The Multiple Imputation DOCtor (midoc) R package is a
guidance system for analysis with missing data. It incorporates expert,
up-to-date methodology to help you choose the most appropriate analysis
method when there are missing data. By examining the available data and
the assumed causal structure, midoc will advise whether
multiple imputation is needed, and if so, how best to perform it.
descMissData lists missing data patterns in the
specified dataset
exploreDAG compares the relationships in the
available data with the proposed DAG
checkCRA checks complete records analysis is valid
under the proposed analysis model
checkMI checks multiple imputation is valid under
the proposed imputation model
checkModSpec explores the parametric specification
of the imputation model
proposeMI suggests multiple imputation options based
on the available data and specified imputation model
doMImice performs multiple imputation based on the
proposeMI options
You can learn more about these commands in
vignette("midoc","midoc").
You can install the development version of midoc from GitHub with:
# install.packages("devtools")
devtools::install_github("elliecurnow/midoc")library(midoc)
head(bmi)
#> bmi7 matage mated pregsize bwt r
#> 1 15.16444 -1.30048035 0 0 3.287754 1
#> 2 18.00250 -0.33689915 0 0 3.770346 1
#> 3 NA -0.22673432 0 1 3.022161 0
#> 4 NA 0.81459107 1 0 3.103251 0
#> 5 17.97791 -0.55260086 0 0 3.830381 1
#> 6 NA -0.03829346 1 0 2.775282 0
descMissData(y="bmi7", covs="matage mated", data=bmi, plot=TRUE)
#> pattern bmi7 matage mated n pct
#> 1 1 1 1 1 592 59
#> 2 2 0 1 1 408 41
exploreDAG(mdag=" matage -> bmi7
mated -> matage
mated -> bmi7
sep_unmeas -> mated
sep_unmeas -> r
pregsize -> bmi7
pregsize -> bwt
sep_unmeas -> bwt",
data=bmi)
#> The proposed directed acyclic graph (DAG) implies the following
#> conditional independencies (where, for example, 'X _||_ Y | Z' should
#> be read as 'X is independent of Y conditional on Z'). Note that
#> variable names are abbreviated:
#>
#> bmi7 _||_ bwt | prgs, sp_n
#>
#> bmi7 _||_ bwt | matd, prgs
#>
#> bmi7 _||_ r | sp_n
#>
#> bmi7 _||_ r | matd
#>
#> bmi7 _||_ sp_n | matd
#>
#> bwt _||_ matg | matd
#>
#> bwt _||_ matg | sp_n
#>
#> bwt _||_ matd | sp_n
#>
#> bwt _||_ r | sp_n
#>
#> matg _||_ prgs
#>
#> matg _||_ r | sp_n
#>
#> matg _||_ r | matd
#>
#> matg _||_ sp_n | matd
#>
#> matd _||_ prgs
#>
#> matd _||_ r | sp_n
#>
#> prgs _||_ r
#>
#> prgs _||_ sp_n
#>
#> These (conditional) independence statements are explored below using
#> the canonical correlations approach for mixed data. See
#> ??dagitty::localTests for further details. Results are shown for
#> variables that are fully observed in the specified dataset. The null
#> hypothesis is that the stated variables are (conditionally)
#> independent.
#>
#> estimate p.value 2.5% 97.5%
#>
#> bwt _||_ matage | mated 0.05018898 0.1127099 -0.01184095 0.11183410
#>
#> matage _||_ pregsize 0.03029139 0.3386080 -0.03176134 0.09211150
#>
#> matage _||_ r | mated 0.02998323 0.3435470 -0.03206946 0.09180567
#>
#> mated _||_ pregsize 0.01594976 0.6144181 -0.04608889 0.07786585
#>
#> pregsize _||_ r 0.01482015 0.6397174 -0.04721631 0.07674273
#>
#> Interpretation: A small p-value means the stated variables may not be
#> (conditionally) independent in the specified dataset: your data may not
#> be consistent with the proposed DAG. A large p-value means there is
#> little evidence of inconsistency between your data and the proposed
#> DAG.
