SimMissing-class: Class '"SimMissing"'

In simsem: SIMulated Structural Equation Modeling

Description

Missing information imposing on the complete dataset

Objects from the Class

Objects can be created by miss function.

Slots

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.

Methods

• summary To summarize the object

• impose To impose missing information into data

Author(s)

Patrick Miller (University of Notre Dame; [e-mail]) Alexander M. Schoemann (East Carolina University; [e-mail]) Kyle Lang (University of Kansas; [e-mail]) Sunthud Pornprasertmanit ([e-mail])

See Also

• imposeMissing for directly imposing missingness into a dataset.

Examples

misstemplate <- miss(pmMCAR=0.2)
summary(misstemplate)

Example output

Loading required package: lavaan
This is lavaan 0.6-3
lavaan is BETA software! Please report any bugs.
 
#################################################################
This is simsem 0.5-14
simsem is BETA software! Please report any bugs.
simsem was first developed at the University of Kansas Center for
Research Methods and Data Analysis, under NSF Grant 1053160.
#################################################################

Attaching package: 'simsem'

The following object is masked from 'package:lavaan':

    inspect

MISSING OBJECT
The method of missing data handling: Maximum Likelihood 
Covariates: none 
Ignored Variables: none 
Proportion of MCAR: 0.2 

simsem documentation built on March 29, 2021, 1:07 a.m.