summary.krls {KRLS} | R Documentation |
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.
## S3 method for class 'krls'
summary(object, probs=c(.25,.5,.75),...)
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 |
... |
additional arguments to be passed to lower level functions |
Notice that the partial derivatives can only be summarized if the krls object was computed with krls(,derivatives=TRUE)
.
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. |
Jens Hainmueller (Stanford) and Chad Hazlett (MIT)
# 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)