,
Christina Kihn [aut],
Sebastian Döhler [ctb] | Title: | Vectorised Computation of P-Values and Their Supports for Several Discrete Statistical Tests |
|---|---|
| Description: | Provides vectorised functions for computing p-values of various common discrete statistical tests, as described e.g. in Agresti (2002) <doi:10.1002/0471249688>, including their distributions. Exact and approximate computation methods are provided. For exact p-values, several procedures of determining two-sided p-values are included, which are outlined in more detail in Hirji (2006) <doi:10.1201/9781420036190>. |
| Authors: | Florian Junge [cre, aut] ,
Christina Kihn [aut],
Sebastian Döhler [ctb] |
| Maintainer: | Florian Junge <[email protected]> |
| License: | GPL-3 |
| Version: | 0.2.1 |
| Built: | 2024-10-27 18:15:51 UTC |
| Source: | https://github.com/disohda/discretetests |
This package provides vectorised functions for computing p-values of various discrete statistical tests. Exact and approximate computation methods are provided. For exact p-values, several procedures of determining two-sided p-values are included.
Additionally, these functions are capable of returning the discrete p-value
supports, i.e. all observable p-values under a null hypothesis. These
supports can be used for multiple testing procedures in the
DiscreteFDR and
FDX packages.
Maintainer: Florian Junge [email protected] (ORCID)
Authors:
Christina Kihn
Other contributors:
Sebastian Döhler [email protected] (ORCID) [contributor]
Fisher, R. A. (1935). The logic of inductive inference. Journal of the Royal Statistical Society Series A, 98, pp. 39–54. doi:10.2307/2342435
Agresti, A. (2002). Categorical data analysis (2nd ed.). New York: John Wiley & Sons. doi:10.1002/0471249688
Blaker, H. (2000) Confidence curves and improved exact confidence intervals for discrete distributions. Canadian Journal of Statistics, 28(4), pp. 783-798. doi:10.2307/3315916
Hirji, K. F. (2006). Exact analysis of discrete data. New York: Chapman and Hall/CRC. pp. 55-83. doi:10.1201/9781420036190
Useful links:
Report bugs at https://github.com/DISOhda/DiscreteTests/issues
binom_test_pv() performs an exact or approximate binomial test about the
probability of success in a Bernoulli experiment. In contrast to
stats::binom.test(), it is vectorised, only calculates p-values and
offers a normal approximation of their computation. Furthermore, it is
capable of returning the discrete p-value supports, i.e. all observable
p-values under a null hypothesis. Multiple tests can be evaluated
simultaneously. In two-sided tests, several procedures of obtaining the
respective p-values are implemented.
![[Deprecated]](data:image/svg+xml;base64,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)
Note: Please use binom_test_pv()! The older binom.test.pv() is
deprecated in order to migrate to snake case. It will be removed in a future
version.
binom_test_pv( x, n, p = 0.5, alternative = "two.sided", ts_method = "minlike", exact = TRUE, correct = TRUE, simple_output = FALSE ) binom.test.pv( x, n, p = 0.5, alternative = "two.sided", ts.method = "minlike", exact = TRUE, correct = TRUE, simple.output = FALSE )binom_test_pv( x, n, p = 0.5, alternative = "two.sided", ts_method = "minlike", exact = TRUE, correct = TRUE, simple_output = FALSE ) binom.test.pv( x, n, p = 0.5, alternative = "two.sided", ts.method = "minlike", exact = TRUE, correct = TRUE, simple.output = FALSE )
x |
integer vector giving the number of successes. |
n |
integer vector giving the number of trials. |
p |
numerical vector of hypothesised probabilities of success. |
alternative |
character vector that indicates the alternative hypotheses; each value must be one of |
ts_method, ts.method
|
single character string that indicates the two-sided p-value computation method (if any value in |
exact |
logical value that indicates whether p-values are to be calculated by exact computation ( |
correct |
logical value that indicates if a continuity correction is to be applied ( |
simple_output, simple.output
|
logical value that indicates whether an R6 class object, including the tests' parameters and support sets, i.e. all observable p-values under each null hypothesis, is to be returned (see below). |
The parameters x, n, p and alternative are vectorised. They are
replicated automatically to have the same lengths. This allows multiple
hypotheses to be tested simultaneously.
