generate.trend: Generates a set of mean values.

In emon: Tools for Environmental and Ecological Survey Design

Description

This function is used to generate mean value scenarios for use in power.trend.

Usage

 generate.trend(nyears, mu1=0, change, change.type="A", type = c("linear", "incident",
        "step", "updown"), changeyear, symmetric=F) 

Arguments

nyears	Defines the number of time points for X axis
mu1	The mean Y value for the first time point
change	Difference between mu1 and largest (or smallest) mu_i. Can be negative for a decreasing trend.
change.type	Whether the parameter change represents an additive ("A") or percentage ("M") change.
type	Type of trend to assess the power against. Can be any of "linear", "incident", "step" or "updown". See examples below for more details.
changeyear	Year in which change in gradient occurs, for options 'incident' or 'updown'.
symmetric	If symmetric=T, nyears is even and changeyear = nyears / 2 then type='updown' generates a pattern where the two middle years are at level k.

Details

Assumes that surveys take place in years 1 to nyears (or could be any other equally spaced unit). Generates a set of mean values (or signal) for a specified trend over that time period. The approach is based on Fryer and Nicholson (1993, 1999). For type="linear", the slope is generated by b=k/(nyears-1). If type="updown", the slope until changeyear is generated by b=k/(changeyears-1). After changeyear, the slope is -b (except for the special case outlined by the symmetric parameter above, where changeyear and changeyear+1 are k and then the slope continues as -b).

Value

Generates a data frame where the first column ($i) is year and the second column ($mu) is the mean value.

Author(s)

David Maxwell: [e-mail]

References

Fryer RJ & Nicholson MD (1993) The power of a contaminant monitoring programme to detect linear trends and incidents. 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.

See Also

power.trend, addnoise

Examples

lin0 = generate.trend(nyears=10, change=0, type="linear")
lin5 = generate.trend(nyears=10, change=5, type="linear")
inc5 = generate.trend(nyears=10, change=5, type="incident", changeyear=6)
step5 = generate.trend(nyears=10, change=5, type="step", changeyear=6)
updeven = generate.trend(nyears=10, change=5, type="updown", changeyear=5, symmetric=TRUE)
updodd = generate.trend(nyears=9, change=5, type="updown", changeyear=3)

par(mfrow=c(2,3))
plot(lin0$i, lin0$mu, type="o", pch=16, xlab='Time', ylab='Y')
plot(lin5$i, lin5$mu, type="o", pch=16, xlab='Time', ylab='Y')
plot(inc5$i, inc5$mu, type="o", pch=16, xlab='Time', ylab='Y')
plot(step5$i, step5$mu, type="o", pch=16, xlab='Time', ylab='Y')
plot(updeven$i, updeven$mu, type="o", pch=16, xlab='Time', ylab='Y')
plot(updodd$i, updodd$mu, type="o", pch=16, xlab='Time', ylab='Y')

Example output

emon documentation built on May 2, 2019, 10:21 a.m.