| Type: | Package |
| Title: | Statistical Analysis of Count Data and Quantal Data |
| Version: | 0.1.1 |
| Maintainer: | Benjamin Daniels <Benjamin.Daniels@uba.de> |
| Description: | Methods for statistical analysis of count data and quantal data. For the analysis of count data an implementation of the Closure Principle Computational Approach Test ("CPCAT") is provided (Lehmann, R et al. (2016) <doi:10.1007/s00477-015-1079-4>), as well as an implementation of a "Dunnett GLM" approach using a Quasi-Poisson regression (Hothorn, L, Kluxen, F (2020) <doi:10.1101/2020.01.15.907881>). For the analysis of quantal data an implementation of the Closure Principle Fisher–Freeman–Halton test ("CPFISH") is provided (Lehmann, R et al. (2018) <doi:10.1007/s00477-017-1392-1>). P-values and no/lowest observed (adverse) effect concentration values are calculated. All implemented methods include further functions to evaluate the power and the minimum detectable difference using a bootstrapping approach. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | multcomp |
| Suggests: | knitr, rmarkdown, testthat |
| RoxygenNote: | 7.3.2 |
| Depends: | R (≥ 2.10) |
| NeedsCompilation: | no |
| Packaged: | 2025-02-20 12:23:17 UTC; Benni Daniels |
| Author: | Benjamin Daniels 
[cre, ctb],
Christian Dietrich [aut],
Thomas Graeff [ctb],
Magnus Wang [ctb] |
| Repository: | CRAN |
| Date/Publication: | 2025-02-21 11:30:25 UTC |
CP hypotheses
Description
Function to generate hypotheses for the Closure Principle concept using 0/1 contrast matrices
Usage
CP.hypotheses(n, treatment.names = NULL)
Arguments
n |
Number of groups |
treatment.names |
Optional vector with names of treatment groups |
Value
Contrast matrix (all 0/1 combinations)
CPCAT
Description
When conducting statistical tests with multiple treatments, such as a control group and increasing concentrations of a test substance, ANOVA and parametric post-hoc tests (e.g. Dunnett's test) are commonly used. However, these tests require the assumptions of homogeneous variances and normally distributed data. For count data (e.g. counts of animals), these assumptions are typically violated, as the data are usually Poisson-distributed. Additionally, multiple testing using post-hoc tests can lead to alpha-inflation. To address these issues, CPCAT was proposed by Lehmann et al. (2016). CPCAT has two components. The first is the Closure Principle (CP) developed by Bretz et al. (2010), which aims to eliminate alpha-inflation. CP applies a stepwise approach to identify at which concentration effects begin to occur. The second part of CPCAT is the actual significance test, CAT (Computational Approach Test; introduced by Chang et al., 2010), which uses a test based on the Poisson distribution rather than a parametric test based on normal distribution assumptions. For details on the structure of the input data, please refer to the dataset 'Daphnia.counts' provided alongside this package.
