Type:	Package
Title:	Matching on Generalized Propensity Scores with Continuous Exposures
Version:	0.5.0
Maintainer:	Naeem Khoshnevis <nkhoshnevis@g.harvard.edu>
Description:	Provides a framework for estimating causal effects of a continuous exposure using observational data, and implementing matching and weighting on the generalized propensity score. Wu, X., Mealli, F., Kioumourtzoglou, M.A., Dominici, F. and Braun, D., 2022. Matching on generalized propensity scores with continuous exposures. Journal of the American Statistical Association, pp.1-29.
License:	GPL-3
Language:	en-US
URL:	https://github.com/NSAPH-Software/CausalGPS
BugReports:	https://github.com/NSAPH-Software/CausalGPS/issues
Copyright:	Harvard University
Imports:	parallel, data.table, SuperLearner, xgboost, gam, MASS, polycor, wCorr, stats, ggplot2, rlang, logger, Rcpp, gnm, locpol, Ecume, KernSmooth, cowplot
Encoding:	UTF-8
RoxygenNote:	7.2.3
Suggests:	covr, knitr, rmarkdown, ranger, earth, testthat, gridExtra
VignetteBuilder:	knitr
Depends:	R (≥ 3.5.0)
LinkingTo:	Rcpp
NeedsCompilation:	yes
Packaged:	2024-06-19 18:12:02 UTC; rstudio
Author:	Naeem Khoshnevis [aut, cre] (AFFILIATION: Kempner), Xiao Wu [aut] (AFFILIATION: CUMC), Danielle Braun [aut] (AFFILIATION: HSPH)
Repository:	CRAN
Date/Publication:	2024-06-19 18:30:02 UTC

The 'CausalGPS' package.

Description

An R package for implementing matching and weighting on generalized propensity scores with continuous exposures.

Details

We developed an innovative approach for estimating causal effects using observational data in settings with continuous exposures, and introduce a new framework for GPS caliper matching.

Author(s)

Naeem Khoshnevis

Xiao Wu

Danielle Braun

References

Wu, X., Mealli, F., Kioumourtzoglou, M.A., Dominici, F. and Braun, D., 2022. Matching on generalized propensity scores with continuous exposures. Journal of the American Statistical Association, pp.1-29.

Kennedy, E.H., Ma, Z., McHugh, M.D. and Small, D.S., 2017. Non-parametric methods for doubly robust estimation of continuous treatment effects. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 79(4), pp.1229-1245.

Check covariate balance using absolute approach

Description

Checks covariate balance based on absolute correlations for given data sets.

Usage

absolute_corr_fun(w, c)

Arguments

Value

The function returns a list including:

• absolute_corr: the absolute correlations for each pre-exposure covariates;

• mean_absolute_corr: the average absolute correlations for all pre-exposure covariates.

Examples

set.seed(291)
n <- 100
mydata <- generate_syn_data(sample_size=100)
year <- sample(x=c("2001","2002","2003","2004","2005"),size = n,
 replace = TRUE)
region <- sample(x=c("North", "South", "East", "West"),size = n,
 replace = TRUE)
mydata$year <- as.factor(year)
mydata$region <- as.factor(region)
mydata$cf5 <- as.factor(mydata$cf5)
cor_val <- absolute_corr_fun(mydata[,2], mydata[, 3:length(mydata)])
print(cor_val$mean_absolute_corr)

Check Weighted Covariate Balance Using Absolute Approach

Description

Checks covariate balance based on absolute weighted correlations for given data sets.

Usage

absolute_weighted_corr_fun(w, vw, c)

Arguments

Value

The function returns a list saved the measure related to covariate balance absolute_corr: the absolute correlations for each pre-exposure covairates; mean_absolute_corr: the average absolute correlations for all pre-exposure covairates.

