The lpcde package provides R implementation for
bandwidth selection, point estimation and inference for local polynomial
conditional distribution and density methods.
This work was supported by the the National Science Foundation through grants SES-1947805, SES-1947662, DMS-2210561, and SES-2241575, and from the National Institute of Health (R01 GM072611-16).
https://nppackages.github.io/lpcde
To install/update in R type:
install.packages('lpcde')Here we outline some of the simple use cases of the package. For a
given dataset, comprising of a response variable (y_data)
and a collection of regressors (x_data), the
lpcde function estimates the conditional distribution (or
derivatives thereof) at some prescribe value of x. By
default a locally quadratic polynomial in y and locally linear
polynomial in x and Epanechnikov kernel are used on some prescribed
values of y, y_grid, with a bandwidth bw. This
procedure is executed with the following command:
model1 = lpcde::lpcde(x_data=x_data, y_data=y_data, y_grid=y_grid, x=0, bw=1)Standard R methods, coef, confint,
vcov, print, plot and
summary, can be used on objects returned from the
lpcde function to understand the output. For example, the
summary command applied to the object generated from this function,
summary(model1), will output a table that provides the
point estimates of the conditional density, number of data points used
in estimation, standard error, and a robust 95% confidence interval. The
confidence level, robustness of point and standard error estimates,
estimates of uniform confidence bands can all be altered by providing
the correct inputs to the optional variables for the lpcde
function.
By default, the function provides estimates according to the original
formulation of the estimator (see references below for all technical
details). If a constrained density estimate that is non-negative and
integrates to one is desired (see references below), the flags
nonneg and normalize can be turned on:
model_reg = lpcde::lpcde(x_data=x_data, y_data=y_data, y_grid=y_grid, x=0, bw=1,
nonneg=TRUE, normalize=TRUE)The package also provides additional an function for automating the
bandwidth selection, lpbwcde. By default this function
computes the rule-of-thumb MSE optimal bandwidth for the conditional PDF
with locally quadratic polynomial in y and locally linear polynomial in
x and Epanechnikov kernel on the implied support of Y.
model2 = lpbwcde(y_data = y_data, x_data = x_data, x = 0, y_grid = y_grid)
The estimated bandwidth from this function can be used as bandwidth
input to lpcde directly by using the option
bwselect in lpcde to specify bandwidth
selection type instead of running lpbwcde first. R methods
like summary, print and coef can
be applied to the objects returned by lpbwcde for
understanding the output.
Additional replication scripts showcasing the full functionality of
the package along with references for a full description of the methods
along with theoretical guarantees can be found on the GitHub repository
lpcde.