If p = NULL, it is tested if the probability of success is 0.5 with
the alternative being specified by alternative.
For exact computation, various procedures of determining two-sided p-values are implemented.
"minlike"The standard approach in stats::fisher.test() and
stats::binom.test(). The probabilities of the
likelihoods that are equal or less than the observed one
are summed up. In Hirji (2006), it is referred to as the
Probability-based approach.
"blaker"The minima of the observations' lower and upper tail probabilities are combined with the opposite tail not greater than these minima. More details can be found in Blaker (2000) or Hirji (2006), where it is referred to as the Combined Tails method.
"absdist"The probabilities of the absolute distances from the expected value that are greater than or equal to the observed one are summed up. In Hirji (2006), it is referred to as the Distance from Center approach.
"central"The smaller values of the observations' simply doubles the minimum of lower and upper tail probabilities. In Hirji (2006), it is referred to as the Twice the Smaller Tail method.
For non-exact (i.e. continuous approximation) approaches, ts_method is
ignored, since all its methods would yield the same p-values. More
specifically, they all converge to the doubling approach as in
ts_mthod = "central".
If simple.output = TRUE, a vector of computed p-values is returned.
Otherwise, the output is a DiscreteTestResults R6 class object, which
also includes the p-value supports and testing parameters. These have to be
accessed by public methods, e.g. $get_pvalues().
Agresti, A. (2002). Categorical data analysis (2nd ed.). New York: John Wiley & Sons. pp. 14-15. doi:10.1002/0471249688
Blaker, H. (2000) Confidence curves and improved exact confidence intervals for discrete distributions. Canadian Journal of Statistics, 28(4), pp. 783-798. doi:10.2307/3315916
Hirji, K. F. (2006). Exact analysis of discrete data. New York: Chapman and Hall/CRC. pp. 55-83. doi:10.1201/9781420036190
# Constructing k <- c(4, 2, 2, 14, 6, 9, 4, 0, 1) n <- c(18, 12, 10) p <- c(0.5, 0.2, 0.3) # Computation of exact two-sided p-values ("blaker") and their supports results_ex <- binom_test_pv(k, n, p, ts_method = "blaker") raw_pvalues <- results_ex$get_pvalues() pCDFlist <- results_ex$get_pvalue_supports() # Computation of normal-approximated one-sided p-values ("less") and their supports results_ap <- binom_test_pv(k, n, p, "less", exact = FALSE) raw_pvalues <- results_ap$get_pvalues() pCDFlist <- results_ap$get_pvalue_supports()# Constructing k <- c(4, 2, 2, 14, 6, 9, 4, 0, 1) n <- c(18, 12, 10) p <- c(0.5, 0.2, 0.3) # Computation of exact two-sided p-values ("blaker") and their supports results_ex <- binom_test_pv(k, n, p, ts_method = "blaker") raw_pvalues <- results_ex$get_pvalues() pCDFlist <- results_ex$get_pvalue_supports() # Computation of normal-approximated one-sided p-values ("less") and their supports results_ap <- binom_test_pv(k, n, p, "less", exact = FALSE) raw_pvalues <- results_ap$get_pvalues() pCDFlist <- results_ap$get_pvalue_supports()
This is the class used by the statistical test functions of this package for
returning not only p-values, but also the supports of their distributions and
the parameters of the respective tests. Objects of this class are obtained by
setting the simple.output parameter of a test function to FALSE (the
default). All data members of this class are private to avoid inconsistencies
by deliberate or inadvertent changes by the user. However, the results can be
read by public methods.
new()
Creates a new DiscreteTestResults object.
DiscreteTestResults$new( test_name, inputs, p_values, pvalue_supports, support_indices, data_name )
test_namesingle character string with the name of the test(s).
inputsnamed list of exactly three elements
containing the observations, test parameters and
hypothesised null values as data frames;
names of these list fields must be
observations, nullvalues and parameters.