Examples

set.seed(639)
n <- 100
mydata <- generate_syn_data(sample_size=100)
year <- sample(x=c("2001","2002","2003","2004","2005"),size = n,
               replace = TRUE)
region <- sample(x=c("North", "South", "East", "West"),size = n,
                 replace = TRUE)
mydata$year <- as.factor(year)
mydata$region <- as.factor(region)
mydata$cf5 <- as.factor(mydata$cf5)
cor_val <- absolute_weighted_corr_fun(mydata[,2],
                                      runif(n),
                                      mydata[, 3:length(mydata)])
print(cor_val$mean_absolute_corr)

A helper function for cgps_cw object

Description

A helper function to plot cgps_cw object using ggplot2 package.

Usage

## S3 method for class 'cgps_cw'
autoplot(object, ...)

Arguments

Value

Returns a ggplot object.

A helper function for cgps_erf object

Description

A helper function to plot cgps_erf object using ggplot2 package.

Usage

## S3 method for class 'cgps_erf'
autoplot(object, ...)

Arguments

Value

Returns a ggplot object.

A helper function for cgps_gps object

Description

A helper function to plot cgps_gps object using ggplot2 package.

Usage

## S3 method for class 'cgps_gps'
autoplot(object, ...)

Arguments

Value

Returns a ggplot object.

A helper function for cgps_pspop object

Description

A helper function to plot cgps_pspop object using ggplot2 package.

Usage

## S3 method for class 'cgps_pspop'
autoplot(object, ...)

Arguments

Value

Returns a ggplot object.

Check covariate balance

Description

Checks the covariate balance of original population or pseudo population.

Usage

check_covar_balance(
  w,
  c,
  ci_appr,
  counter_weight = NULL,
  covar_bl_method = "absolute",
  covar_bl_trs = 0.1,
  covar_bl_trs_type = "mean"
)

Arguments

Value

output object:

• corr_results

  • absolute_corr

  • mean_absolute_corr

• pass (TRUE,FALSE)

Examples

set.seed(422)
n <- 100
mydata <- generate_syn_data(sample_size=n)
year <- sample(x=c("2001","2002","2003","2004","2005"),size = n,
              replace = TRUE)
region <- sample(x=c("North", "South", "East", "West"),size = n,
                replace = TRUE)
mydata$year <- as.factor(year)
mydata$region <- as.factor(region)
mydata$cf5 <- as.factor(mydata$cf5)

m_xgboost <- function(nthread = 1,
                      ntrees = 35,
                      shrinkage = 0.3,
                      max_depth = 5,
                      ...) {SuperLearner::SL.xgboost(
                        nthread = nthread,
                        ntrees = ntrees,
                        shrinkage=shrinkage,
                        max_depth=max_depth,
                        ...)}

data_with_gps <- estimate_gps(.data = mydata,
                              .formula = w ~ cf1 + cf2 + cf3 + cf4 + cf5 +
                                             cf6 + year + region,
                              sl_lib = c("m_xgboost"),
                              gps_density = "kernel")


cw_object_matching <- compute_counter_weight(gps_obj = data_with_gps,
                                             ci_appr = "matching",
                                             bin_seq = NULL,
                                             nthread = 1,
                                             delta_n = 0.1,
                                             dist_measure = "l1",
                                             scale = 0.5)

pseudo_pop <- generate_pseudo_pop(.data = mydata,
                                  cw_obj = cw_object_matching,
                                  covariate_col_names = c("cf1", "cf2", "cf3",
                                                          "cf4", "cf5", "cf6",
                                                          "year", "region"),
                                  covar_bl_trs = 0.1,
                                  covar_bl_trs_type = "maximal",
                                  covar_bl_method = "absolute")


adjusted_corr_obj <- check_covar_balance(w = pseudo_pop$.data[, c("w")],
                                         c = pseudo_pop$.data[ ,
                                         pseudo_pop$params$covariate_col_names],
                                         counter = pseudo_pop$.data[,
                                                     c("counter_weight")],
                                         ci_appr = "matching",
                                         covar_bl_method = "absolute",
                                         covar_bl_trs = 0.1,
                                         covar_bl_trs_type = "mean")

Check Kolmogorov-Smirnov (KS) statistics

Description

Checks the Kolmogorov-Smirnov (KS) statistics for exposure and confounders in the pseudo-population

Usage

check_kolmogorov_smirnov(w, c, ci_appr, counter_weight = NULL)

Arguments

Value

output object is list including:

• ks_stat

• maximal_val

• mean_val

• median_val

Compile pseudo population

Description

Compiles pseudo population based on the original population and estimated GPS value.

