Description Usage Arguments Details Value See Also Examples
Estimate AMCEs for a conjoint analysis and return a tidy data frame of results
1 2 3 4 5 6 7 8 9 10 11 12 13 |
data |
A data frame containing variables specified in |
formula |
A formula specifying an AMCE model to be estimated. All variables should be factors; all levels across features should be unique. Two-way constraints can be specified with an asterisk (*) between RHS features. The specific constrained level pairs within these features are then detected automatically. Higher-order constraints are not allowed. |
id |
An RHS formula specifying a variable holding respondent identifiers, to be used for clustering standard errors. By default, data are unclustered. |
weights |
An (optional) RHS formula specifying a variable holding survey weights. |
feature_order |
An (optional) character vector specifying the names of feature (RHS) variables in the order they should be encoded in the resulting data frame. |
feature_labels |
A named list of “fancy” feature labels to be used in output. By default, the function looks for a “label” attribute on each variable in |
level_order |
A character string specifying levels (within each feature) should be ordered increasing or decreasing in the final output. This is mostly only consequential for plotting via |
alpha |
A numeric value indicating the significance level at which to calculate confidence intervals for the MMs (by default 0.95, meaning 95-percent CIs are returned). |
... |
For |
variable |
An RHS formula containing a single factor variable from |
amce
provides estimates of AMCEs (or rather, average marginal effects for each feature level). Two-way constraints can be specified with an asterisk (*) between features. The specific constrained level pairs within these features are then detected automatically. The function can also be used for calculating average component interaction effects when combined with interaction
, and for balance testing by specifying a covariate rather outcome on the left-hand side of formula
. See examples.
amce_by_reference
provides a tool for quick sensitivity analysis. AMCEs are defined relative to an arbitrary reference category (i.e., feature level). This function will loop over all feature levels (for a specified feature) to show how interpretation will be affected by choice of reference category. The resulting data frame will be a stacked result from amce
, containing an additional REFERENCE
column specifying which level of variable
was used as the reference category. In unconstrained conjoint designs, only AMCEs for variable
will vary by reference category; in constrained designs, AMCEs for any factor constrained by variable
may also vary.
Users may desire to specify a family
argument via ...
, which should be a “family” object such as gaussian
. Sensible alternatives are binomial
(for binary outcomes) and quasibinomial (for weighted survey data). See family
for details. In such cases, effects are always reported on the link (not outcome) scale.
A data frame of class “cj_amce”
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 | data("taxes")
# estimating AMCEs
amce(taxes, chose_plan ~ taxrate1 + taxrate2 + taxrate3 +
taxrate4 + taxrate5 + taxrate6 + taxrev, id = ~ ID)
data("immigration")
# estimating AMCEs with constraints
amce(immigration, ChosenImmigrant ~ Gender + ReasonForApplication * CountryOfOrigin,
id = ~CaseID)
# estimating average component interaction effects (AMCEs of feature combinations)
immigration$language_entry <- interaction(immigration$LanguageSkills,
immigration$PriorEntry, sep = "_")
amce(immigration,ChosenImmigrant ~ language_entry, id = ~CaseID)
# balance testing example
plot(amce(immigration[!is.na(immigration$ethnocentrism),],
ethnocentrism ~ Gender + Education + LanguageSkills, id = ~ CaseID))
# reference category sensitivity
x <- amce_by_reference(immigration, ChosenImmigrant ~ LanguageSkills + Education,
variable = ~ LanguageSkills, id = ~ CaseID)
# plot
plot(x)
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