mifa_ci_boot: Bootstrap confidence intervals for explained variance

In mifa: Multiple Imputation for Exploratory Factor Analysis

Description

Compute bootstrap confidence intervals for the proportion of explained variance for the covariance of an incomplete data imputed using multiple imputation. For multiple imputation, Multivariate Imputation by Chained Equations (MICE) from the mice package is used.

Usage

mifa_ci_boot(
  data,
  cov_vars = dplyr::everything(),
  n_pc,
  conf = 0.95,
  n_boot = 1000,
  progress = FALSE,
  ...
)

Arguments

data	A data frame with missing values coded as NA.
cov_vars	Variables in data for which to calculate the covariance matrix. Supports (tidy selection)dplyr::select(). This allows to select variables that are used for the imputations of missing values, but not the calculations of the covariance matrix. This is especially useful when there are categorical predictors that can improve the imputation of the response variables, but for which covariance cannot be calculated. By default, all variables in data are used for both, the imputation and the covariance matrix. Note: Variables and rows used for the imputation, as well as the method for imputation can be configured using the .... See also mice::mice().
n_pc	Integer or integer vector indicating number of principal components (eigenvectors) for which explained variance (eigenvalues) should be obtained and for which confidence intervals should be computed. Defaults to all principal components, i.e., the number of variables in the data.
conf	Confidence level for constructing confidence intervals. The default is .95 that is, 95% confidence intervals.
n_boot	Number of bootstrap samples to use for bootstrapped confidence intervals. The default is 1000.
progress	Logical. Whether to show progress bars for computation of bootstrap confidence intervals. Default is FALSE.
...	Arguments passed on to mice::mice m Number of multiple imputations. The default is m=5. method Can be either a single string, or a vector of strings with length length(blocks), specifying the imputation method to be used for each column in data. If specified as a single string, the same method will be used for all blocks. The default imputation method (when no argument is specified) depends on the measurement level of the target column, as regulated by the defaultMethod argument. Columns that need not be imputed have the empty method "". See details. predictorMatrix A numeric matrix of length(blocks) rows and ncol(data) columns, containing 0/1 data specifying the set of predictors to be used for each target column. Each row corresponds to a variable block, i.e., a set of variables to be imputed. A value of 1 means that the column variable is used as a predictor for the target block (in the rows). By default, the predictorMatrix is a square matrix of ncol(data) rows and columns with all 1's, except for the diagonal. Note: For two-level imputation models (which have "2l" in their names) other codes (e.g, 2 or -2) are also allowed. ignore A logical vector of nrow(data) elements indicating which rows are ignored when creating the imputation model. The default NULL includes all rows that have an observed value of the variable to imputed. Rows with ignore set to TRUE do not influence the parameters of the imputation model, but are still imputed. We may use the ignore argument to split data into a training set (on which the imputation model is built) and a test set (that does not influence the imputation model estimates). Note: Multivariate imputation methods, like mice.impute.jomoImpute() or mice.impute.panImpute(), do not honour the ignore argument. where A data frame or matrix with logicals of the same dimensions as data indicating where in the data the imputations should be created. The default, where = is.na(data), specifies that the missing data should be imputed. The where argument may be used to overimpute observed data, or to skip imputations for selected missing values. blocks List of vectors with variable names per block. List elements may be named to identify blocks. Variables within a block are imputed by a multivariate imputation method (see method argument). By default each variable is placed into its own block, which is effectively fully conditional specification (FCS) by univariate models (variable-by-variable imputation). Only variables whose names appear in blocks are imputed. The relevant columns in the where matrix are set to FALSE of variables that are not block members. A variable may appear in multiple blocks. In that case, it is effectively re-imputed each time that it is visited. visitSequence A vector of block names of arbitrary length, specifying the sequence of blocks that are imputed during one iteration of the Gibbs sampler. A block is a collection of variables. All variables that are members of the same block are imputed when the block is visited. A variable that is a member of multiple blocks is re-imputed within the same iteration. The default visitSequence = "roman" visits the blocks (left to right) in the order in which they appear in blocks. One may also use one of the following keywords: "arabic" (right to left), "monotone" (ordered low to high proportion of missing data) and "revmonotone" (reverse of monotone). formulas A named list of formula's, or expressions that can be converted into formula's by as.formula. List elements correspond to blocks. The block to which the list element applies is identified by its name, so list names must correspond to block names. The formulas argument is an alternative to the predictorMatrix argument that allows for more flexibility in specifying imputation models, e.g., for specifying interaction terms. blots A named list of alist's that can be used to pass down arguments to lower level imputation function. The entries of element blots[[blockname]] are passed down to the function called for block blockname. post A vector of strings with length ncol(data) specifying expressions as strings. Each string is parsed and executed within the sampler() function to post-process imputed values during the iterations. The default is a vector of empty strings, indicating no post-processing. defaultMethod A vector of length 4 containing the default imputation methods for 1) numeric data, 2) factor data with 2 levels, 3) factor data with > 2 unordered levels, and 4) factor data with > 2 ordered levels. By default, the method uses pmm, predictive mean matching (numeric data) logreg, logistic regression imputation (binary data, factor with 2 levels) polyreg, polytomous regression imputation for unordered categorical data (factor > 2 levels) polr, proportional odds model for (ordered, > 2 levels). maxit A scalar giving the number of iterations. The default is 5. printFlag If TRUE, mice will print history on console. Use print=FALSE for silent computation. seed An integer that is used as argument by the set.seed() for offsetting the random number generator. Default is to leave the random number generator alone. data.init A data frame of the same size and type as data, without missing data, used to initialize imputations before the start of the iterative process. The default NULL implies that starting imputation are created by a simple random draw from the data. Note that specification of data.init will start all m Gibbs sampling streams from the same imputation.

Details

This function uses the Shao and Sitter (1996) method to combine multiple imputation and bootstrapping. The imputations are done using mice::mice().

Normally, this function does not need to be called directly. Instead, use mifa(..., ci = "boot").

Value

A data frame containing bootstrapped confidence intervals for variance explained by different number of principal components.

References

Shao, J. & Sitter, R. R. (1996). Bootstrap for imputed survey data. Journal of the American Statistical Association 91.435 (1996): 1278-1288. doi: 10.1080/01621459.1996.10476997

See Also

mifa(), mice::mice()

Other mifa confidence intervals: mifa_ci_fieller()

Examples

if(requireNamespace("psych")) {
  data <- psych::bfi[, 1:25]
  mifa_ci_boot(data, n_pc = 3:8, n_boot = 10, print = FALSE)
}

mifa documentation built on Jan. 22, 2021, 5:10 p.m.