Title: Calculating Optimum Sampling Effort in Community Ecology
Version: 0.12.0
Description: A system for calculating the optimal sampling effort, based on the ideas of "Ecological cost-benefit optimization" as developed by A. Underwood (1997, ISBN 0 521 55696 1). Data is obtained from simulated ecological communities with prep_data() which formats and arranges the initial data, and then the optimization follows the following procedure of four functions: (1) scompvar() calculates the variation components necessary for (2) sim_cbo() to calculate the optimal combination of number of sites and samples depending on either an economic budget or on a desired statistical accuracy. Additionally, (3) sim_beta() estimates statistical power and type 2 error by using Permutational Multivariate Analysis of Variance, and (6) plot_power() represents the results of the previous function.
License: GPL (≥ 3)
Encoding: UTF-8
RoxygenNote: 7.3.1
Imports: ggplot2, ggpubr, sampling, stats, rlang, foreach, parallel, doParallel, doSNOW, vegan, SSP
Depends: R (≥ 2.10)
LazyData: true
Suggests: knitr,rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition: 3
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-08-06 21:27:46 UTC; artu
Author: Edlin Guerra-Castro ORCID iD [aut, cph], Arturo Sanchez-Porras ORCID iD [aut, cre]
Maintainer: Arturo Sanchez-Porras <sp.arturo@gmail.com>
Repository: CRAN
Date/Publication: 2024-08-21 08:00:02 UTC

ecocbo: Calculating Optimum Sampling Effort in Community Ecology

Description

A system for calculating the optimal sampling effort, based on the ideas of "Ecological cost-benefit optimization" as developed by A. Underwood (1997, ISBN 0 521 55696 1). Data is obtained from simulated ecological communities, and the optimization follows the following procedure of two functions (1) scompvar() calculates the variation components necessary for (2) sim_cbo() to calculate the optimal combination of number of sites and samples depending on either an economical budget or on a desired statistical accuracy. Additionally, (3) sim_beta() estimates statistical power and type 2 error by using Permutational Multivariate Analysis of Variance, and (4) plot_power() represents the results of the previous function.

Details

The functions in ecocbo package can be used to identify the optimal number of sites and samples that must be considered in a community ecology study by using simulated data. Together with SSP package, ecocbo proposes a novel approach to the determination of he appropriate sampling effort in community ecology studies.

ecocbo is composed by five functions: prep_data gives the appropriate format to the data so that it can be used by the other functions in the package. scompvar calculates the components of variation for the analized dataset, and finally, sim_cbo determines an estimate of the number of sites and samples to consider to optimize the cost-benefit for an ecological sampling study. For getting more information on the data, sim_beta calculates statistical power for different sampling efforts and plot_power plots those results to help the user define the a combination of sampling effort and power to move on.

ecocbo is being developed at Github(https://github.com/arturoSP/ecocbo), where up-to-date versions can be found.

Author(s)

The ecocbo development team is Edlin Guerra-Castro and Arturo Sanchez-Porras.

References

Underwood, A. J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge university press.

Underwood, A. J., & Chapman, M. G. (2003). Power, precaution, Type II error and sampling design in assessment of environmental impacts. Journal of Experimental Marine Biology and Ecology, 296(1), 49-70.

Anderson, M. J. (2014). Permutational multivariate analysis of variance (PERMANOVA). Wiley statsref: statistics reference online, 1-15.

Guerra‐Castro, E. J., Cajas, J. C., Simões, N., Cruz‐Motta, J.J., & Mascaró, M. (2021). SSP: an R package to estimate sampling effort in studies of ecological communities. Ecography, 44(4), 561-573.

Examples


# Load and adjust data.
data(epiDat)

simResults <- prep_data(data = epiDat, type = "counts", Sest.method = "average",
                        cases = 5, N = 100, sites = 10,
                        n = 5, m = 5, k = 30,
                        transformation = "none", method = "bray",
                        dummy = FALSE, useParallel = FALSE,
                        model = "single.factor")

simResults

# Computing components of variation
compVar <- scompvar(data = simResults)
compVar

# Cost-benefit optimization
cboResult <- sim_cbo(comp.var = compVar, ct = 20000, ck = 100, cj = 2500)
cboResult

# Determination of statistical power
epiBetaR <- sim_beta(simResults, alpha = 0.05)
epiBetaR

# Visualization of statistical power
plot_power(data = epiBetaR, n = NULL, m = 3, method = "both")

Sum of squares using Huygen Theorem

Description

Calculates sum of squares using Huygen theorem as implemented by Anderson (2014).

