power.trend {emon} | R Documentation |
Calculates power by simulation to detect a specified trend.
Description
Calculates power for a specified trend wherethe signal for the trend is specified by xvalues and meanvalues (possibly generated by generate.trend), and the error distribution is specified by distribution. The statistical method to detect the trend is specified by method.The power is the proportion of repeat simulations for which the trend is detected with a p-value less than alpha (two-sided test).
Usage
power.trend(xvalues, reps=1, meanvalues, distribution="Normal", sd=NA,
nbsize=NA, method="linear regression", alpha=0.05, nsims=1000, nsims.mk=999,
randeffect=F, randeffect.sd=NA)
Arguments
xvalues |
Vector of, for example, time points at which the trend is evaluated. |
reps |
Vector of number of replicates per time point. |
meanvalues |
Vector of mean values that identify the signal of the trend. |
distribution |
Distribution (must be one of |
sd |
Standard deviation if distribution= |
nbsize |
Size parameter if distribution= |
method |
Method used to identify the trend. Can be one of |
alpha |
Type 1 error for detecting trend. Values less than |
nsims |
The number of simulations to be used in calculating the power. Default is 1000. |
nsims.mk |
The number of replicate permutations used in calculating the p-value for the Mann-Kendall test
when |
randeffect |
Not working yet |
randeffect.sd |
Not working yet |
Details
The Mann Kendall tests are approriate only for monotonic increasing or decreasing trends, the linear regression method is only approriate for linearly increasing or decreasing trend. The GAM is appropriate for changing trends over time.
Several powers can be calculated on a single call to this function by placing more than one value in reps
.
Value
The power is returned.
Author(s)
David Maxwell: david.maxwell@cefas.co.uk
References
Fryer RJ & Nicholson MD (1993) Need paper title. ICES Journal of Marine Science, 50, 161-168.
Fryer & Nicholson 1999 Using smoothers for comprehensive assessments of contaminant time series in marine biota. ICES Journal of Marine Science, 56, 779-790.
Wood S.N. (2006) Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC Press.
See Also
mannkendall.stat
, addnoise
,
gam
, generate.trend
Examples
library(mgcv)
# In practice, \code{nsims} would be set to at least 1000
par(mfrow=c(2,2))
lin5 = generate.trend(nyears=10, change=5, type="linear")
plot(lin5$i, lin5$mu)
updown = generate.trend(nyears=15, change=5, type="updown", changeyear=8)
plot(updown$i, updown$mu)
power.trend(xvalues=lin5$i, meanvalues=lin5$mu, distribution="Normal", sd=2,
method="linear regression", alpha=0.05, nsims=50)
power.trend(xvalues=lin5$i, meanvalues=lin5$mu, distribution="Poisson", method="mk", alpha=0.05,
nsims=50)
power.trend(xvalues=updown$i, meanvalues=updown$mu, distribution="Normal", sd=2,
method="gam", alpha=0.05, nsims=50)