<- pmplots_data_obs()
data
npde_panel(data)
Displays of exploratory or diagnostic plots have several figures sharing a common theme arranged on a single page. These displays tend to have fixed composition and arrangement, with some customization allowed. If you want a totally custom display, it is best to create your own from base pmplots functions, arranging as you like with the patchwork package.
Displays can include ETA, NPDE or CWRES based plots.
12.1 NPDE-based diagnositcs
Use npde_panel()
to create a standardized panel of common NPDE-based diagnostics
12.1.1 Tag levels
You can label the panels with the plot_annotation()
function from the patchwork package
npde_panel(data) +
plot_annotation(tag_levels = "A")
12.1.2 NPDE scatter
This display includes just a subset of the diagnostics which are scatter plots
npde_scatter(data)
12.1.3 NPDE histogram and qq plot
This display is the complement of the scatter plots
npde_hist_q(data)
12.2 NPDE versus covariate
You can enter a mix of categorical and continuous covariates: here, WT
and SCR
are continuous covariates and RF
and CPc
are categorical
<- c(
covs "WT // Weight (kg)",
"SCR // Serum creatinine (mg/dL)",
"RF // Renal group",
"CPc // Child-Pugh"
)npde_covariate(data, covs)
12.3 CWRES-based diagnostics
There are a equivalent set of displays which show CWRES
rather than NPDE
cwres_panel(data)
12.3.1 CWRES scatter
cwres_scatter(data)
12.3.2 CWRES histogram and qq plot
cwres_hist_q(data)
12.4 CWRES versus covariate
<- c(
covs "WT // Weight (kg)",
"SCR // Serum creatinine (mg/dL)",
"RF // Renal group",
"CPc // Child-Pugh"
)cwres_covariate(data, covs)
12.5 ETA versus covariate
These displays are created with eta_covariate()
. Since there are typically several ETAs to look at, this function returns a list of arranged plots
<- pmplots_data_id() id
<- eta_col_labs(CL, V2, KA)
etas etas
ETA-CL ETA-V2 ETA-KA
"ETA1//ETA-CL" "ETA2//ETA-V2" "ETA3//ETA-KA"
For ETA diagnostics, we can plot versus a mix of categorical and continuous covariates
<- c(
covs "WT // Weight (kg)",
"AAG // AAG (mg/dL)",
"AGE // Age (years)",
"STUDYc // Study",
"RF // Renal function",
"CPc // Child-Pugh"
)
<- eta_covariate(id, x = covs , y = etas, byrow = FALSE) cov
By default, the plots are collected by the ETA
names(cov)
[1] "ETA1" "ETA2" "ETA3"
$ETA1 cov
Use the transpose
argument to collect by the covariate
<- eta_covariate(id, covs, etas, transpose = TRUE, ncol = 1)
cov
names(cov)
[1] "WT" "AAG" "AGE" "STUDYc" "RF" "CPc"
$WT cov