# outliergram: Outliergram for univariate functional datasets

### 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 outliergram(fData, MBD_data = NULL, MEI_data = NULL, q_low = 0, q_high = 1, 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 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, centerline = sin( 4 * pi * grid ), Cov = Cov ) Data_extra = array( 0, dim = c( N_extra, P ) ) Data_extra[ 1, ] = generate_gauss_fdata( 1, sin( 4 * pi * grid + pi / 2 ), Cov = Cov ) Data_extra[ 2, ] = generate_gauss_fdata( 1, sin( 4 * pi * grid - pi / 2 ), Cov = Cov ) Data_extra[ 3, ] = generate_gauss_fdata( 1, sin( 4 * pi * grid + pi/ 3 ), Cov = Cov ) Data_extra[ 4, ] = generate_gauss_fdata( 1, sin( 4 * pi * grid - pi / 3), Cov = Cov ) Data = rbind( Data, Data_extra ) fD = fData( grid, Data ) outliergram( fD, display = TRUE ) outliergram( fD, Fvalue = 10, display = TRUE ) ## Not run: outliergram( fD, adjust = list( N_trials = 10, trial_size = 5 * nrow( Data ), TPR = 0.01, VERBOSE = FALSE ), display = TRUE ) ## End(Not run)