Usage

SS(d)

Arguments

d

distance matrix from which the sum of squares will be calculated.

Value

A numeric vector containing the dimension for the distance matrix, and the value for the sum of squares for the matrix.

Author(s)

Edlin Guerra-Castro (edlinguerra@gmail.com), Arturo Sanchez-Porras

References

Anderson, M. J. (2014). Permutational multivariate analysis of variance (PERMANOVA). Wiley statsref: statistics reference online, 1-15.

Balanced sampling

Description

Develops the experimental design based on the provided conditions

Usage

balanced_sampling(
  i,
  Y,
  mm,
  nn,
  YPU,
  H0Sim,
  HaSim,
  resultsHa,
  transformation,
  method
)

Arguments

i

pointer to the index in the list of experimental designs to try.

Y

index to the data.frame the function will work with.

mm

number of site the function is working with in each iteration.

nn

number of samples to consider in each iteration.

YPU

label for the sites in each iteration, as used by sampling::balancedtwostage()

H0Sim

simulated community from SSP::simdata() in which H0 is true.

HaSim

simulated community from SSP::simdata() in which H0 is false.

resultsHa

helper matrix that stores labels and later the results.

transformation

Mathematical function to reduce the weight of very dominant species.

method

appropriate distance/dissimilarity metric (e.g. Gower, Bray–Curtis, Jaccard, etc).

Value

a data frame with values for observed F (for H0 and Ha), and the Ha mean squares for residuals and variation among sites.

Author(s)

Edlin Guerra-Castro (edlinguerra@gmail.com), Arturo Sanchez-Porras

References

Underwood, A. J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge university press.

See Also

sampling::balancedtwostage()

Balanced sampling 2

Description

Develops the experimental design based on the provided conditions

Usage

balanced_sampling2(
  i,
  Y,
  mm,
  nn,
  YPU,
  H0Sim,
  HaSim,
  factEnv,
  resultsHa,
  transformation,
  method,
  model,
  nSect,
  sites,
  N
)

Arguments

i

pointer to the index in the list of experimental designs to try.

Y

index to the data.frame the function will work with.

mm

number of site the function is working with in each iteration.

nn

number of samples to consider in each iteration.

YPU

label for the sites in each iteration, as used by sampling::balancedtwostage()

H0Sim

simulated community from SSP::simdata() in which H0 is true.

HaSim

simulated community from SSP::simdata() in which H0 is false.

resultsHa

helper matrix that stores labels and later the results.

transformation

Mathematical function to reduce the weight of very dominant species.

method

appropriate distance/dissimilarity metric (e.g. Gower, Bray–Curtis, Jaccard, etc).

model

which algorithm to use for the calculation? At the moment, the only option is "nested.symmetric".

nSect

Total number of sectors to be simulated in each data set.

sites

Total number of sites to be simulated in each data set.

N

Total number of samples to be simulated in each site.

Value

a data frame with values for observed F (for H0 and Ha), and the Ha mean squares for residuals and variation among sites.

Author(s)

Edlin Guerra-Castro (edlinguerra@gmail.com), Arturo Sanchez-Porras

References

Underwood, A. J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge university press.

See Also

sampling::balancedtwostage()

Dataset on species count of marine communities

Description

This is a dataset containing a subset from the epibionts dataset from 'SSP' which was made by using the three local communities that differ the most.

Usage

data("epiDat")

Format

A data frame with count of individuals for 24 observations on 151 species.

Source

Data available from the Dryad Digital Repository: <http://dx.doi.org/10.5061/dryad.3bk3j9kj5> (Guerra-Castro et al. 2020).

References

Guerra-Castro, E. J. et al. 2016. Scales of spatial variation in tropical benthic assemblages and their ecological relevance: epibionts on Caribbean mangrove roots as a model system. – Mar. Ecol. Prog. Ser. 548: 97–110.

Examples

data("epiDat")

str(epiDat)

PERMANOVA one-way

Description

Calculates observed F and mean squares for the residuals and among sites. This function is a helper for prep_data().

Usage

permanova_oneway(
  x,
  factEnv,
  type = "P",
  method = "bray",
  transformation = "none"
)

Arguments

x

ecological community data.

factEnv

label for the community data.

type

which algorithm to use for the calculation? At the moment, the only option is "P".

method

appropriate distance/dissimilarity metric (e.g. Gower, Bray–Curtis, Jaccard, etc).

transformation

Mathematical function to reduce the weight of very dominant species.