Usage
CPCAT(
groups,
counts,
control.name = NULL,
bootstrap.runs = 10000,
hampel.threshold = 5,
use.fixed.random.seed = NULL,
get.contrasts.and.p.values = FALSE,
show.output = TRUE
)
Arguments
groups |
Group vector |
counts |
Vector with count data |
control.name |
Character string with control group name (optional) |
bootstrap.runs |
Number of bootstrap runs |
hampel.threshold |
Threshold for Hampel identifier (measure for over-/underdispersion) |
use.fixed.random.seed |
Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set. |
get.contrasts.and.p.values |
Get each row of the contrast matrices evaluated |
show.output |
Show/hide output |
Value
R object with results and information from CPCAT calculations
References
Bretz, F.; Hothorn, T.; Westfall, P. (2010): Multiple comparisons using R. 1st Edition, Chapman and Hall/CRC, New York
Chang, C.-H.; Pal, N.; Lin, J.-J. (2010): A Note on Comparing Several Poisson Means. In: Commun. Stat. Simul. Comput., 2010, 39(8), p. 1605-1627, https://doi.org/10.1080/03610918.2010.508860
Lehmann, R.; Bachmann, J.; Maletzki, D.; Polleichtner, C.; Ratte, H.; Ratte, M. (2016): A new approach to overcome shortcomings with multiple testing of reproduction data in ecotoxicology. In: Stochastic Environmental Research and Risk Assessment, 2016, 30(3), p. 871-882, https://doi.org/10.1007/s00477-015-1079-4
Examples
Daphnia.counts # example data provided alongside the package
# Test CPCAT
CPCAT(groups = Daphnia.counts$Concentration,
counts = Daphnia.counts$Number_Young,
control.name = NULL,
bootstrap.runs = 10000,
use.fixed.random.seed = 123, #fixed seed for reproducible results
get.contrasts.and.p.values = FALSE,
show.output = TRUE)
CPCAT Poisson sub-test
Description
Helper function for CPCAT
Usage
CPCAT.Poisson.sub.test(dat, contrast, bootstrap.runs = 10000)
Arguments
dat |
Data frame to be evaluated |
contrast |
Contrast matrix |
bootstrap.runs |
Number of bootstrap runs |
Value
p-value for tested data and contrast
CPCAT Poisson test
Description
Helper function for CPCAT
Usage
CPCAT.Poisson.test(dat, contrastmatrix, bootstrap.runs = 10000)
Arguments
dat |
Data frame to be evaluated |
contrastmatrix |
Contrast matrix |
bootstrap.runs |
Number of bootstrap runs |
Value
List of p-values for tested contrast matrices
CPCAT bootstrap MDD (bMDD)
Description
The basic idea of the calculation of bootstrap MDD (bMDD) using the CPCAT approach is to shift the lambda parameter of Poisson distribution until there is a certain proportion of results significantly different from the control.
Usage
CPCAT.bMDD(
groups,
counts,
control.name = NULL,
alpha = 0.05,
shift.step = -0.25,
bootstrap.runs = 200,
power = 0.8,
max.iterations = 1000,
use.fixed.random.seed = NULL,
CPCAT.bootstrap.runs = 200,
show.progress = TRUE,
show.results = TRUE
)
Arguments
groups |
Group vector |
counts |
Vector with count data |
control.name |
Character string with control group name (optional) |
alpha |
Significance level |
shift.step |
Step of shift (negative as a reduction is assumed) |
bootstrap.runs |
Number of bootstrap runs |
power |
Proportion of bootstrap.runs that return significant differences |
max.iterations |
Max. number of iterations to not get stuck in the while loop |
use.fixed.random.seed |
Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set. |
CPCAT.bootstrap.runs |
Bootstrap runs within CPCAT method |
show.progress |
Show progress for each shift of lambda |
show.results |
Show results |
Value
Data frame with results from bMDD analysis
Examples
Daphnia.counts # example data provided alongside the package
# Test CPCAT bootstrap MDD
CPCAT.bMDD(groups = Daphnia.counts$Concentration,
counts = Daphnia.counts$Number_Young,
control.name = NULL,
alpha = 0.05,
shift.step = -1, # Caution: big step size for testing
bootstrap.runs = 5, # Caution: low number of bootstrap runs for testing
power = 0.8,
max.iterations = 1000,
use.fixed.random.seed = 123, #fixed seed for reproducible results
CPCAT.bootstrap.runs = 10,
show.progress = TRUE,
show.results = TRUE)
CPCAT power
Description
The basic idea of CPCAT power calculations is to do parametric bootstrapping for each dose/concentration group and to evaluate the proportion of results significantly different from the control.
