fisher.test              package:stats              R Documentation

_F_i_s_h_e_r'_s _E_x_a_c_t _T_e_s_t _f_o_r _C_o_u_n_t _D_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     Performs Fisher's exact test for testing the null of independence
     of rows and columns in a contingency table with fixed marginals.

_U_s_a_g_e:

     fisher.test(x, y = NULL, workspace = 200000, hybrid = FALSE,
                 control = list(), or = 1, alternative = "two.sided",
                 conf.int = TRUE, conf.level = 0.95,
                 simulate.p.value = FALSE, B = 2000)

_A_r_g_u_m_e_n_t_s:

       x: either a two-dimensional contingency table in matrix form, or
          a factor object.

       y: a factor object; ignored if 'x' is a matrix.

workspace: an integer specifying the size of the workspace used in the
          network algorithm.  In units of 4 bytes.  Only used for
          non-simulated p-values larger than 2 by 2 tables.

  hybrid: a logical. Only used for larger than 2 by 2 tables, in which
          cases it indicated whether the exact probabilities (default)
          or a hybrid approximation thereof should be computed. See
          Details.

 control: a list with named components for low level algorithm control.
           At present the only one used is '"mult"', a positive integer
          >= 2 with default 30 used only for larger than 2 by 2 tables.
           This says how many times as much space should be allocated
          to paths as to keys: see file 'fexact.c' in the sources of
          this package.

      or: the hypothesized odds ratio.  Only used in the 2 by 2 case.

alternative: indicates the alternative hypothesis and must be one of
          '"two.sided"', '"greater"' or '"less"'. You can specify just
          the initial letter.  Only used in the 2 by 2 case.

conf.int: logical indicating if a confidence interval should be
          computed (and returned).

conf.level: confidence level for the returned confidence interval. 
          Only used in the 2 by 2 case if 'conf.int = TRUE'.

simulate.p.value: a logical indicating whether to compute p-values by
          Monte Carlo simulation, in larger than 2 by 2 tables.

       B: an integer specifying the number of replicates used in the
          Monte Carlo test.

_D_e_t_a_i_l_s:

     If 'x' is a matrix, it is taken as a two-dimensional contingency
     table, and hence its entries should be nonnegative integers.
     Otherwise, both 'x' and 'y' must be vectors of the same length. 
     Incomplete cases are removed, the vectors are coerced into factor
     objects, and the contingency table is computed from these.

     For 2 by 2 cases, p-values are obtained directly using the
     (central or non-central) hypergeometric distribution. Otherwise,
     computations are based on a C version of the FORTRAN subroutine
     FEXACT which implements the network developed by Mehta and Patel
     (1986) and improved by Clarkson, Fan and Joe (1993). The FORTRAN
     code can be obtained from <URL: http://www.netlib.org/toms/643>. 
     Note this fails (with an error message) when the entries of the
     table are too large.  (It transposes the table if necessary so it
     has no more rows than columns.  One constraint is that the product
     of the row marginals be less than 2^31 - 1.)

     For 2 by 2 tables, the null of conditional independence is
     equivalent to the hypothesis that the odds ratio equals one. 
     'Exact' inference can be based on observing that in general, given
     all marginal totals fixed, the first element of the contingency
     table has a non-central hypergeometric distribution with
     non-centrality parameter given by the odds ratio (Fisher, 1935). 
     The alternative for a one-sided test is based on the odds ratio,
     so 'alternative = "greater"' is a test of the odds ratio being
     bigger than 'or'.

     Two-sided tests are based on the probabilities of the tables, and
     take as 'more extreme' all tables with probabilities less than or
     equal to that of the observed table, the p-value being the sum of
     such probabilities.

     For larger than  2 by 2 tables and 'hybrid = TRUE', asymptotic
     chi-squared probabilities are only used if the 'Cochran
     conditions' are satisfied, that is if no cell has count zero, and
     more than 80% of the cells have counts at least 5.

