The regr function wraps a number of linear regression functions into
one convenient interface that provides similar output to the regression
function in SPSS. It automatically provides confidence intervals and
standardized coefficients. Note that this function is meant for teaching
purposes, and therefore it's only for very basic regression analyses.

The formula of the regression analysis, of the form y ~ x1 + x2, where
y is the dependent variable and x1 and x2 are the predictors.

dat

If the terms in the formula aren't vectors but variable names, this should
be the dataframe where those variables are stored.

conf.level

The confidence of the confidence interval around the regression coefficients.

digits

Number of digits to round the output to.

pvalueDigits

The number of digits to show for p-values; smaller p-values will be shown as
<.001 or <.0001 etc.

coefficients

Which coefficients to show; can be "raw" to only show the raw
(unstandardized) coefficients; "scaled" to only show the scaled
(standardized) coefficients), or c("raw", "scaled') to show both.

plot

For regression analyses with only one predictor (also sometimes confusingly
referred to as 'univariate' regression analyses), scatterplots with
regression lines and their standard errors can be produced.

ci.method, ci.method.note

Which method to use for the confidence interval around R squared, and whether to display a note about this choice.

env

The enviroment where to evaluate the formula.

Value

A list of three elements:

input

List with input arguments

intermediate

List of intermediate objects, such as the lm and
confint objects.

output

List with two dataframes, one with the raw coefficients,
and one with the scaled coefficients.

# NOT RUN {### Do a simple regression analysisregr(age ~ circumference, dat=Orange);
### Show more digits for the p-valueregr(Orange$age ~ Orange$circumference, pvalueDigits=18);
# }