stdz produces a standardized copy of any input variable. It can also standardize a weighted variable to produce a copy of the original variable standardized around its weighted mean and variance.
plotwtdinteraction produces a plot from a regression object to illustrate a two- or three-way interaction for a prototypical individual holding constant all other variables. Prototypical individual is identified as the mean (numeric), median (ordinal), and/or modal (factors and logical variables) values for all measures. Standard errors are illustrated with polygons by default.
wpct produces a weighted table of the proportion of data in each category for any variable. This is simply a weighted frequency table divided by its sum.
wtd.chi.sq produces weighted chi-squared tests for two- and three-variable contingency tables. Decomposes parts of three-variable contingency tables as well. Note that weights run with the default parameters here treat the weights as an estimate of the precision of the information. A prior version of this software was set to default to mean1=FALSE.
wtd.t.test produces either one- or two-sample t-tests comparing weighted data streams to one another. Note that weights run with the default parameters here treat the weights as an estimate of the precision of the information. For survey data, users should run this code with bootstrapped standard errors bootse=TRUE, which are robust to heteroskadesticity, although these will vary slightly each time the weights are run. A prior version of this software was set to default to mean1=FALSE and bootse=FALSE.
dummify creates a matrix with columns signifying separate dummy variables for each level of a factor. The column names are the former levels of the factor.
wtd.cor produces a Pearsons correlation coefficient comparing two variables or matrices. Note that weights run with the default parameters here treat the weights as an estimate of the precision of the information. For survey data, users should run this code with bootstrapped standard errors bootse=TRUE, which are robust to heteroskadesticity, although these will vary slightly each time the weights are run. A prior version of this software was set to default to mean1=FALSE and bootse=FALSE.
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