set if the original model included one.
2. A $n \times k$ non-character (logical or numeric) matrix or data frame of observed covariate data with $n$ data points or units and $k$ covariates. All desired variable transformations and interaction terms should be included in this set of $k$ covariates unless formula is alternatively used to produce them. However, an intercept should not be. Such a matrix may be obtained by passing model output (e.g., output from a call to lm) to model.matrix and excluding the intercept from the resulting matrix if one was fit. Note that whatif will attempt to coerce data frames to their internal numeric values. Hence, data frames should only contain logical, numeric, and factor columns; character columns will lead to an error being returned.
3. A string. Either the complete path (including file name) of the file containing the data or the path relative to your working directory. This file should be a white space delimited text file. If it contains a header, you must include a column of row names as discussed in the help file for the R function read.table. The data in the file should be as otherwise described in (2).

Missing data is allowed and will be dealt with via the argument missing. It should be flagged using R's standard representation for missing data, NA.

cfact

A R object or a string. If a R object, a $m \times k$ non-character matrix or data frame of counterfactuals with $m$ counterfactuals and the same $k$ covariates (in the same order) as in data. However, if formula is used to select a subset of the $k$ covariates, then cfact may contain either only these $j \leq k$ covariates or the complete set of $k$ covariates. An intercept should not be included as one of the covariates. It will be automatically dropped from the counterfactuals generated by Zelig if the original model contained one. Data frames will again be coerced to their internal numeric values if possible. If a string, either the complete path (including file name) of the file containing the counterfactuals or the path relative to your working directory. This file should be a white space delimited text file. See the discussion under data for instructions on dealing with a header. All counterfactuals should be fully observed: if you supply counterfactuals with missing data, they will be list-wise deleted and a warning message will be printed to the screen.

range

An optional numeric vector of length $k$ , where $k$ is the number of covariates. Each element represents the range of the corresponding covariate for use in calculating Gower distances. Use this argument when covariate data do not represent the population of interest, such as selection by stratification or experimental manipulation. By default, the range of each covariate is calculated from the data (the difference of its maximum and minimum values in the sample), which is appropriate when a simple random sampling design was used. To supply your own range for the $k$ th covariate, set the $k$ th element of the vector equal to the desired range and all other elements equal to NA. Default is NULL.

freq

An optional numeric vector of any positive length, the elements of which comprise a set of distances. Used in calculating cumulative frequency distributions for the distances of the data points from each counterfactual. For each such distance and counterfactual, the cumulative frequency is the fraction of observed covariate data points with distance to the counterfactual less than or equal to the supplied distance value. The default varies with the distance measure used. When the Gower distance measure is employed, frequencies are calculated for the sequence of Gower distances from 0 to $1$ in increments of $0.05$ . When the Euclidian distance measure is employed, frequencies are calculated for the sequence of Euclidian distances from the minimum to the maximum observed distances in twenty equal increments, all rounded to two decimal places. Default is NULL.

nearby

An optional scalar indicating which observed data points are considered to be nearby (i.e., within `nearby' geometric variances of) the counterfactuals. Used to calculate the summary statistic returned by the function: the fraction of the observed data nearby each counterfactual. By default, the geometric variability of the covariate data is used. For example, setting nearby to $2$ will identify the proportion of data points within two geometric variances of a counterfactual. Default is 1.

distance

An optional string indicating the distance measure to employ. The choices are either "gower", Gower's non-parametric distance measure ($G^{2}$ ), which is suitable for both qualitative and quantitative data; or "euclidian", (squared) Euclidian distance, which is only suitable for quantitative data. The default is the former, "gower".

miss

An optional string indicating the strategy for dealing with missing data in the observed covariate data set. whatif supports two possible missing data strategies: "list", list-wise deletion of missing cases; and "case", ignoring missing data case-by-case (pairwise deletion). Note that if "case" is selected, observations with missing values are still deleted listwise for the convex hull test and for computing Euclidian distances, but pairwise deletion is used in computing the Gower distances to maximally use available information. The user is strongly encouraged to treat missing data using specialized tools such as Amelia prior to feeding the data to whatif. Default is "list".

choice

An optional string indicating which analyses to undertake. The options are either "hull", only perform the convex hull membership test; "distance", do not perform the convex hull test but do everything else, such as calculating the distance between each counterfactual and data point; or "both", undertake both the convex hull test and the distance calculations (i.e., do everything). Default is "both".

return.inputs

A Boolean; should the processed observed covariate and counterfactual data matrices on which all whatif computations are performed be returned? Processing refers to internal whatif operations such as the subsetting of covariates via formula, the deletion of cases with missing values, and the coercion of data frames to numeric matrices. Primarily intended for diagnostic purposes. If TRUE, these matrices are returned as a list. Default is FALSE.

return.distance

A Boolean; should the matrix of distances between each counterfactual and data point be returned? If TRUE, this matrix is returned as part of the output; if FALSE, it is not. Default is FALSE due to the large size that this matrix may attain.