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

## Description

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

## Usage

 ```1 2 3``` ```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, ...) ```

## Arguments

 `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.

## Details

`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.

## Examples

 ``` 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.