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 0.98603 0.13998 -0.04040 0.08071
#> 2 1 0.98694 0.13522 -0.03438 0.08051
#> 3 2 0.98769 0.13271 -0.03372 0.07557
#> 4 3 0.98851 0.12777 -0.03401 0.07323
#> 5 4 0.98888 0.12328 -0.04087 0.07238
#> 6 5 0.98831 0.12373 -0.05426 0.07058
#> 7 6 0.98847 0.12740 -0.05158 0.06349
#> 8 7 0.98957 0.11967 -0.05403 0.05926
#> 9 8 0.98900 0.12131 -0.06498 0.05425
#> 10 9 0.98894 0.12163 -0.07186 0.04507
#> # ℹ 91 more rows
#>
#> attr(,"class")
#> [1] "qts_sample" "list"