Description Usage Arguments Author(s) See Also Examples
Given alpha
, this function generates plots of response vs out-of-bag predictions across instances, which can be used to assess individual instances, examine outliers, etc.
For linear regression models, it generates a response vs out-of-bag prediction scatterplot;
it also displays the best linear fit and its 95% confidence level region.
For categorical models, it generates a boxplot across classes showing the frequency of out-of-bag correct predictions.
1 2 3 4 5 | plotMeasuredVsOOB(x, alpha.index=NULL, xlab=NULL, ylab=NULL,
cex.lab=0.95, main=NULL, col.main="black", cex.main=0.85, instance.label=TRUE,
instance.label.cex=NULL, instance.label.offset=NULL,
instance.label.added.margin=NULL, col=NULL, box.wex=NULL, box.range=NULL,
box.col=NULL, transparency=NULL, jitter=NULL, cex.pt=NULL, class.color=NULL, ...)
|
x |
|
alpha.index |
Integer indices to select |
xlab |
Custom x-axis label. |
ylab |
Custom y-axis label. |
cex.lab |
Axis label size. |
main |
Custom title. |
col.main |
Title color. |
cex.main |
Title size. |
instance.label |
Logical to display instance labels. Default is |
instance.label.cex |
Size of instance labels. |
instance.label.offset |
Offset of instance labels. |
instance.label.added.margin |
(linear regression only) Margin size to accomodate instance label display. |
col |
(linear regression only) Symbol color. |
box.wex |
(categorical models only) Boxplot |
box.range |
(categorical models only) Boxplot |
box.col |
(categorical models only) Boxplot |
transparency |
(categorical models only) Symbol transparency. Default is 70. |
jitter |
(categorical models only) Symbol jitter. Default is 0.25. |
cex.pt |
(categorical models only) Symbol size. Default is 1.7 |
class.color |
(categorical models only) Vector of class colors. Default is the default palette. |
... |
Additional plotting parameters. |
Julian Candia and John S. Tsang
Maintainer: Julian Candia julian.candia@nih.gov
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | data(QuickStartEx)
fit = eNetXplorer(x=QuickStartEx$predictor,y=QuickStartEx$response,
family="gaussian",n_run=20,n_perm_null=10,seed=111)
plot(x=fit,plot.type="measuredVsOOB")
plotMeasuredVsOOB(x=fit,alpha.index=2)
data(QuickStartEx)
binarized=rep("low",length(QuickStartEx$response))
binarized[QuickStartEx$response>median(QuickStartEx$response)]="high"
fit = eNetXplorer(x=QuickStartEx$predictor,y=binarized,family="binomial",n_run=20,
n_perm_null=10,seed=111)
plot(x=fit,plot.type="measuredVsOOB")
plotMeasuredVsOOB(x=fit,alpha.index=2)
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