# Predict method for Robust Multivariate Outlier Detection `predict` method for class "mvout". ```r ## S3 method for class 'mvout' predict(object, x, type = c("distance", "outlier", "scores"), thresh = 0.01, ...) ``` ## Arguments - `object`: Object of class `mvout` - `x`: Optional matrix of new data used for the predictions. If omitted, the original data are used if `keepx = TRUE` (and error is produced otherwise). - `type`: Type of prediction to return: "distance" returns the predicted Mahalanobis distance, "outlier" returns the predicted outlier status (T/F) using the specified `thresh`, and "scores" returns the predicted principal component or factor scores (if applicable). - `thresh`: Scalar specifying the threshold for flagging outliers (0 < thresh < 1). See `mvout` for details. - `...`: Additional arguments (ignored) ## Details Produces predictions from the input new data `x` using the robust parameter estimates (of location and scatter) from the input "mvout" `object`. ## Returns Returns a vector of numerics ("distance" or "scores") or logicals ("outlier"). ## Author(s) Jesus E. Delgado Nathaniel E. Helwig ## Note If you input the same `x` that was used to estimate the location and scale parameters you will obtain: (1) the same "distance" and "scores" as output by the `mvout` function (2) a potentially different "outlier" result than what is output by the `mvout` function The discrepancy in (2) is because all of the observations are considered to have been excluded from the location/scatter estimation when the `x` argument is provided. This results in a different critical value being used for the observations that were included in the MCD estimate. For boarderline cases, this slight change in the critical value could result in a change of outlier status. ## See Also `mvout` for estimation of (robust) location/scatter. ## Examples ```r # generate some data n <- 200 p <- 2 set.seed(0) x <- matrix(rnorm(n * p), n, p) # thresh = 0.01 set.seed(1) # for reproducible MCD estimate out1 <- mvout(x) # predicted distance (same as before) fit1 <- predict(out1, x = x) max(abs(fit1 - out1$distance)) # predicted outlier (differs from before) fit1 <- predict(out1, x = x, type = "outlier") mean(abs(fit1 == out1$outlier)) ```

predict.mvout function