| Title: | Tools for Analysis of Stacked Multiple Imputations |
| Version: | 0.1.0 |
| Description: | Provides methods for inference using stacked multiple imputations augmented with weights. The vignette provides example R code for implementation in general multiple imputation settings. For additional details about the estimation algorithm, we refer the reader to Beesley, Lauren J and Taylor, Jeremy M G (2020) “A stacked approach for chained equations multiple imputation incorporating the substantive model” <doi:10.1111/biom.13372>, and Beesley, Lauren J and Taylor, Jeremy M G (2021) “Accounting for not-at-random missingness through imputation stacking” <doi:10.48550/arXiv.2101.07954>. |
| Depends: | R (≥ 3.6.0) |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| LazyDataCompression: | xz |
| RoxygenNote: | 7.1.1 |
| Imports: | sandwich, zoo, mice, dplyr, MASS, magrittr, boot |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2021-09-08 18:16:12 UTC; mkleinsa |
| Author: | Lauren Beesley [aut], Mike Kleinsasser [cre] |
| Maintainer: | Mike Kleinsasser <mkleinsa@umich.edu> |
| Repository: | CRAN |
| Date/Publication: | 2021-09-10 11:10:02 UTC |
Pipe operator
Description
See magrittr::%>% for details.
Usage
lhs %>% rhs
Arguments
lhs |
A value or the magrittr placeholder. |
rhs |
A function call using the magrittr semantics. |
Value
The result of calling 'rhs(lhs)'.
Bootstrap_Variance
Description
This function takes a dataset with stacked multiple imputation and a model fit and applies bootstrap to estimate the covariance matrix accounting for imputation uncertainty.
Usage
Bootstrap_Variance(fit, stack, M, n_boot = 100)
Arguments
fit |
object with corresponding vcov method (e.g. glm, coxph, survreg, etc.) from fitting to the (weighted) stacked dataset |
stack |
data frame containing stacked dataset across multiple imputations. Could have 1 or M rows for each subject with complete data. Should have M rows for each subject with imputed data. Must contain the following named columns: (1) stack$.id, which correspond to a unique identifier for each subject. This column can be easily output from MICE. (2) stack$wt, which corresponds to weights assigned to each row. Standard analysis of stacked multiple imputations should set these weights to 1 over the number of times the subject appears in the stack. (3) stack$.imp, which indicates the multiply imputed dataset (from 1 to M). This column can be easily output from MICE. |
M |
number of multiple imputations |
n_boot |
number of bootstrap samples |
Details
This function implements the bootstrap-based estimation method for stacked multiple imputations proposed by Dr. Paul Bernhardt in “A Comparison of Stacked and Pooled Multiple Imputation" at the Joint Statistical Meetings, 2019.
Value
Variance, estimated covariance matrix accounting for within and between imputation variation
Examples
data(stackExample)
fit = stackExample$fit
stack = stackExample$stack
bootcovar = Bootstrap_Variance(fit, stack, M = 5, n_boot = 10)
VARIANCE_boot = diag(bootcovar)
Jackknife_Variance
Description
This function takes a dataset with stacked multiple imputation and a model fit and applies jackknife to estimate the covariance matrix accounting for imputation uncertainty.
Usage
Jackknife_Variance(fit, stack, M)
Arguments
fit |
object with corresponding vcov method (e.g. glm, coxph, survreg, etc.) from fitting to the (weighted) stacked dataset |
stack |
data frame containing stacked dataset across multiple imputations. Could have 1 or M rows for each subject with complete data. Should have M rows for each subject with imputed data. Must contain the following named columns: (1) stack$.id, which correspond to a unique identifier for each subject. This column can be easily output from MICE. (2) stack$wt, which corresponds to weights assigned to each row. Standard analysis of stacked multiple imputations should set these weights to 1 over the number of times the subject appears in the stack. (3) stack$.imp, which indicates the multiply imputed dataset (from 1 to M). This column can be easily output from MICE. |
M |
number of multiple imputations |
Details
This function implements the jackknife-based estimation method for stacked multiple imputations proposed by Beesley and Taylor (2021).
Value
Variance, estimated covariance matrix accounting for within and between imputation variation
Examples
data(stackExample)
fit = stackExample$fit
stack = stackExample$stack
jackcovar = Jackknife_Variance(fit, stack, M = 5)
VARIANCE_jack = diag(jackcovar)
Louis_Information
Description
This function takes a dataset with stacked multiple imputations and a glm or coxph fit and estimates the corresponding information matrix accounting for the imputation uncertainty.
