Model {lessR}

Regression Analysis, ANOVA or t-test

Description

Abbreviation: model, model_brief

Automatically selects and then provides an analysis of a linear model: OLS regression, Logistic 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 d, 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, data=d, brief=getOption("brief"), xlab=NULL, ...)

model_brief(..., brief=TRUE) 

model(...) 

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.

data

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

brief

If set to TRUE, reduced text output. Can change system default with style function.

xlab

x-axis label, defaults to variable name, or, if present, variable label.

...

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, Logit, 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.

Author(s)

David W. Gerbing (Portland State University; [e-mail])

See Also

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

Examples

# Generate random data, place in data frame d
n <- 200
X1 <- rnorm(n)
X2 <- rnorm(n)
Y <- .7*X1 + .2*X2 + .6*rnorm(n)
Ybin <- cut(Y, breaks=2, labels=FALSE)
#  instead, if read data with the Read function
#   then the result is the data frame called d 
d <- round(data.frame(X1, X2, Y, Ybin),2)
rm(Y); rm(Ybin); 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
Model(Y ~ X1 + X2)

# Logit analysis
# Y is binary, 0 or 1
d <- recode(Ybin, old=c(1,2), new=c(0,1), quiet=TRUE)
Model(Ybin ~ X1)

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

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