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
qtsspecifying 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 0.91688 0.33844 0.14417 0.15493
#> 2 1 0.91902 0.33181 0.14549 0.15535
#> 3 2 0.92323 0.32782 0.13435 0.14876
#> 4 3 0.92359 0.32706 0.13479 0.14777
#> 5 4 0.92708 0.31990 0.13011 0.14577
#> 6 5 0.92788 0.32089 0.12790 0.14037
#> 7 6 0.93216 0.31711 0.11702 0.12969
#> 8 7 0.93450 0.30981 0.11048 0.13611
#> 9 8 0.93823 0.30398 0.10235 0.12978
#> 10 9 0.93792 0.30684 0.10035 0.12684
#> # ℹ 91 more rows
#>
#> attr(,"class")
#> [1] "qts_sample" "list"