Description Usage Arguments Value Examples
Creates a dataframe with imputed values using either linear regression or lasso based models. For each variable in given data frame, the function finds the best correlated predictors (number of which is set by top_predictors), and uses these to construct models for predicting missing values.
1 2 3 | regImputation(dataframe, matrix, continuous = "", categorical = "",
method = "lm", parallel = 0, threshold = 0.4, top_predictors = 3,
debug = 0, degree = 1, test = 0, failmode = "skip")
|
method |
method for imputation ( |
parallel |
whether to use parallel processes (for MacOSX only at the moment) |
threshold |
for selection of predictors based on correlation; values between 0 and 1. |
top_predictors |
how many predictors to use in imputation prediction; more values can lead to better quality but more sparsely available predictions. |
debug |
debug mode; shows which models are running, the quality of predictions relative to original data, and any model errors. 1=progress, errors and warnings, 2=progress,errors, warnings and prediction quality. |
degree |
the degree of polynomial effects to estimate (1=main effects only, 2=quadratic, 3=cubic, etc.) |
test |
test mode; runs on only the first 4 variables; helpful for trying out the function options before running full imputation. |
failmode |
what to do if prediction fails for any reason. Defaults to returning the original variable vector (failmode='skip'), but can be told to impute central tendency instead with option (failmode='impute') |
Dataframe containing imputed variables, with imputations performed only on missing values and retaining original data where available.
1 | ## Not run: regImputation(dataframe, matrix, method='polywog', parallel=1, debug=1, test=1)
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