miss_gen {tensorMiss}

Assignment of missingness to tensor time series

Description

Assign missingness to a given order-K tensor time series, where the maximum K is 4

Usage

miss_gen(dt, type = "random", p = 0.7)

Arguments

dt

Tensor time series, written in an array with dimension K+1 and mode-1 as the time mode.

type

Type of missingness, where "random" is random missing with probability p, "simul" is missingness on the last half along all dimensions, "mix" is a mixture of "random" and "simul". Default is "random".

p

If type is "random", then each entry is randomly missing with probability 1-p. Default is 0.7.

Value

A multi-dimensional array with dimension K+1, where mode-1 is the time mode and missing entries are denoted by NA

Examples

K = 3;
TT = 10;
d = c(20,20,20);
r = c(2,2,2);
re = c(2,2,2);
eta = list(c(0,0), c(0,0), c(0,0));
coef_f = c(0.7, 0.3, -0.4, 0.2, -0.1);
coef_fe = c(-0.7, -0.3, -0.4, 0.2, 0.1);
coef_e = c(0.8, 0.4, -0.4, 0.2, -0.1);
tensor_gen(K,TT,d,r,re,eta, coef_f, coef_fe, coef_e);
data_test = tensor_gen(K,TT,d,r,re,eta, coef_f, coef_fe, coef_e);
miss_gen(data_test$X);