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svyglm()
for consistency. Weighted
designs with weights provided to the weight
argument of
cj()
, amce()
, etc. were unaffected. Analysts
using the package for unweighted designs should update results to obtain
appropriately conservative standard errors of AMCEs. Tests of this
behavior have now been added (#15).mm_diffs()
returned 90% rather than
95% confidence intervals. (#37, h/t @hoellers)mm()
wherein alpha
was
ignored.mm_diffs()
wherein h0
was
ignored.by
variables in
cj()
that contain empty character string levels. (#32, h/t
Farsan Ghassim)amce_by_reference()
now returns a
REFERENCE
column rather than a BY
column, for
clarity. (#35, h/t Farsan Ghassim)mm_diffs()
to allow
>= 2 levels for by
factor. (#33, h/t Farsan
Ghassim)cj()
now explicitly requires that all variables in
by
are factor and ensures factor level ordering is carried
forward from input data to output data.cj_df()
now ignores drop
(for behavior
consistent with “tibble” rather than base “data.frame” tables)data
as documented, with the exception of mm()
and mm_diffs()
.mm_diffs()
wherein a by
variable that shared levels with a feature variable led to incorrect
estimates being returned. (#22, h/t Michael Jankowski)plot()
methods. (#18, h/t Mia Costa)plot()
method output. (#14)cj_tidy()
work with tibbles. (#11, Umberto
Mignozzetti)cj()
now imposes class “cj_df” on data
to
preserve attributes during subsetting.cj_table()
, which can be useful in
communicating the set of features and levels used in the design as a
data frame (e.g,. using
knitr::kable(cj_table(data, ~ feat1 + feat2))
).props()
-> cj_props()
and freqs()
to cj_freqs()
for API
consistency.cj_df()
, which provides a modified data
frame class (“cj_df”) that preserves variable “label” attributes when
subsetting.immigration
and taxes
gain a “cj_df” class.cj_tidy()
now returns objects of class
c("cj_df", "data.frame")
.cj_tidy()
to tidy a “wide”
respondent-length conjoint dataset into a “long”
respondenttaskalternative-length dataset. An example dataset,
wide_conjoint
, is provided for examples and testing.amce_diffs()
, limiting it to
work with unconstrained designs. (#6)taxes
, from Ballard-Rosa
et al. (2016).hainmueller
dataset to
immigration
.cj_anova()
currently does not work with weighted data due
to a bug in survey::anova.svyglm()
.amce_diffs()
now respect
clustering. (#9)mm_diffs()
.mm_diffs()
for calculating
differences in marginal means.mm()
gains an h0
argument to specify a
null hypothesis values so that z statistics and p-values are
meaningful.family
argument).amce_diffs()
and amce_anova()
functions to assess differences in AMCEs by a grouping variable.plot()
methods.
(#8)svydesign()
objects that
was generating incorrect variance estimates.props()
function to calculate display proportions
for features or combinations of features (e.g., for examining
constrained designs). Updated documentation accordingly. (#2)amce_by_reference()
function to examine
sensitivity of results to choice of reference category. (#2)level
argument to alpha
to
avoid ambiguity with “levels” in the “feature level” sense used in the
package (as opposed to the intended alpha or significance level).level_order
argument to freqs()
,
mm()
, and amce()
that specifies whether
feature levels are ordered ascending in the output or descending. This
is mostly only useful for plotting to specify whether the levels within
each feature should be ordered with lower factor levels at the top
(“ascending”) or at the bottom (“descending”) of the plot. (#1)cj()
gains a by
argument, which enables
subgroup analyses, for example to investigate profile spillover effects
or analyses by subsets of respondents. (#3)freq()
to freqs()
and
prefixed class names of return values from all functions with
cj_*
.feature_order
argument to all functions to
regulate display order.header_fmt
in
plot()
.freqs()
.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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