# outliergram: Outliergram for univariate functional datasets In roahd: Robust Analysis of High Dimensional Data

## Description

This function performs the outliergram of a univariate functional dataset, possibly with an adjustment of the true positive rate of outliers discovered under assumption of gaussianity.

## Usage

  1 2 3 4 5 6 7 8 9 10 11 12 13 outliergram( fData, MBD_data = NULL, MEI_data = NULL, p_check = 0.05, Fvalue = 1.5, adjust = FALSE, display = TRUE, xlab = NULL, ylab = NULL, main = NULL, ... ) 

## References

Arribas-Gil, A., and Romo, J. (2014). Shape outlier detection and visualization for functional data: the outliergram, Biostatistics, 15(4), 603-619.

fData, MEI, MBD, fbplot
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 set.seed(1618) N <- 200 P <- 200 N_extra <- 4 grid <- seq(0, 1, length.out = P) Cov <- exp_cov_function(grid, alpha = 0.2, beta = 0.8) Data <- generate_gauss_fdata( N = N, centerline = sin(4 * pi * grid), Cov = Cov ) Data_extra <- array(0, dim = c(N_extra, P)) Data_extra[1, ] <- generate_gauss_fdata( N = 1, centerline = sin(4 * pi * grid + pi / 2), Cov = Cov ) Data_extra[2, ] <- generate_gauss_fdata( N = 1, centerline = sin(4 * pi * grid - pi / 2), Cov = Cov ) Data_extra[3, ] <- generate_gauss_fdata( N = 1, centerline = sin(4 * pi * grid + pi / 3), Cov = Cov ) Data_extra[4, ] <- generate_gauss_fdata( N = 1, centerline = sin(4 * pi * grid - pi / 3), Cov = Cov ) Data <- rbind(Data, Data_extra) fD <- fData(grid, Data) # Outliergram with default Fvalue = 1.5 outliergram(fD, display = TRUE) # Outliergram with Fvalue enforced to 2.5 outliergram(fD, Fvalue = 2.5, display = TRUE) # Outliergram with estimated Fvalue to ensure TPR of 1% outliergram( fData = fD, adjust = list( N_trials = 10, trial_size = 5 * nrow(Data), TPR = 0.01, VERBOSE = FALSE ), display = TRUE )