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https://github.com/naoki-egami/factorEx
R package factorEx: Design and Analysis for Factorial Experiments
https://github.com/naoki-egami/factorEx
Last synced: about 2 months ago
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R package factorEx: Design and Analysis for Factorial Experiments
- Host: GitHub
- URL: https://github.com/naoki-egami/factorEx
- Owner: naoki-egami
- Created: 2019-03-03T01:23:02.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-10-02T23:07:27.000Z (almost 2 years ago)
- Last Synced: 2024-07-28T09:33:23.153Z (about 2 months ago)
- Language: R
- Homepage:
- Size: 1.92 MB
- Stars: 8
- Watchers: 4
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
factorEx: Design and Analysis for Factorial Experiments
=======================================================
**Description:**
R package `factorEx` provides design-based and model-based estimators
for the population average marginal component effects (the pAMCE) in
factorial experiments, including conjoint analysis. The package also
implements a series of recommendations offered in de la Cuesta, Egami,
and Imai (2022, PA) and Egami and Imai (2019, JASA).
**Authors:**
- [Naoki Egami](https://naokiegami.com/)
- [Brandon de la Cuesta](https://www.brandondelacuesta.com/)
- [Kosuke Imai](https://imai.fas.harvard.edu/)
**References:**
- de la Cuesta, Egami, and Imai. (2022). [Improving the External
Validity of Conjoint Analysis: The Essential Role of Profile
Distribution.](https://naokiegami.com/paper/conjoint_profile.pdf)
*Political Analysis*, Vol.30, No.1 (January), pp. 19–45.
- Egami and Imai. (2019). [Causal Interaction in Factorial
Experiments: Application to Conjoint
Analysis.](https://naokiegami.com/paper/causal_int_JASA.pdf)
*Journal of the American Statistical Association*, Vol.114, No.526
(June), pp. 529–540.
Installation Instructions
-------------------------
`factorEx` is available on CRAN and can be installed using:
``` r
install.packages("factorEx")
```
You can also install the most recent development version using the
`devtools` package. First you have to install `devtools` using the
following code. Note that you only have to do this once:
``` r
if(!require(devtools)) install.packages("devtools")
```
Then, load `devtools` and use the function `install_github()` to install
`factorEx`:
``` r
library(devtools)
install_github("naoki-egami/factorEx", dependencies=TRUE)
```
Examples
--------
- **Design-based Confirmatory Analysis**
- Case 1: Use Marginal Distributions for Target Profile
Distribution
- Case 2: Use Combination of Marginal and Partial Joint
Distributions for Target Profile Distribution
- **Model-based Exploratory Analysis**
(1) Design-based Confirmatory Analysis
--------------------------------------
Here, we use the conjoint experiment that randomized profiles according
to the marginal population randomization design.
### Case 1: Use Marginal Distributions for Target Profile Distributions
When using marginal distributions, `target_dist` should be a list and
each element should have a factor name. Within each list, a `numeric`
vector should have the same level names as those in `data`.
``` r
## Load the package and data
library(factorEx)
data("OnoBurden")
OnoBurden_data_pr <- OnoBurden$OnoBurden_data_pr # randomization based on marginal population design
# we focus on target profile distributions based on Democratic legislators.
# See de la Cuesta, Egami, and Imai (2019+) for details.
target_dist_marginal <- OnoBurden$target_dist_marginal
target_dist_marginal
```
## $gender
## Male Female
## 0.6778243 0.3221757
##
## $age
## 36 years old 44 years old 52 years old 60 years old 68 years old 76 years old
## 0.05020921 0.13807531 0.23012552 0.22594142 0.25104603 0.10460251
##
## $family
## Single (never married) Single (divorced) Married (no child)
## 0.07729469 0.03864734 0.12560386
## Married (two children)
## 0.75845411
##
## $race
## White Hispanic Asian American Black
## 0.6725664 0.1283186 0.0000000 0.1991150
##
## $experience
## None 4 years 8 years 12 years
## 0.1966527 0.2259414 0.1548117 0.4225941
##
## $party
## Dem Rep
## 1 0
##
## $pos_security
## Cut military budget Maintain strong defense
## 0.98557692 0.01442308
We can estimate the pAMCE with `design_pAMCE` with
`target_type = "marginal"`. Use `factor_name` to specify for which
factors we estimate the pAMCE.
