summary.krls: Summary method for Kernel-based Regularized Least Squares...

In KRLS: Kernel-Based Regularized Least Squares

Description

Summarizes average partial derivatives (i.e. marginal effects) and the distribution of the partial derivatives for each predictor. For binary predictors, the marginal effects are the first differences if krls(,derivatives=TRUE,binary=TRUE) was specified.

Usage

 ## S3 method for class 'krls'
summary(object, probs=c(.25,.5,.75),...)

Arguments

object	Fitted krls model, i.e. an object of class krls
probs	numeric vector with numbers between 0 and 1 that specify the quantiles of the pointwise marginal effects for the summary (see the quantile function for details).
...	additional arguments to be passed to lower level functions

Details

Notice that the partial derivatives can only be summarized if the krls object was computed with krls(,derivatives=TRUE).

Value

coefficients	matrix with average partial derivates and or first differences (point estimates, standart errors, t-values, p-values).
qcoefficients	matrix with 1st, 2nd, and 3rd quatriles of distribution of pointwise marinal effects.

Author(s)

Jens Hainmueller (Stanford) and Chad Hazlett (MIT)

See Also

krls

Examples

# non-linear example
# set up data
N <- 200
x1 <- rnorm(N)
x2 <- rbinom(N,size=1,prob=.2)
y <- x1^3 + .5*x2 + rnorm(N,0,.15)
X <- cbind(x1,x2)

# fit model
krlsout <- krls(X=X,y=y)
# summarize marginal effects and contribution of each variable
summary(krlsout)
# plot marginal effects and conditional expectation plots
plot(krlsout)

KRLS documentation built on May 2, 2019, 5:51 a.m.