### Abstract

GENCAT is a computer program which implements an extremely general methodology for the analysis of multivariate categorical data. This approach essentially involves the construction of test statistics for hypotheses involving functions of the observed proportions which are directed at the relationships under investigation and the estimation of corresponding model parameters via weighted least squares computations. Any compounded function of the observed proportions which can be formulated as a sequence of the following transformations of the data vector - linear, logarithmic, exponential, or the addition of a vector of constants - can be analyzed within this general framework. This algorithm produces minimum modified chi-square statistics which are obtained by partitioning the sums of squares as in ANOVA. The input data can be either: (a) frequencies from a multidimensional contingency table; (b) a vector of functions with its estimated covariance matrix; and (c) raw data in the form of integer-valued variables associated with each subject. The input format is completely flexible for the data as well as for the matrices.

Original language | English (US) |
---|---|

Pages (from-to) | 196-231 |

Number of pages | 36 |

Journal | Computer Programs in Biomedicine |

Volume | 6 |

Issue number | 4 |

DOIs | |

State | Published - 1976 |

Externally published | Yes |

### Fingerprint

### Keywords

- Categorical data
- Computer program
- Contingency tables
- Linear models
- Minimum modified chi-square
- Multivariate analysis
- Rates and proportions
- Weighted least squares

### ASJC Scopus subject areas

- Medicine (miscellaneous)

### Cite this

*Computer Programs in Biomedicine*,

*6*(4), 196-231. https://doi.org/10.1016/0010-468X(76)90037-4

**A computer program for the generalized chi-square analysis of categorical data using weighted least squares (GENCAT).** / Landis, J. Richard; Stanish, William M.; Freeman, Jean L.; Koch, Gary G.

Research output: Contribution to journal › Article

*Computer Programs in Biomedicine*, vol. 6, no. 4, pp. 196-231. https://doi.org/10.1016/0010-468X(76)90037-4

}

TY - JOUR

T1 - A computer program for the generalized chi-square analysis of categorical data using weighted least squares (GENCAT)

AU - Landis, J. Richard

AU - Stanish, William M.

AU - Freeman, Jean L.

AU - Koch, Gary G.

PY - 1976

Y1 - 1976

N2 - GENCAT is a computer program which implements an extremely general methodology for the analysis of multivariate categorical data. This approach essentially involves the construction of test statistics for hypotheses involving functions of the observed proportions which are directed at the relationships under investigation and the estimation of corresponding model parameters via weighted least squares computations. Any compounded function of the observed proportions which can be formulated as a sequence of the following transformations of the data vector - linear, logarithmic, exponential, or the addition of a vector of constants - can be analyzed within this general framework. This algorithm produces minimum modified chi-square statistics which are obtained by partitioning the sums of squares as in ANOVA. The input data can be either: (a) frequencies from a multidimensional contingency table; (b) a vector of functions with its estimated covariance matrix; and (c) raw data in the form of integer-valued variables associated with each subject. The input format is completely flexible for the data as well as for the matrices.

AB - GENCAT is a computer program which implements an extremely general methodology for the analysis of multivariate categorical data. This approach essentially involves the construction of test statistics for hypotheses involving functions of the observed proportions which are directed at the relationships under investigation and the estimation of corresponding model parameters via weighted least squares computations. Any compounded function of the observed proportions which can be formulated as a sequence of the following transformations of the data vector - linear, logarithmic, exponential, or the addition of a vector of constants - can be analyzed within this general framework. This algorithm produces minimum modified chi-square statistics which are obtained by partitioning the sums of squares as in ANOVA. The input data can be either: (a) frequencies from a multidimensional contingency table; (b) a vector of functions with its estimated covariance matrix; and (c) raw data in the form of integer-valued variables associated with each subject. The input format is completely flexible for the data as well as for the matrices.

KW - Categorical data

KW - Computer program

KW - Contingency tables

KW - Linear models

KW - Minimum modified chi-square

KW - Multivariate analysis

KW - Rates and proportions

KW - Weighted least squares

UR - http://www.scopus.com/inward/record.url?scp=0017049426&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0017049426&partnerID=8YFLogxK

U2 - 10.1016/0010-468X(76)90037-4

DO - 10.1016/0010-468X(76)90037-4

M3 - Article

C2 - 1009762

AN - SCOPUS:0017049426

VL - 6

SP - 196

EP - 231

JO - Computer Methods and Programs in Biomedicine

JF - Computer Methods and Programs in Biomedicine

SN - 0169-2607

IS - 4

ER -