#>
#> Note that these results assume that relationships between variables are
#> linear. Consider exploring the specification of each relationship in
#> your model. Also consider whether it is valid and possible to explore
#> relationships between partially observed variables using the observed
#> data, e.g. avoiding perfect prediction.
checkCRA(y="bmi7", covs="matage mated", r_cra="r",
mdag=" matage -> bmi7
mated -> matage
mated -> bmi7
sep_unmeas -> mated
sep_unmeas -> r
pregsize -> bmi7
pregsize -> bwt
sep_unmeas -> bwt")
#> Based on the proposed directed acyclic graph (DAG), the analysis model
#> outcome and complete record indicator are independent given analysis
#> model covariates. Hence, complete records analysis is valid.
checkMI(dep="bmi7", preds="matage mated pregsize", r_dep="r",
mdag=" matage -> bmi7
mated -> matage
mated -> bmi7
sep_unmeas -> mated
sep_unmeas -> r
pregsize -> bmi7
pregsize -> bwt
sep_unmeas -> bwt")
#> Based on the proposed directed acyclic graph (DAG), the incomplete
#> variable and its missingness indicator are independent given imputation
#> model predictors. Hence, multiple imputation methods which assume data
#> are missing at random are valid in principle.
mimod_bmi7 <- checkModSpec(formula="bmi7~matage+I(matage^2)+mated+pregsize",
family="gaussian(identity)", data=bmi)
#> Model mis-specification method: regression of model residuals on a
#> fractional polynomial of the fitted values
#>
#> P-value: 1
#>
#> A large p-value means there is little evidence of model
#> mis-specification.
miprop <- proposeMI(mimodobj=mimod_bmi7, data=bmi)
#> Based on your proposed imputation model and dataset, your mice() call
#> should be as follows:
#>
#> mice(data = bmi , # You may need to specify a subset of the columns in
#> your dataset
#>
#> m = 41 , # You should use at least this number of imputations based on
#> the proportion of complete records in your dataset
#>
#> method = c( 'norm' ) # Specify a method for each incomplete variable.
#> If displayed, the box-and-whisker plots can be used to inform your
#> choice of method(s): for example, if the imputation model does not
#> predict extreme values appropriately, consider a different imputation
#> model/method e.g. PMM. Note the distribution of imputed and observed
#> values is displayed for numeric variables only. The distribution may
#> differ if data are missing at random or missing not at random. If you
#> suspect data are missing not at random, the plots can also inform your
#> choice of sensitivity parameter.
#>
#> formulas = formulas_list , # Note that you do not additionally need to
#> specify a 'predmatrix'
#>
#> # The formulas_list specifies the conditional imputation models, which
#> are as follows:
#>
#> 'bmi7 ~ matage + I(matage^2) + mated + pregsize'
#>
#> maxit = 10 , # If you have more than one incomplete variable, you
#> should check this number of iterations is sufficient by inspecting the
#> trace plots, if displayed. Consider increasing the number of iterations
#> if there is a trend that does not stabilise by the 10th iteration. Note
#> that iteration is not performed when only one variable is partially
#> observed.
#>
#> printFlag = FALSE , # Change to printFlag=TRUE to display the history
#> as imputation is performed
#>
#> seed = NA) # It is good practice to choose a seed so your results are
#> reproducible
doMImice(miprop, 123, substmod="lm(bmi7 ~ matage + I(matage^2) + mated)")
#> Given the substantive model: lm(bmi7 ~ matage + I(matage^2) + mated) ,
#> multiple imputation estimates are as follows:
#>
#> term estimate std.error statistic df p.value
#>
#> 1 (Intercept) 17.6607324 0.07126548 247.816079 233.1668 2.116834e-284
#>
#> 2 matage 1.1504545 0.05230345 21.995769 184.5081 1.863532e-53
#>
#> 3 I(matage^2) 0.8414975 0.03231752 26.038433 257.1270 4.754845e-74
#>
#> 4 mated1 -1.0026194 0.10787751 -9.294054 159.1101 1.094881e-16
#>
#> 2.5 % 97.5 %
#>
#> 1 17.5203258 17.8011389
#>
#> 2 1.0472648 1.2536442
#>
#> 3 0.7778567 0.9051382
#>
#> 4 -1.2156760 -0.7895629