QTS Random Sampling — rnorm

This function adds uncorrelated Gaussian noise to the logarithm QTS using an exponential covariance function.

Usage

rnorm_qts(n, mean_qts, alpha = 0.01, beta = 0.001)

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   0.98741  -0.09893   0.09124  -0.08312
#>  2     1   0.98698  -0.10397   0.08881  -0.08466
#>  3     2   0.98748  -0.10539   0.08356  -0.08242
#>  4     3   0.98638  -0.11948   0.07363  -0.08578
#>  5     4   0.98630  -0.12358   0.06865  -0.08498
#>  6     5   0.98699  -0.12099   0.06539  -0.08333
#>  7     6   0.98839  -0.11697   0.06529  -0.07175
#>  8     7   0.98716  -0.12607   0.06561  -0.07287
#>  9     8   0.98673  -0.12654   0.06605  -0.07739
#> 10     9   0.98706  -0.12486   0.05998  -0.08080
#> # ℹ 91 more rows
#> 
#> attr(,"class")
#> [1] "qts_sample" "list"