Stepwise linear regression analysis selects model based on information criteria and F or approximate F test with 'forward', 'backward', 'bidirection' and 'score' model selection method.
stepwise(
formula,
data,
include = NULL,
selection = c("forward", "backward", "bidirection", "score"),
select = c("AIC", "AICc", "BIC", "CP", "HQ", "HQc", "Rsq", "adjRsq", "SL", "SBC"),
sle = 0.15,
sls = 0.15,
multivarStat = c("Pillai", "Wilks", "Hotelling-Lawley", "Roy"),
weights = NULL,
best = NULL
)Model formulae. The models fitted by the lm functions are specified in a compact symbolic form. The basic structure of a formula is the tilde symbol (~) and at least one independent (righthand) variable. In most (but not all) situations, a single dependent (lefthand) variable is also needed. Thus we can construct a formula quite simple formula (y ~ x). Multiple independent variables by simply separating them with the plus (+) symbol (y ~ x1 + x2). Variables in the formula are removed with a minus(-) symbol (y ~ x1 - x2). One particularly useful feature is the . operator when modelling with lots of variables (y ~ .). The %in% operator indicates that the terms on its left are nested within those on the right. For example y ~ x1 + x2 %in% x1 expands to the formula y ~ x1 + x1:x2. A model with no intercept can be specified as y ~ x - 1 or y ~ x + 0 or y ~ 0 + x. Multivariate multiple regression can be specified as cbind(y1,y2) ~ x1 + x2.
Data set including dependent and independent variables to be analyzed
Force vector of effects name to be included in all models.
Model selection method including "forward", "backward", "bidirection" and 'score',forward selection starts with no effects in the model and adds effects, backward selection starts with all effects in the model and removes effects, while bidirection regression is similar to the forward method except that effects already in the model do not necessarily stay there, and score method requests specifies the best-subset selection method, which uses the branch-and-bound technique to efficiently search for subsets of model effects that best predict the response variable.
Specify the criterion that uses to determine the order in which effects enter and leave at each step of the specified selection method including "AIC","AICc","BIC","CP","HQ","HQc","Rsq","adjRsq","SBC" and "SL".
Specify the significance level for entry, default is 0.15
Specify the significance level for staying in the model, default is 0.15
Statistic for multivariate regression analysis, including Wilks' lamda ("Wilks"), Pillai Trace ("Pillai"), Hotelling-Lawley's Trace ("Hotelling"), Roy's Largest Root ("Roy")
Numeric vector to provide a weight for each observation in the input data set. Note that weights should be ranged from 0 to 1, while negative numbers are forcibly converted to 0, and numbers greater than 1 are forcibly converted to 1. If you do not specify a weight vector, each observation has a default weight of 1.
Control the number of models displayed in the output, default is NULL, which means all possible model will be displayed.
Junhui Li
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data(mtcars)
mtcars$yes <- mtcars$wt
formula <- cbind(mpg,drat) ~ . + 0
stepwise(formula=formula,
data=mtcars,
selection="bidirection",
select="AIC")
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