Description Usage Arguments Details Value Author(s) References See Also Examples

tabCoord computes a system of orthonormal coordinates of a compositional table. Computation of either pivot coordinates or a coordinate system based on the given SBP is possible.

tabCoordWrapper: For each compositional table in the sample `tabCoordWrapper`

computes a system of orthonormal coordinates and provide a simple descriptive analysis.
Computation of either pivot coordinates or a coordinate system based on the given SBP is possible.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 |

`x` |
a data frame containing variables representing row and column factors of the respective compositional table and variable with the values of the composition. |

`row.factor` |
name of the variable representing the row factor. Needs to be stated with the quotation marks. |

`col.factor` |
name of the variable representing the column factor. Needs to be stated with the quotation marks. |

`value` |
name of the variable representing the values of the composition. Needs to be stated with the quotation marks. |

`SBPr` |
an |

`SBPc` |
an |

`pivot` |
logical, default is FALSE. If TRUE, or one of the SBPs is not defined, its pivot version is used. |

`print.res` |
logical, default is FALSE. If TRUE, the output is displayed in the Console. |

`X` |
a data frame containing variables representing row and column factors of the respective compositional tables, variable with the values of the composition and variable distinguishing the observations. |

`obs.ID` |
name of the variable distinguishing the observations. Needs to be stated with the quotation marks. |

`test` |
logical, default is |

`n.boot` |
number of bootstrap samples. |

tabCoord

This transformation moves the IJ-part compositional tables from the simplex into a (IJ-1)-dimensional real space isometrically with respect to its two-factorial nature. The coordinate system is formed by two types of coordinates - balances and log odds-ratios.

tabCoordWrapper: Each of n IJ-part compositional tables from the sample is with respect to its two-factorial nature isometrically transformed from the simplex into a (IJ-1)-dimensional real space. Sample mean values and standard deviations are computed and using bootstrap an estimate of 95 % confidence interval is given.

`Coordinates` |
an array of orthonormal coordinates. |

`Grap.rep` |
graphical representation of the coordinates. Parts denoted by |

`Ind.coord` |
an array of row and column balances. Coordinate representation of the independent part of the table. |

`Int.coord` |
an array of OR coordinates. Coordinate representation of the interactive part of the table. |

`Contrast.matrix` |
contrast matrix. |

`Log.ratios` |
an array of pure log-ratios between groups of parts without the normalizing constant. |

`Coda.table` |
table form of the given composition. |

`Bootstrap` |
array of sample means, standard deviations and bootstrap confidence intervals. |

`Tables` |
Table form of the given compositions. |

Kamila Facevicova

Facevicova, K., Hron, K., Todorov, V. and M. Templ (2018) General approach to coordinate representation of compositional tables. Scandinavian Journal of Statistics, 45(4), 879–899.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | ```
###################
### Coordinate representation of a CoDa Table
# example from Fa\v cevicov\'a (2018):
data(manu_abs)
manu_USA <- manu_abs[which(manu_abs$country=='USA'),]
manu_USA$output <- factor(manu_USA$output, levels=c('LAB', 'SUR', 'INP'))
# pivot coordinates
tabCoord(manu_USA, row.factor = 'output', col.factor = 'isic', value='value')
# SBPs defined in paper
r <- rbind(c(-1,-1,1), c(-1,1,0))
c <- rbind(c(-1,-1,-1,-1,1), c(-1,-1,-1,1,0), c(-1,-1,1,0,0), c(-1,1,0,0,0))
tabCoord(manu_USA, row.factor = 'output', col.factor = 'isic', value='value', SBPr=r, SBPc=c)
###################
### Analysis of a sample of CoDa Tables
# example from Fa\v cevicov\'a (2018):
data(manu_abs)
### Compositional tables approach,
### analysis of the relative structure.
### An example from Facevi\v cov\'a (2018)
manu_abs$output <- factor(manu_abs$output, levels=c('LAB', 'SUR', 'INP'))
# pivot coordinates
tabCoordWrapper(manu_abs, obs.ID='country',
row.factor = 'output', col.factor = 'isic', value='value')
# SBPs defined in paper
r <- rbind(c(-1,-1,1), c(-1,1,0))
c <- rbind(c(-1,-1,-1,-1,1), c(-1,-1,-1,1,0),
c(-1,-1,1,0,0), c(-1,1,0,0,0))
tabCoordWrapper(manu_abs, obs.ID='country',row.factor = 'output',
col.factor = 'isic', value='value', SBPr=r, SBPc=c, test=TRUE)
### Classical approach,
### generalized linear mixed effect model.
## Not run:
library(lme4)
glmer(value~output*as.factor(isic)+(1|country),data=manu_abs,family=poisson)
## End(Not run)
``` |

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