vignettes/Introduction.Rmd
Introduction.Rmdmm()
freqs() and cross-tabulation of feature restrictions using props()
+cj_table() and cj_freqs() and cross-tabulation of feature restrictions using cj_props()
cj_tidy()
For example, to check display frequencies of conjoint features (to ensure equal - or unequal - display frequency):
# plotting of display frequencies
-plot(freqs(immigration, f1, id = ~CaseID))
As can be clear in the above, constraints were imposed in the original study on the allowed combinations of Job and Education and also on combinations of CountryOfOrigin and ReasonForApplication. The props() function provides tidy proportions tables to quickly
subset(props(immigration, ~Job + Education, id = ~CaseID), Proportion == 0)As can be clear in the above, constraints were imposed in the original study on the allowed combinations of Job and Education and also on combinations of CountryOfOrigin and ReasonForApplication. The cj_props() function provides tidy proportions tables to quickly
subset(cj_props(immigration, ~Job + Education, id = ~CaseID), Proportion == 0) Job Education Proportion
5 Financial Analyst No Formal 0
8 Computer Programmer No Formal 0
@@ -286,7 +286,7 @@
41 Computer Programmer High School 0
43 Research Scientist High School 0
44 Doctor High School 0
subset(props(immigration, ~CountryOfOrigin + ReasonForApplication, id = ~CaseID), Proportion == 0)subset(cj_props(immigration, ~CountryOfOrigin + ReasonForApplication, id = ~CaseID), Proportion == 0)CountryOfOrigin ReasonForApplication Proportion 21 India Escape Persecution 0 22 Germany Escape Persecution 0 diff --git a/docs/articles/Introduction_files/figure-html/amce_diagnostic-1.png b/docs/articles/Introduction_files/figure-html/amce_diagnostic-1.png index 24023c1..088c30d 100644 Binary files a/docs/articles/Introduction_files/figure-html/amce_diagnostic-1.png and b/docs/articles/Introduction_files/figure-html/amce_diagnostic-1.png differ diff --git a/docs/articles/Introduction_files/figure-html/amce_vs_mm-1.png b/docs/articles/Introduction_files/figure-html/amce_vs_mm-1.png index 15b8eee..0924dcf 100644 Binary files a/docs/articles/Introduction_files/figure-html/amce_vs_mm-1.png and b/docs/articles/Introduction_files/figure-html/amce_vs_mm-1.png differ diff --git a/docs/articles/Introduction_files/figure-html/balance_testing-1.png b/docs/articles/Introduction_files/figure-html/balance_testing-1.png index 7344059..2b26b3c 100644 Binary files a/docs/articles/Introduction_files/figure-html/balance_testing-1.png and b/docs/articles/Introduction_files/figure-html/balance_testing-1.png differ diff --git a/docs/articles/Introduction_files/figure-html/conditional_amces-1.png b/docs/articles/Introduction_files/figure-html/conditional_amces-1.png index cba990f..68af422 100644 Binary files a/docs/articles/Introduction_files/figure-html/conditional_amces-1.png and b/docs/articles/Introduction_files/figure-html/conditional_amces-1.png differ diff --git a/docs/articles/Introduction_files/figure-html/conditional_differences-1.png b/docs/articles/Introduction_files/figure-html/conditional_differences-1.png index a675517..2818153 100644 Binary files a/docs/articles/Introduction_files/figure-html/conditional_differences-1.png and b/docs/articles/Introduction_files/figure-html/conditional_differences-1.png differ diff --git a/docs/articles/Introduction_files/figure-html/conditional_mms-1.png b/docs/articles/Introduction_files/figure-html/conditional_mms-1.png