funs.class.spec | R Documentation |

Two functions: `class.spec.ct`

and `class.spec`

.

Both functions are objects of class `"classhtest"`

but with different arguments (see the parameter list below).
Each one performs class specific segregation tests for the rows if `type="base"`

and
columns if `type="NN"`

for *k ≥ 2* classes.
That is,
each one performs hypothesis tests of deviations of
entries in each row (column) of NNCT from the expected values under RL or CSR for each row (column)
if `type="base"`

(`"NN"`

).
Recall that row labels of the NNCT are base class labels and
column labels in the NNCT are NN class labels.
The test for each row (column) *i* is based on the chi-squared approximation of the corresponding quadratic form
and are due to \insertCitedixon:NNCTEco2002;textualnnspat
(\insertCiteceyhan:stat-neer-class2009;textualnnspat).

The argument `covN`

must be covariance of row-wise (column-wise) vectorization of NNCT if `type="base"`

(`type="NN"`

).

Each function yields the test statistic, *p*-value and `df`

for each base class *i*, description of the
alternative with the corresponding null values (i.e. expected values) for the row (column) *i*, estimates for the entries in
row (column) *i* for *i=1,…,k* if `type="base"`

(`type="NN"`

).
The functions also provide names of the test statistics, the method and the data set used.

The null hypothesis for each row (column) is that the corresponding *N_{ij}* entries in row (column) *i* are
equal to their expected values under RL or CSR.

See also (\insertCitedixon:NNCTEco2002,ceyhan:stat-neer-class2009;textualnnspat) and the references therein.

class.spec.ct(ct, covN, type = "base") class.spec(dat, lab, type = "base", ...)

`ct` |
A nearest neighbor contingency table, used in |

`covN` |
The |

`type` |
The type of the class-specific tests with default= |

`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

`type` |
Type of the class-specific test, which is |

`statistic` |
The |

`stat.names` |
Name of the test statistics |

`p.value` |
The |

`df` |
Degrees of freedom for the chi-squared test, which is |

`estimate` |
Estimates of the parameters, NNCT, i.e., the matrix of the
observed |

`null.value` |
The |

`null.name` |
Name of the null values |

`method` |
Description of the hypothesis test |

`ct.name` |
Name of the contingency table, |

`data.name` |
Name of the data set, |

`base.class.spec.ct`

, `base.class.spec`

, `NN.class.spec.ct`

and `NN.class.spec`

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) W<-Wmat(ipd) Qv<-Qvec(W)$q Rv<-Rval(W) varN<-var.nnct(ct,Qv,Rv) covN<-cov.nnct(ct,varN,Qv,Rv) #default is byrow class.spec(Y,cls) class.spec(Y,cls,type="NN") class.spec.ct(ct,covN) class.spec.ct(ct,covN,type="NN") class.spec(Y,cls,method="max") #cls as a factor na<-floor(n/2); nb<-n-na fcls<-rep(c("a","b"),c(na,nb)) ct<-nnct(ipd,fcls) class.spec(Y,fcls) class.spec(Y,fcls,type="NN") class.spec.ct(ct,covN) class.spec.ct(ct,covN,type="NN") ############# n<-40 Y<-matrix(runif(3*n),ncol=3) ipd<-ipd.mat(Y) cls<-sample(1:4,n,replace = TRUE) #or try cls<-rep(1:2,c(10,10)) ct<-nnct(ipd,cls) W<-Wmat(ipd) Qv<-Qvec(W)$q Rv<-Rval(W) varN<-var.nnct(ct,Qv,Rv) covN<-cov.nnct(ct,varN,Qv,Rv) class.spec(Y,cls) class.spec(Y,cls,type="NN") class.spec.ct(ct,covN) class.spec.ct(ct,covN,type="NN")

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