powEx {sse} | R Documentation |
A function for constructing an object of class power
used for
drawing an example in a sensitivity plot and for evaluating sample size.
powEx(x, theta, xi = NA, endpoint = NA, power = 0.9, drop = 0, method = c("default", "lm", "step"), lm.range = NA, forceDivisor = FALSE)
x |
An object of class |
theta |
a numeric value indicating for which |
xi |
a numeric value, as |
endpoint |
Object of class |
power |
Object of class |
method |
Defining the method how the sample size for the indicated theta, xi, and power is "estimated". If the power object was created using resampling the "default" evaluates to "lm", otherwise to "step". |
lm.range |
The range of evaluations that are used for estimating
the sample size if the |
drop |
Object of class |
forceDivisor |
If |
For method equal to "lm" a linear model is fit as lm(sample.size ~
transformed(power)) with all data where theta, and xi are equal
to the theta and xi of the example and within the power-range as
defined by the argument lm.range
. This model is then used for
predicting the sample size. Always inspect the result using inspect
!
The method "step" returns the last element in the sequence of sample sizes - power pairs, sorted with decreasing power, where the power is above the power defined for the example.
An object of class power
.
In older verstions of the package: The function
merge
was used together with an object
of class powEx
to form an object of class power
## defining the range of n and theta to be evaluated psi <- powPar(theta = seq(from = 0.5, to = 1.5, by = 0.1), n = seq(from = 20, to = 60, by = 2), muA = 0, muB = 1) ## defining a power-function powFun <- function(psi){ power.t.test(n = n(psi)/2, delta = pp(psi, "muA") - pp(psi, "muB"), sd = theta(psi) )$power } ## evaluating the power-function for all combinations of n and theta calc <- powCalc(psi, powFun) ## adding example at theta of 1 and power of 0.9 pow <- powEx(calc, theta = 1, power = 0.9) ## drawing the power plot with 3 contour lines plot(pow, xlab = "Standard Deviation", ylab = "Total Sample Size", at = c(0.85, 0.9, 0.95)) ## changing the estimation method pow2 <- powEx(calc, theta = 1, power = 0.9, method = "lm") ## drawing an inspection plot inspect(pow2)