Package 'qountstat' reference manual

Title:	Statistical Analysis of Count Data and Quantal Data
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.
Authors:	Benjamin Daniels [cre, ctb] , Christian Dietrich [aut], Thomas Graeff [ctb], Magnus Wang [ctb]
Maintainer:	Benjamin Daniels <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.1
Built:	2025-02-21 13:30:57 UTC
Source:	CRAN

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
)
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)
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 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
)
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)

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 Poisson sub-test

Description

Helper function for CPCAT

Usage

CPCAT.Poisson.sub.test(dat, contrast, bootstrap.runs = 10000)
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)
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 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
)
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)
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
)
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.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
)
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	# 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
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)
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
)
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)
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
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)
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
)
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)
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
)
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)
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
)
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)
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"
)
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"
)
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

`n`	Number of groups
`treatment.names`	Optional vector with names of treatment groups

Package 'qountstat'

Help Index

CP hypotheses

Description

Usage

Arguments

Value

CPCAT

Description

Usage

Arguments

Value

References

Examples

CPCAT bootstrap MDD (bMDD)

Description

Usage

Arguments

Value

Examples

CPCAT Poisson sub-test

Description

Usage

Arguments

Value

CPCAT Poisson test

Description

Usage

Arguments

Value

CPCAT power

Description

Usage

Arguments

Value

Examples

CPFISH

Description

Usage

Arguments

Value

References

Examples

CPFISH bootstrap MDD (bMDD)

Description

Usage

Arguments

Value

Examples

CPFISH.contingency.table is a dataset to showcase analyses with CPFISH

Description

Usage

Format

Source

Examples

CPFISH power

Description

Usage

Arguments

Value

Examples

Daphnia.counts is a dataset to showcase analyses with CPCAT and Dunnett.GLM

Description

Usage

Format

Source

Examples

Dunnett.GLM

Description

Usage

Arguments

Value

References

Examples

Dunnett.GLM bootstrap MDD (bMDD)

Description

Usage

Arguments

Value

Examples