Value

A data frame containing the resulting PERMANOVA table.

Author(s)

Edlin Guerra-Castro (edlinguerra@gmail.com), Arturo Sanchez-Porras

References

Underwood, A. J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge university press.

Anderson, M. J. (2014). Permutational multivariate analysis of variance (PERMANOVA). Wiley statsref: statistics reference online, 1-15.

See Also

vegan::vegdist()

PERMANOVA two-way

Description

Calculates observed F and mean squares for the residuals and among sites. This function is a helper for prep_data_nestedsymmetric().

Usage

permanova_twoway(
  x,
  factEnv,
  method = "bray",
  transformation = "none",
  model = "nested.symmetric"
)

Arguments

x

ecological community data.

factEnv

label for the community data.

method

appropriate distance/dissimilarity metric (e.g. Gower, Bray–Curtis, Jaccard, etc).

transformation

Mathematical function to reduce the weight of very dominant species.

model

which algorithm to use for the calculation? At the moment, the only option is "nested.symmetric".

Value

A data frame containing the resulting PERMANOVA table.

Author(s)

Edlin Guerra-Castro (edlinguerra@gmail.com), Arturo Sanchez-Porras

References

Underwood, A. J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge university press.

Anderson, M. J. (2014). Permutational multivariate analysis of variance (PERMANOVA). Wiley statsref: statistics reference online, 1-15.

See Also

vegan::vegdist()

Power curves for different sampling efforts

Description

plot_power() can be used to visualize the power of a study as a function of the sampling effort. The power curve plot shows that the power of the study increases as the sample size increases, and the density plot shows the overlapping areas where \alpha and \beta are significant.

Usage

plot_power(data, n = NULL, m = NULL, method = "power")

Arguments

data

Object of class "ecocbo_beta" that results from sim_beta().

n

Defaults to NULL, and then the function computes the number of samples 'n', within the selected 'm', that result in a sampling effort close to (1 - alpha) in power. If provided, said number of samples will be used.

m

Defaults to NULL, and then the function computes the number of sites 'm' that result in a sampling effort that is close to (1 - alpha) in power. If provided, said number of site will be used.

method

The desired plot. Options are "power", "density" or "both". "power" plots the power curve, "density" plots the density distribution of pseudoF, and "both" draws both plots one next to the other.

Value

If the method is "power", then the power curves for the different values of 'm'. The selected, or computed, 'n' is marked in red. If the method is "density", then a density plot for the observed pseudoF values and a line marking the value of pseudoF that marks the significance level indicated in sim_beta(). If the method is "both", then a composite with power curves and a density plot side by side.

The value of the selected 'm', 'n' and the corresponding component of variation are presented in all methods.

Author(s)

Edlin Guerra-Castro (edlinguerra@gmail.com), Arturo Sanchez-Porras

References

Underwood, A. J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge university press.

Underwood, A. J., & Chapman, M. G. (2003). Power, precaution, Type II error and sampling design in assessment of environmental impacts. Journal of Experimental Marine Biology and Ecology, 296(1), 49-70.

See Also

sim_beta() scompvar() sim_cbo() prep_data()

Examples

epiBetaR <- sim_beta(simResults, alpha = 0.05)

plot_power(data = epiBetaR, n = NULL, m = 3, method = "power")
plot_power(data = epiBetaR, n = NULL, m = 3, method = "density")
plot_power(data = epiBetaR, n = 4, m = 3, method = "both")

Prepare data for evaluation

Description

prep_data() formats and arranges the initial data so that it can be readily used by the other functions in the package. The function first gets the species names and the number of samples for each species from the input data frame. Then, it permutes the sampling efforts and calculates the pseudo-F statistic and the mean squares for each permutation. Finally, it returns a data frame with the permutations, pseudo-F statistic, and mean squares.

Usage

prep_data(
  data,
  type = "counts",
  Sest.method = "average",
  cases = 5,
  N = 100,
  sites = 10,
  n,
  m,
  k = 50,
  transformation = "none",
  method = "bray",
  dummy = FALSE,
  useParallel = TRUE,
  model = "single.factor"
)

Arguments

data

Data frame with species names (columns) and samples (rows) information. The first column should indicate the site to which the sample belongs, regardless of whether a single site has been sampled.

type

Nature of the data to be processed. It may be presence / absence ("P/A"), counts of individuals ("counts"), or coverage ("cover")

Sest.method

Method for estimating species richness. The function specpool is used for this. Available methods are the incidence-based Chao "chao", first order jackknife "jack1", second order jackknife "jack2" and Bootstrap "boot". By default, the "average" of the four estimates is used.

cases

Number of data sets to be simulated.