Usage

compile_pseudo_pop(
  data_obj,
  ci_appr,
  gps_density,
  exposure_col_name,
  nthread,
  ...
)

Arguments

Details

For matching approach, use an extra parameter, bin_seq, which is sequence of w (treatment) to generate pseudo population. If NULL is passed the default value will be used, which is seq(min(w)+delta_n/2,max(w), by=delta_n).

Value

compile_pseudo_pop returns the pseudo population data that is compiled based on the selected causal inference approach.

Examples

set.seed(112)
m_d <- generate_syn_data(sample_size = 100)

m_xgboost <- function(nthread = 1,
                      ntrees = 35,
                      shrinkage = 0.3,
                      max_depth = 5,
                      ...) {SuperLearner::SL.xgboost(
                        nthread = nthread,
                        ntrees = ntrees,
                        shrinkage=shrinkage,
                        max_depth=max_depth,
                        ...)}

data_with_gps <- estimate_gps(.data = m_d,
                              .formula = w ~ cf1 + cf2 + cf3 +
                                             cf4 + cf5 + cf6,
                              gps_density = "normal",
                              sl_lib = c("m_xgboost")
                             )


pd <- compile_pseudo_pop(data_obj = data_with_gps,
                         ci_appr = "matching",
                         gps_density = "normal",
                         bin_seq = NULL,
                         exposure_col_name = c("w"),
                         nthread = 1,
                         dist_measure = "l1",
                         covar_bl_method = 'absolute',
                         covar_bl_trs = 0.1,
                         covar_bl_trs_type= "mean",
                         delta_n = 0.5,
                         scale = 1)

Find the closest data in subset to the original data

Description

A function to compute the closest data in subset of data to the original data based on two attributes: vector and scalar (vector of size one).

Usage

compute_closest_wgps(a, b, c, d, sc, nthread)

Arguments

Value

The function returns index of subset data that is closest to the original data sample.

Compute counter or weight of data samples

Description

Computes counter (for matching approach) or weight (for weighting) approach.

Usage

compute_counter_weight(gps_obj, ci_appr, nthread = 1, ...)

Arguments

Details

Additional parameters

Causal Inference Approach (ci_appr)

• if ci_appr = 'matching':

  • bin_seq: A sequence of w (treatment) to generate pseudo population. If NULL is passed the default value will be used, which is seq(min(w)+delta_n/2,max(w), by=delta_n).

  • dist_measure: Matching function. Available options:

    • l1: Manhattan distance matching

  • delta_n: caliper parameter.

  • scale: a specified scale parameter to control the relative weight that is attributed to the distance measures of the exposure versus the GPS.

Value

Returns a counter_weight (cgps_cw) object that includes .data and params attributes.

• .data: includes id and counter_weight columns. In case of matching the counter_weight column is integer values, which represent how many times the provided observational data was mached during the matching process. In case of weighting the column is double values.

• params: Include related parameters that is used for the process.

Examples

m_d <- generate_syn_data(sample_size = 100)
gps_obj <- estimate_gps(.data = m_d,
                        .formula = w ~ cf1 + cf2 + cf3 + cf4 + cf5 + cf6,
                        gps_density = "normal",
                        sl_lib = c("SL.xgboost"))

cw_object <- compute_counter_weight(gps_obj = gps_obj,
                                    ci_appr = "matching",
                                    bin_seq = NULL,
                                    nthread = 1,
                                    delta_n = 0.1,
                                    dist_measure = "l1",
                                    scale = 0.5)

Approximate density based on another vector

Description

A function to impute missing values based on density estimation of another vector or itself after removing the missing values.