     Simulation is done conditional on the row and column marginals,
     and works only if the marginals are strictly positive.  (A C
     translation of the algorithm of Patefield (1981) is used.)

_V_a_l_u_e:

     A list with class '"htest"' containing the following components: 

 p.value: the p-value of the test.

conf.int: a confidence interval for the odds ratio. Only present in the
          2 by 2 case if argument 'conf.int = TRUE'.

estimate: an estimate of the odds ratio.  Note that the _conditional_
          Maximum Likelihood Estimate (MLE) rather than the
          unconditional MLE (the sample odds ratio) is used. Only
          present in the 2 by 2 case.

null.value: the odds ratio under the null, 'or'. Only present in the 2
          by 2 case.

alternative: a character string describing the alternative hypothesis.

  method: the character string '"Fisher's Exact Test for Count Data"'.

data.name: a character string giving the names of the data.

_R_e_f_e_r_e_n_c_e_s:

     Agresti, A. (1990) _Categorical data analysis_. New York: Wiley.
     Pages 59-66.

     Fisher, R. A. (1935) The logic of inductive inference. _Journal of
     the Royal Statistical Society Series A_ *98*, 39-54.

     Fisher, R. A. (1962) Confidence limits for a cross-product ratio.
     _Australian Journal of Statistics_ *4*, 41.

     Fisher, R. A. (1970) _Statistical Methods for Research Workers._ 
     Oliver & Boyd.

     Mehta, C. R. and Patel, N. R. (1986) Algorithm 643. FEXACT: A
     Fortran subroutine for Fisher's exact test on unordered r*c
     contingency tables. _ACM Transactions on Mathematical Software_,
     *12*, 154-161.

     Clarkson, D. B., Fan, Y. and Joe, H. (1993) A Remark on Algorithm
     643: FEXACT: An Algorithm for Performing Fisher's Exact Test in r
     x c Contingency Tables. _ACM Transactions on Mathematical
     Software_, *19*, 484-488.

     Patefield, W. M. (1981) Algorithm AS159.  An efficient method of
     generating r x c tables with given row and column totals. _Applied
     Statistics_ *30*, 91-97.

_S_e_e _A_l_s_o:

     'chisq.test'

_E_x_a_m_p_l_e_s:

     ## Agresti (1990), p. 61f, Fisher's Tea Drinker
     ## A British woman claimed to be able to distinguish whether milk or
     ##  tea was added to the cup first.  To test, she was given 8 cups of
     ##  tea, in four of which milk was added first.  The null hypothesis
     ##  is that there is no association between the true order of pouring
     ##  and the woman's guess, the alternative that there is a positive
     ##  association (that the odds ratio is greater than 1).
     TeaTasting <-
     matrix(c(3, 1, 1, 3),
            nrow = 2,
            dimnames = list(Guess = c("Milk", "Tea"),
                            Truth = c("Milk", "Tea")))
     fisher.test(TeaTasting, alternative = "greater")
     ## => p=0.2429, association could not be established

     ## Fisher (1962, 1970), Criminal convictions of like-sex twins
     Convictions <-
     matrix(c(2, 10, 15, 3),
            nrow = 2,
            dimnames =
            list(c("Dizygotic", "Monozygotic"),
                 c("Convicted", "Not convicted")))
     Convictions
     fisher.test(Convictions, alternative = "less")
     fisher.test(Convictions, conf.int = FALSE)
     fisher.test(Convictions, conf.level = 0.95)$conf.int
     fisher.test(Convictions, conf.level = 0.99)$conf.int

     ## A r x c table  Agresti (2002, p. 57) Job Satisfaction
     Job <- matrix(c(1,2,1,0, 3,3,6,1, 10,10,14,9, 6,7,12,11), 4, 4,
     dimnames = list(income=c("< 15k", "15-25k", "25-40k", "> 40k"),
                     satisfaction=c("VeryD", "LittleD", "ModerateS", "VeryS")))
     fisher.test(Job)
     fisher.test(Job, simulate.p.value=TRUE, B=1e5)