N

Total number of samples to be simulated in each site.

sites

Total number of sites to be simulated in each data set.

n

Maximum number of samples to consider.

m

Maximum number of sites.

k

Number of resamples the process will take. Defaults to 50.

transformation

Mathematical function to reduce the weight of very dominant species: 'square root', 'fourth root', 'Log (X+1)', 'P/A', 'none'

method

The appropriate distance/dissimilarity metric (e.g. Gower, Bray–Curtis, Jaccard, etc). The function vegan::vegdist() is called for that purpose.

dummy

Logical. It is recommended to use TRUE in cases where there are observations that are empty.

useParallel

Logical. Perform the analysis in parallel? Defaults to TRUE.

model

Select the model to use. Options, so far, are 'single.factor' and 'nested.symmetric'.

Value

prep_data() returns an object of class "ecocbo_data".

An object of class "ecocbo_data" is a list containing: $Results, a data frame that lists the estimates of pseudoF for simH0 and simHa that can be used to compute the statistical power for different sampling efforts, as well as the square means necessary for calculating the variation components.

Author(s)

Edlin Guerra-Castro (edlinguerra@gmail.com), Arturo Sanchez-Porras

References

Underwood, A. J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge university press.

Underwood, A. J., & Chapman, M. G. (2003). Power, precaution, Type II error and sampling design in assessment of environmental impacts. Journal of Experimental Marine Biology and Ecology, 296(1), 49-70.

See Also

sim_beta() plot_power() sim_cbo() scompvar()

Examples


simResults <- prep_data(data = epiDat, type = "counts", Sest.method = "average",
                        cases = 5, N = 100, sites = 10,
                        n = 5, m = 5, k = 30,
                        transformation = "none", method = "bray",
                        dummy = FALSE, useParallel = FALSE,
                        model = "single.factor")

simResults

Prepare data for evaluation in nested symmetric double-factor experiments

Description

prep_data() formats and arranges the initial data so that it can be readily used by the other functions in the package. The function first gets the species names and the number of samples for each species from the input data frame. Then, it permutes the sampling efforts and calculates the pseudo-F statistic and the mean squares for each permutation. Finally, it returns a data frame with the permutations, pseudo-F statistic, and mean squares.

Usage

prep_data_nestedsymmetric(
  data,
  type = "counts",
  Sest.method = "average",
  cases = 5,
  N = 100,
  sites = 10,
  n,
  m,
  k = 50,
  transformation = "none",
  method = "bray",
  dummy = FALSE,
  useParallel = TRUE
)

Arguments

data

Data frame with species names (columns) and samples (rows) information. The first column should indicate the site to which the sample belongs, regardless of whether a single site has been sampled.

type

Nature of the data to be processed. It may be presence / absence ("P/A"), counts of individuals ("counts"), or coverage ("cover")

Sest.method

Method for estimating species richness. The function specpool is used for this. Available methods are the incidence-based Chao "chao", first order jackknife "jack1", second order jackknife "jack2" and Bootstrap "boot". By default, the "average" of the four estimates is used.

cases

Number of data sets to be simulated.

N

Total number of samples to be simulated in each site.

sites

Total number of sites to be simulated in each data set.

n

Maximum number of samples to consider.

m

Maximum number of sites.

k

Number of resamples the process will take. Defaults to 50.

transformation

Mathematical function to reduce the weight of very dominant species: 'square root', 'fourth root', 'Log (X+1)', 'P/A', 'none'

method

The appropriate distance/dissimilarity metric (e.g. Gower, Bray–Curtis, Jaccard, etc). The function vegan::vegdist() is called for that purpose.

dummy

Logical. It is recommended to use TRUE in cases where there are observations that are empty.

useParallel

Logical. Perform the analysis in parallel? Defaults to FALSE.

Value

prep_data() returns an object of class "ecocbo_data".

An object of class "ecocbo_data" is a list containing: $Results, a data frame that lists the estimates of pseudoF for simH0 and simHa that can be used to compute the statistical power for different sampling efforts, as well as the square means necessary for calculating the variation components.

Author(s)

Edlin Guerra-Castro (edlinguerra@gmail.com), Arturo Sanchez-Porras

References

Underwood, A. J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge university press.

Underwood, A. J., & Chapman, M. G. (2003). Power, precaution, Type II error and sampling design in assessment of environmental impacts. Journal of Experimental Marine Biology and Ecology, 296(1), 49-70.

