funsNNclass.spec | R Documentation |

Two functions: `NN.class.spec.ct`

and `NN.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 columns, i.e., NN categories for *k ≥ 2* classes.
That is,
each one performs hypothesis tests of deviations of
entries in each column of NNCT from the expected values under RL or CSR for each column.
Recall that column labels in the NNCT are NN class labels.
The test for each column *i* is based on the chi-squared approximation of the corresponding quadratic form
and are due to \insertCiteceyhan:stat-neer-class2009;textualnnspat.

The argument `covN`

must be covariance of column-wise vectorization of NNCT if the logical argument `byrow=FALSE`

otherwise the function converts `covN`

(which is done row-wise) to columnwise version with `covNrow2col`

function.

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 column *i*, estimates for the entries in column *i*
for *i=1,…,k*. The functions also provide names of the test statistics, the method and the data set used.

The null hypothesis for each column is that the corresponding *N_{ij}* entries in 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.

NN.class.spec.ct(ct, covN, byrow = TRUE) NN.class.spec(dat, lab, ...)

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

`covN` |
The |

`byrow` |
A logical argument (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, transpose of the NNCT, i.e., transpose of the matrix of the
observed |

`null.value` |
Transpose of the matrix of hypothesized null values for the parameters which are expected
values of 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, |

Elvan Ceyhan

`base.class.spec.ct`

, `base.class.spec`

, `class.spec.ct`

and `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)$q Rv<-Rval(W) varN<-var.nnct(ct,Qv,Rv) covNrow<-cov.nnct(ct,varN,Qv,Rv) covNcol<-covNrow2col(covNrow) NN.class.spec(Y,cls) NN.class.spec(Y,cls,method="max") NN.class.spec.ct(ct,covNrow) NN.class.spec.ct(ct,covNcol,byrow = FALSE) #cls as a factor na<-floor(n/2); nb<-n-na fcls<-rep(c("a","b"),c(na,nb)) ct<-nnct(ipd,fcls) NN.class.spec(Y,fcls) NN.class.spec.ct(ct,covNrow) ############# n<-40 Y<-matrix(runif(3*n),ncol=3) ipd<-ipd.mat(Y) cls<-sample(1:4,n,replace = TRUE) #or try cls<-rep(1:2,c(10,10)) ct<-nnct(ipd,cls) W<-Wmat(ipd) Qv<-Qvec(W)$q Rv<-Rval(W) varN<-var.nnct(ct,Qv,Rv) covNrow<-cov.nnct(ct,varN,Qv,Rv) covNcol<-covNrow2col(covNrow) NN.class.spec(Y,cls) NN.class.spec.ct(ct,covNrow) NN.class.spec.ct(ct,covNcol,byrow = FALSE)

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.