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Showing content from http://cran.rstudio.com/web/packages/cowplot/../CausalGPS/vignettes/CausalGPS.html below:

CausalGPS

• if ci.appr = ‘matching’:
  • dist_measure: Distance measuring 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.
    
  • 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)
  • max_attempt: maximum number of attempt to satisfy covariate balance.
    See create_matching() for more details about the parameters and default values.
    
• if ci.appr = ‘weighting’:
  • covar_bl_method: Covariate balance method.
    
  • covar_bl_trs: Covariate balance threshold
    
  • max_attempt: Maximum number of attempt to satisfy covariate balance.
• Generating Pseudo Population

 set.seed(422)
 n <- 1000
 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)
                             
 pseudo_pop <- generate_pseudo_pop(
                             mydata[, c("id", "w")],
                             mydata[, c("id", "cf1", "cf2", "cf3", "cf4", 
                                        "cf5", "cf6","year","region")],
                             ci_appr = "matching",
                             gps_density = "kernel",
                             use_cov_transform = TRUE,
                             transformers = list("pow2", "pow3", "abs", 
                                                 "scale"),
                             exposure_trim_qtls = c(0.01,0.99),
                             sl_lib = c("m_xgboost"),
                             covar_bl_method = "absolute",
                             covar_bl_trs = 0.1,
                             covar_bl_trs_type = "mean",
                             max_attempt = 4,
                             dist_measure = "l1",
                             delta_n = 1,
                             scale = 0.5,
                             nthread = 1)                            
                             
 plot(pseudo_pop)

matching_fn is Manhattan distance matching approach. For prediction model we use SuperLearner package. SuperLearner supports different machine learning methods and packages. params is a list of hyperparameters that users can pass to the third party libraries in the SuperLearner package. All hyperparameters go into the params list. The prefixes are used to distinguished parameters for different libraries. The following table shows the external package names, their equivalent name that should be used in sl_lib, the prefixes that should be used for their hyperparameters in the params list, and available hyperparameters.

nthread is the number of available threads (cores). XGBoost needs OpenMP installed on the system to parallelize the processing.

• Estimating GPS

data_with_gps <- estimate_gps(w,
                              c,
                              params = list(xgb_max_depth = c(3,4,5),
                                            xgb_rounds = c(10,20,30,40)),
                              nthread = 1,                                
                              sl_lib = c("m_xgboost")
                              )

• Estimating Exposure Rate Function

estimate_npmetric_erf<-function(matched_Y,
                                matched_w,
                                matched_counter = NULL,
                                bw_seq=seq(0.2,2,0.2),
                                w_vals,
                                nthread)

• Generating Synthetic Data

syn_data <- generate_syn_data(sample_size=100,
                              outcome_sd = 10,
                              gps_spec = 1,
                              cova_spec = 1)

• Logging

The CausalGPS package is logging internal activities into the CausalGPS.log file. The file is located in the source file location and will be appended. Users can change the logging file name (and path) and logging threshold. The logging mechanism has different thresholds (see logger package). The two most important thresholds are INFO and DEBUG levels. The former, which is the default level, logs more general information about the process. The latter, if activated, logs more detailed information that can be used for debugging purposes.

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