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Usage:
predlimits(x, confLevel), x REAL scalar, vector or matrix with no
MISSING elements, 0 < confLevel < 1 scalar
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Keywords:
regression, prediction limits
Usage
You can use macro predlimits() to compute prediction limits for y =
E(y|x) + epsilon or y = E(y | x1, x2 ...) + epsilon after running
regress("y=x") or regress("y=x1+x2+..+xk"). These are limits on the a
future value of y for specified values of the predictor variable or
variables.
predlimits(x, confLevel), where x is a REAL vector with length(x) =
number of predictors (1 for simple linear regression), returns
vector(lower,upper), where lower and upper are prediction limit with
confidence level confLevel. Argument confLevel must be a REAL scalar
between 0.5 and 1.
When confLevel < .5, a warning message is printed and 1 - confLevel is
used.
Example of single prediction
Example:
After regress("y=x1 + x2 + x3"), predlimits(vector(2,3,4), .95)
returns vector(lower,upper) where lower and upper are the limits when
x1=2, x2=3 and x3=4
Limits for several predictions
You can use predlimits() to get limits for several values at once,
returning hconcat(lower,upper), where lower and upper are vectors of
limits for the various values.
For simple linear regression, x should be a vector containing the
values for which you want prediction limits.
When there are k predictors, x should be a matrix with k columns, and
lower and upper will have length(nrows(x)).
Example of several predictions
Example:
After regress("y = x1 + x2"), predlimits(vconcat(vector(1,2)',
vector(2,1.5)',vector(3,3.2)'), .95) returns hconcat(lower,upper)
where, for example, lower[2] and upper [2] are the limits when x1 = 2
and x2 = 1.5.
Cross references
See also estimlimits(), regpred(), glmpred().
Gary Oehlert
2005-08-12