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formula naming #70

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Sep 15, 2023
Merged

formula naming #70

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6 changes: 4 additions & 2 deletions R/calcBalStats.R
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Original file line number Diff line number Diff line change
Expand Up @@ -138,8 +138,10 @@ calcBalStats <- function(home_dir = NA, data, formulas, exposure, exposure_time_
lagged_time_pts <- exposure_time_pts[exposure_time_pts<exposure_time_pt]

# GETS COVARIATES FROM FORM FOR ASSESSING BALANCE
full_form <- formulas[[names(formulas)[grepl(paste0("form_", exposure, "-", outcome, "-", exposure_time_pt),
names(formulas))]]]
# full_form <- formulas[[names(formulas)[grepl(paste0("-", exposure_time_pt),
# names(formulas))]]]
full_form <- formulas[[names(formulas)[as.numeric(sapply(strsplit(names(formulas), "-"), "[",2)) == exposure_time_pt]]]

covars <- paste(deparse(full_form[[3]], width.cutoff = 500), collapse = "") # gets covariates
covar_time <- sapply(strsplit(unlist(strsplit(as.character(covars), "\\+")), "\\."), "[", 2)
covars <- as.character(unlist(strsplit(covars, "\\+")))
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2 changes: 1 addition & 1 deletion R/createFormulas.R
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Expand Up @@ -282,7 +282,7 @@ createFormulas <- function(home_dir, exposure, exposure_time_pts, outcome, tv_co
forms_csv <- c(forms_csv, paste(exposure, "~", paste0(vars_to_include[order(vars_to_include)], sep = "", collapse = " + ")))

# Assigns the form to forms list
forms[[paste(type, "_form_", exposure, "-", outcome, "-", time, sep = "")]] <- f
forms[[paste(type, "_form", "-", time, sep = "")]] <- f
}

if(save.out){
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10 changes: 9 additions & 1 deletion examplePipelineRevised.Rmd
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Expand Up @@ -83,6 +83,14 @@ data_mids <- readRDS(paste0(home_dir, "/imputed_data.rds"))
#example path to imputed data
# data_path <- "/Users/isabella/Library/CloudStorage/Box-Box/BSL General/MSMs/testing/imputations/"

#MD data
data <- read.csv("/Users/isabella/Library/CloudStorage/Box-Box/BSL General/MSMs/testing/meriah/imputations/ESETA1-CORTB.154_imp1.csv")
factor_confounders = c("state","TcBlac2", "PmBlac2")

data[, factor_confounders] <- lapply(data[, factor_confounders], as.factor)
numeric_vars <- colnames(data)[!colnames(data) %in% c(factor_confounders, "ID")]
data[, numeric_vars] <- lapply(data[, numeric_vars], as.numeric)

library(dplyr)
data_df <- data_mids[[1]]
data_df <- data_df %>% dplyr::select(-c(contains(c(":", "Childhood", "Infancy", "Toddlerhood", "pcx"))))
Expand Down Expand Up @@ -126,7 +134,7 @@ data_wide = data_wide[, !colnames(data_wide) %in% c("RHasSO.6","RHasSO.15", "RHa
m <- 1
method <- "pmm"; #pmm, midastouch, sample, cart , rf (default)
imputed_data <- imputeData(data_wide, m, method, home_dir, exposure, outcome, tv_confounders, ti_confounders,
para_proc = FALSE, read_imps_from_file = "no")
para_proc = TRUE, read_imps_from_file = "no")

data = complete(imputed_data, 1)

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