Usage

compute_density(x0, x1)

Arguments

Value

Returns approximation of density value of vector x1 based on vector x0.

Compute minimum and maximum

Description

Function to compute minimum and maximum of the input vector

Usage

compute_min_max(x)

Arguments

Value

Returns a vector of length 2. The first element is min value, and the second element is max value.

Computes distance on all possible combinations

Description

Computes the distance between all combination of elements in two vector. a is vector of size n, and b is a vector of size m, the result, will be a matrix of size(n,m)

Usage

compute_outer(a, b, op)

Arguments

Value

A n by m matrix that includes abs difference between elements of vector a and b.

Compute residual

Description

Function to compute residual

Usage

compute_resid(a, b, c)

Arguments

Value

returns a residual values.

Compute risk value

Description

Calculates the cross-validated risk for the optimal bandwidth selection in kernel smoothing approach.

Usage

compute_risk(h, matched_Y, matched_w, matched_cw, x_eval, w_vals, kernel_appr)

Arguments

Value

returns a cross-validated risk value for the input bandwidth

Create pseudo population using matching casual inference approach

Description

Generates pseudo population based on matching casual inference method.

Usage

create_matching(
  .data,
  exposure_col_name,
  matching_fn,
  dist_measure = dist_measure,
  gps_density = gps_density,
  delta_n = delta_n,
  scale = scale,
  bin_seq = NULL,
  nthread = 1
)

Arguments

Value

Returns data.table of matched set.

Create pseudo population using weighting casual inference approach

Description

Generates pseudo population based on weighting casual inference method.

Usage

create_weighting(dataset, exposure_col_name)

Arguments

Value

Returns a data table which includes the following columns:

• Y

• w

• gps

• counter

• row_index

• ipw

• covariates

Estimate Exposure Response Function

Description

Estimates the exposure-response function (ERF) for a matched and weighted dataset using parametric, semiparametric, and nonparametric models.

Usage

estimate_erf(.data, .formula, weights_col_name, model_type, w_vals, ...)

Arguments

Value

Returns an S3 object containing the following data and parameters:

• .data_original <- result_data_original

• .data_prediction <- result_data_prediction

• params

Estimate generalized propensity score (GPS) values

Description

Estimates GPS value for each observation using normal or kernel approaches.

Usage

estimate_gps(
  .data,
  .formula,
  gps_density = "normal",
  sl_lib = c("SL.xgboost"),
  ...
)

Arguments

Value

The function returns a S3 object. Including the following:

• .data : id, exposure_var, gps, e_gps_pred, e_gps_std_pred, w_resid

• params: Including the following fields:

  • gps_mx (min and max of gps)

  • w_mx (min and max of w).

  • .formula

  • gps_density

  • sl_lib

  • fcall (function call)

Examples

m_d <- generate_syn_data(sample_size = 100)
data_with_gps <- estimate_gps(.data = m_d,
                              .formula = w ~ cf1 + cf2 + cf3 + cf4 + cf5 + cf6,
                              gps_density = "normal",
                              sl_lib = c("SL.xgboost")
                             )

Estimate hat (fitted) values

Description

Estimates the fitted values based on bandwidth value

Usage

estimate_hat_vals(bw, matched_w, w_vals)

Arguments

Value

Returns fitted values, or the prediction made by the model for each observation.

Estimate smoothed exposure-response function (ERF) for pseudo population

Description

Estimate smoothed exposure-response function (ERF) for matched and weighted data set using non-parametric models.

Usage

estimate_npmetric_erf(
  m_Y,
  m_w,
  counter_weight,
  bw_seq,
  w_vals,
  nthread,
  kernel_appr = "locpol"
)

Arguments

Details

Estimate Functions Using Local Polynomial kernel regression.

Value

The function returns a gpsm_erf object. The object includes the following attributes:

• params

• m_Y

• m_w

• bw_seq

• w_vals

• erf

• fcall

Estimate Parametric Exposure Response Function

Description

Estimate a constant effect size for matched and weighted data set using parametric models

Usage

estimate_pmetric_erf(formula, family, data, ...)

