Description Usage Arguments Details References
Generalized Regression.
1 2 3 4 5 6 7 8 9 10  Regression(formula, data = NULL, subset = NULL, weights = NULL,
missing = "Exclude cases with missing data", type = "Linear",
robust.se = FALSE, method = "default", output = "Coefficients",
detail = FALSE, m = 10, seed = 12321, statistical.assumptions,
auxiliary.data = NULL, show.labels = FALSE, internal = FALSE,
contrasts = c("contr.treatment", "contr.treatment"),
relative.importance = FALSE, importance.absolute = FALSE,
interaction = NULL, correction = "None",
interaction.formula = NULL, recursive.call = FALSE,
effects.format = list(max.label = 10), ...)

formula 
An object of class 
data 
A 
subset 
An optional vector specifying a subset of observations to be
used in the fitting process, or, the name of a variable in 
weights 
An optional vector of sampling weights, or, the name or, the
name of a variable in 
missing 
How missing data is to be treated in the regression. Options:

type 
Defaults to 
robust.se 
If 
method 
The method to be used; for fitting. This will only do something if method = "model.frame", which returns the model frame. 
output 

detail 
This is a deprecated function. If 
m 
The number of imputed samples, if using multiple imputation. 
seed 
The random number seed used in imputation. 
statistical.assumptions 
A Statistical Assumptions object. 
auxiliary.data 
A 
show.labels 
Shows the variable labels, as opposed to the names, in the outputs, where a variables label is an attribute (e.g., attr(foo, "label")). 
internal 
If 
contrasts 
A vector of the contrasts to be used for 
relative.importance 
Deprecated. To run Relative Importance Analysis, use the output variable. 
importance.absolute 
Whether the absolute value of the relative importance should be shown. 
interaction 
Optional variable to test for interaction with other variables in the model. Output will be a crosstab showing coefficients from both both models. 
correction 
Method to correct for multiple comparisons. Can be one of 
interaction.formula 
Used internally for multiple imputation. 
recursive.call 
Used internally to indicate if call is a result of recursion (e.g., multiple imputation). 
effects.format 
A list of items 
... 
Additional argments to be past to 
"Imputation (replace missing values with estimates)". All selected
outcome and predictor variables are included in the imputation, along with
all auxiliary.data
, excluding cases that are excluded via subset or
have invalid weights, but including cases with missing values of the outcome variable.
Then, cases with missing values in the outcome variable are excluded from
the analysis (von Hippel 2007). See Imputation
.
von Hippel, Paul T. 2007. "Regression With Missing Y's: An Improved Strategy for Analyzing Multiply Imputed Data." Sociological Methodology 37:83117. White, H. (1980), A heteroskedasticconsistent covariance matrix estimator and a direct test of heteroskedasticity. Econometrica, 48, 817838. Long, J. S. and Ervin, L. H. (2000). Using heteroscedasticity consistent standard errors in the linear regression model. The American Statistician, 54(3): 217224.
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