View source: R/emxModelBuilders.R

emxRegressionModel | R Documentation |

This function creates a regression model as an MxModel object.

emxRegressionModel(model, data, type='Steven', run, ...) emxModelRegression(model, data, type='Steven', run, ...)

`model` |
formula. See Details. |

`data` |
data used for the model |

`run` |
logical. Whether to run the model before returning. |

`type` |
character. Either 'Steven' or 'Joshua'. See Details. |

`...` |
Further named arguments to be passed to |

The `model`

argument is a formula identical to what is used in `lm`

.

The `type`

argument switches the kind of regression model that is specified. When there are no missing data, the two versions will estimate the same regression parameters but `type='Steven'`

will estimate addition parameters that are not estimated by `type='Joshua'`

. The `type='Steven'`

model is due to Steven Boker and many others. It estimates more parameters than a typical regression analysis and has a different set of assumptions. More exactly, `type='Steven'`

models the outcome and all of the predictors as a multivariate Normal distribution. By contrast, `type='Joshua'`

is due to Joshua Pritikin and exactly replicates the typical regression model with its usual assumptions. In particular, `type='Joshua'`

models the regression residual as a univariate Normal distribution. Predictors are assumed to have no measurement error (see Westfall & Yarkoni, 2016).

The benefit of `type='Steven'`

is that it handles missing data with
full-information maximum likelihood (FIML; Enders & Bandalos, 2001), at the cost of using a different model with different assumptions from ordinary least squares regression. The benefit of `type='Joshua'`

is that it exactly replicates regression as a maximum likelhood model, at the cost of having the same weakness in terms of missing data as OLS regression.

An MxModel.

Enders, C. K. & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. <i>Structural Equation Modeling, 8</i>(3), 430-457.

Westfall, J. & Yarkoni, T. (2016). Statistically controlling for confounding constructs is harder than you think. <i>PLoS ONE, 11</i>(3). doi:10.1371/journal.pone.0152719

lm

# Example require(EasyMx) data(myRegDataRaw) myrdr <- myRegDataRaw myrdr[1, 4] <- NA ## Not run: run <- emxRegressionModel(y~1+x*z, data=myrdr, run=TRUE) summary(run) ## End(Not run) summary(lm(y~1+x*z, data=myrdr))

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