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 0.94948 0.12891 0.27792 0.06805
#> 2 1 0.95119 0.11564 0.27752 0.06965
#> 3 2 0.95546 0.10827 0.26726 0.06286
#> 4 3 0.95658 0.10276 0.26463 0.06614
#> 5 4 0.95668 0.10068 0.26571 0.06347
#> 6 5 0.95608 0.09286 0.27107 0.06171
#> 7 6 0.95665 0.09197 0.26969 0.06026
#> 8 7 0.95603 0.09135 0.27240 0.05889
#> 9 8 0.95751 0.08389 0.27093 0.05232
#> 10 9 0.95810 0.08561 0.26768 0.05534
#> # ℹ 91 more rows
#>
#> attr(,"class")
#> [1] "qts_sample" "list"