funsZnnsym.dx | R Documentation |
Two functions: Znnsym.dx.ct
and Znnsym.dx
.
Both functions are objects of class "cellhtest"
but with different arguments (see the parameter list below).
Each one performs hypothesis tests of equality of the expected values of the off-diagonal
cell counts (i.e., entries) for each pair i,j of classes under RL or CSR in the NNCT for k ≥ 2 classes.
That is, each performs Dixon's NN symmetry test which is appropriate
(i.e. have the appropriate asymptotic sampling distribution)
for completely mapped data.
(See \insertCitedixon:1994,ceyhan:SWJ-spat-sym2014;textualnnspat for more detail).
Each symmetry test is based on the normal approximation of the difference of the off-diagonal entries in the NNCT and are due to \insertCitedixon:1994;textualnnspat.
Each function yields a contingency table of the test statistics, p-values for the corresponding alternative, expected values (i.e. null value(s)), lower and upper confidence levels and sample estimates (i.e. observed values) for the N_{ij}-N_{ji} values for i \ne j (all in the upper-triangular form except for the null value, which is 0 for all pairs) and also names of the test statistics, estimates, null values and the method and the data set used.
The null hypothesis is that all E(N_{ij})=E(N_{ji}) for i \ne j in the k \times k NNCT (i.e., symmetry in the mixed NN structure) for k ≥ 2. In the output, the test statistic, p-value and the lower and upper confidence limits are valid for completely mapped data.
See also (\insertCitedixon:1994,ceyhan:SWJ-spat-sym2014;textualnnspat) and the references therein.
Znnsym.dx.ct( ct, varS, alternative = c("two.sided", "less", "greater"), conf.level = 0.95 ) Znnsym.dx( dat, lab, alternative = c("two.sided", "less", "greater"), conf.level = 0.95, ... )
ct |
A nearest neighbor contingency table, used in |
varS |
The variance vector of differences of off-diagonal cell counts in NNCT, |
alternative |
Type of the alternative hypothesis in the test, one of |
conf.level |
Level of the upper and lower 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 |
stat.names |
Name of the test statistics |
p.value |
The |
LCL,UCL |
Matrix of Lower and Upper Confidence Levels (in the upper-triangular form) for the N_{ij}-N_{ji}
values for i \ne j at the given confidence level |
conf.int |
The confidence interval for the estimates, it is |
cnf.lvl |
Level of the upper and lower confidence limits (i.e., conf.level) of the differences of the off-diagonal entries. |
estimate |
Estimates of the parameters, i.e., matrix of the difference of the off-diagonal entries (in the upper-triangular form) of the k \times k NNCT, N_{ij}-N_{ji} for i \ne j. |
est.name,est.name2 |
Names of the estimates, former is a shorter description of the estimates than the latter. |
null.value |
Hypothesized null value for the expected difference between the off-diagonal entries, E(N_{ij})-E(N_{ji}) for i \ne j in the k \times k NNCT, which is 0 for this function. |
null.name |
Name of the null values |
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
Znnsym2cl.dx.ct
, Znnsym2cl.dx
, Znnsym.ss.ct
,
Znnsym.ss
, Xsq.nnsym.dx.ct
and Xsq.nnsym.dx
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 W<-Wmat(ipd) Qv<-Qvec(W)$q Rv<-Rval(W) varN<-var.nnct(ct,Qv,Rv) covN<-cov.nnct(ct,varN,Qv,Rv) #default is byrow varS<-var.nnsym(covN) Znnsym.dx(Y,cls) Znnsym.dx.ct(ct,varS) Znnsym.dx(Y,cls,method="max") Znnsym.dx(Y,cls,alt="g") Znnsym.dx.ct(ct,varS,alt="g") #cls as a factor na<-floor(n/2); nb<-n-na fcls<-rep(c("a","b"),c(na,nb)) Znnsym.dx(Y,fcls) ############# n<-40 Y<-matrix(runif(3*n),ncol=3) ipd<-ipd.mat(Y) cls<-sample(1:4,n,replace = TRUE) #or try cls<-rep(1:2,c(10,10)) ct<-nnct(ipd,cls) W<-Wmat(ipd) Qv<-Qvec(W)$q Rv<-Rval(W) varN<-var.nnct(ct,Qv,Rv) covN<-cov.nnct(ct,varN,Qv,Rv) #default is byrow varS<-var.nnsym(covN) Znnsym.dx(Y,cls) Znnsym.dx.ct(ct,varS)
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