EMT (version 1.1)

multinomial.test: Exact Multinomial Test: Goodness-of-Fit Test for Discrete Multivariate data

Description

This function runs a Goodness-of-fit test for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. A p-value can be calculated using different distance measures between observed and expected frequencies. A Monte Carlo method is provided to make the function capable of solving high-dimensional problems.

Usage

multinomial.test(observed, prob, useChisq = FALSE, MonteCarlo = FALSE, ntrial = 100000, atOnce = 1000000)

Arguments

observed

vector describing the observation: contains the observed numbers of items in each category.

prob

vector describing the model: contains the hypothetical probabilities corresponding to each category.

useChisq

if TRUE, Pearson's chisquare is used as a distance measure between observed and expected frequencies.

MonteCarlo

if TRUE, the Monte Carlo approach is used.

ntrial

number of simulated samples in the Monte Carlo approach.

atOnce

a parameter of more technical nature. Determines how much memory is used for big arrays.

Value

id

textual description of the method used.

size

sample size n, equals the sum of the components of the vector observed.

groups

number of categories k in the experiment, equals the number of components of the vector observed.

stat

textual description of the distance measure used.

allProb

vector containing the probabilities (rel. frequencies for the Monte Carlo approach) of all possible outcomes (might be huge for big n and k).

ntrial

number of trials if the Monte Carlo approach was used, NULL otherwise.

p.value

the calculated p-value rounded to four significant digits.

Details

The Exact Multinomial Test is a Goodness-of-fit test for discrete multivariate data. It is tested if a given observation is likely to have occurred under the assumption of an ab-initio model. In the experimental setup belonging to the test, n items fall into k categories with certain probabilities (sample size n with k categories). The observation, described by the vector observed, indicates how many items have been observed in each category. The model, described by the vector prob, assigns to each category the hypothetical probability that an item falls into it. Now, if the observation is unlikely to have occurred under the assumption of the model, it is advisible to regard the model as not valid. The p-value estimates how likely the observation is, given the model. In particular, low p-values suggest that the model is not valid. The default approach used by multinomial.test obtains the p-values by calculating the exact probabilities of all possible outcomes given n and k, using the multinomial probability distribution function dmultinom provided by R. Then, by default, the p-value is obtained by summing the probabilities of all outcomes which are less likely than the observed outcome (or equally likely as the observed outcome), i.e. by summing all \(p(i) <= p(observed)\) (distance measure based on probabilities). Alternatively, the p-value can be obtained by summing the probabilities of all outcomes connected with a chisquare no smaller than the chisquare connected with the actual observation (distance measure based on chisquare). The latter is triggered by setting useChisq = TRUE. Having a sample of size n in an experiment with k categories, the number of distinct possible outcomes is the binomial coefficient choose(n+k-1,k-1). This number grows rapidly with increasing parameters n and k. If the parameters grow too big, numerical calculation might fail because of time or memory limitations. In this case, usage of the Monte Carlo approach provided by multinomial.test is suggested. The Monte Carlo approach, activated by setting MonteCarlo = TRUE, simulates withdrawal of ntrial samples of size n from the hypothetical distribution specified by the vector prob. The default value for ntrial is 100000 but might be incremented for big n and k. The advantage of the Monte Carlo approach is that memory requirements and running time are essentially determined by ntrial but not by n or k. By default, the p-value is then obtained by summing the relative frequencies of occurrence of unusual outcomes, i.e. of outcomes occurring less frequently than the observed one (or equally frequent as the observed one). Alternatively, as above, Pearson's chisquare can be used as a distance measure by setting useChisq = TRUE. The parameter atOnce is of more technical nature, with a default value of \(1000000\). This value should be decremented for computers with low memory to avoid overflow, and can be incremented for large-CPU computers to speed up calculations. The parameter is only effective for Monte Carlo calculations.

References

H. Bayo Lawal (2003) Categorical data analysis with SAS and SPSS applications, Volume 1, Chapter 3 ISBN: 978-0-8058-4605-8

Read, T. R. C. and Cressie, N. A. C. (1988). Goodness-of-fit statistics for discrete multivariate data. Springer, New York.

See Also

The Multinomial Distribution: dmultinom

Examples

# NOT RUN {
## Load the EMT package:
library(EMT)


## Input data for a three-dimensional case:
observed <- c(5,2,1)   		# observed data: 5 items in category one, 2 items in category two, 1 item in category three
prob <- c(0.25, 0.5, 0.25) 	# model: hypothetical probability that an item falls into category one, two, or three


## Calculate p-value using default options:
out <- multinomial.test(observed, prob)        
# p.value = 0.0767


## Plot the probabilities for each event:
plotMultinom(out)


## Calculate p-value for the same input using Pearson's chisquare as a distance measure:
out <- multinomial.test(observed, prob, useChisq = TRUE)  
# p.value = 0.0596 ; not the same!


## Test the hypothesis that all sides of a dice pop up with the same probability (a 6-dimesional problem):
pdice = 1/6
prob <- c(pdice, pdice, pdice, pdice, pdice, pdice)	# the model, determined by the hypothetical probabilities
observed <- c(4, 5, 2, 7, 0, 1)				# the observation consisting of 19 throws ( = sample size)
out <- multinomial.test(observed, prob) 
# p.value = 0.0357 ; better get another dice, this one seems to be biased
plotMultinom(out, showmax = 10000)   


# the same problem using a Monte Carlo approach:
# we have about 40.000 outcomes and choose at least 400000 trials (probably to be increased):
out <- multinomial.test(observed, prob, MonteCarlo = TRUE, ntrial = 400000)   
# p.value = 0.0343 ; takes a few minutes on a laptop with 2 GB memory, 1.5 GHz speed
plotMultinom(out, showmax = 5000)  

# }