index 3e8798c..0b36a0f 100644 Binary files a/docs/articles/Introduction_files/figure-html/conditional_mms-1.png and b/docs/articles/Introduction_files/figure-html/conditional_mms-1.png differ diff --git a/docs/articles/Introduction_files/figure-html/leftright-1.png b/docs/articles/Introduction_files/figure-html/leftright-1.png index 3245277..127a99c 100644 Binary files a/docs/articles/Introduction_files/figure-html/leftright-1.png and b/docs/articles/Introduction_files/figure-html/leftright-1.png differ diff --git a/docs/articles/Introduction_files/figure-html/mm_by-1.png b/docs/articles/Introduction_files/figure-html/mm_by-1.png index 6c22cd1..fd9b14a 100644 Binary files a/docs/articles/Introduction_files/figure-html/mm_by-1.png and b/docs/articles/Introduction_files/figure-html/mm_by-1.png differ diff --git a/docs/articles/Introduction_files/figure-html/mmplot-1.png b/docs/articles/Introduction_files/figure-html/mmplot-1.png index 41793f0..f5a4aae 100644 Binary files a/docs/articles/Introduction_files/figure-html/mmplot-1.png and b/docs/articles/Introduction_files/figure-html/mmplot-1.png differ diff --git a/docs/articles/Introduction_files/figure-html/plot_amce-1.png b/docs/articles/Introduction_files/figure-html/plot_amce-1.png index fdb39f3..6998c41 100644 Binary files a/docs/articles/Introduction_files/figure-html/plot_amce-1.png and b/docs/articles/Introduction_files/figure-html/plot_amce-1.png differ diff --git a/docs/articles/Introduction_files/figure-html/plot_freqs-1.png b/docs/articles/Introduction_files/figure-html/plot_freqs-1.png index b7bf237..0a120ac 100644 Binary files a/docs/articles/Introduction_files/figure-html/plot_freqs-1.png and b/docs/articles/Introduction_files/figure-html/plot_freqs-1.png differ diff --git a/docs/articles/index.html b/docs/articles/index.html index 88394ca..6e2b853 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -58,7 +58,7 @@ diff --git a/docs/authors.html b/docs/authors.html index 28f34f0..cc1c3ec 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -58,7 +58,7 @@ @@ -114,13 +114,13 @@Citation
Leeper TJ (2018). cregg: Simple Conjoint Analyses and Visualization. -R package version 0.2.1. +R package version 0.2.3.
@Manual{, title = {cregg: Simple Conjoint Analyses and Visualization}, author = {Thomas J. Leeper}, year = {2018}, - note = {R package version 0.2.1}, + note = {R package version 0.2.3}, }@@ -149,6 +149,7 @@Authors
diff --git a/docs/index.html b/docs/index.html index b02e449..cdd30fe 100644 --- a/docs/index.html +++ b/docs/index.html @@ -5,12 +5,12 @@ -Simple Conjoint Analyses and Visualization • cregg +Simple Conjoint Tidying, Analysis, and Visualization • cregg - - + + + + + + + + + +Create a “cj_df” data frame — cj_df • cregg + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +++ + + + + + diff --git a/docs/reference/cj_freqs.html b/docs/reference/cj_freqs.html new file mode 100644 index 0000000..e269c24 --- /dev/null +++ b/docs/reference/cj_freqs.html @@ -0,0 +1,299 @@ + + + + + + + + ++ + + + + +++ + ++ + ++ ++ ++ +A simple data frame extension that preserves attributes during subsetting operations.
+ +cj_df(x) + +# S3 method for data.frame +cj_df(x) + +# S3 method for cj_df +[(x, i, j, drop = FALSE)+ +Arguments
++
+ ++ + +x ++ A data frame
+ +i ++ See
[.data.frame+ +j ++ See
[.data.frame+ +drop ++ See
[.data.frameValue
+ +An data frame with additional “cj_df” class, which has subsetting methods that preserve variables attributes.
