# missing data in the LMS and QML approaches

    library(modsem)

# Options for Handling Missing Values

By default, missing values for both the LMS and QML are removed list-wise (`missing="listwise"`), such that only complete observations are kept. It is also possible to set `missing="impute"` to impute missing values, or to use the `modsem_mimpute()` function to perform multiple imputation. For the LMS approach it is also possible to use Full Information Maximum Likelihood (FIML)

## Full Information Maximum Likelihood (FIML)

If you’re using the LMS approach, it is possible to estimate your model using Full Information Maximum Likelihood (FIML), by setting `missing="fiml"`. Here is an example, where we generate some missing data using the `oneInt` dataset.

    set.seed(2834027) # set seed for reproducibility
    n.missing <- 200L # generate 200 missing values
    oneInt2 <- as.matrix(oneInt)
    oneInt2[sample(length(oneInt2), n.missing)] <- NA

    m1 <- '
    # Outer Model
      X =~ x1 + x2 + x3
      Z =~ z1 + z2 + z3
      Y =~ y1 + y2 + y3

    # Inner Model
      Y ~ X + Z + X:Z
    '

    lms_fiml <- modsem(m1, oneInt2, method = "lms", missing = "fiml")
    summary(lms_fiml)

## (Multiple) Imputation

If you’re using the QML approach it is not possible (yet) to use FIML, and FIML can also be very computationally intensive when using the LMS approach. Another option is to use (multiple) imputation.

If `missing="impute"` is set, single imputation is performed.

    qml_impute <- modsem(m1, oneInt2, method = "qml", missing = "impute")
    summary(qml_impute)

A better option than single imputation, is multiple imputation, which can be performed both for the LMS and QML approaches, using the `modsem_mimpute()` function.

    lms_mimpute <- modsem_mimpute(m1, oneInt2, method = "lms")
    summary(lms_mimpute)