cubeCoord: Coordinate representation of a compositional cube and of a...

In robCompositions: Compositional Data Analysis

Coordinate representation of a compositional cube and of a sample of compositional cubes

Description

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

Wrapper (cubeCoordWrapper): For each compositional cube in the sample cubeCoordWrapper 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.

Usage

cubeCoord(
  x,
  row.factor = NULL,
  col.factor = NULL,
  slice.factor = NULL,
  value = NULL,
  SBPr = NULL,
  SBPc = NULL,
  SBPs = NULL,
  pivot = FALSE,
  print.res = FALSE
)

cubeCoordWrapper(
  X,
  obs.ID = NULL,
  row.factor = NULL,
  col.factor = NULL,
  slice.factor = NULL,
  value = NULL,
  SBPr = NULL,
  SBPc = NULL,
  SBPs = NULL,
  pivot = FALSE,
  test = FALSE,
  n.boot = 1000
)

Arguments

x	a data frame containing variables representing row, column and slice factors of the respective compositional cube 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.
slice.factor	name of the variable representing the slice 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 I-1\times I array defining the sequential binary partition of the values of the row factor, where I is the number of the row factor levels. The values assigned in the given step to the + group are marked by 1, values from the - group by -1 and the rest by 0. If it is not provided, the pivot version of coordinates is constructed automatically.
SBPc	an J-1\times J array defining the sequential binary partition of the values of the column factor, where J is the number of the column factor levels. The values assigned in the given step to the + group are marked by 1, values from the - group by -1 and the rest by 0. If it is not provided, the pivot version of coordinates is constructed automatically.
SBPs	an K-1\times K array defining the sequential binary partition of the values of the slice factor, where K is the number of the slice factor levels. The values assigned in the given step to the + group are marked by 1, values from the - group by -1 and the rest by 0. If it is not provided, the pivot version of coordinates is constructed automatically.
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, column and slice factors of the respective compositional cubes, 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 FALSE. If TRUE, the bootstrap analysis of coordinates is provided.
n.boot	number of bootstrap samples.

Details

cubeCoord

This transformation moves the IJK-part compositional cubes from the simplex into a (IJK-1)-dimensional real space isometrically with respect to its three-factorial nature.

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

Value

Coordinates	an array of orthonormal coordinates.
Grap.rep	graphical representation of the coordinates. Parts denoted by + form the groups in the numerator of the respective computational formula, parts - form the denominator and parts . are not involved in the given coordinate.
Row.balances	an array of row balances.
Column.balances	an array of column balances.
Slice.balances	an array of slice balances.
Row.column.OR	an array of row-column OR coordinates.
Row.slice.OR	an array of row-slice OR coordinates.
Column.slice.OR	an array of column-slice OR coordinates.
Row.col.slice.OR	an array of coordinates describing the mutual interaction between all three factors.
Contrast.matrix	contrast matrix.
Log.ratios	an array of pure log-ratios between groups of parts without the normalizing constant.
Coda.cube	cube form of the given composition.
Bootstrap	array of sample means, standard deviations and bootstrap confidence intervals.
Cubes	Cube form of the given compositions.

Author(s)

Kamila Facevicova

References

Facevicova, K., Filzmoser, P. and K. Hron (2019) Compositional Cubes: Three-factorial Compositional Data. Under review.

See Also

tabCoord tabCoordWrapper

Examples

###################
### Coordinate representation of a CoDa Cube
## Not run: 
### example from Fa\v cevicov\'a (2019)
data(employment2)
CZE <- employment2[which(employment2$Country == 'CZE'), ]

# pivot coordinates
cubeCoord(CZE, "Sex", 'Contract', "Age", 'Value')

# coordinates with given SBP

r <- t(c(1,-1))
c <- t(c(1,-1))
s <- rbind(c(1,-1,-1), c(0,1,-1))

cubeCoord(CZE, "Sex", 'Contract', "Age", 'Value', r,c,s)

## End(Not run)

###################
### Analysis of a sample of CoDa Cubes
## Not run: 
### example from Fa\v cevicov\'a (2019)
data(employment2)
### Compositional tables approach,
### analysis of the relative structure.
### An example from Facevi\v cov\'a (2019)

# pivot coordinates
cubeCoordWrapper(employment2, 'Country', 'Sex', 'Contract', 'Age', 'Value',  
test=TRUE)

# coordinates with given SBP (defined in the paper)

r <- t(c(1,-1))
c <- t(c(1,-1))
s <- rbind(c(1,-1,-1), c(0,1,-1))

res <- cubeCoordWrapper(employment2, 'Country', 'Sex', 'Contract', 
"Age", 'Value', r,c,s, test=TRUE)

### Classical approach,
### generalized linear mixed effect model.

library(lme4)
employment2$y <- round(employment2$Value*1000)
glmer(y~Sex*Age*Contract+(1|Country),data=employment2,family=poisson)

### other relations within cube (in the log-ratio form)
### e.g. ratio between women and man in the group FT, 15to24
### and ratio between age groups 15to24 and 55plus

# transformation matrix
T <- rbind(c(1,rep(0,5), -1, rep(0,5)), c(rep(c(1/4,0,-1/4), 4)))
T %*% t(res$Contrast.matrix) %*%res$Bootstrap[,1]

## End(Not run)

robCompositions documentation built on Aug. 25, 2023, 5:13 p.m.