View source: R/VariationTable.R
VariationTable  R Documentation 
This function allows to evaluate how a change in an explanatory variables impacts the response variable in a CoDa regression model. The changes are calculated based from the approximate formal presented in Dargel and ThomasAgnan (2024). Changes in the response variables are provided as data.frame and the underlying changes in the explanatory variable are given as attributes.
VariationTable(
object,
Xvar,
Xdir,
obs = 1,
inc_size = 0.1,
inc_rate = NULL,
Ytotal = 1,
normalize_Xdir = TRUE
)
object 
an object of class "lmCoDa" 
Xvar 
a character indicating the name of the explanatory variable that changes 
Xdir 
either character or numeric, to indicate the direction in which Xvar should change

obs 
a numeric indicating the observation used for the scenario 
inc_size 
a numeric indicating the distance between each point in the scenario of X 
inc_rate 
a numeric that can be used as a parameterization of the step size 
Ytotal 
a numeric indicating the total of Y 
normalize_Xdir 
a logical, if 
data.frame
Lukas Dargel
Rodrigue Nasr
Dargel, Lukas and Christine ThomasAgnan, “Pairwise share ratio interpretations of compositional regression models”, Computational Statistics & Data Analysis 195 (2024), p. 107945
# XYcompositional model
res < lmCoDa(
ilr(cbind(left, right, extreme_right)) ~
ilr(cbind(Educ_BeforeHighschool, Educ_Highschool, Educ_Higher)),
data = head(election, 20))
# Focus on changes in the education composition
educ_comp < "cbind(Educ_BeforeHighschool, Educ_Highschool, Educ_Higher)"
# ... changes towards a summit towards a summit (higher share of people with lower education)
VariationTable(res, educ_comp, Xdir = "Educ_BeforeHighschool")
# ... same changes using a compositional vector as direction
VariationTable(res, educ_comp, Xdir = c(.5,.25,.25))
# ... changes in a more general direction and for a different observation
VariationTable(res, educ_comp, Xdir = c(.35,.45,.10), obs = 2)
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