"SimMissing"
Missing information imposing on the complete dataset
Objects can be created by miss
function.
cov
:Column indices of any normally distributed covariates used in the data set.
pmMCAR
:Decimal percent of missingness to introduce completely at random on all variables.
pmMAR
:Decimal percent of missingness to introduce using the listed covariates as predictors.
logit
:The script used for imposing missing values by logistic regression. See miss
for further details.
nforms
:The number of forms for planned missing data designs, not including the shared form.
itemGroups
:List of lists of item groupings for planned missing data forms. Without this, items will be divided into groups sequentially (e.g. 1-3,4-6,7-9,10-12)
twoMethod
:Vector of (percent missing, column index). Will put a given percent missing on that column in the matrix to simulate a two method planned missing data research design.
prAttr
:Probability (or vector of probabilities) of an entire case being removed due to attrition at a given time point. See imposeMissing
for further details.
m
:The number of imputations. The default is 0 such that the full information maximum likelihood is used.
package
:The package to be used in multiple imputation. The default value of this function is "default"
. For the default option, if m
is 0, the full information maximum likelihood is used. If m
is greater than 0, the mice
package is used.
convergentCutoff
:If the proportion of convergent results across imputations are greater than the specified value (the default is 80%), the analysis on the dataset is considered as convergent. Otherwise, the analysis is considered as nonconvergent. This attribute is applied for multiple imputation only.
timePoints
:Number of timepoints items were measured over. For longitudinal data, planned missing designs will be implemented within each timepoint.
ignoreCols
:The columns not imposed any missing values for any missing data patterns
threshold
:The threshold of covariates that divide between the area to impose missing and the area not to impose missing. The default threshold is the mean of the covariate.
covAsAux
:If TRUE
, the covariate listed in the object will be used as auxiliary variables when putting in the model object. If FALSE
, the covariate will be included in the analysis.
logical
:A matrix of logical values (TRUE/FALSE
). If a value in the dataset is corresponding to the TRUE
in the logical matrix, the value will be missing.
args
:A list of additional options to be passed to the multiple impuatation function in each package.
imposeMissing
for directly imposing missingness into a dataset.
# NOT RUN {
misstemplate <- miss(pmMCAR=0.2)
summary(misstemplate)
# }
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