funsZdir.nnct.ss | R Documentation |
Two functions: Zdir.nnct.ss.ct
and Zdir.nnct.ss
.
Both functions are objects of class "htest"
but with different arguments (see the parameter list below).
Each one performs hypothesis tests of independence in the 2 \times 2 NNCT which implies Z_P=0
or equivalently N_{11}/n_1=N_{21}/n_2.
Z_P=(N_{11}/n_1-N_{21}/n_2)√{n_1 n_2 n/(C_1 C_2)}
where N_{ij} is the cell count in entry i,j, n_i is the sum of row i (i.e. size of class i),
c_j is the sum of column j in the 2 \times 2 NNCT;
N_{11}/n_1 and N_{21}/n_2 are also referred to as the phat estimates in row-wise binomial framework
for 2 \times 2 NNCT (see \insertCiteceyhan:jnps-NNCT-2010;textualnnspat).
That is, each performs directional (i.e. one-sided) tests based on the 2 \times 2 NNCT and is appropriate (i.e. have the appropriate asymptotic sampling distribution) when that data is obtained by sparse sampling. (See \insertCiteceyhan:jnps-NNCT-2010;textualnnspat for more detail).
Each test is based on the normal approximation of Z_P which is the directional Z-tests for the chi-squared tests of independence for the contingency tables \insertCitebickel:1977nnspat.
Each function yields the test statistic, p-value for the corresponding alternative, the confidence interval, sample estimate (i.e. observed value) and null (i.e., expected) value for the difference in the phat values (which is 0 for this test) in an NNCT, and method and name of the data set used.
The null hypothesis is that E[Z_P] = 0 or equivalently N_{11}/n_1 = N_{21}/n_2.
Zdir.nnct.ss.ct( ct, alternative = c("two.sided", "less", "greater"), conf.level = 0.95 ) Zdir.nnct.ss( dat, lab, alternative = c("two.sided", "less", "greater"), conf.level = 0.95, ... )
ct |
The NNCT, used in |
alternative |
Type of the alternative hypothesis in the test, one of |
conf.level |
Level of the confidence limits, default is |
dat |
The data set in one or higher dimensions, each row corresponds to a data point,
used in |
lab |
The |
... |
are for further arguments, such as |
A list
with the elements
statistic |
The Z test statistic for the directional (i.e. one-sided) test of segregation based on the NNCT |
p.value |
The p-value for the hypothesis test for the corresponding alternative |
conf.int |
Confidence interval for the difference in phat values in the NNCT
at the given confidence level |
estimate |
Estimate of the parameter, i.e., the observed difference in phat values in the NNCT. |
null.value |
Hypothesized null value for the difference in phat values in the NNCT which is 0 for this function. |
alternative |
Type of the alternative hypothesis in the test, one of |
method |
Description of the hypothesis test |
ct.name |
Name of the contingency table, |
data.name |
Name of the data set, |
Elvan Ceyhan
Zdir.nnct.ct
, Zdir.nnct
, Pseg.ss.ct
and Pseg.ss
n<-20 #or try sample(1:20,1) 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 Zdir.nnct.ss(Y,cls) Zdir.nnct.ss.ct(ct) Zdir.nnct.ss(Y,cls,alt="g") Zdir.nnct.ss(Y,cls,method="max") #cls as a factor na<-floor(n/2); nb<-n-na fcls<-rep(c("a","b"),c(na,nb)) ct<-nnct(ipd,fcls) Zdir.nnct.ss(Y,fcls) Zdir.nnct.ss.ct(ct) ############# ct<-matrix(1:4,ncol=2) Zdir.nnct.ss.ct(ct) #gives an error message if ct<-matrix(1:9,ncol=3)
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