confint.lmCoDa {CoDaImpact}

Confidence Intervals for CoDa Models

Description

Dargel and Thomas-Agnan (2024) show to compute variances and confidence intervals for parameters of CoDa models in log-ratio spaces.

Of particular interest are the clr parameters since they can be directly interpreted as differences from an average elasticity.

Another option is interpret the difference in clr parameters as these coincide with the difference in elasticities.

Usage

## S3 method for class 'lmCoDa'
confint(object, parm, level = 0.95, y_ref = NULL, obs = NULL, ...)

Arguments

object

class "lmCoDa"

parm

a character, indicating the name of one explanatory variable

level

a numeric, indicating the confidence level required

y_ref

an optional argument that indicates the reference component of the response variable using its name or its position.
This argument is only used in the Y-compositional model. If it is supplied confidence intervals of difference are used instead of the direct intervals of the parameters.

obs

an optional integer that indicates one observation when this argument is supplied the function return the observation dependent elasticity

...

passed on to confit()

Details

Since CoDa models are often multivariate this function only allows to specify one explanatory variable at a time. The output is also more complex than the usual one for "lm" classes, because we have to indicate the component of Y and X. With confint.lm() it is still possible to compute the usual the confidence intervals.

Value

data.frame

Author(s)

Lukas Dargel

References

• Dargel, Lukas and Christine Thomas-Agnan, “Pairwise share ratio interpretations of compositional regression models”, Computational Statistics & Data Analysis 195 (2024), p. 107945

Examples

## ==== Y-compositional model ====
res <- lmCoDa(
  ilr(cbind(left, right, extreme_right)) ~
  ilr(cbind(Age_1839, Age_4064)) +
  ilr(cbind(Educ_BeforeHighschool, Educ_Highschool, Educ_Higher)) +
  unemp_rate,
  data = head(election, 20))

## ---- CI for scalar X
# CI for clr parameters
confint(res, "unemp_rate")
# CI for difference in clr parameters (coincides with difference in the semi elasticity)
confint(res, "unemp_rate", y_ref = 1)

## ---- CI for compositional X
# CI for clr parameters
confint(res, "cbind(Age_1839, Age_4064)")

# CI for difference in clr parameters (coincides with difference in the elasticity)
confint(res, "cbind(Age_1839, Age_4064)", y_ref = 1)