Arguments

Details

This method uses generalized nonlinear model (gnm) from gnm package.

Value

returns an object of class gnm

Estimate semi-exposure-response function (semi-ERF).

Description

Estimates the smoothed exposure-response function using a generalized additive model with splines.

Usage

estimate_semipmetric_erf(formula, family, data, ...)

Arguments

Details

This approach uses Generalized Additive Model (gam) using mgcv package.

Value

returns an object of class gam

Generate kernel function

Description

Generates a kernel function

Usage

generate_kernel(t)

Arguments

Value

probability distribution

Generate pseudo population

Description

Generates pseudo population data set based on user-defined causal inference approach. The function uses an adaptive approach to satisfies covariate balance requirements. The function terminates either by satisfying covariate balance or completing the requested number of iteration, whichever comes first.

Usage

generate_pseudo_pop(
  .data,
  cw_obj,
  covariate_col_names,
  covar_bl_trs = 0.1,
  covar_bl_trs_type = "maximal",
  covar_bl_method = "absolute"
)

Arguments

Value

Returns a pseudo population (gpsm_pspop) object that is generated or augmented based on the selected causal inference approach (ci_appr). The object includes the following objects:

• params

  • ci_appr

  • params

• pseudo_pop

• adjusted_corr_results

• original_corr_results

• best_gps_used_params

• effect size of generated pseudo population

Examples

set.seed(967)

m_d <- generate_syn_data(sample_size = 200)
m_d$id <- seq_along(1:nrow(m_d))

m_xgboost <- function(nthread = 4,
                      ntrees = 35,
                      shrinkage = 0.3,
                      max_depth = 5,
                      ...) {SuperLearner::SL.xgboost(
                        nthread = nthread,
                        ntrees = ntrees,
                        shrinkage=shrinkage,
                        max_depth=max_depth,
                        ...)}

data_with_gps_1 <- estimate_gps(
  .data = m_d,
  .formula = w ~ I(cf1^2) + cf2 + I(cf3^2) + cf4 + cf5 + cf6,
  sl_lib = c("m_xgboost"),
  gps_density = "normal")

cw_object_matching <- compute_counter_weight(gps_obj = data_with_gps_1,
                                             ci_appr = "matching",
                                             bin_seq = NULL,
                                             nthread = 1,
                                             delta_n = 0.1,
                                             dist_measure = "l1",
                                             scale = 0.5)

pseudo_pop <- generate_pseudo_pop(.data = m_d,
                                  cw_obj = cw_object_matching,
                                  covariate_col_names = c("cf1", "cf2",
                                                          "cf3", "cf4",
                                                          "cf5", "cf6"),
                                  covar_bl_trs = 0.1,
                                  covar_bl_trs_type = "maximal",
                                  covar_bl_method = "absolute")

Generate synthetic data for the CausalGPS package

Description

Generates synthetic data set based on different GPS models and covariates.

Usage

generate_syn_data(
  sample_size = 1000,
  outcome_sd = 10,
  gps_spec = 1,
  cova_spec = 1,
  vectorized_y = FALSE
)

Arguments

Value

synthetic_data: The function returns a data.frame saved the constructed synthetic data.

Examples

set.seed(298)
s_data <- generate_syn_data(sample_size = 100,
                            outcome_sd = 10,
                            gps_spec = 1,
                            cova_spec = 1)

Get Logger Settings

Description

Returns current logger settings.

Usage

get_logger()

Value

Returns a list that includes logger_file_path and logger_level.

Examples

set_logger("mylogger.log", "INFO")
log_meta <- get_logger()

Log system information

Description

Logs system related information into the log file.

Usage

log_system_info()

Value

No return value. This function is called for side effects.