Usage
CPCAT.power(
groups,
counts,
control.name = NULL,
alpha = 0.05,
bootstrap.runs = 200,
use.fixed.random.seed = NULL,
CPCAT.bootstrap.runs = 200,
show.progress = TRUE,
show.results = TRUE
)
Arguments
groups |
Group vector |
counts |
Vector with count data |
control.name |
Character string with control group name (optional) |
alpha |
Significance level |
bootstrap.runs |
Number of bootstrap runs |
use.fixed.random.seed |
Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set. |
CPCAT.bootstrap.runs |
Bootstrap runs within CPCAT method |
show.progress |
Show progress for each shift of lambda |
show.results |
Show results |
Value
Data frame with results from power analysis
Examples
Daphnia.counts # example data provided alongside the package
# Test CPCAT power
CPCAT.power(groups = Daphnia.counts$Concentration,
counts = Daphnia.counts$Number_Young,
control.name = NULL,
alpha = 0.05,
bootstrap.runs = 10, # Caution: low number of bootstrap runs for testing
use.fixed.random.seed = 123, #fixed seed for reproducible results
CPCAT.bootstrap.runs = 10,# Caution: low number of bootstrap runs for testing
show.progress = TRUE,
show.results = TRUE)
CPFISH
Description
For quantal data (e.g. survival data, '14 out of 20 animals died') e.g. in the form of a contingency table, CPFISH was proposed by Lehmann et al. (2018). Like CPCAT, CPFISH is based on the Closure Principle (CP), but instead of a bootstrapping approach, a Fisher test is performed for all sub-hypotheses to be analyzed. For details on the structure of the input table, please refer to the dataset 'CPFISH.contingency.table' provided alongside this package.
Usage
CPFISH(
contingency.table,
control.name = NULL,
simulate.p.value = TRUE,
use.fixed.random.seed = NULL,
show.output = TRUE
)
Arguments
contingency.table |
Matrix with observed data (e.g. survival counts, survival must be in first row) |
control.name |
Character string with control group name (optional) |
simulate.p.value |
Use simulated p-values in Fisher test or not |
use.fixed.random.seed |
Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set. |
show.output |
Show/hide output |
Value
R object with results and information from CPFISH calculations
References
Lehmann, R.; Bachmann, J.; Karaoglan, B.; Lacker, J.; Polleichtner, C.; Ratte, H.; Ratte, M. (2018): An alternative approach to overcome shortcomings with multiple testing of binary data in ecotoxicology. In: Stochastic Environmental Research and Risk Assessment, 2018, 32(1), p. 213-222, https://doi.org/10.1007/s00477-017-1392-1
Examples
CPFISH.contingency.table # example data provided alongside the package
# Test CPFISH
CPFISH(contingency.table = CPFISH.contingency.table,
control.name = NULL,
simulate.p.value = TRUE,
use.fixed.random.seed = 123, #fixed seed for reproducible results
show.output = TRUE)
CPFISH bootstrap MDD (bMDD)
Description
The basic idea of the calculation of bootstrap MDD (bMDD) using the CPCAT approach is to shift the probability of binomial distribution until there is a certain proportion of results significantly different from the control.
Usage
CPFISH.bMDD(
contingency.table,
control.name = NULL,
alpha = 0.05,
shift.step = -0.01,
bootstrap.runs = 200,
power = 0.8,
max.iterations = 1000,
simulate.p.value = TRUE,
use.fixed.random.seed = NULL,
show.progress = TRUE,
show.results = TRUE
)
Arguments
contingency.table |
Matrix with observed data (e.g. survival counts, survival must be in first row) |
control.name |
Character string with control group name (optional) |
alpha |
Significance level |
shift.step |
Step of shift (negative as a reduction is assumed) |
bootstrap.runs |
Number of bootstrap runs (draw Poisson data n times) |
power |
Proportion of bootstrap.runs that return significant differences |
max.iterations |
Max. number of iterations to not get stuck in the while loop |
simulate.p.value |
Use simulated p-values in Fisher test or not |
use.fixed.random.seed |
Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set. |
show.progress |
Show progress for each shift of the probability |
show.results |
Show results |
Value
Data frame with results from bMDD analysis
Examples
CPFISH.contingency.table # example data provided alongside the package
# Test CPFISH bootstrap MDD
CPFISH.bMDD(contingency.table = CPFISH.contingency.table,
control.name = NULL,
alpha = 0.05,
shift.step = -0.1, # Caution: big step size for testing
bootstrap.runs = 10, # Caution: low number of bootstrap runs for testing
power = 0.8,
max.iterations = 1000,
simulate.p.value = TRUE,
use.fixed.random.seed = 123, #fixed seed for reproducible results
show.progress = TRUE,
show.results = TRUE)
CPFISH.contingency.table is a dataset to showcase analyses with CPFISH
Description
CPFISH.contingency.table is a dataset to showcase analyses with CPFISH
Usage
CPFISH.contingency.table
Format
An object of class matrix (inherits from array) with 2 rows and 3 columns.
