Description Usage Arguments Value References See Also Examples
GAM-style effects plots provide a graphical means of interpreting
fitted covariate/response relationships. 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.
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x |
matrix with number of rows equal to the number of samples and number of columns equal to the number of covariate variables. |
weights |
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 |
... |
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.
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