emon (version 1.3)

power.BACI: Calculates power for a Before and After Control Impact (BACI) design.

Description

BACI designs are commonly used in environmental monitoring. They are relevant where you want to measure the effect of an impact (e.g. the effect on benthic ecology of dredging in an area). Observations for treatment and control areas are measured BEFORE and after the impact. This function allows you to examine the power of particular BACI designs to detect differences between the control and the treatment.

Usage

power.BACI(change, change.type="M", nt, nc, parst, parsc, distribution, test="P", alpha=0.05, nsims=1000)

Arguments

change

AFTER treatment mean minus BEFORE treatment mean or percentage change of AFTER treatment mean relative to BEFORE treatment mean (depending on value of change.type).

change.type

Whether the parameter change represents an additive ("A") or percentage ("M") change.

nt

Vector of sample sizes for treatment group. Must be of same dimension as nc.

nc

Vector of sample sizes for control group. Must be of same dimension as nt.

parst

Parameters for the treatment data. If distribution="Normal", parst[1] contains the BEFORE mean and parst[2] contains the BEFORE standard deviation. If distribution="Poisson", parst[1] contains the BEFORE mean. If distribution="Lognormal", parst[1] contains the BEFORE mean of the natural log data and parst[2] contains the BEFORE standard deviation of the log data. If distribution="Negbin", parst[1] contains the BEFORE mean, parst[2] contains the BEFORE size, and parst[3] is the AFTER size.

parsc

Parameters for the control data. If distribution="Normal", parsc[1] contains the BEFORE mean and parsc[2] contains the BEFORE standard deviation. If distribution="Poisson", parsc[1] contains the BEFORE mean. If distribution="Lognormal", parsc[1] contains the BEFORE mean of the natural log data and parsc[2] contains the BEFORE standard deviation of the log data. If distribution="Negbin", parsc[1] contains the BEFORE mean, and parsc[2] contains the BEFORE size.

distribution

The statistical distribution for the two groups. Can be either: "Normal", "Poisson", "Lognormal" or "Negbin".

test

The statistical test used to compare the interaction between the control and treatment means at the BEFORE and AFTER sampling occasions. If test="NP", then the test will be a non-parametric randomisation test, in the spirit of Manly (1997), using the function permute.BACI.

If test="P", then parametric tests are made to compare the treatment (i.e. a factor indicating whether an observation is from the treatmment or the control) by time (i.e. a factor indicating whether observations are BEFORE or AFTER) interaction. If distribution="Normal" then this is calculated from the usual Analysis of Variance. The same method is used (but on the log data) if distribution="Lognormal".

When distribution="Poisson" or distribution="Negbin", interactions from analysis of deviance tables are used to measure the interaction. The p-value is calculated by assuming that this interaction deviance has a chi-squared distribution on 1 df. For the Negative Binomial distribution, terms in the analysis of deviance table use the same value for the size parameter as that estimated from the null model.

alpha

If the p-value for the interaction is less than alpha, a change is deemed to have been detected. Used to assess power from the nreps simulations.

nsims

Number of repeat simulations used to calculate the power. Default is 1000.

Value

Details

BACI designs are relevant where you want to measure the effect of an impact (e.g. the effect on benthic ecology of dredging in an area). You take a number of samples both in (treatment) and outside (control) the affected area BEFORE the impact. Those in the control area should be as similar to the treatment area as possible in terms of benthic ecology. You then sample the areas again AFTER the impact has taken place. If there is an interaction for the 2x2 crossed design then there is an effect of the impact. That is, if the control area has changed differently to the treatment area.

This function allows you to examine the power of particular BACI designs. The distribution of the measure being used can be Normal, Poisson, Negative Binomial or Lognormal.

It is also assumed that the sample sizes before and after the impact are the same, although the sample size in the treatment area can be different to the control area. Thus, if 10 and 8 samples are taken in the treatment and control areas before the impact, then 10 and 8 samples are assumed to be taken after the impact.

For the Normal, Poisson and Negative Binomial distributions, the parameter change is simply the percentage or additive change of the treatment mean from the BEFORE to the AFTER sampling occasions. For the Lognormal distribution, the percentage change is relative to the BEFORE treatment mean on the non-log scale. The BEFORE treatment mean is estimated from the mean of the log data (parst[1]), the standard deviation of the log data (parst[2]) and the proposed sampling size nt.

The estimator used is the one proposed by Shen (2006), which did better in terms of mean squared error than both the sample mean on the non-log scale and the maximum likelihood estimators. This is given by mean.before = exp(parst[1] + (nt-1)*ss / (2*(nt+4)*(nt-1) + 3*ss)), where ss = (nt-1)*parst[2]**2.

The Negative Binomial distribution option (parst[3] allows the user to specify the size parameter of the AFTER treatment distribution. One possibility is to keep the size the same for both the BEFORE and AFTER distributions. However, because the mean changes and because the variance V = mu+mu^2/size, this means that V will be different for the BEFORE and AFTER distributions. If you want to keep the variance the same, you can use the function size2.samevar.

Several powers can be calculated per call to this function by specifying more than one values for the sample sizes nt and nc.

References

Shen H, Brown LD and Zhi H (2006) Efficient estimation of log-normal means with application to pharmacokinetic data. Statistics in Medicine, 25, 3023 to 3038.

See Also

power.trend, power.groups, size2.samevar

Examples

# Data is richness (number of species) and abundance from grab samples from
# the Dogger Bank, UK. In practice, \code{nsims} would be set to at least 1000.

rich = c(15,16,37,12,15,5,13,16,17,34,23,20,22,30,85,55,13,19,30,41,22,8,43,10,38,24,17,
         23,17,17,24,33,30,18,26,18,12,50,19,21,35)
abun = c(50,91,140,21,25,8,28,37,30,90,56,50,40,83,964,180,21,60,81,138,67,17,250,63,152,
         68,42,69,57,67,74,96,75,44,61,49,62,281,55,50,198)

par(mfrow=c(2,2))
hist(rich)
hist(abun)
hist(sqrt(rich))
hist(log(abun))

ssize = seq(10, 50, 10)
parsc.rich = mean(rich); parst.rich = mean(rich)
parsc.abun = rep(0,2); parst.abun = parsc.abun
parst.abun[1] = mean(log(abun)); parst.abun[2] = sd(log(abun))
parsc.abun = parst.abun
power.rich = rep(0, length(ssize))
power.abun = rep(0, length(ssize))

power.rich = power.BACI(change=35, change.type="M", nt=ssize, nc=ssize,
                parst=parst.rich, parsc=parsc.rich,
                distribution="Poisson", test="P", nsims=50)$power

power.abun = power.BACI(change=35, change.type="M", nt=ssize, nc=ssize,
                parst=parst.abun, parsc=parsc.abun, distribution="Lognormal",
                test="P", nsims=50)$power

par(mfrow=c(1,1))
plot(ssize, power.rich, ylim=c(0,1), ylab="Power", xlab="Sample size", type="l")
lines(ssize, power.abun, lty=2, col=2)
legend("bottomright", legend=c("Richness power", "Abundance power"), lty=c(1,2),
       col=c(1,2))
title("BACI power plots")