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   0.99567   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"