See details for further information about the
requirements for these fields.
p_valuesnumeric vector of the p-values calculated by each hypothesis test.
pvalue_supportslist of unique numeric vectors containing
all p-values that are observable under the
respective hypothesis; each value of p_values
must occur in its respective p-value support.
support_indiceslist of numeric vectors containing the test indices that indicates to which individual testing scenario each unique parameter set and each unique support belongs.
data_namesingle character string with the name of the variable that contains the observed data.
The fields of the inputs have the following requirements:
$observationsdata frame that contains the observed data; if
the observed data is a matrix, it must be
converted to a data frame; must not be NULL,
only numerical and character values are
allowed.
$nullvaluesdata frame that contains the hypothesised values
of the tests, e.g. the rate parameters for Poisson
tests; must not be NULL, only numerical values
are allowed.
$parametersdata frame that holds the parameter combinations
of the null distribution of each test (e.g.
numbers of Bernoulli trials for binomial tests, or
m, n and k for the hypergeometric
distribution used by Fisher's Exact Test, which
have to be derived from the observations first);
must include a mandatory column named
alternative; only numerical and character values
are allowed.
Missing values or NULLs are not allowed for any of these fields. All
data frames must have the same number of rows. Their column names are
used by the print method for producing text output, therefore they
should be informative, i.e. short and (if necessary) non-syntactic,
like e.g. `number of success`.
get_pvalues()
Returns the computed p-values.
DiscreteTestResults$get_pvalues(named = TRUE)
namedsingle logical value that indicates whether the vector is to be returned as a named vector (if names are present)
A numeric vector of the p-values of all null hypotheses.
get_inputs()
Return the list of the test inputs.
DiscreteTestResults$get_inputs(unique = FALSE)
uniquesingle logical value that indicates whether only unique
combinations of parameter sets and null values are to be
returned. If unique = FALSE (the default), the returned
data frames may contain duplicate sets.
A list of three elements. The first one contains a data frame with the
observations for each tested null hypothesis, while the second is another
data frame with the hypothesised null values (e.g. p for binomial
tests). The third list field holds the parameter sets (e.g. n in case
of a binomial test). If unique = TRUE, only unique combinations of
parameter sets and null values are returned, but observations remain
unchanged.
get_pvalue_supports()
Returns the p-value supports, i.e. all observable p-values under the respective null hypothesis of each test.
DiscreteTestResults$get_pvalue_supports(unique = FALSE)
uniquesingle logical value that indicates whether only unique
p-value supports are to be returned. If unique = FALSE
(the default), the returned supports may be duplicated.
A list of numeric vectors containing the supports of the p-value null distributions.
get_support_indices()
Returns the indices that indicate to which testing scenario each unique support belongs.
DiscreteTestResults$get_support_indices()
A list of numeric vectors. Each one contains the indices of the null hypotheses to which the respective support and/or unique parameter set belongs.
print()
Prints the computed p-values.
DiscreteTestResults$print( inputs = TRUE, pvalues = TRUE, supports = FALSE, test_idx = NULL, limit = 10, ... )
inputssingle logical value that indicates if the inputs
values (i.e. observations and parameters) are to be
printed; defaults to TRUE.
pvaluessingle logical value that indicates if the resulting
p-values are to be printed; defaults to TRUE.
supportssingle logical value that indicates if the p-value
supports are to be printed; defaults to FALSE.
test_idxinteger vector giving the indices of the tests whose
results are to be printed; if NULL (the default),
results of every test up to the index specified by
limit (see below) are printed
limitsingle integer that indicates the maximum number of
test results to be printed; if limit = 0, results of
every test are printed; ignored if test_idx is not
set to NULL
...further arguments passed to print.default.
Prints a summary of the tested null hypotheses. The object itself is invisibly returned.
clone()
The objects of this class are cloneable with this method.
DiscreteTestResults$clone(deep = FALSE)
deepWhether to make a deep clone.
