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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   0.98603   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"