funs.cell.spec.ss | R Documentation |

Two functions: `cell.spec.ss.ct`

and `cell.spec.ss`

.

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
cell counts (i.e., entries) in the NNCT for *k ≥ 2* classes.
Each test is appropriate (i.e. have the appropriate asymptotic sampling distribution)
when that data is obtained by sparse sampling.

Each cell-specific segregation test is based on the normal approximation of the entries in the NNCT and are due to \insertCitepielou:1961;textualnnspat.

Each function yields a contingency table of the test statistics, *p*-values for the corresponding
alternative, expected values, lower and upper confidence levels, sample estimates (i.e. observed values)
and null value(s) (i.e. expected values) for the *N_{ij}* values for *i,j=1,2,…,k* 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})=n_i c_j /n* where *n_i* is the sum of row *i* (i.e. size of class *i*)
*c_j* is the sum of column *j* in the *k \times k* NNCT for *k ≥ 2*.
In the output, the test statistic, *p*-value and the lower and upper confidence limits are valid only
for (properly) sparsely sampled data.

See also (\insertCitepielou:1961,ceyhan:eest-2010;textualnnspat) and the references therein.

cell.spec.ss.ct( ct, alternative = c("two.sided", "less", "greater"), conf.level = 0.95 ) cell.spec.ss( dat, lab, alternative = c("two.sided", "less", "greater"), conf.level = 0.95, ... )

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

`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 for the entries |

`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 NNCT entries. |

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

`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 values of the NNCT entries, E(Nij) for i,j=1,2,...,k. |

`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

`cell.spec.ct`

and `cell.spec`

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) cell.spec.ss(Y,cls) cell.spec.ss.ct(ct) cell.spec.ss.ct(ct,alt="g") cell.spec.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) cell.spec.ss(Y,fcls) cell.spec.ss.ct(ct) ############# 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) cell.spec.ss(Y,cls,alt="l") cell.spec.ss.ct(ct) cell.spec.ss.ct(ct,alt="l")

nnspat documentation built on Aug. 30, 2022, 9:06 a.m.

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