Combine parameter estimates with parameter key

Combines model output parameter estimates with information in parameter key. Performs some formatting of this combined data.frame.

Usage

define_param_table(.estimates, .key, .ci = 95, .zscore = NULL)

Arguments

.estimates

One of the following:

• Path to a bbr model

• A bbr model. (output of bbr::read_model())

• A bbr model summary object (output of bbr::model_summary())

• A data frame of the parameter estimates (e.g., output of bbr::param_estimates())

.key

Path to parameter key or data.frame of parameter key. Described in more detail in param_key

.ci

Confidence interval. A value from 1 to 99 denoting the percent confidence interval. Default is 95.

.zscore

Deprecated. Please use the .ci argument.

Details

Below is the expected format of .estimates if a data frame is provided:

parameter_names estimate  stderr random_effect_sd random_effect_sdse fixed diag  shrinkage
<chr>              <dbl>   <dbl>            <dbl>              <dbl> <lgl> <lgl>     <dbl>
THETA1            0.443  0.0643            NA               NA       FALSE NA       NA
THETA2            4.12   0.0275            NA               NA       FALSE NA       NA
THETA3            1.17   0.0280            NA               NA       FALSE NA       NA
THETA4            4.21   0.0190            NA               NA       FALSE NA       NA
THETA5            1.28   0.0348            NA               NA       FALSE NA       NA
THETA6            0.485  0.0395            NA               NA       FALSE NA       NA
THETA7           -0.0378 0.0635            NA               NA       FALSE NA       NA
THETA8            0.419  0.0863            NA               NA       FALSE NA       NA

Notes:

• Some parameter names may have punctuation (such as OMEGA(1,1)). A new name column is automatically added that specifies the name without punctuation (i.e. OMEGA11).

• Parameter details from the parameter key are joined to the bootstrap parameter estimates. A dplyr::inner_join is used so that only parameters in the model output are kept in the table. This was done so that, if your base and final model used the same structural THETAs and random parameters, the same parameter key could be used for both.

  • This join adds the following columns: abb (abbreviation), desc (parameter description), panel, trans (transformation).

• A final check is performed to determine whether parameters with special transformation rules were defined correctly. In addition, a series of TRUE/ FALSE columns are added that will be used downstream.

See also

param_key(), define_boot_table()

Examples

model_dir <- system.file("model/nonmem", package = "pmparams")
paramKey <-  file.path(model_dir, "pk-parameter-key-new.yaml")

# Using a file path
param_est_path <- file.path(model_dir, "param_est_102.csv")
param_ests <- readr::read_csv(param_est_path)
#> Rows: 12 Columns: 8
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): parameter_names
#> dbl (5): estimate, stderr, random_effect_sd, random_effect_sdse, shrinkage
#> lgl (2): fixed, diag
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

define_param_table(param_ests, paramKey, .ci = 95)
#> # A tibble: 12 × 33
#>    parameter_names estimate  stderr random_effect_sd random_effect_sdse fixed
#>    <chr>              <dbl>   <dbl>            <dbl>              <dbl> <lgl>
#>  1 THETA1            0.434  0.0629            NA               NA       FALSE
#>  2 THETA2            4.12   0.0276            NA               NA       FALSE
#>  3 THETA3            1.12   0.0328            NA               NA       FALSE
#>  4 THETA4            4.21   0.0192            NA               NA       FALSE
#>  5 THETA5            1.29   0.0354            NA               NA       FALSE
#>  6 OMEGA(1,1)        0.221  0.0530             0.470            0.0564  FALSE
#>  7 OMEGA(2,2)        0.0827 0.00983            0.288            0.0171  FALSE
#>  8 OMEGA(3,3)        0.169  0.0197             0.411            0.0240  FALSE
#>  9 OMEGA(2,1)        0.0690 0.0200             0.511            0.0975  FALSE
#> 10 OMEGA(3,1)        0.134  0.0236             0.694            0.0686  FALSE
#> 11 OMEGA(3,2)        0.0735 0.0105             0.622            0.0524  FALSE
#> 12 SIGMA(1,1)        0.0399 0.00123            0.200            0.00307 FALSE
#> # ℹ 27 more variables: diag <lgl>, shrinkage <dbl>, name <chr>, abb <chr>,
#> #   desc <chr>, panel <chr>, trans <chr>, nrow <int>, transTHETA <lgl>,
#> #   THETAERR <lgl>, TH <lgl>, OM <lgl>, S <lgl>, LOG <lgl>, LOGIT <lgl>,
#> #   lognormO <lgl>, Osd <lgl>, logitOsd <lgl>, propErr <lgl>, addErr <lgl>,
#> #   addErrLogDV <lgl>, value <dbl>, se <dbl>, corr_SD <dbl>, lower <dbl>,
#> #   upper <dbl>, ci_level <dbl>


# Using a `bbr` model
if (FALSE) { # \dontrun{
mod <- bbr::read_model(file.path(model_dir, "102"))

define_param_table(mod, paramKey, .ci = 95)
} # }