+ + +Examples
++x1 <- data.frame(a = 1:3, b = 4:6) +attr(x1$a, "label") <- "Variable A" + +# cj_df() returns a data frame +inherits(x1, "data.frame")#> [1] TRUEclass(x1)#> [1] "data.frame"+# attributes dropped for data frames +attr(x1[1:2,]$a, "label")#> NULL+# attributes preserved with a cj_df +attr(cj_df(x1)[1:2,]$a, "label")#> [1] "Variable A"+Conjoint feature frequencies — cj_freqs • cregg + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +++ + + + + + diff --git a/docs/reference/cj_tidy.html b/docs/reference/cj_tidy.html index b58c66b..d47e606 100644 --- a/docs/reference/cj_tidy.html +++ b/docs/reference/cj_tidy.html @@ -61,7 +61,7 @@+ + + + + +++ + ++ + ++ ++ ++ +Tabulate and visualize conjoint features, and their display frequencies and proportions
+ +cj_freqs(data, formula, id = NULL, weights = NULL, feature_order = NULL, + feature_labels = NULL, level_order = c("ascending", "descending"), ...) + +cj_props(data, formula, id, weights = NULL, margin = NULL, ...) + +cj_table(data, formula, feature_order = NULL, feature_labels = NULL, + level_order = c("ascending", "descending"), include_reference = FALSE, + ...)+ +Arguments
++
+ ++ + +data ++ A data frame containing variables specified in
formula. All RHS variables should be factors.+ +formula ++ An RHS formula specifying conjoint features to tabulate. All RHS variables should be factors.
+ +id ++ An RHS formula specifying a variable holding respondent identifiers, to be used for clustering standard errors.
+ +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
formulaand uses that for pretty printing. This argument overrides those attributes or otherwise provides fancy labels for this purpose. This should be a list with names equal to variables on the righthand side offormulaand character string values; arguments passed here override variable attributes.+ +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
plot.cj_mm, etc.+ +… ++ Ignored.
+ +margin ++ A numeric value passed to
prop.table. IfNULLoverall proportions are calculated.+ +include_reference ++ A logical indicating whether to include a “reference” column that indicates whether a feature level is the reference category for that feature. Default is
FALSE.Details
+ +These functions provide related but slightly different functionality.
+cj_tablesimply creates a data frame of features and their levels, which is useful for printing.cj_propsprovides tidy proportion tables to examine cross-feature restrictions in conjoint designs that are not equally randomized. This enables, for example, tabulation and visualization of complete restrictions (where combinations of two or more features are not permitted), as well as calculation of AMCEs for constrained designs appropriately weighted by the display proportions for particular combinations of features.+ +
cj_freqsprovides marginal display frequencies, which are a descriptive check on the presentation of individual conjoint features (for example, to ensure equal or intentionally unequal appearance of levels). This is mostly useful for plotting functionality provided inplot.cj_freqs, which provides barcharts for the frequency with which each level of each feature was presented.See also
+ ++ + +
plot.cj_mmExamples