See Also

prep_data() sim_beta() plot_power() sim_cbo() scompvar()

Prepare data for evaluation in single-factor experiments

Description

prep_data() formats and arranges the initial data so that it can be readily used by the other functions in the package. The function first gets the species names and the number of samples for each species from the input data frame. Then, it permutes the sampling efforts and calculates the pseudo-F statistic and the mean squares for each permutation. Finally, it returns a data frame with the permutations, pseudo-F statistic, and mean squares.

Usage

prep_data_single(
  data,
  type = "counts",
  Sest.method = "average",
  cases = 5,
  N = 100,
  sites = 10,
  n,
  m,
  k = 50,
  transformation = "none",
  method = "bray",
  dummy = FALSE,
  useParallel = TRUE
)

Arguments

data

Data frame with species names (columns) and samples (rows) information. The first column should indicate the site to which the sample belongs, regardless of whether a single site has been sampled.

type

Nature of the data to be processed. It may be presence / absence ("P/A"), counts of individuals ("counts"), or coverage ("cover")

Sest.method

Method for estimating species richness. The function specpool is used for this. Available methods are the incidence-based Chao "chao", first order jackknife "jack1", second order jackknife "jack2" and Bootstrap "boot". By default, the "average" of the four estimates is used.

cases

Number of data sets to be simulated.

N

Total number of samples to be simulated in each site.

sites

Total number of sites to be simulated in each data set.

n

Maximum number of samples to consider.

m

Maximum number of sites.

k

Number of resamples the process will take. Defaults to 50.

transformation

Mathematical function to reduce the weight of very dominant species: 'square root', 'fourth root', 'Log (X+1)', 'P/A', 'none'

method

The appropriate distance/dissimilarity metric (e.g. Gower, Bray–Curtis, Jaccard, etc). The function vegan::vegdist() is called for that purpose.

dummy

Logical. It is recommended to use TRUE in cases where there are observations that are empty.

useParallel

Logical. Perform the analysis in parallel? Defaults to FALSE.

Value

prep_data() returns an object of class "ecocbo_data".

An object of class "ecocbo_data" is a list containing: $Results, a data frame that lists the estimates of pseudoF for simH0 and simHa that can be used to compute the statistical power for different sampling efforts, as well as the square means necessary for calculating the variation components.

Author(s)

Edlin Guerra-Castro (edlinguerra@gmail.com), Arturo Sanchez-Porras

References

Underwood, A. J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge university press.

Underwood, A. J., & Chapman, M. G. (2003). Power, precaution, Type II error and sampling design in assessment of environmental impacts. Journal of Experimental Marine Biology and Ecology, 296(1), 49-70.

See Also

prep_data() sim_beta() plot_power() sim_cbo() scompvar()

S3Methods for Printing

Description

prints for ecocbo::sim_beta() objects.

Usage

## S3 method for class 'ecocbo_beta'
print(x, ...)

Arguments

x

Object from ecocbo::sim_beta() function.

...

Additional arguments

Value

Prints the result of ecocbo::sim_beta() function, showing in an ordered matrix the estimated power for the different experimental designs that were considered.

Simulated components of variation

Description

scompvar can be used to calculate the average component of variation among units and the average component of variation within samples in terms of sampling effort.

Usage

scompvar(data, n = NULL, m = NULL)

Arguments

data

Object of class "ecocbo_data" that results from prep_data().

n

Number of samples to be considered. Defaults to NULL.

m

Site label to be used as basis for the computation. Defaults to NULL.

Value

A data frame containing the values for the variation component among sites compVarA and in the residuals compVarR.

Note

If m or n are left as NULL, the function will calculate the components of variation using the largest available values as set in the experimental design in sim_beta().

Author(s)

Edlin Guerra-Castro (edlinguerra@gmail.com), Arturo Sanchez-Porras

References

Underwood, A. J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge university press.

Underwood, A. J., & Chapman, M. G. (2003). Power, precaution, Type II error and sampling design in assessment of environmental impacts. Journal of Experimental Marine Biology and Ecology, 296(1), 49-70.

See Also

sim_beta() plot_power() sim_cbo() prep_data()

Examples

scompvar(data = simResults)
scompvar(data = simResults, n = 5, m = 2)

Data set containing the results of applying ecocbo::prep_data().

Description

The dataset contains the results of applying ecocbo::prep_data() to epiDat. The result is a list with one level: $Results is a data frame with the results of applying PERMANOVA to epiDat a number of times, it contains the values of pseudoF and the mean squares for different repeated sampling efforts.