Source
The CPFISH data was artificially created.
Examples
data(CPFISH.contingency.table)
CPFISH power
Description
The basic idea of CPFISH power calculations is to do parametric bootstrapping for each dose/concentration group and to evaluate the proportion of results significantly different from the control.
Usage
CPFISH.power(
contingency.table,
control.name = NULL,
alpha = 0.05,
bootstrap.runs = 200,
simulate.p.value = TRUE,
use.fixed.random.seed = NULL,
show.progress = TRUE,
show.results = TRUE
)
Arguments
contingency.table |
Matrix with observed data (e.g. survival counts, survival must be in first row) |
control.name |
Character string with control group name (optional) |
alpha |
Significance level |
bootstrap.runs |
Number of bootstrap runs (draw Poisson data n times) |
simulate.p.value |
Use simulated p-values in Fisher test or not |
use.fixed.random.seed |
Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set. |
show.progress |
Show progress for each shift of the probability |
show.results |
Show results |
Value
Data frame with results from power analysis
Examples
CPFISH.contingency.table # example data provided alongside the package
# Test CPFISH power
CPFISH.power(contingency.table = CPFISH.contingency.table,
control.name = NULL,
alpha = 0.05,
bootstrap.runs = 10, # Caution: low number of bootstrap runs for testing
simulate.p.value = TRUE,
use.fixed.random.seed = 123, #fixed seed for reproducible results
show.progress = TRUE,
show.results = TRUE)
Daphnia.counts is a dataset to showcase analyses with CPCAT and Dunnett.GLM
Description
Daphnia.counts is a dataset to showcase analyses with CPCAT and Dunnett.GLM
Usage
Daphnia.counts
Format
An object of class data.frame with 60 rows and 3 columns.
Source
The daphnia data was taken from Hothorn and Kluxen (2020).
Examples
data(Daphnia.counts)
Dunnett.GLM
Description
When conducting statistical tests with multiple treatments, such as a control group and increasing concentrations of a test substance, ANOVA and parametric post-hoc tests (e.g. Dunnett's test) are commonly used. However, these tests require the assumptions of homogeneous variances and normally distributed data. For count data (e.g. counts of animals), these assumptions are typically violated, as the data are usually Poisson-distributed. The Dunnett.GLM function is based on a GLM followed by a Dunnett test to the model estimates. It was implemented to serve as an alternative approach to CPCAT while using a Quasi-Poisson regression. The basic approach from Hothorn and Kluxen (2020) was adjusted to overcome methodological issues (see description of 'zero.treatment.action parameter'). For details on the structure of the input data, please refer to the dataset 'Daphnia.counts' provided alongside this package.