++data(immigration) +# identify all levels +cj_table(immigration, ~ Gender + Education + LanguageSkills)#> feature level +#> 1 Gender Female +#> 2 Gender Male +#> 3 Educational Attainment No Formal +#> 4 Educational Attainment 4th Grade +#> 5 Educational Attainment 8th Grade +#> 6 Educational Attainment High School +#> 7 Educational Attainment Two-Year College +#> 8 Educational Attainment College Degree +#> 9 Educational Attainment Graduate Degree +#> 10 Language Skills Fluent English +#> 11 Language Skills Broken English +#> 12 Language Skills Tried English but Unable +#> 13 Language Skills Used Interpretercj_table(immigration, ~ Gender + Education + LanguageSkills, include_ref = TRUE)#> feature level reference +#> 1 Gender Female TRUE +#> 2 Gender Male FALSE +#> 3 Educational Attainment No Formal TRUE +#> 4 Educational Attainment 4th Grade FALSE +#> 5 Educational Attainment 8th Grade FALSE +#> 6 Educational Attainment High School FALSE +#> 7 Educational Attainment Two-Year College FALSE +#> 8 Educational Attainment College Degree FALSE +#> 9 Educational Attainment Graduate Degree FALSE +#> 10 Language Skills Fluent English TRUE +#> 11 Language Skills Broken English FALSE +#> 12 Language Skills Tried English but Unable FALSE +#> 13 Language Skills Used Interpreter FALSE+# display frequencies +cj_freqs(immigration, ~ Gender + Education + LanguageSkills, id = ~ CaseID)#> statistic feature level estimate +#> 1 frequencies Gender Female 7130 +#> 2 frequencies Gender Male 6830 +#> 3 frequencies Educational Attainment No Formal 1964 +#> 4 frequencies Educational Attainment 4th Grade 2018 +#> 5 frequencies Educational Attainment 8th Grade 1998 +#> 6 frequencies Educational Attainment High School 1994 +#> 7 frequencies Educational Attainment Two-Year College 2005 +#> 8 frequencies Educational Attainment College Degree 1963 +#> 9 frequencies Educational Attainment Graduate Degree 2018 +#> 10 frequencies Language Skills Fluent English 3576 +#> 11 frequencies Language Skills Broken English 3440 +#> 12 frequencies Language Skills Tried English but Unable 3501 +#> 13 frequencies Language Skills Used Interpreter 3443+# restrictions +## check display proportions +cj_props(immigration, ~ Job, id = ~ CaseID)#> Job Proportion +#> 1 Janitor 0.11697708 +#> 2 Waiter 0.12335244 +#> 3 Child Care Provider 0.12578797 +#> 4 Gardener 0.11833811 +#> 5 Financial Analyst 0.03488539 +#> 6 Construction Worker 0.11876791 +#> 7 Teacher 0.12098854 +#> 8 Computer Programmer 0.03882521 +#> 9 Nurse 0.12399713 +#> 10 Research Scientist 0.03868195 +#> 11 Doctor 0.03939828## check which combinations were not allowed +subset(cj_props(immigration, ~ Job + Education, id = ~ CaseID), Proportion == 0)#> Job Education Proportion +#> 5 Financial Analyst No Formal 0 +#> 8 Computer Programmer No Formal 0 +#> 10 Research Scientist No Formal 0 +#> 11 Doctor No Formal 0 +#> 16 Financial Analyst 4th Grade 0 +#> 19 Computer Programmer 4th Grade 0 +#> 21 Research Scientist 4th Grade 0 +#> 22 Doctor 4th Grade 0 +#> 27 Financial Analyst 8th Grade 0 +#> 30 Computer Programmer 8th Grade 0 +#> 32 Research Scientist 8th Grade 0 +#> 33 Doctor 8th Grade 0 +#> 38 Financial Analyst High School 0 +#> 41 Computer Programmer High School 0 +#> 43 Research Scientist High School 0 +#> 44 Doctor High School 0+Arg
Value
A data frame with rows equal to the number of respondents times the number of tasks times the number of profiles (fixed at 2), to be fed into any other function in the package. The columns will include the names of elements in
+profile_variablesandtask_variables, andid, along with an indicatortask(from 1 to the number of tasks),pair(an indicator for each task pair from 1 to the number of pairs),profile(a fator indicator for profile, either “A” or “B”), and any other respondent-varying covariates not specified. As such, respondent-varying variables do not need to be specified tocj_tidyat all.The returned data frame carries an additional S3 class (“cj_df”) with methods that preserve column attributes. See
cj_df.Details
@@ -160,7 +161,7 @@Details
See also
-+
cj
cj,cj_dfExamples
