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Set the multiple imputation methodology

Source: R/methods.R
method.Rd

These functions determine what methods rbmi should use when creating the imputation models, generating imputed values and pooling the results.

Usage

method_bayes(
  burn_in = 200,
  burn_between = 50,
  same_cov = TRUE,
  n_samples = 20,
  seed = NULL
)

method_approxbayes(
  covariance = c("us", "ad", "adh", "ar1", "ar1h", "cs", "csh", "toep", "toeph"),
  threshold = 0.01,
  same_cov = TRUE,
  REML = TRUE,
  n_samples = 20
)

method_condmean(
  covariance = c("us", "ad", "adh", "ar1", "ar1h", "cs", "csh", "toep", "toeph"),
  threshold = 0.01,
  same_cov = TRUE,
  REML = TRUE,
  n_samples = NULL,
  type = c("bootstrap", "jackknife")
)

method_bmlmi(
  covariance = c("us", "ad", "adh", "ar1", "ar1h", "cs", "csh", "toep", "toeph"),
  threshold = 0.01,
  same_cov = TRUE,
  REML = TRUE,
  B = 20,
  D = 2
)

Arguments

burn_in

a numeric that specifies how many observations should be discarded prior to extracting actual samples. Note that the sampler is initialized at the maximum likelihood estimates and a weakly informative prior is used thus in theory this value should not need to be that high.

burn_between

a numeric that specifies the "thinning" rate i.e. how many observations should be discarded between each sample. This is used to prevent issues associated with autocorrelation between the samples.

same_cov

a logical, if TRUE the imputation model will be fitted using a single shared covariance matrix for all observations. If FALSE a separate covariance matrix will be fit for each group as determined by the group argument of set_vars().

n_samples

a numeric that determines how many imputed datasets are generated. In the case of method_condmean(type = "jackknife") this argument must be set to NULL. See details.

seed

deprecated. Please use set.seed() instead.

covariance

a character string that specifies the structure of the covariance matrix to be used in the imputation model. Must be one of "us" (default), "ad", "adh", "ar1", "ar1h", "cs", "csh", "toep", or "toeph"). See details.

threshold

a numeric between 0 and 1, specifies the proportion of bootstrap datasets that can fail to produce valid samples before an error is thrown. See details.

REML

a logical indicating whether to use REML estimation rather than maximum likelihood.

type

a character string that specifies the resampling method used to perform inference when a conditional mean imputation approach (set via method_condmean()) is used. Must be one of "bootstrap" or "jackknife".

B

a numeric that determines the number of bootstrap samples for method_bmlmi.

D

a numeric that determines the number of random imputations for each bootstrap sample. Needed for method_bmlmi().

Details

In the case of method_condmean(type = "bootstrap") there will be n_samples + 1 imputation models and datasets generated as the first sample will be based on the original dataset whilst the other n_samples samples will be bootstrapped datasets. Likewise, for method_condmean(type = "jackknife") there will be length(unique(data$subjid)) + 1 imputation models and datasets generated. In both cases this is represented by n + 1 being displayed in the print message.

The user is able to specify different covariance structures using the the covariance argument. Currently supported structures include:

  • Unstructured ("us") (default)

  • Ante-dependence ("ad")

  • Heterogeneous ante-dependence ("adh")

  • First-order auto-regressive ("ar1")

  • Heterogeneous first-order auto-regressive ("ar1h")

  • Compound symmetry ("cs")

  • Heterogeneous compound symmetry ("csh")

  • Toeplitz ("toep")

  • Heterogeneous Toeplitz ("toeph")

For full details please see mmrm::cov_types().

Note that at present Bayesian methods only support unstructured.

In the case of method_condmean(type = "bootstrap"), method_approxbayes() and method_bmlmi() repeated bootstrap samples of the original dataset are taken with an MMRM fitted to each sample. Due to the randomness of these sampled datasets, as well as limitations in the optimisers used to fit the models, it is not uncommon that estimates for a particular dataset can't be generated. In these instances rbmi is designed to throw out that bootstrapped dataset and try again with another. However to ensure that these errors are due to chance and not due to some underlying misspecification in the data and/or model a tolerance limit is set on how many samples can be discarded. Once the tolerance limit has been reached an error will be thrown and the process aborted. The tolerance limit is defined as ceiling(threshold * n_samples). Note that for the jackknife method estimates need to be generated for all leave-one-out datasets and as such an error will be thrown if any of them fail to fit.

Please note that at the time of writing (September 2021) Stan is unable to produce reproducible samples across different operating systems even when the same seed is used. As such care must be taken when using Stan across different machines. For more information on this limitation please consult the Stan documentation https://mc-stan.org/docs/2_27/reference-manual/reproducibility-chapter.html