Match observations

Description

Matching function using L1 distance on single exposure level w

Usage

matching_fn(
  w,
  dataset,
  exposure_col_name,
  e_gps_pred,
  e_gps_std_pred,
  w_resid,
  gps_mx,
  w_mx,
  dist_measure = "l1",
  gps_density = "normal",
  delta_n = 1,
  scale = 0.5,
  nthread = 1
)

Arguments

Value

dp: The function returns a data.table saved the matched points on by single exposure level w by the proposed GPS matching approaches.

Extend generic plot functions for cgps_cw class

Description

A wrapper function to extend generic plot functions for cgps_cw class.

Usage

## S3 method for class 'cgps_cw'
plot(x, ...)

Arguments

Details

Additional parameters:

• every_n: Puts label to ID at every n interval (default = 10)

• subset_id: A vector of range of ids to be included in the plot (default = NULL)

Value

Returns a ggplot2 object, invisibly. This function is called for side effects.

Extend generic plot functions for cgps_cw class

Description

A wrapper function to extend generic plot functions for cgps_cw class.

Usage

## S3 method for class 'cgps_erf'
plot(x, ...)

Arguments

Details

TBD

Value

Returns a ggplot2 object, invisibly. This function is called for side effects.

Extend generic plot functions for cgps_gps class

Description

A wrapper function to extend generic plot functions for cgps_gps class.

Usage

## S3 method for class 'cgps_gps'
plot(x, ...)

Arguments

Value

Returns a ggplot2 object, invisibly. This function is called for side effects.

Extend generic plot functions for cgps_pspop class

Description

A wrapper function to extend generic plot functions for cgps_pspop class.

Usage

## S3 method for class 'cgps_pspop'
plot(x, ...)

Arguments

Details

Additional parameters

• include_details: If set to TRUE, the plot will include run details (Default = FALSE).

Value

Returns a ggplot2 object, invisibly. This function is called for side effects.

Extend print function for cgps_cw object

Description

Extend print function for cgps_cw object

Usage

## S3 method for class 'cgps_cw'
print(x, ...)

Arguments

Value

No return value. This function is called for side effects.

Extend print function for cgps_erf object

Description

Extend print function for cgps_erf object

Usage

## S3 method for class 'cgps_erf'
print(x, ...)

Arguments

Value

No return value. This function is called for side effects.

Extend print function for cgps_gps object

Description

Extend print function for cgps_gps object

Usage

## S3 method for class 'cgps_gps'
print(x, ...)

Arguments

Value

No return value. This function is called for side effects.

Extend print function for cgps_pspop object

Description

Extend print function for cgps_pspop object

Usage

## S3 method for class 'cgps_pspop'
print(x, ...)

Arguments

Value

No return value. This function is called for side effects.

Set Logger Settings

Description

Updates logger settings, including log level and location of the file.

Usage

set_logger(logger_file_path = "CausalGPS.log", logger_level = "INFO")

Arguments

Value

No return value. This function is called for side effects.

Examples

set_logger("Debug")

Smooth exposure response function

Description

Smooths exposure response function based on bandwidth

Usage

smooth_erf(matched_Y, bw, matched_w, matched_cw, x_eval, kernel_appr)

Arguments

Value

Smoothed value of ERF

Compute smoothed erf with kernsmooth approach

Description

Compute smoothed erf with kernsmooth approach

Usage

smooth_erf_kernsmooth(matched_Y, matched_w, matched_cw, x_eval, bw)

Arguments

Value

A vector of smoothed ERF.

Compute smoothed erf with locpol approach

Description

Compute smoothed erf with locpol approach

Usage

smooth_erf_locpol(matched_Y, matched_w, matched_cw, x_eval, bw)

Arguments

Value

A vector of smoothed ERF.

print summary of cgps_cw object

Description

print summary of cgps_cw object

Usage

## S3 method for class 'cgps_cw'
summary(object, ...)

Arguments

Value

Returns summary of data

print summary of cgps_erf object

Description

print summary of cgps_erf object

Usage

## S3 method for class 'cgps_erf'
summary(object, ...)

Arguments

Value

Returns summary of data

print summary of cgps_gps object

Description

print summary of cgps_gps object

Usage

## S3 method for class 'cgps_gps'
summary(object, ...)

Arguments

Value

Returns summary of data

print summary of cgps_pspop object

Description

print summary of cgps_pspop object

Usage

## S3 method for class 'cgps_pspop'
summary(object, ...)

Arguments

Value

Returns summary of data

Public data set for air pollution and health studies, case study: 2010 county-Level data set for the contiguous United States

