Bernardo Rangel Tura wrote:
On Fri, 2009-03-20 at 18:29 +0000, Helena Mouriño wrote:
Dear all,
Im having an awkward problem in R. When I write the command
Fisher.test(school.data,workspace=2e+07), where school.data is the matrix
corresponding to the data set,
I get the error message:
FEXACT error 7.
LDSTP is too small for this problem.
Try increasing the size of the workspace.
Increasing the workspace:
Fisher.test(school.data,workspace=1e+10),
I get a different message, but it still doesnt work:
NAs in foreign function call (arg 10)
In addition: Warning message:
In fisher.test(dados, workspace = 1e+10, alternative = "two.sided") :
NAs introduced by coercion
Hi Helena,
In this case you can try 3 solutions:
1- chisq.test(school.data), but pay attention if expected value of any
cell is < 5
Note that this requirement was never really checked by Pearson (as
pointed out by Cochran) and is overly cautious. Generally the
Pearson-Cochran chi-square test works well much more frequently than
previously thought, and usually better than Fisher's exact test. See
the reference below. -Frank
@Article{cam07chi,
author = {Campbell, Ian},
title = {Chi-squared and {Fisher-Irwin} tests of
two-by-two tab
les with small sample recommendations},
journal = Stat in Med,
year = 2007,
volume = 26,
pages = {3661-3675},
annote = {2x2 table;chi-squared test;Fisher-Irwin
test;exact tests;small sample recommendations;latest edition of
Armitage's book recommends that continuity adjustments never be used for
contingency table chi-square tests;E. Pearson modification of Pearson
chi-square test, differing from the original by a factor of
(N-1)/N;Cochran noted that the number 5 in "expected frequency less than
5" was arbitrary;findings of published studies may be summarized as
follows, for comparative trials:``1. Yate's chi-squared test has type I
error rates less than the nominal, often less than half the nominal; 2.
The Fisher-Irwin test has type I error rates less than the nominal; 3. K
Pearson's version of the chi-squared test has type I error rates closer
to the nominal than Yate's chi-squared test and the Fisher-Irwin test,
but in some situations gives type I errors appreciably larger than the
nominal value; 4. The 'N-1' chi-squared test, behaves like K. Pearson's
'N' version, but the tendency for higher than nominal values is reduced;
5. The two-sided Fisher-Irwin test using Irwin's rule is less
conservative than the method doubling the one-sided probability; 6. The
mid-P Fisher-Irwin test by doubling the one-sided probability performs
better than standard versions of the Fisher-Irwin test, and the mid-P
method by Irwin's rule performs better still in having actual type I
errors closer to nominal levels."; strong support for the 'N-1' test
provided expected frequencies exceed 1;flaw in Fisher test which was
based on Fisher's premise that marginal totals carry no useful
information;demonstration of their useful information in very small
sample sizes;Yate's continuity adjustment of N/2 is a large over
correction and is inappropriate;counter arguments exist to the use of
randomization tests in randomized trials;calculations of worst
cases;overall recommendation: use the 'N-1' chi-square test when all
expected frequencies are at least 1, otherwise use the Fisher-Irwin test
using Irwin's rule for two-sided tests, taking tables from either tail
as likely, or less, as that observed; see letter to the editor by
Antonio Andres and author's reply in 27:1791-1796; 2008.}
}
2- Fisher.test(school.data,workspace=2e+07,hybrid=TRUE) from Help
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.
3- Use "large tables" approach from Sir David Cox:
Law, G. R. and Cox, D. R. and Machonochie, N. E. S. and Simpson, J. and
Roman, E. and Carpenter, L. M. (2001) Large tables. Biostatistics
2(2):pp. 163-171.
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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