bmop_fit: Estimation of bmop density or conditional density

In gherardovarando/Rbmop: B-spline MoPs densities and conditional densities

Description

Estimation of bmop density or conditional density

Usage

bmop_fit(data, conditional = FALSE, Min = NULL, Max = NULL, bmop = NULL,
  ...)

Arguments

data	data.frame, matrix, vector or object of class "histogram" or "bins"
conditional	logic, if TRUE a conditional density is learned
Min	vector of lower bounds
Max	vector of upper bounds
bmop	a bmop object
...	see bmopPar

Value

a bmop object, a density if conditional=FALSE or a conditional density if conditional=TRUE. In the latter case the first variable of the dataset is considered as the conditioned one and the rest of the variables as the conditioning ones. If the dataset has only one variable a normal density is generated discarding the value of conditional.

Examples

plot(bmop_fit(rnorm(100)))
plot(bmop_fit(hist(rnorm(100000))))
#############################
Data<-data.frame(rnorm(100),rexp(100))
bmop<-bmop_fit(Data)
plot(bmop)
#############################
X<-rnorm(100)
Y<-rnorm(100,mean=X)
Data<-data.frame(X,Y)
bmopPar(mle=TRUE)
bmopC<-bmop_fit(Data,conditional=TRUE)
bmopPar(mle=FALSE)
plot(bmopC)

gherardovarando/Rbmop documentation built on May 17, 2019, 4:17 a.m.