Description

A dataset containing exposure, confounders, and outcome for causal inference studies. The dataset is hosted on Harvard dataverse doi:10.7910/DVN/L7YF2G. This dataset was produced from five different resources. Please see https://github.com/NSAPH-Projects/synthetic_data/ for the data processing pipelines. In the following

Exposure Data

The exposure parameter is PM2.5. Di et al. (2019) provided daily, and annual PM2.5 estimates at 1 km×1 km grid cells in the entire United States. The data can be downloaded from Di et al. (2021). Features in this category starts with qd_ prefix.

Census Data

The main reference for getting the census data is the United States Census Bureau. There are numerous studies and surveys for different geographical resolutions. We use 2010 county level American County Survey at the county level (acs5). Features in this category starts with cs_ prefix.

CDC Data

The Centers for Disease Control and Prevention (CDC), provides the Behavioral Risk Factor Surveillance System (Centers for Disease Control and Prevention (2021)), which is the nation’s premier system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors.

GridMET Data

Climatology Lab at the University of California, Merced, provides the GridMET data (Abatzoglou (2013)). The data set is daily surface meteorological data covering the contiguous United States.

CMS Data

The Centers for Medicare and Medicaid Services(CMS) provides synthetic data at the county level for 2008-2010 (Centers for Medicare & Medicaid Services (2021)).

The definition of each variables are provided below. All data are collected for 2010 and aggregated into the county level and in the contiguous United States.

Usage

data(synthetic_us_2010)

Format

A data frame with 3109 rows and 46 variables:

qd_mean_pm25

Mean PM2.5 (microgram/m3)

cs_poverty

The proportion of below poverty level population among 65+ years old.

cs_hispanic

The proportion of Hispanic or Latino population among 65+ years old.

cs_black

The proportion of Black or African American population among 65+ years old.

cs_white

The proportion of White population among 65 years and over.

cs_native

The proportion of American Indian or Alaska native population among 65 years and over.

cs_asian

The proportion of Asian population among 65 years and over.

cs_other

The proportion of other races population among 65 years and over.

cs_ed_below_highschool

The proportion of the population with below high school level education among 65 years and over.

cs_household_income

Median Household income in the past 12 months (in 2010 inflation-adjusted dollars) where householder is 65 years and over.

cs_median_house_value

Median house value (USD)

cs_total_population

Total Population

cs_area

Area of each county (square miles)

cs_population_density

The number of the population in one square mile.

cdc_mean_bmi

Body Mass Index.

cdc_pct_cusmoker

The proportion of current smokers.

cdc_pct_sdsmoker

The proportion of some days smokers.

cdc_pct_fmsmoker

The proportion of former smokers.

cdc_pct_nvsmoker

The proportion of never smokers.

cdc_pct_nnsmoker

The proportion of not known smokers.

gmet_mean_tmmn

Annual mean of daily minimum temperature (K)

gmet_mean_summer_tmmn

The mean of daily minimum temperature during summer (K)

gmet_mean_winter_tmmn

The mean of daily minimum temperature during winter (K)

gmet_mean_tmmx

Annual mean of daily maximum temperature (K)

gmet_mean_summer_tmmx

The mean of daily maximum temperature during summer (K)

gmet_mean_winter_tmmx

The mean of daily maximum temperature during winter (K)

gmet_mean_rmn

Annual mean of daily minimum relative humidity (%)

gmet_mean_summer_rmn

The mean of daily minimum relative humidity during summer (%)

gmet_mean_winter_rmn

The mean of daily minimum relative humidity during winter (%)

gmet_mean_rmx

Annual mean of daily maximum relative humidity (%)

gmet_mean_summer_rmx

The mean of daily maximum relative humidity during summer (%)

gmet_mean_winter_rmx

The mean of daily maximum relative humidity during winter (%)

gmet_mean_sph

Annual mean of daily mean specific humidity (kg/kg)

gmet_mean_summer_sph

The mean of daily mean specific humidity during summer(kg/kg)

gmet_mean_winter_sph

The mean of daily mean specific humidity during winter(kg/kg)

cms_mortality_pct

The proportion of deceased patients.

