R: Class for a lavaan Model Fitted to Multiple Imputations

lavaan.mi-class {semTools} | R Documentation

## Class for a lavaan Model Fitted to Multiple Imputations

### Description

This class extends the `lavaanList` class, created by fitting a lavaan model to a list of data sets. In this case, the list of data sets are multiple imputations of missing data.

### Usage

    ## S4 method for signature 'lavaan.mi'
    show(object)

    ## S4 method for signature 'lavaan.mi'
    summary(object, se = TRUE, ci = FALSE,
      level = 0.95, standardized = FALSE, rsquare = FALSE, fmi = FALSE,
      header = TRUE, scale.W = TRUE, asymptotic = FALSE,
      add.attributes = TRUE)

    ## S4 method for signature 'lavaan.mi'
    nobs(object, total = TRUE)

    ## S4 method for signature 'lavaan.mi'
    coef(object, type = "free", labels = TRUE)

    ## S4 method for signature 'lavaan.mi'
    vcov(object, type = c("pooled", "between",
      "within", "ariv"), scale.W = TRUE)

    ## S4 method for signature 'lavaan.mi'
    anova(object, ...)

    ## S4 method for signature 'lavaan.mi'
    fitMeasures(object, fit.measures = "all",
      baseline.model = NULL)

    ## S4 method for signature 'lavaan.mi'
    fitmeasures(object, fit.measures = "all",
      baseline.model = NULL)

    ## S4 method for signature 'lavaan.mi'
    fitted(object)

    ## S4 method for signature 'lavaan.mi'
    fitted.values(object)

    ## S4 method for signature 'lavaan.mi'
    residuals(object, type = c("raw", "cor"))

    ## S4 method for signature 'lavaan.mi'
    resid(object, type = c("raw", "cor"))

### Arguments

`object` | An object of class `lavaan.mi`
`se, ci, level, standardized, rsquare, header, add.attributes` | See `parameterEstimates`.
`fmi` | `logical` indicating whether to include the Fraction Missing Information (FMI) for parameter estimates in the `summary` output (see Value section).
`scale.W` | `logical`. If `TRUE` (default), the `vcov` method will calculate the pooled covariance matrix by scaling the within-imputation component by the ARIV (see Enders, 2010, p. 235, for definition and formula). Otherwise, the pooled matrix is calculated as the weighted sum of the within-imputation and between-imputation components (see Enders, 2010, ch. 8, for details). This in turn affects how the `summary` method calcualtes its pooled standard errors, as well as the Wald test (`lavTestWald.mi`).
`asymptotic` | `logical`. If `FALSE` (typically a default, but see Value section for details using various methods), pooled tests (of fit or pooled estimates) will be F or t statistics with associated degrees of freedom (df). If `TRUE`, the (denominator) df are assumed to be sufficiently large for a t statistic to follow a normal distribution, so it is printed as a z statisic; likewise, F times its numerator df is printed, assumed to follow a χ^2 distribution.
`total` | `logical` (default: `TRUE`) indicating whether the `nobs` method should return the total sample size or (if `FALSE`) a vector of group sample sizes.
`type` | The meaning of this argument varies depending on which method it it used for. Find detailed descriptions in the Value section under `coef`, `vcov`, and `residuals`.
`labels` | `logical` indicating whether the `coef` output should include parameter labels. Default is `TRUE`.
`...` | Additional arguments passed to `lavTestLRT.mi`, or subsequently to `lavTestLRT`.
`fit.measures, baseline.model` | See `fitMeasures`.

