gam.style {CaDENCE} | R Documentation |

GAM-style effects plots provide a graphical means of interpreting
relationships between predictors and conditional pdf parameter values
predicted by a CDEN. 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.

gam.style(x, fit, column, baseline = mean(x[,column]), additive.scale = FALSE, epsilon = 1e-5, seg.len = 0.02, seg.cols = "black", plot = TRUE, return.results = FALSE, ...)

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

`fit` |
element from list returned by |

`column` |
column of |

`baseline` |
value of |

`additive.scale` |
if |

`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 |

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

A list with elements:

`effects` |
a matrix of predictor effects. |

`partials` |
a matrix of predictor 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.

data(FraserSediment) set.seed(1) lnorm.distribution <- list(density.fcn = dlnorm, parameters = c("meanlog", "sdlog"), parameters.fixed = NULL, output.fcns = c(identity, exp)) x <- FraserSediment$x.1970.1976[c(TRUE, rep(FALSE, 24)),] y <- FraserSediment$y.1970.1976[c(TRUE, rep(FALSE, 24)),,drop=FALSE] fit.nlin <- cadence.fit(x, y, n.hidden = 2, n.trials = 1, hidden.fcn = tanh, distribution = lnorm.distribution, maxit.Nelder = 100, trace.Nelder = 1, trace = 1) fit.lin <- cadence.fit(x, y, hidden.fcn = identity, n.trials = 1, distribution = lnorm.distribution, maxit.Nelder = 100, trace.Nelder = 1, trace = 1) gam.style(x, fit = fit.nlin[[1]], column = 1, main = "Nonlinear") gam.style(x, fit = fit.lin[[1]], column = 1, additive.scale = TRUE, main = "Linear (additive.scale = TRUE)")

[Package *CaDENCE* version 1.2.5 Index]