Description Usage Arguments Value Author(s) See Also Examples
Specifying the missing template (SimMissing
) to impose on a dataset. The template will be used in Monte Carlo simulation such that, in the sim
function, datasets are created and imposed by missing values created by this template. See imposeMissing
for further details of each argument.
1 2 3 4 
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. The script is similar to the specification of regression in 
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. 13,46,79,1012) 
timePoints 
Number of timepoints items were measured over. For longitudinal data, planned missing designs will be implemented within each timepoint. 
twoMethod 
With missing on one variable: vector of (column index, percent missing). Will put a given percent missing on that column in the matrix to simulate a two method planned missing data research design. With missing on two or more variables: list of (column indices, percent missing). 
prAttr 
Probability (or vector of probabilities) of an entire case being removed due to attrition at a given time point. See 
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 
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. 
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 
logical 
A matrix of logical values ( 
... 
Additional arguments used in multiple imputation function. 
A missing object that contains missingdata template (SimMissing
)
Alexander M. Schoemann (East Carolina University; schoemanna@ecu.edu), Patrick Miller (University of Notre Dame; pmille13@nd.edu), Sunthud Pornprasertmanit (psunthud@gmail.com)
SimMissing
The resulting missing object
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44  #Example of imposing 10% MCAR missing in all variables with no imputations (FIML method)
Missing < miss(pmMCAR=0.1, ignoreCols="group")
summary(Missing)
loading < matrix(0, 6, 1)
loading[1:6, 1] < NA
LY < bind(loading, 0.7)
RPS < binds(diag(1))
RTE < binds(diag(6))
CFA.Model < model(LY = LY, RPS = RPS, RTE = RTE, modelType="CFA")
#Create data
dat < generate(CFA.Model, n = 20)
#Impose missing
datmiss < impose(Missing, dat)
#Analyze data
out < analyze(CFA.Model, datmiss)
summary(out)
#Missing using logistic regression
script < 'y1 ~ 0.05 + 0.1*y2 + 0.3*y3
y4 ~ 2 + 0.1*y4
y5 ~ 0.5'
Missing2 < miss(logit=script, pmMCAR=0.1, ignoreCols="group")
summary(Missing2)
datmiss2 < impose(Missing2, dat)
#Missing using logistic regression (2)
script < 'y1 ~ 0.05 + 0.5*y3
y2 ~ p(0.2)
y3 ~ p(0.1) + 1*y1
y4 ~ p(0.3) + 0.2*y1 + 0.3*y2
y5 ~ 0.5'
Missing2 < miss(logit=script)
summary(Missing2)
datmiss2 < impose(Missing2, dat)
#Example to create simMissing object for 3 forms design at 3 timepoints with 10 imputations
Missing < miss(nforms=3, timePoints=3, numImps=10)
#Missing template for data analysis with multiple imputation
Missing < miss(package="mice", m=10, convergentCutoff=0.6)

Loading required package: lavaan
This is lavaan 0.63
lavaan is BETA software! Please report any bugs.
#################################################################
This is simsem 0.514
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: group
Proportion of MCAR: 0.1
Warning message:
In (function (model = NULL, data = NULL, ordered = NULL, sampling.weights = NULL, :
lavaan WARNING: the optimizer warns that a solution has NOT been found!
lavaan 0.63 did NOT end normally after 10000 iterations
** WARNING ** Estimates below are most likely unreliable
Optimization method NLMINB
Number of free parameters 18
Number of observations 20
Number of missing patterns 7
Estimator ML
Model Fit Test Statistic NA
Degrees of freedom NA
Pvalue NA
Parameter Estimates:
Information Observed
Observed information based on Hessian
Standard Errors Standard
Latent Variables:
Estimate Std.Err zvalue P(>z)
f1 =~
y1 0.006 NA
y2 0.002 NA
y3 0.006 NA
y4 29.492 NA
y5 0.003 NA
y6 0.013 NA
Covariances:
Estimate Std.Err zvalue P(>z)
.y1 ~~
.y2 0.000
.y3 0.000
.y4 0.000
.y5 0.000
.y6 0.000
.y2 ~~
.y3 0.000
.y4 0.000
.y5 0.000
.y6 0.000
.y3 ~~
.y4 0.000
.y5 0.000
.y6 0.000
.y4 ~~
.y5 0.000
.y6 0.000
.y5 ~~
.y6 0.000
Intercepts:
Estimate Std.Err zvalue P(>z)
f1 0.000
.y1 0.175 NA
.y2 0.305 NA
.y3 0.150 NA
.y4 0.063 NA
.y5 0.158 NA
.y6 0.021 NA
Variances:
Estimate Std.Err zvalue P(>z)
f1 1.000
.y1 0.500 NA
.y2 0.747 NA
.y3 0.828 NA
.y4 869.256 NA
.y5 0.488 NA
.y6 0.677 NA
MISSING OBJECT
The method of missing data handling: Maximum Likelihood
Covariates: none
Ignored Variables: group
Proportion of MCAR: 0.1
Logisticregression MAR:
y1 ~ 0.05 + 0.1*y2 + 0.3*y3
y4 ~ 2 + 0.1*y4
y5 ~ 0.5
MISSING OBJECT
The method of missing data handling: Maximum Likelihood
Covariates: none
Ignored Variables: none
Logisticregression MAR:
y1 ~ 0.05 + 0.5*y3
y2 ~ p(0.2)
y3 ~ p(0.1) + 1*y1
y4 ~ p(0.3) + 0.2*y1 + 0.3*y2
y5 ~ 0.5
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