Usage
Dunnett.GLM(
groups,
counts,
control.name = NULL,
zero.treatment.action = "identity.link",
show.output = TRUE
)
Arguments
groups |
Group vector |
counts |
Vector with count data |
control.name |
Character string with control group name (optional) |
zero.treatment.action |
Method for dealing with treatments only containing zeros (use either "identity.link" or "log(x+1)"). The method is only used if the data set contains dose/concentration groups that exclusively contain zero values (since the basic method provides for a logarithmic transformation of the data averages, it would lead to incorrect results). To deal with this methodological shortcoming, two options were implemented. The 'identity.link' option: the 'identity' link is used in the GLM instead of the 'log' link, i.e. the data are no longer transformed. The 'log(x+1)' option: The 'log' link is retained and 1 is added to each count value at the start of the procedure so that the subsequent log-transformation can be carried out without any problems. Note that both options may slightly distort the results. |
show.output |
Show/hide output |
Value
R object with results and information from Dunnett.GLM calculations
References
Hothorn, L.; Kluxen, F. (2020): Statistical analysis of no observed effect concentrations or levels in eco-toxicological assays with overdispersed count endpoints. In: bioRxiv, 2020, https://doi.org/10.1101/2020.01.15.907881
Examples
Daphnia.counts # example data provided alongside the package
# Test Dunnett.GLM with 'identity.link' option
Dunnett.GLM(groups = Daphnia.counts$Concentration,
counts = Daphnia.counts$Number_Young,
control.name = NULL,
zero.treatment.action = "identity.link",
show.output = TRUE)
# Test Dunnett.GLM with 'log(x+1)' option
Dunnett.GLM(groups = Daphnia.counts$Concentration,
counts = Daphnia.counts$Number_Young,
control.name = NULL,
zero.treatment.action = "log(x+1)",
show.output = TRUE)
Dunnett.GLM bootstrap MDD (bMDD)
Description
The basic idea of the calculation of bootstrap MDD (bMDD) using the Dunnett.GLM approach is to shift the lambda parameter of Poisson distribution until there is a certain proportion of results significantly different from the control.
Usage
Dunnett.GLM.bMDD(
groups,
counts,
control.name = NULL,
alpha = 0.05,
shift.step = -0.25,
bootstrap.runs = 200,
power = 0.8,
max.iterations = 1000,
use.fixed.random.seed = NULL,
Dunnett.GLM.zero.treatment.action = "log(x+1)",
show.progress = TRUE,
show.results = TRUE
)
Arguments
groups |
Group vector |
counts |
Vector with count data |
control.name |
Character string with control group name (optional) |
alpha |
Significance level |
shift.step |
Step of shift (negative as a reduction is assumed) |
bootstrap.runs |
Number of bootstrap runs |
power |
Proportion of bootstrap.runs that return significant differences |
max.iterations |
Max. number of iterations to not get stuck in the while loop |
use.fixed.random.seed |
Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set. |
Dunnett.GLM.zero.treatment.action |
Dunnett.GLM method to be used for treatments only containing zeros |
show.progress |
Show progress for each shift of lambda |
show.results |
Show results |
Value
Data frame with results from bMDD analysis
Examples
Daphnia.counts # example data provided alongside the package
# Test Dunnett.GLM bootstrap MDD
Dunnett.GLM.bMDD(groups = Daphnia.counts$Concentration,
counts = Daphnia.counts$Number_Young,
control.name = NULL,
alpha = 0.05,
shift.step = -1, # Caution: big step size for testing
bootstrap.runs = 5, # Caution: low number of bootstrap runs for testing
power = 0.8,
max.iterations = 1000,
use.fixed.random.seed = 123, #fixed seed for reproducible results
Dunnett.GLM.zero.treatment.action = "log(x+1)",
show.progress = TRUE,
show.results = TRUE)
Dunnett.GLM power
Description
The basic idea of Dunnett.GLM power calculations is to do parametric bootstrapping for each dose/concentration group and to evaluate the proportion of results significantly different from the control.
