For piping, set is passed, but with S3 method transformations are applied to the focused xpdb object.
Usage
focus_xpdb(xpdb_s, ..., .add = FALSE)
unfocus_xpdb(xpdb_s)
focused_xpdbs(xpdb_s)
focus_function(xpdb_s, fn, ...)
focus_qapply(xpdb_s, fn, ..., .mods = everything())Arguments
- xpdb_s
<
xpose_set> An xpose_set object- ...
<
dynamic-dots> One or more xpdb objects to focus on- .add
<
logical> Should the focus be added to the existing focus? (default:FALSE)- fn
<
function> to apply to focusedxpose_dataobjects- .mods
<
tidyselect> Model names in set to quick-apply a function. See Details.
Details
While these functions are used internally, it is recognized that they may have value in user scripting. It is hoped these are self-explanatory, but the examples should address common uses.
Note: focus_qapply() (re)focuses as specified in .mods and then un-focuses all elements
of the set so should only be used in the case where a quick application
suffices. Otherwise, focusing with a sequence of focus_function calls
(or a monolithic single focus_function call with a custom function)
should be preferred.
Examples
# Select two xpdb objects to focus on
xpdb_set %>% focus_xpdb(mod2,fix1)
#>
#> ── xpose_set object ────────────────────────────────────────────────────────────
#> • Number of models: 4
#> • Model labels: mod1, mod2, fix1, and fix2
#> • Number of relationships: 3
#> • Focused xpdb objects: mod2 and fix1
#> • Exposed properties: none
#> • Base model: none
# Add a focus
xpdb_set %>% focus_xpdb(mod2,fix1) %>% focus_xpdb(mod1, .add=TRUE)
#>
#> ── xpose_set object ────────────────────────────────────────────────────────────
#> • Number of models: 4
#> • Model labels: mod1, mod2, fix1, and fix2
#> • Number of relationships: 3
#> • Focused xpdb objects: mod1, mod2, and fix1
#> • Exposed properties: none
#> • Base model: none
# Remove focus
xpdb_set %>% focus_xpdb(mod2,fix1) %>% focus_xpdb()
#>
#> ── xpose_set object ────────────────────────────────────────────────────────────
#> • Number of models: 4
#> • Model labels: mod1, mod2, fix1, and fix2
#> • Number of relationships: 3
#> • Focused xpdb objects: none
#> • Exposed properties: none
#> • Base model: none
# Focus function and tidyselect
pheno_set %>%
focus_xpdb(everything()) %>%
# Add iOFV col and iofv type to all xpdbs in set
focus_function(backfill_iofv) %>%
# Show 1... can do all like this, too, but no need
unfocus_xpdb() %>%
select(run6) %>%
{.[[1]]$xpdb} %>%
list_vars()
#> List of available variables for problem no. 1
#> - Subject identifier (id) : ID
#> - Dependent variable (dv) : DV
#> - Independent variable (idv) : TIME
#> - Dose amount (amt) : AMT
#> - Event identifier (evid) : EVID
#> - Missing dependent variable (mdv) : MDV
#> - Model typical predictions (pred) : PRED
#> - Model individual predictions (ipred) : IPRED
#> - Eta (eta) : ETA1, ETA2
#> - Individual OFV (iofv) : iOFV
#> - Residuals (res) : IWRES, CWRES, NPDE, RES, WRES
#> - Categorical covariates (catcov) : APGR ('Apgar score') [10]
#> - Continuous covariates (contcov) : WT ('Weight', kg)
#> - Not attributed (na) : IRES, CL, V, CRES
# Quick-apply version of previous example
pheno_set %>%
focus_qapply(backfill_iofv) %>%
select(run6) %>%
{.[[1]]$xpdb} %>%
list_vars()
#> List of available variables for problem no. 1
#> - Subject identifier (id) : ID
#> - Dependent variable (dv) : DV
#> - Independent variable (idv) : TIME
#> - Dose amount (amt) : AMT
#> - Event identifier (evid) : EVID
#> - Missing dependent variable (mdv) : MDV
#> - Model typical predictions (pred) : PRED
#> - Model individual predictions (ipred) : IPRED
#> - Eta (eta) : ETA1, ETA2
#> - Individual OFV (iofv) : iOFV
#> - Residuals (res) : IWRES, CWRES, NPDE, RES, WRES
#> - Categorical covariates (catcov) : APGR ('Apgar score') [10]
#> - Continuous covariates (contcov) : WT ('Weight', kg)
#> - Not attributed (na) : IRES, CL, V, CRES