alm: ALM
In Paulms/RegUtils: Tools For Model Regressions

Description Usage Arguments Details Examples

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

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alm(formula, data, subset, weights, absorb = NULL, na.action,
   method = "qr", model = TRUE, x = FALSE, y = FALSE, qr = TRUE,
   singular.ok = TRUE, contrasts = NULL, offset, ...)

`formula`	a symbolic description for the model to be estimated,
`data`	a `data.frame`,
`subset`	see `lm` for `"alm"`, a character or numeric vector indicaing asubset of the table of coefficient to be printed for `"print.summary.alm"`,
`absorb`	a variable encode as factors.
`na.action`	see `lm`,
`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 `contrasts.arg` of `model.matrix.default`.
`model, x, y`	logicals. If `TRUE` the corresponding components of the fit (the model frame, the model matrices , the response) are returned.
`...`	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.

# 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)