``` r
out_design_mar <-
design_pAMCE(formula = Y ~ gender + age + family + race + experience + party + pos_security,
factor_name = c("gender", "age", "experience"),
data = OnoBurden_data_pr,
pair_id = OnoBurden_data_pr$pair_id,
cluster_id = OnoBurden_data_pr$id,
target_dist = target_dist_marginal, target_type = "marginal")
summary(out_design_mar)
```
##
## ----------------
## Population AMCEs:
## ----------------
## target_dist factor level Estimate Std. Error p value
## target gender Female 0.027987587 0.005861738 0.000 ***
## target age 44 years old 0.019219282 0.014421828 0.183
## target age 52 years old -0.008792916 0.013765415 0.523
## target age 60 years old -0.006826945 0.013875303 0.623
## target age 68 years old 0.011247969 0.013569292 0.407
## target age 76 years old -0.052741541 0.014775629 0.000 ***
## target experience 12 years 0.041672460 0.007627281 0.000 ***
## target experience 4 years 0.046173813 0.008868432 0.000 ***
## target experience 8 years 0.040752213 0.009313376 0.000 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use `plot` to visualize the estimated pAMCEs.
``` r
plot(out_design_mar, factor_name = c("gender", "experience"))
```
### Case 2: Use Combination of Marginal and Partial Joint Distributions for Target Profile Distribution
The use of partial joint distributions is useful because it can relax
the assumption of no three-way or higher-order interactions (see de la
Cuesta, Egami, and Imai (2019+)).
When using a combination of marginal and partial joint distributions,
`target_dist` should be a list and each element should be a `numeric`
vector (if marginal) or an `array`/`table` (if partial joint). Then, use
argument `partial_joint_name` to specify which factors are marginal and
partial joints. In the following example, `c("gender", "age", "family")`
has the partial joint distributions over the three factors. `race` and
`party` are based on the marginal distributions, respectively.
`c("experience", "pos_security")` has the partial joint distributions
over the two factors. Within each list, a `numeric` vector or an
`array`/`table` should have the same level names as those in `data`.
``` r
target_dist_partial <- OnoBurden$target_dist_partial
target_dist_partial
```
## $`gender:age:family`
## , , family = Single (never married)
##
## age
## gender 36 years old 44 years old 52 years old 60 years old 68 years old
## Male 0.004184100 0.004184100 0.004184100 0.004184100 0.008368201
## Female 0.000000000 0.004184100 0.004184100 0.008368201 0.016736402
## age
## gender 76 years old
## Male 0.004184100
## Female 0.004184100
##
## , , family = Single (divorced)
##
## age
## gender 36 years old 44 years old 52 years old 60 years old 68 years old
## Male 0.004184100 0.000000000 0.004184100 0.004184100 0.004184100
## Female 0.000000000 0.004184100 0.004184100 0.004184100 0.000000000
## age
## gender 76 years old
## Male 0.000000000
## Female 0.004184100
##
## , , family = Married (no child)
##
## age
## gender 36 years old 44 years old 52 years old 60 years old 68 years old
## Male 0.008368201 0.008368201 0.025104603 0.008368201 0.029288703
## Female 0.000000000 0.000000000 0.004184100 0.008368201 0.012552301
## age
## gender 76 years old
## Male 0.000000000
## Female 0.004184100
##
## , , family = Married (two children)
##
## age
## gender 36 years old 44 years old 52 years old 60 years old 68 years old
## Male 0.025104603 0.079497908 0.117154812 0.092050209 0.092050209
## Female 0.004184100 0.020920502 0.050209205 0.062761506 0.033472803
## age
## gender 76 years old
## Male 0.041841004
## Female 0.037656904
##
##
## $race
## White Hispanic Asian American Black
## 0.6725664 0.1283186 0.0000000 0.1991150
##
## $party
## Dem Rep
## 1 0
##
## $`experience:pos_security`
## pos_security
## experience Cut military budget Maintain strong defense
## None 0.066945607 0.000000000
## 4 years 0.221757322 0.004184100
## 8 years 0.154811715 0.000000000
## 12 years 0.414225941 0.008368201
``` r
partial_joint_name <- list(c("gender", "age", "family"), "race", "party", c("experience", "pos_security"))
```
We can estimate the pAMCE with `design_pAMCE` with
`target_type = "partial_joint"` and appropriate `partial_joint_name`.