### Value

`coef` | `signature(object = "lavaan.mi", type = "free", labels = TRUE)`: See `lavaan`. Returns the pooled point estimates (i.e., averaged across imputed data sets; see Rubin, 1987).
`vcov` | `signature(object = "lavaan.mi", scale.W = TRUE, type = c("pooled","between","within","ariv"))`: By default, returns the pooled covariance matrix of parameter estimates (`type = "pooled"`), the within-imputations covariance matrix (`type = "within"`), the between-imputations covariance matrix (`type = "between"`), or the average relative increase in variance (`type = "ariv"`) due to missing data.
`fitted.values` | `signature(object = "lavaan.mi")`: See `lavaan`. Returns model-implied moments, evaluated at the pooled point estimates.
`fitted` | `signature(object = "lavaan.mi")`: alias for `fitted.values`
`residuals` | `signature(object = "lavaan.mi", type = c("raw","cor"))`: See `lavaan`. By default (`type = "raw"`), returns the difference between the model-implied moments from `fitted.values` and the pooled observed moments (i.e., averaged across imputed data sets). Standardized residuals are also available, using Bollen's (`type = "cor"` or `"cor.bollen"`) or Bentler's (`type = "cor.bentler"`) formulas.
`resid` | `signature(object = "lavaan.mi", type = c("raw","cor"))`: alias for `residuals`
`nobs` | `signature(object = "lavaan.mi", total = TRUE)`: either the total (default) sample size or a vector of group sample sizes (`total = FALSE`).
`anova` | `signature(object = "lavaan.mi", ...)`: Returns a test of model fit for a single model (`object`) or test(s) of the difference(s) in fit between nested models passed via `...`. See `lavTestLRT.mi` and `compareFit` for details.
`fitMeasures` | `signature(object = "lavaan.mi", fit.measures = "all", baseline.model = NULL)`: See lavaan's `fitMeasures` for details. Pass additional arguments to `lavTestLRT.mi` via `...`.
`fitmeasures` | alias for `fitMeasures`.
`show` | `signature(object = "lavaan.mi")`: returns a message about convergence rates and estimation problems (if applicable) across imputed data sets.
`summary` | `signature(object = "lavaan.mi", se = TRUE, ci = FALSE, level = .95, standardized = FALSE, rsquare = FALSE, fmi = FALSE, scale.W = FALSE, asymptotic = FALSE, add.attributes = TRUE)`: see `parameterEstimates` for details. By default, `summary` returns pooled point and SE estimates, along with t test statistics and their associated df and p values. If `ci = TRUE`, confidence intervales are returned with the specified confidence `level` (default 95% CI). If `asymptotic = TRUE`, z instead of t tests are returned. `standardized` solution(s) can also be requested by name (`"std.lv"` or `"std.all"`) or both are returned with `TRUE`. R-squared for endogenous variables can be requested, as well as the Fraction Missing Information (FMI) for parameter estimates. By default, the output will appear like `lavaan`'s `summary` output, but if `add.attributes = FALSE`, the returned `data.frame` will resemble the `parameterEstimates` output. The `scale.W` argument is passed to `vcov` (see description above).

### Slots

`coefList`

  `list` of estimated coefficients in matrix format (one per imputation) as output by `lavInspect(fit, "est")`

`GLIST`

  pooled `list` of coefficients in GLIST format

`miList`

  `list` of modification indices output by `modindices`

`seed`

  `integer` seed set before running imputations

`lavListCall`

  call to `lavaanList` used to fit the model to the list of imputed data sets in `@DataList`, stored as a `list` of arguments

`imputeCall`

  call to imputation function (if used), stored as a `list` of arguments

`convergence`

  `list` of `logical` vectors indicating whether, for each imputed data set, (1) the model converged on a solution, (2) SEs could be calculated, (3) the (residual) covariance matrix of latent variables (Ψ) is non-positive-definite, and (4) the residual covariance matrix of observed variables (Θ) is non-positive-definite.

`lavaanList_slots`

  All remaining slots are from `lavaanList`, but `runMI` only populates a subset of the `list` slots, two of them with custom information:

`DataList`

  The `list` of imputed data sets

`SampleStatsList`

  List of output from `lavInspect(fit, "sampstat")` applied to each fitted model

`ParTableList`

  See `lavaanList`

`vcovList`

  See `lavaanList`

`testList`

  See `lavaanList`

### Objects from the Class

See the `runMI` function for details. Wrapper functions include `lavaan.mi`, `cfa.mi`, `sem.mi`, and `growth.mi`.

### Author(s)

Terrence D. Jorgensen (University of Amsterdam; TJorgensen314@gmail.com)

### References

Asparouhov, T., & Muthen, B. (2010). Chi-square statistics with multiple imputation. Technical Report. Retrieved from www.statmodel.com

Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford.

Li, K.-H., Meng, X.-L., Raghunathan, T. E., & Rubin, D. B. (1991). Significance levels from repeated p-values with multiply-imputed data. Statistica Sinica, 1(1), 65–92. Retrieved from https://www.jstor.org/stable/24303994

Meng, X.-L., & Rubin, D. B. (1992). Performing likelihood ratio tests with multiply-imputed data sets. Biometrika, 79(1), 103–111. Retrieved from https://www.jstor.org/stable/2337151

Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York, NY: Wiley.

### Examples

    ## See ?runMI help page

[Package semTools version 0.5-1 Index]