Linear Regression
This function builds a linear regression model. Standard least square method, variable selection, factorial methods are available.
LINREG( x, y, formula = ".", reg = c("linear", "subset", "ridge", "lasso", "elastic", "pcr", "plsr"), regeval = c("r2", "bic", "adjr2", "cp", "msep"), scale = TRUE, lambda = 10^seq(-5, 5, length.out = 101), alpha = 0.5, graph = TRUE, tune = FALSE, ... )
x |
Predictor |
y |
Response |
formula |
A symbolic description of the model to be fitted (as a character string). |
reg |
The algorithm. |
regeval |
The evaluation criterion for subset selection. |
scale |
If true, PCR and PLS use scaled dataset. |
lambda |
The lambda parameter of Ridge, Lasso and Elastic net regression. |
alpha |
The elasticnet mixing parameter. |
graph |
A logical indicating whether or not graphics should be plotted (ridge, LASSO and elastic net). |
tune |
If true, the function returns paramters instead of a classification model. |
... |
Other parameters. |
The classification model, as an object of class model-class
.
lm
, regsubsets
, mvr
, glmnet
## Not run: require (datasets) # With one independant variable data (cars) LINREG (cars [, -2], cars [, 2]) # With two independant variables data (trees) LINREG (trees [, -3], trees [, 3]) # With non numeric variables data (ToothGrowth) LINREG (ToothGrowth [, -1], ToothGrowth [, 1], formula = "-1+supp+dose") # Different intersept LINREG (ToothGrowth [, -1], ToothGrowth [, 1], formula = "dose:supp") # Different slope LINREG (ToothGrowth [, -1], ToothGrowth [, 1], formula = "-1+supp+dose:supp") # Complete model # With multiple numeric variables data (mtcars) LINREG (mtcars [, -1], mtcars [, 1]) LINREG (mtcars [, -1], mtcars [, 1], reg = "subset", regeval = "adjr2") LINREG (mtcars [, -1], mtcars [, 1], reg = "ridge") LINREG (mtcars [, -1], mtcars [, 1], reg = "lasso") LINREG (mtcars [, -1], mtcars [, 1], reg = "elastic") LINREG (mtcars [, -1], mtcars [, 1], reg = "pcr") LINREG (mtcars [, -1], mtcars [, 1], reg = "plsr") ## End(Not run)
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