# 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

 ```1 2``` ``` ## 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)

`krls`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```# 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) ```