Description Usage Arguments Details Value Author(s) See Also Examples

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.

1 2 |

`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)

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)
``` |

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