## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE) ## ----------------------------------------------------------------------------- ymodel_fit_custom <- function(ymodel, obs_data){ return(randomForest::randomForest(formula = ymodel, data = obs_data)) } ## ----------------------------------------------------------------------------- ymodel_predict_custom <- function(fit, newdf){ return(as.numeric(predict(object = fit, newdata = newdf))) } ## ----echo=FALSE--------------------------------------------------------------- library('gfoRmula') library('data.table') ## ----------------------------------------------------------------------------- library('Hmisc') id <- 'id' time_name <- 't0' covnames <- c('L1', 'L2', 'A') outcome_name <- 'Y' outcome_type <- 'continuous_eof' covtypes <- c('categorical', 'normal', 'binary') histories <- c(lagged) histvars <- list(c('A', 'L1', 'L2')) covparams <- list(covmodels = c(L1 ~ lag1_A + lag1_L1 + L3 + t0 + rcspline.eval(lag1_L2, knots = c(-1, 0, 1)), L2 ~ lag1_A + L1 + lag1_L1 + lag1_L2 + L3 + t0, A ~ lag1_A + L1 + L2 + lag1_L1 + lag1_L2 + L3 + t0)) ymodel <- Y ~ A + L1 + L2 + lag1_A + lag1_L1 + lag1_L2 + L3 intervention1.A <- list(static, rep(0, 7)) intervention2.A <- list(static, rep(1, 7)) int_descript <- c('Never treat', 'Always treat') nsimul <- 10000 gform_cont_eof <- gformula(obs_data = continuous_eofdata, id = id, time_name = time_name, covnames = covnames, outcome_name = outcome_name, outcome_type = outcome_type, covtypes = covtypes, covparams = covparams, ymodel = ymodel, ymodel_fit_custom = ymodel_fit_custom, ymodel_predict_custom = ymodel_predict_custom, intervention1.A = intervention1.A, intervention2.A = intervention2.A, int_descript = int_descript, histories = histories, histvars = histvars, basecovs = c("L3"), nsimul = nsimul, seed = 1234) gform_cont_eof