Description Usage Arguments Details Examples
sensitivity.plot
visualises the sensitivity indices of a given smooth
supersaturated model using barplot
. If the SA
flag was not set
to TRUE when fit.ssm
was run to fit the model, then
update.sensitivity
should be used to compute the model
variances. If not, this function will return an error message.
1 | sensitivity.plot(ssm, type = "main_total", ...)
|
ssm |
An SSM object. Must have relevant sensitivity indices
in the appropriate slots, either from setting |
type |
(optional) Character. Determines the type of barplot. One of
|
... |
arguments passed to the |
There are four classes of plot available:
"sobol"
Produces a barplot of all Sobol indices. If there
are more than 10 factors then Sobol indices will not have been computed for
interactions and only the Sobol indices for main effects will be plotted.
Main effects are in red, interactions are in grey.
"main_sobol"
Produces a barplot of Sobol indices for main
effects only.
"main_total"
Produces a barplot of Total indices for main
effects only. This is the default behaviour.
"total"
Produces a barplot of Total indices for main effects
and all second order interactions. Main effects are in red, interactions
are in grey.
Note that variables and interactions are not labelled in the plots since there can be too many bars to label clearly.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | # A 20 point design in four variables
X <- matrix(runif(80, -1, 1), ncol = 4)
Y <- runif(20)
s <- fit.ssm(X, Y, SA = TRUE)
sensitivity.plot(s)
# In the next plots, the grey bars indicate interactions.
sensitivity.plot(s, "sobol")
sensitivity.plot(s, "total")
# Identifying particular indices is best done using the information held in
# the SSM object. The following orders s@total_int_factors so the
# interaction indicated by the top row is the most important.
ind <- order(s@total_int, decreasing = TRUE)
s@total_int_factors[ind, ]
|
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