Description Usage Arguments Value References See Also Examples

Generalized additive model (GAM)-style effects plots provide a graphical
means of interpreting relationships between covariates and conditional
quantiles predicted by a QRNN. From Plate et al. (2000): The effect of the
`i`

th input variable at a particular input point `Delta.i.x`

is the change in `f`

resulting from changing `X1`

to `x1`

from `b1`

(the baseline value [...]) while keeping the other
inputs constant. The effects are plotted as short line segments, centered
at (`x.i`

, `Delta.i.x`

), where the slope of the segment
is given by the partial derivative. Variables that strongly influence
the function value have a large total vertical range of effects.
Functions without interactions appear as possibly broken straight lines
(linear functions) or curves (nonlinear functions). Interactions show up as
vertical spread at a particular horizontal location, that is, a vertical
scattering of segments. Interactions are present when the effect of
a variable depends on the values of other variables.

1 2 3 4 |

`x` |
matrix with number of rows equal to the number of samples and number of columns equal to the number of covariate variables. |

`parms` |
list returned by |

`column` |
column of |

`baseline` |
value of |

`epsilon` |
step-size used in the finite difference calculation of the partial derivatives. |

`seg.len` |
length of effects line segments expressed as a fraction of the range of |

`seg.cols` |
colors of effects line segments. |

`plot` |
if |

`return.results` |
if |

`trim` |
if |

`...` |
further arguments to be passed to |

A list with elements:

`effects` |
a matrix of covariate effects. |

`partials` |
a matrix of covariate partial derivatives. |

Cannon, A.J. and I.G. McKendry, 2002. A graphical sensitivity analysis for interpreting statistical climate models: Application to Indian monsoon rainfall prediction by artificial neural networks and multiple linear regression models. International Journal of Climatology, 22:1687-1708.

Plate, T., J. Bert, J. Grace, and P. Band, 2000. Visualizing the function computed by a feedforward neural network. Neural Computation, 12(6): 1337-1354.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ```
## YVR precipitation data with seasonal cycle and NCEP/NCAR Reanalysis
## covariates
data(YVRprecip)
y <- YVRprecip$precip
x <- cbind(sin(2*pi*seq_along(y)/365.25),
cos(2*pi*seq_along(y)/365.25),
YVRprecip$ncep)
## Fit QRNN, additive QRNN (QADD), and quantile regression (QREG)
## models for the conditional 75th percentile
set.seed(1)
train <- c(TRUE, rep(FALSE, 49))
w.qrnn <- qrnn.fit(x=x[train,], y=y[train,,drop=FALSE],
n.hidden=2, tau=0.75, iter.max=500,
n.trials=1, lower=0, penalty=0.01)
w.qadd <- qrnn.fit(x=x[train,], y=y[train,,drop=FALSE],
n.hidden=ncol(x), tau=0.75, iter.max=250,
n.trials=1, lower=0, additive=TRUE)
w.qreg <- qrnn.fit(x=x[train,], y=y[train,,drop=FALSE],
tau=0.75, iter.max=100, n.trials=1,
lower=0, Th=linear, Th.prime=linear.prime)
## GAM-style plots for slp, sh700, and z500
for (column in 3:5) {
gam.style(x[train,], parms=w.qrnn, column=column,
main="QRNN")
gam.style(x[train,], parms=w.qadd, column=column,
main="QADD")
gam.style(x[train,], parms=w.qreg, column=column,
main="QREG")
}
``` |

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