Usage
Louis_Information(fit, stack, M, IMPUTED = NULL)
Arguments
fit |
object of class glm or coxph from fitting to the (weighted) stacked dataset |
stack |
data frame containing stacked dataset across multiple imputations. Could have 1 or M rows for each subject with complete data. Should have M rows for each subject with imputed data. Must contain the following named columns: (1) stack$.id, which correspond to a unique identifier for each subject. This column can be easily output from MICE. (2) stack$wt, which corresponds to weights assigned to each row. Standard analysis of stacked multiple imputations should set these weights to 1 over the number of times the subject appears in the stack. |
M |
number of multiple imputations |
IMPUTED |
deprecated parameter, not used in current version |
Details
This function uses the observed information matrix principle proposed in Louis (1982) and applied to imputations in Wei and Tanner (1990). This estimator is a further extension specifically designed for analyzing stacks of multiply imputed data as proposed in Beesley and Taylor (2019) https://arxiv.org/abs/1910.04625.
Value
Info, estimated information matrix accounting for within and between imputation variation
Examples
data(stackExample)
Info = Louis_Information(stackExample$fit, stackExample$stack, M = 50)
VARIANCE = diag(solve(Info))
Louis_Information_Custom
Description
This function takes a dataset with stacked multiple imputations and a score matrix and covariance matrix from stacked and weighted analysis as inputs to estimates the corresponding information matrix accounting for the imputation uncertainty.
Usage
Louis_Information_Custom(score, covariance_weighted, stack, M)
Arguments
score |
n x p matrix containing the contribution to the outcome model score matrix for each subject (n rows) and each model parameter (p columns). |
covariance_weighted |
p x p matrix containing the estimated covariance matrix from fitting the desired model to the stacked and weighted multiple imputations. Note: For GLM models, use summary(fit)$cov.unscaled*StackImpute::glm.weighted.dispersion(fit) as the default dispersion parameter will be incorrect. |
stack |
data frame containing stacked dataset across multiple imputations. Could have 1 or M rows for each subject with complete data. Should have M rows for each subject with imputed data. Must contain the following named columns: (1) stack$.id, which correspond to a unique identifier for each subject. This column can be easily output from MICE. (2) stack$wt, which corresponds to weights assigned to each row. Standard analysis of stacked multiple imputations should set these weights to 1 over the number of times the subject appears in the stack. |
M |
number of multiple imputations |
Details
This function uses the observed information matrix principle proposed in Louis (1982) and applied to imputations in Wei and Tanner (1990). This estimator is a further extension specifically designed for analyzing stacks of multiply imputed data as proposed in Beesley and Taylor (2019) https://arxiv.org/abs/1910.04625.
Value
Info, estimated information matrix accounting for within and between imputation variation
Examples
data(stackExample)
fit = stackExample$fit
stack = stackExample$stack
covariates = as.matrix(cbind(1, stack$X, stack$B))
score = sweep(covariates, 1, stack$Y - covariates %*%
matrix(coef(fit)), '*') / glm.weighted.dispersion(fit)
covariance_weighted = summary(fit)$cov.unscaled * glm.weighted.dispersion(fit)
Info = Louis_Information_Custom(score, covariance_weighted, stack, M = 50)
VARIANCE_custom = diag(solve(Info))
func.boot
Description
This function is called internal to Bootstrap_Variance and re-estimates glm model parameters
Usage
func.boot(data, indices)
Arguments
data |
matrix with indices of possible imputed datasets to sample |
indices |
sampled indices |
Value
numeric vector of parameter coefficients
func.jack
Description
This function is internal to Jackknife_Variance. This estimates model parameters using a subset of the stacked data.
Usage
func.jack(leaveout, stack)
Arguments
leaveout |
indexes the multiple imputation being excluded from estimation |
stack |
data frame containing stacked dataset across multiple imputations. Could have 1 or M rows for each subject with complete data. Should have M rows for each subject with imputed data. Must contain the following named columns: (1) stack$.id, which correspond to a unique identifier for each subject. This column can be easily output from MICE. (2) stack$wt, which corresponds to weights assigned to each row. Standard analysis of stacked multiple imputations should set these weights to 1 over the number of times the subject appears in the stack. (3) stack$.imp, which indicates the multiply imputed dataset (from 1 to M). This column can be easily output from MICE. |
Value
numeric vector of parameter coefficients
glm.weighted.dispersion
Description
The goal of this function is to estimate the glm dispersion parameter using data across imputed datasets while correctly accounting for the weights.
Usage
glm.weighted.dispersion(fit)
Arguments
fit |
an object of class glm |
Value
an estimate of the glm dispersion parameter
Examples
data(stackExample)
glm.weighted.dispersion(stackExample$fit)
my_update
Description
Function for updating a model fit using either new data or a new model structure
Usage
my_update(mod, formula = NULL, data = NULL, weights = NULL)
Arguments
mod |
object of class 'glm' or 'coxph' |
formula |
formula for updated model fit, default = no change |
data |
data used for updated model fit, default = no change |
weights |
weights used for updated model fit, default = no change |
Value
the updated model fit object of the same class as the given model
Example data for Louis_Information()
Description
Example data set for Louis_Information()
Format
a list with
fit glm fit from vignette example
stack stacked imputed data sets from vignette example