Provides diagnositic graphs and score tests to evaluate linear model assumptions of normality, constant variance and linearity. Follows best practices and uses many functions from car package.

1 | ```
modelAssumptions(Model, Type = "NORMAL", ID=row.names(Model$model), one.page = TRUE)
``` |

`Model` |
a linear model produced by |

`Type` |
Type =c('NORMAL', 'CONSTANT', 'LINEAR') for normally distributed residuals with constant variance, and linear (e.g., mean of residuals 0 for all Y') |

`ID` |
Use to identify points. Default = row.names(model$model). NULL = no identification |

`one.page` |
logical; display all graphs on one page if TRUE (Default). |

John J. Curtin jjcurtin@wisc.edu

Fox, J. (1991). Regression diagnostics. SAGE Series (79) Quantitative Applictions in the Social Science.

1 2 3 4 | ```
m = lm(interlocks~assets+nation, data=car::Ornstein)
modelAssumptions(m,'NORMAL')
modelAssumptions(m,'CONSTANT')
modelAssumptions(m,'LINEAR', ID=NULL)
``` |

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