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Simulate raw data

Source: R/sim_data.R
sim_data.Rd

Simulates data from a user-supplied distribution and creates missing values artificially. Functions mcar and mcar2 provides an example mechanisms for doing this based on a Missing Completely At Random (MCAR) assumption.

Usage

sim_data(
  blocks = 50,
  block_length = 365,
  distn = "exp",
  missing_fn = mcar,
  missing_args = formals(missing_fn)$missing_args,
  ...
)

mcar(
  sim_data,
  blocks,
  block_length,
  missing_args = list(p0miss = 0, min = 0, max = 0.5)
)

mcar2(sim_data, missing_args = list(pmiss = 0.5))

Arguments

blocks

A numeric scalar. The number of blocks of data required. Usually, this will be a positive integer, but blocks = 0 returns a list containing in the input arguments, in particular, distn, distn_args and block_length. This feature is provided so that a simulation setup could be replicated in without actually simulating data.

block_length

A numeric scalar. The number of raw observations per block.

distn

A character scalar. Specifies the distribution from which raw data are simulated. The name in the xxx part of the dxxx, pxxx, qxxx and rxxx distributional functions in the stats package. See stats::Distributions.

missing_fn

A function to simulate the positions of the missing values within each block year. See Details.

missing_args

Arguments to be passed to missing_fn. If missing_fn is mcar then a subset of p0miss, min and max may be supplied in the list missing_args. The values of the remaining components will be set at their default values.

...

Further arguments to the function stats::rxxx. The argument n is set within sim_data to be equal to block_length * blocks.

sim_data

A numeric vector of raw observations into, some of which will be made missing.

Value

If blocks > 0, a list with the following components:

  • data_full: simulated raw data with no missing values.

  • data_miss: simulated data after missing values have been created.

  • blocks, block_length: the respective input values of blocks and block_length.

  • block: a block indicator vector, suitable as an argument to gev_mle.

  • distn: the input argument distn.

  • distn_args: further arguments to stats::rxxx supplied via ....

If blocks = 0, a list containing all the inputs arguments.

Details

The function missing_fn must return a, possibly empty, subset of c(1, 2, ..., block_length). This function is applied within each simulated block, independently of other blocks.

The default function mcar simulates the numbers of missing values in the blocks as follows.

  • A proportion p0miss of the blocks have no missing values.

  • In the other blocks, the number of missing values is ceiling(prop_miss * block_length), where prob_miss is a value simulated from a Uniform(min, max) distribution. The positions of these missing values within the block is random.

The function mcar2 identifies at random a proportion pmiss of the simulated raw observations to become missing.

Care may need to be taken if these simulated data are used as input to gev_mle using an approach that discards block maxima based on more than a certain percentage of missing values, that is, with discard > 0. For example, using the default argument blocks = 50 and missing_fn = mcar, with its default missing_args, may result in a sample size of retained block maxima that contains insufficient information to make reliable inferences, leading to difficulties finding an appropriate MLE for the shape parameter \(\xi\) and/or a singular observed information matrix.

See also

gev_mle and gev_bayes.

Examples

# Using missing_fn = mcar
sdata <- sim_data()

# Using missing_fn = mcar2
sdata2 <- sim_data(missing_fn = mcar2)