For categorical DVs or similar endpoints (such as censoring
flag columns, like BLQ), this function allows probability
columns to be defined for each level.
Usage
set_dv_probs(
xpdb,
.problem = NULL,
...,
.dv_var = NULL,
.handle_missing = c("quiet", "warn", "error")
)Arguments
- xpdb
<
xp_xtras> object- .problem
<
numeric> Problem number to use. Uses all problems ifNULL(the default). May be omitted entirely and left to default, even when formulas are supplied positionally in....- ...
Formulas where LHS are levels or pseudo-functions (see Details), and RHS are columns with probabilities of those levels.
- .dv_var
<
tidyselect> of column having the categorical observation. Default is first-listedcatdv.- .handle_missing
<
character> How to handle missing levels: "quiet", "warn", or "error"
Details
The same probability cannot be assigned to multiple values. Pseudo-functions can be used, or
new columns can be created to overcome this limitation. The available pseudo-functions should
be written like ge(value) (for >=), gt(value) (for >), etc. These comparison names
are those used in Perl, Fortran and many other languages. The function eq() should not be used,
but it will be ignored either way; equivalence is implied with the base syntax.
Examples
pkpd_m3 %>%
# Not necessary, but correct to set var type before using this
set_var_types(.problem=1, catdv=BLQ, dvprobs=LIKE) %>%
# Set var type. Warnings can be helpful unless an inverse likelihood column is available
set_dv_probs(.problem=1, 1~LIKE, .dv_var = BLQ, .handle_missing = "warn") %>%
list_vars()
#> Warning: BLQ values are missing in probabilities: BLQ. If other probabilities don't add
#> up 1, the inverse will apply to uncounted levels.
#> Warning: This check does not consider qualifiers that may implicitly include uncounted
#> levels (eg, `lt(1)`).
#> List of available variables for problem no. 1
#> - Subject identifier (id) : ID
#> - Dependent variable (dv) : DV
#> - Categorical endpoint (catdv) : BLQ [0]
#> - DV Probabilities (dvprobs) : LIKE [P(*.eq.1)]
#> - Independent variable (idv) : TIME
#> - Dose amount (amt) : AMT
#> - Event identifier (evid) : EVID
#> - Missing dependent variable (mdv) : MDV
#> - Model typical predictions (pred) : PRED
#> - Eta (eta) : ETA1, ETA2
#> - Residuals (res) : NPDE, IWRES, CWRES, RES, WRES
#> - Not attributed (na) : DOSE, LLOQ, IPRE, IRES, BASE, KDEG, IMAX, IC50, CL, VC, Q, VP, KA
# Same as above with demo of inverse column
pkpd_m3 %>%
xpose::mutate(INVLIKE = 1-LIKE) %>%
set_var_types(.problem=1, catdv=BLQ, dvprobs=c(LIKE,INVLIKE)) %>%
# Note no warning
set_dv_probs(.problem=1, 1~LIKE, 0~INVLIKE, .dv_var = BLQ, .handle_missing = "warn")%>%
list_vars()
#> List of available variables for problem no. 1
#> - Subject identifier (id) : ID
#> - Dependent variable (dv) : DV
#> - Categorical endpoint (catdv) : BLQ [0]
#> - DV Probabilities (dvprobs) : LIKE [P(*.eq.1)], INVLIKE [P(*.eq.0)]
#> - Independent variable (idv) : TIME
#> - Dose amount (amt) : AMT
#> - Event identifier (evid) : EVID
#> - Missing dependent variable (mdv) : MDV
#> - Model typical predictions (pred) : PRED
#> - Eta (eta) : ETA1, ETA2
#> - Residuals (res) : NPDE, IWRES, CWRES, RES, WRES
#> - Not attributed (na) : DOSE, LLOQ, IPRE, IRES, BASE, KDEG, IMAX, IC50, CL, VC, Q, VP, KA
# With categorical model
vismo_pomod %>%
# Update var types
set_var_types(.problem=1, catdv=DV, dvprobs=matches("^P\\d+$")) %>%
# Warning (as noted), does not recognize 3 is covered implicitly. That's ok!
set_dv_probs(.problem=1, 0~P0,1~P1,ge(2)~P23, .handle_missing = "warn")%>%
list_vars()
#> Warning: DV values are missing in probabilities: DV. If other probabilities don't add up
#> 1, the inverse will apply to uncounted levels.
#> Warning: This check does not consider qualifiers that may implicitly include uncounted
#> levels (eg, `lt(1)`).
#> List of available variables for problem no. 1
#> - Subject identifier (id) : ID
#> - Categorical endpoint (catdv) : DV [0]
#> - DV Probabilities (dvprobs) : P0 [P(*.eq.0)], P1 [P(*.eq.1)], P23 [P(*.ge.2)]
#> - Independent variable (idv) : TIME
#> - Dose amount (amt) : AMT
#> - Event identifier (evid) : EVID
#> - Model typical predictions (pred) : PRED
#> - Eta (eta) : ETA1
#> - Residuals (res) : RES, WRES
#> - Not attributed (na) : STUDY, COHORT, PTNM, FRMU, BAAG, AGE, BWT, PTS, B1, B2, Y, U449E, CE
# Same as above, but...
vismo_pomod %>%
set_var_types(.problem=1, catdv=DV, dvprobs=matches("^P\\d+$")) %>%
# Default is to not bother users with a warning
set_dv_probs(.problem=1, 0~P0,1~P1,ge(2)~P23)%>%
list_vars()
#> List of available variables for problem no. 1
#> - Subject identifier (id) : ID
#> - Categorical endpoint (catdv) : DV [0]
#> - DV Probabilities (dvprobs) : P0 [P(*.eq.0)], P1 [P(*.eq.1)], P23 [P(*.ge.2)]
#> - Independent variable (idv) : TIME
#> - Dose amount (amt) : AMT
#> - Event identifier (evid) : EVID
#> - Model typical predictions (pred) : PRED
#> - Eta (eta) : ETA1
#> - Residuals (res) : RES, WRES
#> - Not attributed (na) : STUDY, COHORT, PTNM, FRMU, BAAG, AGE, BWT, PTS, B1, B2, Y, U449E, CE
# .problem can be omitted for single-problem models
pkpd_m3 %>%
set_var_types(catdv=BLQ, dvprobs=LIKE) %>%
set_dv_probs(1~LIKE, .dv_var = BLQ) %>%
list_vars()
#> List of available variables for problem no. 1
#> - Subject identifier (id) : ID
#> - Dependent variable (dv) : DV
#> - Categorical endpoint (catdv) : BLQ [0]
#> - DV Probabilities (dvprobs) : LIKE [P(*.eq.1)]
#> - Independent variable (idv) : TIME
#> - Dose amount (amt) : AMT
#> - Event identifier (evid) : EVID
#> - Missing dependent variable (mdv) : MDV
#> - Model typical predictions (pred) : PRED
#> - Eta (eta) : ETA1, ETA2
#> - Residuals (res) : NPDE, IWRES, CWRES, RES, WRES
#> - Not attributed (na) : DOSE, LLOQ, IPRE, IRES, BASE, KDEG, IMAX, IC50, CL, VC, Q, VP, KA