# Summary method for Kernel-based Regularized Least Squares (KRLS) Model Fits

### 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 |

### 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 |

`...` |
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

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)
``` |

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker. Vote for new features on Trello.