Description Usage Arguments Details Examples

`alm`

performs linear regression by absorbing one categorical variable.
Model adjustment is reported with Wald Test.

1 2 3 |

`formula` |
a symbolic description for the model to be estimated, |

`data` |
a |

`subset` |
see |

`absorb` |
a variable encode as factors. |

`na.action` |
see |

`weights` |
an optional vector of weights to be used in the fitting process. |

`offset` |
an optional offset that can be used to specify an a priori known component to be included during fitting. |

`contrasts` |
an optional list. See the |

`model, x, y` |
logicals. If |

`...` |
further arguments. |

`alm`

fits a linear model, absorbing a set of k mutually exclusive and exhaustive binary variables, based on Frisch-Waugh-Lovell Theorem.
The intercept reported by `alm`

, is calculated by choosing the intercept that
makes the prediction calculated at the means of the independent variables equal to the mean of the dependent variable.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
# Without absorb it performs like lm
## Annette Dobson (1990) "An Introduction to Generalized Linear Models".
## Page 9: Plant Weight Data.
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm.D9 <- alm(weight ~ group)
lm.D90 <- lm(weight ~ group)
summary(lm.D9)
summary(lm.D90)
#Using Chile dataframe from car package, to absorb categorical region variable:
data(Chile, package="car")
fit1 = alm(formula = income ~ education + age + statusquo + region,
absorb="region", data = Chile)
summary(fit1)
``` |

Paulms/RegUtils documentation built on May 8, 2019, 1:27 a.m.

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