| Title: | Conditional Expectation Function Estimation with K-Conditional-Means |
| Version: | 0.1.0 |
| Date: | 2023-11-28 |
| Description: | Implementation of the KCMeans regression estimator studied by Wiemann (2023) <doi:10.48550/arXiv.2311.17021> for expectation function estimation conditional on categorical variables. Computation leverages the unconditional KMeans implementation in one dimension using dynamic programming algorithm of Wang and Song (2011) <doi:10.32614/RJ-2011-015>, allowing for global solutions in time polynomial in the number of observed categories. |
| License: | GPL (≥ 3) |
| URL: | https://github.com/thomaswiemann/kcmeans |
| BugReports: | https://github.com/thomaswiemann/kcmeans/issues |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| Depends: | R (≥ 3.6) |
| Imports: | stats, Ckmeans.1d.dp, MASS, Matrix |
| Suggests: | testthat (≥ 3.0.0), covr, knitr, rmarkdown |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2023-11-30 08:37:07 UTC; thomas |
| Author: | Thomas Wiemann [aut, cre] |
| Maintainer: | Thomas Wiemann <wiemann@uchicago.edu> |
| Repository: | CRAN |
| Date/Publication: | 2023-11-30 10:50:02 UTC |
K-Conditional-Means Estimator
Description
Implementation of the K-Conditional-Means estimator.
Usage
kcmeans(y, X, which_is_cat = 1, K = 2)
Arguments
y |
The outcome variable, a numerical vector. |
X |
A (sparse) feature matrix where one column is the categorical predictor. |
which_is_cat |
An integer indicating which column of |
K |
The number of support points, an integer greater than 2. |
Value
kcmeans returns an object of S3 class kcmeans. An
object of class kcmeans is a list containing the following
components:
cluster_mapA matrix that characterizes the estimated predictor of the residualized outcome
\tilde{Y} \equiv Y - X_{2:}^\top \hat{\pi}. The first columnxdenotes the value of the categorical variable that corresponds to the unrestricted sample meanmean_xof\tilde{Y}, the sample sharep_x, the estimated clustercluster_x, and the estimated restricted sample meanmean_xKof\tilde{Y}with justKsupport points.mean_yThe unconditional sample mean of
\tilde{Y}.piThe best linear prediction coefficients of
YonXcorresponding to the non-categorical predictorsX_{2:}.which_is_cat,KPassthrough of user-provided arguments. See above for details.
References
Wang H and Song M (2011). "Ckmeans.1d.dp: optimal k-means clustering in one dimension by dynamic programming." The R Journal 3(2), 29–33.
Wiemann T (2023). "Optimal Categorical Instruments." https://arxiv.org/abs/2311.17021
Examples
# Simulate simple dataset with n=800 observations
X <- rnorm(800) # continuous predictor
Z <- sample(1:20, 800, replace = TRUE) # categorical predictor
Z0 <- Z %% 4 # lower-dimensional latent categorical variable
y <- Z0 + X + rnorm(800) # outcome
# Compute kcmeans with four support points
kcmeans_fit <- kcmeans(y, cbind(Z, X), K = 4)
# Print the estimated support points of the categorical predictor
print(unique(kcmeans_fit$cluster_map[, "mean_xK"]))
Prediction Method for the K-Conditional-Means Estimator.
Description
Prediction method for the K-Conditional-Means estimator.
Usage
## S3 method for class 'kcmeans'
predict(object, newdata, clusters = FALSE, ...)
Arguments
object |
An object of class |
newdata |
A (sparse) feature matrix where the first column corresponds to the categorical predictor. |
clusters |
A boolean indicating whether estimated clusters should be returned. |
... |
Currently unused. |
Value
A numerical vector with predicted values (if clusters = FALSE)
or predicted clusters (if clusters = FALSE).
References
Wiemann T (2023). "Optimal Categorical Instruments." https://arxiv.org/abs/2311.17021
Examples
# Simulate simple dataset with n=800 observations
X <- rnorm(800) # continuous predictor
Z <- sample(1:20, 800, replace = TRUE) # categorical predictor
Z0 <- Z %% 4 # lower-dimensional latent categorical variable
y <- Z0 + X + rnorm(800) # outcome
# Compute kcmeans with four support points
kcmeans_fit <- kcmeans(y, cbind(Z, X), K = 4)
# Calculate in-sample predictions
fitted_values <- predict(kcmeans_fit, cbind(Z, X))
# Print sample share of estimated clusters
clusters <- predict(kcmeans_fit, cbind(Z, X), clusters = TRUE)
table(clusters)