The function can use `factor_name` to specify for which factors we
estimate the pAMCE.
``` r
out_design_par <-
design_pAMCE(formula = Y ~ gender + age + family + race + experience + party + pos_security,
factor_name = c("gender", "age", "race"),
data = OnoBurden_data_pr,
pair_id = OnoBurden_data_pr$pair_id,
cluster_id = OnoBurden_data_pr$id,
target_dist = target_dist_partial, target_type = "partial_joint",
partial_joint_name = partial_joint_name)
summary(out_design_par)
```
##
## ----------------
## Population AMCEs:
## ----------------
## target_dist factor level Estimate Std. Error p value
## target gender Female 0.024756315 0.006362147 0.000 ***
## target age 44 years old 0.024750351 0.015045579 0.100
## target age 52 years old -0.006198274 0.014335803 0.665
## target age 60 years old -0.001011886 0.014397430 0.944
## target age 68 years old 0.016337413 0.014132614 0.248
## target age 76 years old -0.046107728 0.015464360 0.003 **
## target race Black -0.025770076 0.008043842 0.001 **
## target race Hispanic -0.028217748 0.009332710 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(2) Model-based Exploratory Analysis
------------------------------------
Here, we use the conjoint experiment that randomized profiles according
to the uniform distribution and incorporate the target profile
distribution in the analysis stage.
``` r
OnoBurden_data <- OnoBurden$OnoBurden_data # randomization based on uniform
# due to large sample size, focus on "congressional candidates" for this example
OnoBurden_data_cong <- OnoBurden_data[OnoBurden_data$office == "Congress", ]
out_model <-
model_pAMCE(formula = Y ~ gender + age + family + race + experience + party + pos_security,
data = OnoBurden_data_cong,
reg = TRUE,
pair_id = OnoBurden_data_cong$pair_id,
cluster_id = OnoBurden_data_cong$id,
target_dist = target_dist_marginal, target_type = "marginal")
summary(out_model, factor_name = c("gender"))
```
##
## ----------------
## Population AMCEs:
## ----------------
## target_dist factor level Estimate Std. Error p value
## target_1 gender Female 0.02485328 0.01783633 0.163
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
When `sample = TRUE`, the function also reports the AMCE based on the
in-sample profile distributions (`sample AMCE`), which is the uniform
AMCE in this example.
``` r
summary(out_model, factor_name = c("gender"), sample = TRUE)
```
##
## ----------------
## Population AMCEs:
## ----------------
## target_dist factor level Estimate Std. Error p value
## sample AMCE gender Female -0.002290771 0.008321458 0.783
## target_1 gender Female 0.024853283 0.017836332 0.163
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Use `plot` to visualize the estimated pAMCEs. When `diagnose = TRUE`, it
provides two diagnostic checks; specification tests and the check of
bootstrap distributions.
``` r
plot(out_model, factor_name = c("gender"), diagnose = TRUE)
```
In the model-based analysis, we can also decompose the difference
between the pAMCE and the uniform AMCE. Use `effect_name` to specify
which pAMCE we want to decompose. `effect_name` has two elements; the
first is a factor name and the second is a level name of interest.
``` r
decompose_pAMCE(out_model, effect_name = c("gender", "Female"))
```
## type factor estimate se low.95ci
## 1 target_1 - sample age -4.476601e-03 0.002526321 -9.542598e-03
## 2 target_1 - sample family -1.028249e-03 0.002956149 -6.693040e-03
## 3 target_1 - sample race 5.505289e-03 0.007778271 -9.474694e-03
## 4 target_1 - sample experience 6.965927e-05 0.000791340 -1.264228e-03
## 5 target_1 - sample party 1.061621e-02 0.007640463 -6.015014e-03
## 6 target_1 - sample pos_security 1.685740e-02 0.008586040 -2.671033e-05
## high.95ci
## 1 -0.0003348427
## 2 0.0058319109
## 3 0.0219185310
## 4 0.0015099003
## 5 0.0247779725
## 6 0.0315124642
Or use `plot_decompose` to visualize the decomposition.
``` r
plot_decompose(out_model, effect_name = c("gender", "Female"))
```