Package 'CausalGPS'

Title: Matching on Generalized Propensity Scores with Continuous Exposures
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.
Authors: Naeem Khoshnevis [aut, cre] (<https://orcid.org/0000-0003-4315-1426>, Kempner), Xiao Wu [aut] (<https://orcid.org/0000-0002-4884-657X>, CUMC), Danielle Braun [aut] (<https://orcid.org/0000-0002-5177-8598>, HSPH)
Maintainer: Naeem Khoshnevis <[email protected]>
License: GPL-3
Version: 0.5.0
Built: 2024-11-17 06:35:17 UTC
Source: CRAN

Help Index

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

w

A vector of observed continuous exposure variable.
c

A data.frame of observed covariates variable.

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

w

A vector of observed continuous exposure variable.
vw

A vector of weights.
c

A data.table of observed covariates variable.

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)

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

w

A vector of observed continuous exposure variable.
c

A data.frame of observed covariates variable.
ci_appr

The causal inference approach.
counter_weight

A weight vector in different situations. If the matching approach is selected, it is an integer data.table of counters. In the case of the weighting approach, it is weight data.table.
covar_bl_method

Covariate balance method. Available options: - 'absolute'
covar_bl_trs

Covariate balance threshold.
covar_bl_trs_type

Covariate balance type (mean, median, maximal).

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")

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

data_obj

A S3 object including the following:

  • Original data set + GPS values

  • e_gps_pred

  • e_gps_std_pred

  • w_resid

  • gps_mx (min and max of gps)

  • w_mx (min and max of w).

ci_appr

Causal inference approach.
gps_density

Model type which is used for estimating GPS value, including normal and kernel.
exposure_col_name

Exposure data column name.
nthread

An integer value that represents the number of threads to be used by internal packages.
...

Additional parameters.

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)

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

gps_obj

A gps object that is generated with estimate_gps function. If it is provided, the number of iteration will forced to 1 (Default: NULL).
ci_appr

The causal inference approach. Possible values are:

  • "matching": Matching by GPS

  • "weighting": Weighting by GPS

nthread

An integer value that represents the number of threads to be used by internal packages.
...

Additional arguments passed to different models.

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)

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

.data

A data frame containing an observed continuous exposure variable, weights, and an observed outcome variable. Includes an id column for future reference.
.formula

A formula specifying the relationship between the exposure variable and the outcome variable. For example, Y ~ w.
weights_col_name

A string representing the weight or counter column name in .data.
model_type

A string representing the model type based on preliminary assumptions, including parametric, semiparametric, and nonparametric models.
w_vals

A numeric vector of values at which you want to calculate the ERF.
...

Additional arguments passed to the model.

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

.data

A data frame of observed continuous exposure variable and observed covariates variable. Also includes id column for future references.
.formula

A formula specifying the relationship between the exposure variable and the covariates. For example, w ~ I(cf1^2) + cf2.
gps_density

Model type which is used for estimating GPS value, including normal (default) and kernel.
sl_lib

A vector of prediction algorithms to be used by the SuperLearner packageg.
...

Additional arguments passed to the model.

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")
                             )

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

.data

A data.frame of observation data with id column.
cw_obj

An S3 object of counter_weight.
covariate_col_names

A list of covariate columns.
covar_bl_trs

Covariate balance threshold
covar_bl_trs_type

Type of the covariance balance threshold.
covar_bl_method

Covariate balance method.

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

sample_size

A positive integer number that represents a number of data samples.
outcome_sd

A positive double number that represents standard deviation used to generate the outcome in the synthetic data set.
gps_spec

A numerical integer values ranging from 1 to 7. The complexity and form of the relationship between covariates and treatment variables are determined by the gps_spec. Below, you will find a concise definition for each of these values:

  • gps_spec: 1: The treatment is generated using a normal distributionMay 24, 2023 (stats::rnorm) and a linear function of covariates (cf1 to cf6).

  • gps_spec: 2: The treatment is generated using a Student's t-distribution (stats::rt) and a linear function of covariates, but is also truncated to be within a specific range (-5 to 25).

  • gps_spec: 3: The treatment includes a quadratic term for the third covariate.

  • gps_spec: 4: The treatment is calculated using an exponential function within a fraction, creating logistic-like model.

  • gps_spec: 5: The treatment also uses logistic-like model but with different parameters.

  • gps_spec: 6: The treatment is calculated using the natural logarithm of the absolute value of a linear combination of the covariates.

  • gps_spec: 7: The treatment is generated similarly to gps_spec = 2, but without truncation.

cova_spec

A numerical value (1 or 2) to modify the covariates. It determines how the covariates in the synthetic data set are transformed. If cova_spec equals 2, the function applies non-linear transformation to the covariates, which can add complexity to the relationships between covariates and outcomes in the synthetic data. See the code for more details.
vectorized_y

A Boolean value indicates how Y internally is generated. (Default = FALSE). This parameter is introduced for backward compatibility. vectorized_y = TRUE performs better.

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()

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

x

A cgps_cw object.
...

Additional arguments passed to customize the plot.

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

x

A cgps_erf object.
...

Additional arguments passed to customize the plot.

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

x

A cgps_gps object.
...

Additional arguments passed to customize the plot.

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

x

A cgps_pspop object.
...

Additional arguments passed to customize the plot.

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

x

A cgps_cw object.
...

Additional arguments passed to customize the results.

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

x

A cgps_erf object.
...

Additional arguments passed to customize the results.

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

x

A cgps_gps object.
...

Additional arguments passed to customize the results.

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

x

A cgps_pspop object.
...

Additional arguments passed to customize the results.

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

logger_file_path

A path (including file name) to log the messages. (Default: CausalGPS.log)
logger_level

The log level. Available levels include:

  • TRACE

  • DEBUG

  • INFO (Default)

  • SUCCESS

  • WARN

  • ERROR

  • FATAL

Value

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

Examples

set_logger("Debug")

print summary of cgps_cw object

Description

print summary of cgps_cw object

Usage

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

Arguments

object

A cgps_cw object.
...

Additional arguments passed to customize the results.

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

object

A cgps_erf object.
...

Additional arguments passed to customize the results.

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

object

A cgps_gps object.
...

Additional arguments passed to customize the results.

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

object

A cgps_pspop object.
...

Additional arguments passed to customize the results.

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.

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

data_obj

A data frame or an S3 object containing the data to be trimmed. For a data frame, the function operates directly on it. For an S3 object, the function expects a .data attribute containing the data.
trim_quantiles

A numeric vector of length 2 specifying the lower and upper quantiles used for trimming the data.
variable

The name of the variable in the data on which the trimming is to be applied.

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")