## one-sided binomial test # parameters x <- 2:4 n <- 5 p <- 0.4 m <- length(x) # support (same for all three tests) and p-values support <- sapply(0:n, function(k) binom.test(k, n, p, "greater")$p.value) pv <- support[x + 1] # DiscreteTestResults object res <- DiscreteTestResults$new( # string with name of the test test_name = "Exact binomial test", # list of data frames inputs = list( observations = data.frame( `number of successes` = x, # no name check of column header to have a speaking name for 'print' check.names = FALSE ), parameters = data.frame( # parameter 'n', needs to be replicated to length of 'x' `number of trials` = rep(n, m), # mandatory parameter 'alternative', needs to be replicated to length of 'x' alternative = rep("greater", m), # no name check of column header to have a speaking name for 'print' check.names = FALSE ), nullvalues = data.frame( # here: only one null value, 'p'; needs to be replicated to length of 'x' `probability of success` = rep(p, m), # no name check of column header to have a speaking name for 'print' check.names = FALSE ) ), # numerical vector of p-values p_values = pv, # list of supports (here: only one support); values must be sorted and unique pvalue_supports = list(unique(sort(support))), # list of indices that indicate which p-value/hypothesis each support belongs to support_indices = list(1:m), # name of input data variables data_name = "x, n and p" ) # print results without supports print(res) # print results with supports print(res, supports = TRUE)## one-sided binomial test # parameters x <- 2:4 n <- 5 p <- 0.4 m <- length(x) # support (same for all three tests) and p-values support <- sapply(0:n, function(k) binom.test(k, n, p, "greater")$p.value) pv <- support[x + 1] # DiscreteTestResults object res <- DiscreteTestResults$new( # string with name of the test test_name = "Exact binomial test", # list of data frames inputs = list( observations = data.frame( `number of successes` = x, # no name check of column header to have a speaking name for 'print' check.names = FALSE ), parameters = data.frame( # parameter 'n', needs to be replicated to length of 'x' `number of trials` = rep(n, m), # mandatory parameter 'alternative', needs to be replicated to length of 'x' alternative = rep("greater", m), # no name check of column header to have a speaking name for 'print' check.names = FALSE ), nullvalues = data.frame( # here: only one null value, 'p'; needs to be replicated to length of 'x' `probability of success` = rep(p, m), # no name check of column header to have a speaking name for 'print' check.names = FALSE ) ), # numerical vector of p-values p_values = pv, # list of supports (here: only one support); values must be sorted and unique pvalue_supports = list(unique(sort(support))), # list of indices that indicate which p-value/hypothesis each support belongs to support_indices = list(1:m), # name of input data variables data_name = "x, n and p" ) # print results without supports print(res) # print results with supports print(res, supports = TRUE)
This is the class used by DiscreteTests for summarising
DiscreteTestResults objects. It contains the summarised objects itself, as
well as a summary data frame as private members. Both can be read by public
methods.
new()
Creates a new summary.DiscreteTestResults object.
DiscreteTestResultsSummary$new(test_results)
test_resultsthe DiscreteTestResults class object to be
summarised.
get_test_results()
Returns the underlying DiscreteTestResults object.
DiscreteTestResultsSummary$get_test_results()
A DiscreteTestResults R6 class object.
get_summary_table()
Returns the summary table of the underlying DiscreteTestResults object.
DiscreteTestResultsSummary$get_summary_table()
A data frame.
print()
Prints the summary.
DiscreteTestResultsSummary$print(...)
...further arguments passed to print.data.frame.
Prints a summary table of the tested null hypotheses. The object itself is invisibly returned.
clone()
The objects of this class are cloneable with this method.
DiscreteTestResultsSummary$clone(deep = FALSE)
deepWhether to make a deep clone.
