vignette fix and doc update

R/fn_make.R

@@ -162,8 +162,8 @@ make_formula <- function (depnt_var_nm_1L_chr, predictors_chr, environment_env =
  " ~ ", paste0(predictors_chr, collapse = " + ")), env = environment_env)
  return(formula_fml)
 }
-#' Make hlth ec summary
-#' @description make_hlth_ec_smry() is a Make function that creates a new R object. Specifically, this function implements an algorithm to make hlth ec summary. The function returns He summary (a list).
+#' Make health economic summary
+#' @description make_hlth_ec_smry() is a Make function that creates a new R object. Specifically, this function implements an algorithm to make health economic summary. The function returns He summary (a list).
 #' @param ds_tb Dataset (a tibble)
 #' @param change_vars_chr Change variables (a character vector), Default: 'NA'
 #' @param wtp_dbl Willingness to pay (a double vector), Default: 50000

data-raw/MAKE_HOUSESTYLE_DSS.R

@@ -55,10 +55,14 @@ abbreviations_lup <- abbreviations_lup %>%
  "willingness to pay threshold"),
  no_plural_chr = c("effective","effectiveness",
  "named","willingness to pay"))
+abbreviations_lup <- abbreviations_lup %>%
+ ready4fun::update_abbr_lup(short_name_chr = c("ec", "hlth"),
+ long_name_chr = c("economic", "health"))
+
 # Push updates to dataverse
-seed_obj_lup_tb %>%
- ready4use::write_paired_ds_fls_to_dv(fl_nm_1L_chr = "seed_obj_lup_tb",
- desc_1L_chr = "Seed object type lookup table")
+# seed_obj_lup_tb %>%
+#  ready4use::write_paired_ds_fls_to_dv(fl_nm_1L_chr = "seed_obj_lup_tb",
+#  desc_1L_chr = "Seed object type lookup table")
 object_type_lup %>%
  ready4use::write_paired_ds_fls_to_dv(fl_nm_1L_chr = "object_type_lup",
  desc_1L_chr = "Object type lookup table")

vignettes/Economic_Analysis.Rmd

@@ -67,7 +67,7 @@ sngl_grp_ds_tb <- make_sngl_grp_ds(seed_ds_tb,ds_smry_ls = ds_smry_ls)
 We can split a subset of our single group dataset into two propensity score matched groups - one each for intervention and control - using the `make_matched_ds`{.R} function. We also use this function to build in our assumptions about group differences in changes in PHQ9, SOFAS and costs measured at follow-up. The below example assumes **population** (not sample) mean change differences between Intervention and Control arms of -2 for PHQ9, -2 for SOFAS and +\$300 for costs.
 
 ```{r}
-matched_ds_tb <- make_matched_ds(sngl_grp_ds, 
+matched_ds_tb <- make_matched_ds(sngl_grp_ds_tb, 
  cmprsn_smry_tb = tibble::tibble(var_nms_chr = c(ds_smry_ls$predr_var_nms, ds_smry_ls$costs_var_nm_1L_chr),
  fns_ls = list(stats::rnorm,stats::rnorm,stats::rnorm),
  abs_mean_diff_dbl = c(2,2,300),