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predict is a function for predictions of the scalability of a system
modeled with the Universal Scalability Law. It evaluates the regression
function in the frame newdata (which defaults to
model.frame(object)). Setting interval to "confidence"
requests the computation of confidence intervals at the specified
level.
# S4 method for USL
predict(object, newdata, sigma, kappa, interval = c("none",
"confidence"), level = 0.95)A USL model object for which prediction is desired.
An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
Optional parameter to be used for evaluation instead of the parameter computed for the model.
Optional parameter to be used for evaluation instead of the parameter computed for the model.
Type of interval calculation. Default is to calculate no confidence interval.
Confidence level. Default is 0.95.
predict produces a vector of predictions or a matrix of
predictions and bounds with column names fit, lwr, and
upr if interval is set to "confidence".
The parameters sigma or kappa are useful to do a what-if
analysis. Setting these parameters override the model parameters and show
how the system would behave with a different contention or coherency delay
parameter.
predict internally uses the function returned by
scalability,USL-method to calculate the result.
Neil J. Gunther. Guerrilla Capacity Planning: A Tactical Approach to Planning for Highly Scalable Applications and Services. Springer, Heidelberg, Germany, 1st edition, 2007.
# NOT RUN {
require(usl)
data(raytracer)
## Print predicted result from USL model for demo dataset
predict(usl(throughput ~ processors, raytracer))
## The same prediction with confidence intervals at the 99% level
predict(usl(throughput ~ processors, raytracer),
interval = "confidence", level = 0.99)
# }
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