# binomial tests obj <- binom.test.pv(0:5, 5, 0.5) # create DiscreteTestResultsSummary object res <- DiscreteTestResultsSummary$new(obj) # print summary print(res) # extract summary table res$get_summary_table()# binomial tests obj <- binom.test.pv(0:5, 5, 0.5) # create DiscreteTestResultsSummary object res <- DiscreteTestResultsSummary$new(obj) # print summary print(res) # extract summary table res$get_summary_table()
fisher_test_pv() performs Fisher's exact test or a chi-square approximation
to assess if rows and columns of a 2-by-2 contingency table with fixed
marginals are independent. In contrast to stats::fisher.test(), it is
vectorised, only calculates p-values and offers a normal approximation of
their computation. Furthermore, it is capable of returning the discrete
p-value supports, i.e. all observable p-values under a null hypothesis.
Multiple tables can be analysed simultaneously. In two-sided tests, several
procedures of obtaining the respective p-values are implemented.
Note: Please use fisher_test_pv()! The older fisher.test.pv() is
deprecated in order to migrate to snake case. It will be removed in a future
version.
fisher_test_pv( x, alternative = "two.sided", ts_method = "minlike", exact = TRUE, correct = TRUE, simple_output = FALSE ) fisher.test.pv( x, alternative = "two.sided", ts.method = "minlike", exact = TRUE, correct = TRUE, simple.output = FALSE )fisher_test_pv( x, alternative = "two.sided", ts_method = "minlike", exact = TRUE, correct = TRUE, simple_output = FALSE ) fisher.test.pv( x, alternative = "two.sided", ts.method = "minlike", exact = TRUE, correct = TRUE, simple.output = FALSE )
x |
integer vector with four elements, a 2-by-2 matrix or an integer matrix (or data frame) with four columns, where each line represents a 2-by-2 table to be tested. |
alternative |
character vector that indicates the alternative hypotheses; each value must be one of |
ts_method, ts.method
|
single character string that indicates the two-sided p-value computation method (if any value in |
exact |
logical value that indicates whether p-values are to be calculated by exact computation ( |
correct |
logical value that indicates if a continuity correction is to be applied ( |
simple_output, simple.output
|
logical value that indicates whether an R6 class object, including the tests' parameters and support sets, i.e. all observable p-values under each null hypothesis, is to be returned (see below). |
The parameters x and alternative are vectorised. They are replicated
automatically, such that the number of x's rows is the same as the length
of alternative. This allows multiple null hypotheses to be tested
simultaneously. Since x is (if necessary) coerced to a matrix with four
columns, it is replicated row-wise.
If exact = TRUE, Fisher's exact test is performed (the specific hypothesis
depends on the value of alternative). Otherwise, if exact = FALSE, a
chi-square approximation is used for two-sided hypotheses or a normal
approximation for one-sided tests, based on the square root of the
chi-squared statistic. This is possible because the degrees of freedom of
chi-squared tests on 2-by-2 tables are limited to 1.
For exact computation, various procedures of determining two-sided p-values are implemented.
"minlike"The standard approach in stats::fisher.test() and
stats::binom.test(). The probabilities of the
likelihoods that are equal or less than the observed one
are summed up. In Hirji (2006), it is referred to as the
Probability-based approach.
"blaker"The minima of the observations' lower and upper tail probabilities are combined with the opposite tail not greater than these minima. More details can be found in Blaker (2000) or Hirji (2006), where it is referred to as the Combined Tails method.
"absdist"The probabilities of the absolute distances from the expected value that are greater than or equal to the observed one are summed up. In Hirji (2006), it is referred to as the Distance from Center approach.
"central"The smaller values of the observations' simply doubles the minimum of lower and upper tail probabilities. In Hirji (2006), it is referred to as the Twice the Smaller Tail method.
For non-exact (i.e. continuous approximation) approaches, ts_method is
ignored, since all its methods would yield the same p-values. More
specifically, they all converge to the doubling approach as in
ts_mthod = "central".
If simple.output = TRUE, a vector of computed p-values is returned.
Otherwise, the output is a DiscreteTestResults R6 class object, which
also includes the p-value supports and testing parameters. These have to be
accessed by public methods, e.g. $get_pvalues().