This dataset can be used to study the variability of the pseudoF-statistic, beta and the power when an experiment is applied to a varying number of samples, sampling units, or sampling sites.

Usage

data("simResults")

Format

An object of class "ecocbo_data", also a list containing one data frame. The format is:

$Results a data frame that contains the results of the evaluation of sim_beta.
dat.sim simulation from which the results are obtained.
k number of resample for the result.
m number of sites considered for the result.
n number of replicates within each site for the result.
pseudoFH0 observed F value for the experimental design, when all observations belong to one site.
pseudoFHa observed F value for the experimental design, when observations belong to different sites.
AMSHa calculated mean squares among sites in the experiment.
RMSHa calculated mean squares for the residuals in the experiment.

Details

This dataset comes from applying ecocbo::prep_data() to the basic data from ecocbo::epiDat.

Source

Data available from the Dryad Digital Repository: <http://dx.doi.org/10.5061/dryad.3bk3j9kj5> (Guerra-Castro et al. 2020).

References

Guerra-Castro, E. J. et al. 2016. Scales of spatial variation in tropical benthic assemblages and their ecological relevance: epibionts on Caribbean mangrove roots as a model system. – Mar. Ecol. Prog. Ser. 548: 97–110.

Examples

data(simResults)

sim_beta(simResults, alpha = 0.05)

Calculate beta and power out of simulated samples

Description

sim_beta() can be used to assess the power of a study by comparing the variation when one can assume whether an ecological community does not have composition differences (H0 true) or it does (H0 false). For example, if the beta error is 0.25, then there is a 25% chance of failing to detect a difference even if the difference is real. The power of the study is 1 - \beta, so in this example, the power of the study is 0.75.

Usage

sim_beta(data, alpha = 0.05)

Arguments

data

An object of class "ecocbo_data" that results from applying prep_data() to a community data frame.

alpha

Level of significance for Type I error. Defaults to 0.05.

Value

sim_data() returns an object of class "ecocbo_beta".

The function print() is used to present a matrix that summarizes the results by showing the estimate power according to different sampling efforts.

An object of class "ecocbo_beta" is a list containing the following components:

Author(s)

Edlin Guerra-Castro (edlinguerra@gmail.com), Arturo Sanchez-Porras

References

Underwood, A. J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge university press.

Underwood, A. J., & Chapman, M. G. (2003). Power, precaution, Type II error and sampling design in assessment of environmental impacts. Journal of Experimental Marine Biology and Ecology, 296(1), 49-70.

Anderson, M. J. (2014). Permutational multivariate analysis of variance (PERMANOVA). Wiley statsref: statistics reference online, 1-15.

Guerra‐Castro, E. J., Cajas, J. C., Simões, N., Cruz‐Motta, J. J., & Mascaró, M. (2021). SSP: an R package to estimate sampling effort in studies of ecological communities. Ecography, 44(4), 561-573.

See Also

plot_power() scompvar() sim_cbo() prep_data() SSP::assempar() SSP::simdata()

Examples

sim_beta(data = simResults, alpha = 0.05)

Simulated cost-benefit optimization

Description

sim_cbo() can be used to apply a cost-benefit optimization model that depends either on a desired level of precision or on a budgeted total cost, as proposed by Underwood (1997).

Usage

sim_cbo(comp.var, multSE = NULL, ct = NULL, ck, cj = NULL)

Arguments

comp.var

Data frame as obtained from scompvar().

multSE

Optional. Required multivariate standard error for the sampling experiment.

ct

Optional. Total cost for the sampling experiment.

ck

Cost per replicate.

cj

Cost per unit.

Value

A data frame containing the optimized values for m number of sites and n number of samples to consider.

Author(s)

Edlin Guerra-Castro (edlinguerra@gmail.com), Arturo Sanchez-Porras

References

Underwood, A. J. (1997). Experiments in ecology: their logical design and interpretation using analysis of variance. Cambridge university press.

Underwood, A. J., & Chapman, M. G. (2003). Power, precaution, Type II error and sampling design in assessment of environmental impacts. Journal of Experimental Marine Biology and Ecology, 296(1), 49-70.

See Also

sim_beta() plot_power() scompvar()

Examples

compVar <- scompvar(data = simResults)

sim_cbo(comp.var = compVar, multSE = NULL, ct = 20000, ck = 100, cj = 2500)
sim_cbo(comp.var = compVar, multSE = 0.15, ct = NULL, ck = 100, cj = 2500)