Compute Fit Results — compute_fit

Computes all fitted results and residuals for GOF plots.

Usage

compute_fit_results(data, fit, dv_col, conc_col, ntime_col, trt_col = NULL)

Arguments

data: A data frame containing C-QT analysis dataset
fit: An nlme::lme model object from model fitting
dv_col: An unquoted column name for observed model dependent variable
conc_col: An unquoted column name for observed Concentration measurements
ntime_col: An unquoted column name for time points for measurements
trt_col: An unquoted column name for treatment group, default NULL

Value

A tibble with observed DV, concentration, time, population/individual predictions (PRED/IPRED), and residuals (RES/IRES/WRES/IWRES)

Examples

data_proc <- preprocess(cqtkit_data_verapamil)

fit <- fit_prespecified_model(
  data_proc,
  deltaQTCF,
  ID,
  CONC,
  deltaQTCFBL,
  TRTG,
  TAFD,
  "REML",
  TRUE
)

compute_fit_results(data_proc, fit, deltaQTCF, CONC, NTLD)
#> # A tibble: 643 × 10
#>        dv  conc  time   PRED   IPRED     RES   IRES     WRES   IWRES TRTG 
#>     <dbl> <dbl> <dbl>  <dbl>   <dbl>   <dbl>  <dbl>    <dbl>   <dbl> <chr>
#>  1  -6.00     0   0.5  -5.97  -6.15  -0.0329  0.153 -0.00532  0.0248 ""   
#>  2 -12.1      0   0.5 -14.4  -12.4    2.30    0.292  0.373    0.0472 ""   
#>  3  -5.54     0   0.5  -8.82  -6.94   3.28    1.40   0.530    0.226  ""   
#>  4 -22.5      0   0.5 -36.7  -21.8   14.2    -0.722  2.30    -0.117  ""   
#>  5   1.33     0   0.5  -4.37   0.515  5.70    0.811  0.921    0.131  ""   
#>  6  -6.58     0   0.5  -6.22 -10.0   -0.353   3.46  -0.0572   0.559  ""   
#>  7 -12.4      0   0.5 -26.1  -14.2   13.7     1.79   2.22     0.289  ""   
#>  8 -11.1      0   0.5 -15.6   -9.95   4.46   -1.14   0.722   -0.185  ""   
#>  9  -7.66     0   0.5  -7.11  -6.21  -0.557  -1.45  -0.0901  -0.235  ""   
#> 10  -2.73     0   0.5 -22.5   -6.11  19.8     3.37   3.20     0.545  ""   
#> # ℹ 633 more rows