Fisher, R. A. (1935). The logic of inductive inference. Journal of the Royal Statistical Society Series A, 98, pp. 39–54. doi:10.2307/2342435
Agresti, A. (2002). Categorical data analysis (2nd ed.). New York: John Wiley & Sons. pp. 91–97. doi:10.1002/0471249688
Blaker, H. (2000) Confidence curves and improved exact confidence intervals for discrete distributions. Canadian Journal of Statistics, 28(4), pp. 783-798. doi:10.2307/3315916
Hirji, K. F. (2006). Exact analysis of discrete data. New York: Chapman and Hall/CRC. pp. 55-83. doi:10.1201/9781420036190
# Constructing S1 <- c(4, 2, 2, 14, 6, 9, 4, 0, 1) S2 <- c(0, 0, 1, 3, 2, 1, 2, 2, 2) N1 <- rep(148, 9) N2 <- rep(132, 9) F1 <- N1 - S1 F2 <- N2 - S2 df <- data.frame(S1, F1, S2, F2) # Computation of Fisher's exact p-values (default: "minlike") and their supports results_f <- fisher_test_pv(df) raw_pvalues <- results_f$get_pvalues() pCDFlist <- results_f$get_pvalue_supports() # Computation of p-values of chi-square tests and their supports results_c <- fisher_test_pv(df, exact = FALSE) raw_pvalues <- results_c$get_pvalues() pCDFlist <- results_c$get_pvalue_supports()# Constructing S1 <- c(4, 2, 2, 14, 6, 9, 4, 0, 1) S2 <- c(0, 0, 1, 3, 2, 1, 2, 2, 2) N1 <- rep(148, 9) N2 <- rep(132, 9) F1 <- N1 - S1 F2 <- N2 - S2 df <- data.frame(S1, F1, S2, F2) # Computation of Fisher's exact p-values (default: "minlike") and their supports results_f <- fisher_test_pv(df) raw_pvalues <- results_f$get_pvalues() pCDFlist <- results_f$get_pvalue_supports() # Computation of p-values of chi-square tests and their supports results_c <- fisher_test_pv(df, exact = FALSE) raw_pvalues <- results_c$get_pvalues() pCDFlist <- results_c$get_pvalue_supports()
Performs McNemar's chi-square test or an exact variant to assess the symmetry
of rows and columns in a 2-by-2 contingency table. In contrast to
stats::mcnemar.test(), it is vectorised, only calculates p-values and
offers their exact computation. Furthermore, it is capable of returning the
discrete p-value supports, i.e. all observable p-values under a null
hypothesis. Multiple tables can be analysed simultaneously. In two-sided
tests, several procedures of obtaining the respective p-values are
implemented. It is a special case of the binomial test.
Note: Please use mcnemar_test_pv()! The older mcnemar.test.pv() is
deprecated in order to migrate to snake case. It will be removed in a future
version.
mcnemar_test_pv( x, alternative = "two.sided", exact = TRUE, correct = TRUE, simple_output = FALSE ) mcnemar.test.pv( x, alternative = "two.sided", exact = TRUE, correct = TRUE, simple.output = FALSE )mcnemar_test_pv( x, alternative = "two.sided", exact = TRUE, correct = TRUE, simple_output = FALSE ) mcnemar.test.pv( x, alternative = "two.sided", exact = TRUE, correct = TRUE, simple.output = FALSE )
x |
integer vector with four elements, a 2-by-2 matrix or an integer matrix (or data frame) with four columns where each line represents a 2-by-2 table to be tested. |
alternative |
character vector that indicates the alternative hypotheses; each value must be one of |
exact |
logical value that indicates whether p-values are to be calculated by exact computation ( |
correct |
logical value that indicates if a continuity correction is to be applied ( |
simple_output, simple.output
|
logical value that indicates whether an R6 class object, including the tests' parameters and support sets, i.e. all observable p-values under each null hypothesis, is to be returned (see below). |
The parameters x and alternative are vectorised. They are replicated
automatically, such that the number of x's rows is the same as the length
of alternative. This allows multiple null hypotheses to be tested
simultaneously. Since 'x is (if necessary) coerced to a matrix with four
columns, it is replicated row-wise.