cms_white_pct

The proportion of White patients.

cms_black_pct

The proportion of Black patients.

cms_hispanic_pct

The proportion of Hispanic patients.

cms_others_pct

The proportion of Other patients.

cms_female_pct

The proportion of Female patients.

region

The region that the county is located in.

  NORTHEAST=("NY","MA","PA","RI","NH","ME","VT","CT","NJ")
  SOUTH=("DC","VA","NC","WV","KY","SC","GA","FL","AL","TN","MS","AR","MD","DE","OK","TX","LA")
  MIDWEST=c("OH","IN","MI","IA","MO","WI","MN","SD","ND","IL","KS","NE")
  WEST=c("MT","CO","WY","ID","UT","NV","CA","OR","WA","AZ","NM")

FIPS

Federal Information Processing Standards, a unique ID for each county.

NAME

County, State name.

STATE

State abbreviation.

STATE_CODE

State numerical code.

References

Abatzoglou, John T. 2013. “Development of Gridded Surface Meteorological Data for Ecological Applications and Modelling.” International Journal of Climatology 33 (1): 121–31. doi:10.1002/joc.3413.

Centers for Disease Control and Prevention. 2021. “Behavioral Risk Factor Surveillance System.” https://www.cdc.gov/brfss/annual_data/annual_2010.htm/.

Centers for Medicare & Medicaid Services. 2021. “CMS 2008-2010 Data Entrepreneurs’ Synthetic Public Use File (DE-SynPUF).” https://www.cms.gov/data-research/statistics-trends-and-reports/medicare-claims-synthetic-public-use-files/cms-2008-2010-data-entrepreneurs-synthetic-public-use-file-de-synpuf.

Di, Qian, Heresh Amini, Liuhua Shi, Itai Kloog, Rachel Silvern, James Kelly, M Benjamin Sabath, et al. 2019. “An Ensemble-Based Model of Pm2. 5 Concentration Across the Contiguous United States with High Spatiotemporal Resolution.” Environment International 130: 104909. doi:10.1016/j.envint.2019.104909.

Di, Qian, Yaguang Wei, Alexandra Shtein, Carolynne Hultquist, Xiaoshi Xing, Heresh Amini, Liuhua Shi, et al. 2021. “Daily and Annual Pm2.5 Concentrations for the Contiguous United States, 1-Km Grids, V1 (2000 - 2016).” NASA Socioeconomic Data; Applications Center (SEDAC). doi:10.7927/0rvr-4538.

Generate Prediction Model

Description

Function to develop prediction model based on user's preferences.

Usage

train_it(target, input, sl_lib_internal = NULL, ...)

Arguments

Value

prediction model

Trim a data frame or an S3 object

Description

Trims a data frame or an S3 object's .data attributs.

Usage

trim_it(data_obj, trim_quantiles, variable)

Arguments

Value

Returns a trimmed data frame or an S3 object with the $.data attribute trimmed, depending on the input type.

Examples

# Example usage with a data frame
df <- data.frame(id = 1:10, value = rnorm(100))
trimmed_df <- trim_it(df, c(0.1, 0.9), "value")

# Example usage with an S3 object
data_obj <- list()
class(data_obj) <- "myobject"
data_obj$.data <- df
trimmed_data_obj <- trim_it(data_obj, c(0.1, 0.9), "value")

Helper function

Description

Helper function

Usage

w_fun(bw, matched_w, w_vals)

Arguments

Value

return value (TODO)