exact.nnct | R Documentation |
An object of class "htest"
performing exact version of Pearson's chi-square test on nearest neighbor contingency
tables (NNCTs) for the RL or CSR independence for 2 classes.
Pearson's χ^2 test is based on the test statistic
\mathcal X^2=∑_{j=1}^2∑_{i=1}^2 (N_{ij}-μ_{ij})^2/μ_{ij},
which has χ^2_1 distribution in the limit provided
that the contingency table is constructed under the independence null hypothesis.
The exact version of Pearson's test uses the exact distribution of \mathcal X^2 rather than large sample
χ^2 approximation.
That is, for the one-sided alternative, we calculate
the p-values as in the function exact.pval1s
;
and for the two-sided alternative, we calculate
the p-values as in the function exact.pval2s
with double argument determining
the type of the correction.
This test would be equivalent to Fisher's exact test fisher.test
if the odds ratio=1
(which can not be specified in the current version), and the odds ratio for the RL or CSR independence null
hypothesis is θ_0=(n_1-1)(n_2-1)/(n_1 n_2) which is used in the function and
the p-value and confidence interval computations are are adapted from fisher.test
.
See \insertCiteceyhan:SWJ-spat-sym2014;textualnnspat for more details.
exact.nnct( ct, alternative = "two.sided", conf.level = 0.95, pval.type = "inc", double = FALSE )
ct |
A 2 \times 2 NNCT |
alternative |
Type of the alternative hypothesis in the test, one of |
conf.level |
Level of the upper and lower confidence limits, default is |
pval.type |
The type of the p-value correction for the exact test on the NNCT, default= |
double |
A logical argument (default is |
A list
with the elements
statistic |
The test statistic, it is |
p.value |
The p-value for the hypothesis test for the corresponding alternative |
conf.int |
Confidence interval for the odds ratio in the 2 \times 2 NNCT
at the given confidence level |
estimate |
Estimate, i.e., the observed odds ratio the 2 \times 2 NNCT. |
null.value |
Hypothesized null value for the odds ratio in the 2 \times 2 NNCT, which is θ_0=(n_1-1)(n_2-1)/(n_1 n_2) for this function. |
alternative |
Type of the alternative hypothesis in the test, one of |
method |
Description of the hypothesis test |
data.name |
Name of the contingency table, |
Elvan Ceyhan
fisher.test
, exact.pval1s
, and exact.pval2s
n<-20 Y<-matrix(runif(3*n),ncol=3) ipd<-ipd.mat(Y) cls<-sample(1:2,n,replace = TRUE) #or try cls<-rep(1:2,c(10,10)) ct<-nnct(ipd,cls) ct exact.nnct(ct) fisher.test(ct) exact.nnct(ct,alt="g") fisher.test(ct,alt="g") exact.nnct(ct,alt="l",pval.type = "mid") ############# ct<-matrix(sample(10:20,9),ncol=3) fisher.test(ct) #here exact.nnct(ct) gives error message, since number of classes > 2
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