This function adds uncorrelated Gaussian noise to the logarithm QTS using an exponential covariance function.
Arguments
- n
An integer specifying how many QTS should be generated.
- mean_qts
An object of class
qts
specifying the mean QTS.- alpha
A positive scalar specifying the variance of each component of the log-QTS. Defaults to
0.01
.- beta
A positive scalar specifying the exponential weight. Defaults to
0.001
.
Value
A list of n
objects of class qts
with added noise as
specified by parameters alpha
and beta
.
Details
See exp_cov_function
for details about the roles of
alpha
and beta
in the definition of the covariance operator.
Examples
rnorm_qts(1, vespa64$igp[[1]])
#> [[1]]
#> # A tibble: 101 × 5
#> time w x y z
#> <int> <dec:.5!> <dec:.5!> <dec:.5!> <dec:.5!>
#> 1 0 0.99567 0.02874 0.08679 -0.01708
#> 2 1 0.99587 0.01955 0.08752 -0.01446
#> 3 2 0.99632 0.01059 0.08432 -0.01088
#> 4 3 0.99664 0.01609 0.07978 -0.00876
#> 5 4 0.99727 0.01224 0.07279 0.00028
#> 6 5 0.99688 0.00632 0.07856 0.00414
#> 7 6 0.99689 0.00142 0.07864 0.00382
#> 8 7 0.99650 0.00134 0.08356 -0.00100
#> 9 8 0.99669 -0.00345 0.08058 0.00987
#> 10 9 0.99713 -0.01140 0.07414 0.01048
#> # … with 91 more rows
#>
#> attr(,"class")
#> [1] "qts_sample" "list"