It can be shown that McNemar's test is a special case of the binomial test.
Therefore, its computations are handled by binom_test_pv(). In
contrast to that function, mcnemar_test_pv() does not allow specifying
exact two-sided p-value calculation procedures. The reason is that McNemar's
exact test always tests for a probability of 0.5, in which case all these
exact two-sided p-value computation methods yield exactly the same results.
If simple.output = TRUE, a vector of computed p-values is returned.
Otherwise, the output is a DiscreteTestResults R6 class object, which
also includes the p-value supports and testing parameters. These have to be
accessed by public methods, e.g. $get_pvalues().
Agresti, A. (2002). Categorical data analysis (2nd ed.). New York: John Wiley & Sons. pp. 411–413. doi:10.1002/0471249688
stats::mcnemar.test(), binom_test_pv()
# Constructing S1 <- c(4, 2, 2, 14, 6, 9, 4, 0, 1) S2 <- c(0, 0, 1, 3, 2, 1, 2, 2, 2) N1 <- rep(148, 9) N2 <- rep(132, 9) F1 <- N1 - S1 F2 <- N2 - S2 df <- data.frame(S1, F1, S2, F2) # Computation of exact p-values and their supports results_ex <- mcnemar_test_pv(df) raw_pvalues <- results_ex$get_pvalues() pCDFlist <- results_ex$get_pvalue_supports() # Computation of chisquare p-values and their supports results_cs <- mcnemar_test_pv(df, exact = FALSE) raw_pvalues <- results_cs$get_pvalues() pCDFlist <- results_cs$get_pvalue_supports()# Constructing S1 <- c(4, 2, 2, 14, 6, 9, 4, 0, 1) S2 <- c(0, 0, 1, 3, 2, 1, 2, 2, 2) N1 <- rep(148, 9) N2 <- rep(132, 9) F1 <- N1 - S1 F2 <- N2 - S2 df <- data.frame(S1, F1, S2, F2) # Computation of exact p-values and their supports results_ex <- mcnemar_test_pv(df) raw_pvalues <- results_ex$get_pvalues() pCDFlist <- results_ex$get_pvalue_supports() # Computation of chisquare p-values and their supports results_cs <- mcnemar_test_pv(df, exact = FALSE) raw_pvalues <- results_cs$get_pvalues() pCDFlist <- results_cs$get_pvalue_supports()
poisson_test_pv() performs an exact or approximate Poisson test about the
rate parameter of a Poisson distribution. In contrast to
stats::poisson.test(), it is vectorised, only calculates p-values and
offers a normal approximation of their computation. Furthermore, it is
capable of returning the discrete p-value supports, i.e. all observable
p-values under a null hypothesis. Multiple tests can be evaluated
simultaneously. In two-sided tests, several procedures of obtaining the
respective p-values are implemented.
Note: Please use poisson_test_pv()! The older poisson.test.pv() is
deprecated in order to migrate to snake case. It will be removed in a future
version.
poisson_test_pv( x, lambda = 1, alternative = "two.sided", ts_method = "minlike", exact = TRUE, correct = TRUE, simple_output = FALSE ) poisson.test.pv( x, lambda = 1, alternative = "two.sided", ts.method = "minlike", exact = TRUE, correct = TRUE, simple.output = FALSE )poisson_test_pv( x, lambda = 1, alternative = "two.sided", ts_method = "minlike", exact = TRUE, correct = TRUE, simple_output = FALSE ) poisson.test.pv( x, lambda = 1, alternative = "two.sided", ts.method = "minlike", exact = TRUE, correct = TRUE, simple.output = FALSE )
x |
integer vector giving the number of events. |
lambda |
non-negative numerical vector of hypothesised rate(s). |
alternative |
character vector that indicates the alternative hypotheses; each value must be one of |
ts_method, ts.method
|
single character string that indicates the two-sided p-value computation method (if any value in |
exact |
logical value that indicates whether p-values are to be calculated by exact computation ( |
correct |
logical value that indicates if a continuity correction is to be applied ( |
simple_output, simple.output
|
logical value that indicates whether an R6 class object, including the tests' parameters and support sets, i.e. all observable p-values under each null hypothesis, is to be returned (see below). |
The parameters x, lambda and alternative are vectorised. They are
replicated automatically to have the same lengths. This allows multiple null
hypotheses to be tested simultaneously.