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estimate -+ A character string specifying an estimate type. Current options are average marginal component effects (or AMCEs, “amce”, estimated via
amce), display frequencies (“frequncies”, estimated viafreqs), marginal means (or AMMs, “mm”, estimated viamm), differences in MMs (“mm_differences”, viamm_diffs), or differences in AMCEs (“amce_differences”, viaamce_diffs). Additional options may be made available in the future. Non-ambiguous abbreviations are allowed.A character string specifying an estimate type. Current options are average marginal component effects (or AMCEs, “amce”, estimated via
amce), display frequencies (“frequncies”, estimated viacj_freqs), marginal means (or AMMs, “mm”, estimated viamm), differences in MMs (“mm_differences”, viamm_diffs), or differences in AMCEs (“amce_differences”, viaamce_diffs). Additional options may be made available in the future. Non-ambiguous abbreviations are allowed.feature_order @@ -169,19 +169,19 @@Arg
… -+ Additional arguments to
amce,freqs,mm,mm_diffs, oramce_diffs.Additional arguments to
amce,cj_freqs,mm,mm_diffs, oramce_diffs.Details
-The main function
+cjis a convenience function wrapper around the underlying estimation functions that provide for average marginal component effects (AMCEs), by default, via theamcefunction, marginal means (MMs) via themmfunction, and display frequencies viafreqsandprops. Additional estimands may be supported in the future through their own functions and through thecjinterface. Plotting is provided via ggplot2 for all types of estimates.The main function
cjis a convenience function wrapper around the underlying estimation functions that provide for average marginal component effects (AMCEs), by default, via theamcefunction, marginal means (MMs) via themmfunction, and display frequencies viacj_freqsandcj_props. Additional estimands may be supported in the future through their own functions and through thecjinterface. Plotting is provided via ggplot2 for all types of estimates.The only additional functionality provided by
cjover the underlying functions is thebyargument, which will perform operations on subsets ofdata, returning a single data frame. This can be useful, for example, for evaluating profile spillover effects and subgroup results, or in any situation where one might be inclined to use aforloop orlapply, callingcjrepeatedly on subgroups.Note: Some features of cregg (namely, the
amce_diffs) function, orestimate = "amce_diff"here) only work with full factorial conjoint experiments. Designs involving two-way constraints between features are supported simply by expressing interactions between constrained terms informula(again, except foramce_diffs). Higher-order constraints may be supported in the future.See also
-@@ -174,7 +174,7 @@Functions:
amce,mm,freqs,mm_diffs,plot.cj_amce,cj_tidy+diff --git a/docs/reference/diffs.html b/docs/reference/diffs.html index c5110a3..a2ffc33 100644 --- a/docs/reference/diffs.html +++ b/docs/reference/diffs.html @@ -61,7 +61,7 @@Functions:
amce,mm,cj_freqs,mm_diffs,plot.cj_amce,cj_tidyData:immigration,taxesArg
@@ -192,7 +192,7 @@ … -+ Details
See also
- +Examples
diff --git a/docs/reference/immigration.html b/docs/reference/immigration.html index c750fe9..3c3bfc7 100644 --- a/docs/reference/immigration.html +++ b/docs/reference/immigration.html @@ -61,7 +61,7 @@ @@ -127,7 +127,7 @@Immigration Conjoint Experiment Dataset from Hainmueller et. al. (2014)
Format
-A data frame with 13960 observations on the following 16 variables.
+
A data frame (with additional “cj_df” class) with 13960 observations on the following 16 variables.
