# Specifying the missing template to impose on a dataset 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. ```r miss(cov = 0, pmMCAR = 0, pmMAR = 0, logit = "", nforms = 0, itemGroups = list(), timePoints = 1, twoMethod = 0, prAttr = 0, m = 0, package = "default", convergentCutoff = 0.8, ignoreCols = 0, threshold = 0, covAsAux = TRUE, logical = NULL, ...) ``` ## Arguments - `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 `lavaan` such that each line begins with a dependent variable, then '~' is used as regression sign, and the formula of a linear combination of independent variable plus constant, such as y1 ~ 0.5 + 0.2*y2. '#' and '!' can be used as a comment (like `lavaan`). For the intercept, users may use 'p()' to specify the average proportion of missing, such as y1 ~ p(0.2) + 0.3*y2, which the average missing proportion of y1 is 0.2 and the missing of y1 depends on y2. Users may visualize the missing proportion from the logistic specification by the `plotLogitMiss` function. - `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) - `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 `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. The possible inputs are `"default"`, `"Amelia"`, or `"mice"`. - `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 `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. - ``...``: Additional arguments used in multiple imputation function. ## Returns A missing object that contains missing-data template (`SimMissing`) ## Author(s) Alexander M. Schoemann (East Carolina University; schoemanna@ecu.edu ), Patrick Miller (University of Notre Dame; pmille13@nd.edu ), Sunthud Pornprasertmanit (psunthud@gmail.com ) ## See Also * `SimMissing` The resulting missing object ## Examples ```r #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) ```

miss function