Since the Poisson distribution itself has an infinite support, so do the p-values of exact Poisson tests. Thus supports only contain p-values that are not rounded off to 0.
For exact computation, various procedures of determining two-sided p-values are implemented.
"minlike"The standard approach in stats::fisher.test() and
stats::binom.test(). The probabilities of the
likelihoods that are equal or less than the observed one
are summed up. In Hirji (2006), it is referred to as the
Probability-based approach.
"blaker"The minima of the observations' lower and upper tail probabilities are combined with the opposite tail not greater than these minima. More details can be found in Blaker (2000) or Hirji (2006), where it is referred to as the Combined Tails method.
"absdist"The probabilities of the absolute distances from the expected value that are greater than or equal to the observed one are summed up. In Hirji (2006), it is referred to as the Distance from Center approach.
"central"The smaller values of the observations' simply doubles the minimum of lower and upper tail probabilities. In Hirji (2006), it is referred to as the Twice the Smaller Tail method.
For non-exact (i.e. continuous approximation) approaches, ts_method is
ignored, since all its methods would yield the same p-values. More
specifically, they all converge to the doubling approach as in
ts_mthod = "central".
If simple.output = TRUE, a vector of computed p-values is returned.
Otherwise, the output is a DiscreteTestResults R6 class object, which
also includes the p-value supports and testing parameters. These have to be
accessed by public methods, e.g. $get_pvalues().
Blaker, H. (2000) Confidence curves and improved exact confidence intervals for discrete distributions. Canadian Journal of Statistics, 28(4), pp. 783-798. doi:10.2307/3315916
Hirji, K. F. (2006). Exact analysis of discrete data. New York: Chapman and Hall/CRC. pp. 55-83. doi:10.1201/9781420036190
stats::poisson.test(), binom_test_pv()
# Constructing k <- c(4, 2, 2, 14, 6, 9, 4, 0, 1) lambda <- c(3, 2, 1) # Computation of exact two-sided p-values ("blaker") and their supports results_ex <- poisson_test_pv(k, lambda, ts_method = "blaker") raw_pvalues <- results_ex$get_pvalues() pCDFlist <- results_ex$get_pvalue_supports() # Computation of normal-approximated one-sided p-values ("less") and their supports results_ap <- poisson_test_pv(k, lambda, "less", exact = FALSE) raw_pvalues <- results_ap$get_pvalues() pCDFlist <- results_ap$get_pvalue_supports()# Constructing k <- c(4, 2, 2, 14, 6, 9, 4, 0, 1) lambda <- c(3, 2, 1) # Computation of exact two-sided p-values ("blaker") and their supports results_ex <- poisson_test_pv(k, lambda, ts_method = "blaker") raw_pvalues <- results_ex$get_pvalues() pCDFlist <- results_ex$get_pvalue_supports() # Computation of normal-approximated one-sided p-values ("less") and their supports results_ap <- poisson_test_pv(k, lambda, "less", exact = FALSE) raw_pvalues <- results_ap$get_pvalues() pCDFlist <- results_ap$get_pvalue_supports()
summary method for class DiscreteTestResults.
## S3 method for class 'DiscreteTestResults' summary(object, ...)## S3 method for class 'DiscreteTestResults' summary(object, ...)
object |
object of class |
... |
further arguments passed to or from other methods. |
A summary.DiscreteTestResults R6 class
object.
# binomial tests obj <- binom.test.pv(0:5, 5, 0.5) # print summary summary(obj) # extract summary table smry <- summary(obj) smry$get_summary_table()# binomial tests obj <- binom.test.pv(0:5, 5, 0.5) # print summary summary(obj) # extract summary table smry <- summary(obj) smry$get_summary_table()