Usage
Dunnett.GLM.power(
groups,
counts,
control.name = NULL,
alpha = 0.05,
bootstrap.runs = 200,
use.fixed.random.seed = NULL,
Dunnett.GLM.zero.treatment.action = "log(x+1)",
show.progress = TRUE,
show.results = TRUE
)
Arguments
groups |
Group vector |
counts |
Vector with count data |
control.name |
Character string with control group name (optional) |
alpha |
Significance level |
bootstrap.runs |
Number of bootstrap runs |
use.fixed.random.seed |
Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set. |
Dunnett.GLM.zero.treatment.action |
GLM.Dunnett method to be used for treatments only containing zeros |
show.progress |
Show progress for each shift of lambda |
show.results |
Show results |
Value
Data frame with results from power analysis
Examples
Daphnia.counts # example data provided alongside the package
# Test Dunnett.GLM power
Dunnett.GLM.power(groups = Daphnia.counts$Concentration,
counts = Daphnia.counts$Number_Young,
control.name = NULL,
alpha = 0.05,
bootstrap.runs = 10, # Caution: low number of bootstrap runs for testing
use.fixed.random.seed = 123, #fixed seed for reproducible results
Dunnett.GLM.zero.treatment.action = "log(x+1)",
show.progress = TRUE,
show.results = TRUE)
Multi-group test bMDD
Description
Idea: shift lambda of Poisson distribution until there is a certain proportion of significant results
Usage
Multi.group.test.bMDD(
groups,
counts,
control.name = NULL,
alpha = 0.05,
shift.step = -0.25,
bootstrap.runs = 200,
power = 0.8,
max.iterations = 1000,
use.fixed.random.seed = NULL,
CPCAT.bootstrap.runs = 200,
Dunnett.GLM.zero.treatment.action = "log(x+1)",
show.progress = TRUE,
show.results = TRUE,
get.effect.and.power = FALSE,
use.CMP.distribution = FALSE,
CMP.dispersion.factor = 1,
test = "CPCAT"
)
Arguments
groups |
Group vector |
counts |
Vector with count data |
control.name |
Character string with control group name (optional) |
alpha |
Significance level |
shift.step |
Step of shift (negative as a reduction is assumed) |
bootstrap.runs |
Number of bootstrap runs |
power |
Proportion of bootstrap.runs that return significant differences |
max.iterations |
Max. number of iterations to not get stuck in the while loop |
use.fixed.random.seed |
Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set. |
CPCAT.bootstrap.runs |
Bootstrap runs within CPCAT method |
Dunnett.GLM.zero.treatment.action |
Dunnett.GLM method to be used for treatments only containing zeros |
show.progress |
Show progress for each shift of lambda |
show.results |
Show results |
get.effect.and.power |
Return effect size (percent of control) and power for each step (only for last treatment) |
use.CMP.distribution |
Use Conway-Maxwell-Poisson distribution for sampling |
CMP.dispersion.factor |
Dispersion parameter phi has to be sqrt(factor) to scale the variance by this factor |
test |
Either "CPCAT" or "GLM.Dunnett" |
Value
Data frame with results from bMDD analysis
Multi-group test power
Description
Idea: Do parametric bootstrapping for each group and evaluate the proportion of significant results.
Usage
Multi.group.test.power(
groups,
counts,
control.name = NULL,
alpha = 0.05,
bootstrap.runs = 200,
use.fixed.random.seed = NULL,
CPCAT.bootstrap.runs = 200,
Dunnett.GLM.zero.treatment.action = "log(x+1)",
show.progress = TRUE,
show.results = TRUE,
test = "CPCAT"
)
Arguments
groups |
Group vector |
counts |
Vector with count data |
control.name |
Character string with control group name (optional) |
alpha |
Significance level |
bootstrap.runs |
Number of bootstrap runs |
use.fixed.random.seed |
Use fixed seed, e.g. 123, for reproducible results. If NULL no seed is set. |
CPCAT.bootstrap.runs |
Bootstrap runs within CPCAT method |
Dunnett.GLM.zero.treatment.action |
GLM.Dunnett method to be used for treatments only containing zeros |
show.progress |
Show progress for each shift of lambda |
show.results |
Show results |
test |
Either "CPCAT" or "GLM.Dunnett" |
Value
Data frame with results from power analysis