lessR (version 2.4.2)

Model: Regression Analysis, ANOVA or t-test

Description

Abbreviation: model, model.brief

Automatically selects and then provides an analysis of a linear model: OLS regression, ANOVA, or a t-test depending on the proprieties of the data. Comprehensive regression analysis with graphics from a single, simple function call with many default settings, each of which can be re-specified. By default the data exists as a data frame with the default name of mydata, such as data read by the lessR rad function. Specify the model in the function call according to an R formula, that is, the response variable followed by a tilde, followed by the list of predictor variables, each pair separated by a plus sign.

Usage

Model(my.formula, dframe=mydata, brief=FALSE, ...)
model.brief(my.formula, brief=TRUE, ...) 
model(my.formula, ...)

Arguments

my.formula

Standard R formula for specifying a model. For example, for a response variable named Y and two predictor variables, X1 and X2, specify the corresponding linear model as Y ~ X1 + X2.

dframe

The default name of the data frame that contains the data for analysis is mydata, otherwise explicitly specify.

brief

Reduced output.

...

Other parameter values for R functions such as lm which provide the core computations.

Details

OVERVIEW The purpose of Model is to combine many standard R function calls into one, as well as provide ancillary analyses such as as graphics, organizing output into tables and sorting to assist interpretation of the output, all from a single function. Currently the supported models are OLS regression, ANOVA and the t-test. For more details of each of these methods, see the lessR functions Regression, ANOVA and ttest, respectively, which, in turn are based on many standard R functions.

All invocations of the model function are based on the standard R formula.

See Also

formula, lm, summary.lm, anova, confint, fitted, resid, rstudent, cooks.distance

Examples

# Generate random data, place in data frame mydata
X1 <- rnorm(20)
X2 <- rnorm(20)
Y <- .7*X1 + .2*X2 + .6*rnorm(20)
#  instead, if read data with the rad function
#   then the result is the data frame called mydata 
mydata <- data.frame(X1, X2, Y)
rm(Y); rm(X1); rm(X2)

# One-predictor regression
# Provide all default analyses including scatterplot etc.
Model(Y ~ X1)
# alternate form
model(Y ~ X1)

# Multiple regression model
# Provide all default analyses, including interactive 3d scatterplot
Model(Y ~ X1 + X2)

# t-test
Model(breaks ~ wool, dframe=warpbreaks)

# ANOVA analysis
# from another data frame other than the default \code{mydata}
# breaks is numerical, wool and tension are categorical
Model(breaks ~ wool + tension, dframe=warpbreaks)