- CaseID
a numeric vector indicating the respondent to which the particular profile corresponds
- contest_no
a numeric vector indicating the number of the task to which the profile corresponds
- Education
- @@ -156,14 +156,14 @@
a factor with levels “No formal”, “4th grade”, “8th grade”, “High school”, “Two-year college”, “college Degree”, “Graduate degree”
Note
See also
-+
cjtaxesExamples
data("immigration") # view constraints between features -subset(props(immigration, ~ Job + Education, id = ~ CaseID), Proportion == 0)#> Job Education Proportion +subset(cj_props(immigration, ~ Job + Education, id = ~ CaseID), Proportion == 0)#> Job Education Proportion #> 5 Financial Analyst No Formal 0 #> 8 Computer Programmer No Formal 0 #> 10 Research Scientist No Formal 0 @@ -179,8 +179,8 @@Examp #> 38 Financial Analyst High School 0 #> 41 Computer Programmer High School 0 #> 43 Research Scientist High School 0 -#> 44 Doctor High School 0
subset(props(immigration, ~ ReasonForApplication + CountryOfOrigin, - id = ~ CaseID), Proportion == 0)#> ReasonForApplication CountryOfOrigin Proportion +#> 44 Doctor High School 0subset(cj_props(immigration, ~ ReasonForApplication + CountryOfOrigin, + id = ~ CaseID), Proportion == 0)#> ReasonForApplication CountryOfOrigin Proportion #> 3 Escape Persecution India 0 #> 6 Escape Persecution Germany 0 #> 9 Escape Persecution France 0 diff --git a/docs/reference/index.html b/docs/reference/index.html index e3175ab..c65ad2d 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -58,7 +58,7 @@@@ -135,6 +135,18 @@cj_df()
`[`(<cj_df>)+ ++ Create a “cj_df” data frame
+ + + + ++ Conjoint feature frequencies
+ @@ -153,12 +165,6 @@ freqs()
props()- -- Conjoint feature frequencies
- diff --git a/docs/reference/mm.html b/docs/reference/mm.html index bf6e3a3..77fc4c7 100644 --- a/docs/reference/mm.html +++ b/docs/reference/mm.html @@ -61,7 +61,7 @@ diff --git a/docs/reference/plot-1.png b/docs/reference/plot-1.png index 864a9a0..62930c2 100644 Binary files a/docs/reference/plot-1.png and b/docs/reference/plot-1.png differ diff --git a/docs/reference/plot-2.png b/docs/reference/plot-2.png index 5ce0528..588bb8e 100644 Binary files a/docs/reference/plot-2.png and b/docs/reference/plot-2.png differ diff --git a/docs/reference/plot-3.png b/docs/reference/plot-3.png index 9a99c4a..0b4bfde 100644 Binary files a/docs/reference/plot-3.png and b/docs/reference/plot-3.png differ diff --git a/docs/reference/plot-4.png b/docs/reference/plot-4.png index 4d6f8ba..28c1fa7 100644 Binary files a/docs/reference/plot-4.png and b/docs/reference/plot-4.png differ diff --git a/docs/reference/plot-5.png b/docs/reference/plot-5.png index 449ef63..06d280b 100644 Binary files a/docs/reference/plot-5.png and b/docs/reference/plot-5.png differ diff --git a/docs/reference/plot.html b/docs/reference/plot.html index 31d69b0..e9f20dc 100644 --- a/docs/reference/plot.html +++ b/docs/reference/plot.html @@ -61,7 +61,7 @@ diff --git a/docs/reference/taxes.html b/docs/reference/taxes.html index dba8e10..88e282d 100644 --- a/docs/reference/taxes.html +++ b/docs/reference/taxes.html @@ -61,7 +61,7 @@ @@ -127,7 +127,7 @@ Tax Preference Conjoint Experiment Dataset from Ballard-Rosa et al. (2016)Format -
A data frame with 32000 observations on the following 13 variables. Each row corresponds to a single profile presented to a respondent.
+
A data frame (with additional “cj_df” class) with 32000 observations on the following 13 variables. Each row corresponds to a single profile presented to a respondent.
- chose_plan
A numeric vector denoting whether the immigrant profile was selected (=1) or not (=0).
- taxrate1
An experimental factor with levels “<10k: 0%”, dQuote<10k: 5%, dQuote<10k: 15%, dQuote<10k: 25%.
- taxrate2
- @@ -149,7 +149,7 @@
An experimental factor with levels “10-35k: 5%”, dQuote10-35k: 15%, dQuote10-35k: 25%, dQuote10-35k: 35%.
SourceSee also
-+
cjimmigration
cjimmigrationcj_dfExamples
diff --git a/docs/reference/wide_conjoint.html b/docs/reference/wide_conjoint.html index 9a7eb3f..9fb5a32 100644 --- a/docs/reference/wide_conjoint.html +++ b/docs/reference/wide_conjoint.html @@ -61,7 +61,7 @@