knit_print | R Documentation |
CohortSizeConst
ObjectWe provide additional utility functions to allow human-friendly rendition of crmPack objects in Markdown and Quarto files
We provide additional utility functions to allow human-friendly rendition of
crmPack objects in Markdown and Quarto files. This file contains methods for
all design classes, not just those that are direct descendants of Design
.
We provide additional utility functions to allow human-friendly rendition of crmPack objects in Markdown and Quarto files
## S3 method for class 'CohortSizeConst'
knit_print(x, ..., asis = TRUE, label = c("participant", "participants"))
## S3 method for class 'CohortSizeRange'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'CohortSizeDLT'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
## S3 method for class 'CohortSizeParts'
knit_print(x, ..., asis = TRUE, label = c("participant", "participants"))
## S3 method for class 'CohortSizeMax'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'CohortSizeMin'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'CohortSizeOrdinal'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
## S3 method for class 'StartingDose'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'RuleDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
## S3 method for class 'Design'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
## S3 method for class 'DualDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
## S3 method for class 'DADesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
## S3 method for class 'TDDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
## S3 method for class 'DualResponsesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
## S3 method for class 'DesignOrdinal'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
## S3 method for class 'DesignGrouped'
knit_print(
x,
...,
level = 2L,
title = "Design",
sections = c(model = "Dose toxicity model", mono = "Monotherapy rules", combo =
"Combination therapy rules", other = "Other details"),
asis = TRUE
)
## S3 method for class 'TDsamplesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
## S3 method for class 'DualResponsesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
## S3 method for class 'DualResponsesSamplesDesign'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
## S3 method for class 'RuleDesignOrdinal'
knit_print(x, ..., level = 2L, title = "Design", sections = NA, asis = TRUE)
## S3 method for class 'GeneralData'
knit_print(
x,
...,
asis = TRUE,
label = c("participant", "participants"),
full_grid = FALSE,
summarise = c("none", "dose", "cohort"),
summarize = summarise,
units = NA,
format_func = function(x) x
)
## S3 method for class 'DataParts'
knit_print(
x,
...,
asis = TRUE,
label = c("participant", "participants"),
full_grid = FALSE,
summarise = c("none", "dose", "cohort"),
summarize = summarise,
units = NA,
format_func = function(x) x
)
## S3 method for class 'DualEndpoint'
knit_print(
x,
...,
asis = TRUE,
use_values = TRUE,
fmt = "%5.2f",
units = NA,
tox_label = "toxicity",
biomarker_label = "PD biomarker"
)
## S3 method for class 'ModelParamsNormal'
knit_print(
x,
use_values = TRUE,
fmt = "%5.2f",
params = c("alpha", "beta"),
preamble = "The prior for θ is given by\\n",
asis = TRUE,
theta = "\\theta",
...
)
## S3 method for class 'GeneralModel'
knit_print(
x,
...,
params = c("alpha", "beta"),
asis = TRUE,
use_values = TRUE,
fmt = "%5.2f",
units = NA
)
## S3 method for class 'LogisticKadane'
knit_print(
x,
...,
asis = TRUE,
use_values = TRUE,
fmt = "%5.2f",
units = NA,
tox_label = "toxicity"
)
## S3 method for class 'LogisticKadaneBetaGamma'
knit_print(
x,
...,
asis = TRUE,
use_values = TRUE,
fmt = "%5.2f",
tox_label = "toxicity",
units = NA
)
## S3 method for class 'LogisticLogNormal'
knit_print(
x,
...,
use_values = TRUE,
fmt = "%5.2f",
params = c(`\\alpha` = "alpha", `log(\\beta)` = "beta"),
preamble = "The prior for θ is given by\\n",
asis = TRUE
)
## S3 method for class 'LogisticLogNormalMixture'
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)
## S3 method for class 'LogisticLogNormalSub'
knit_print(
x,
...,
use_values = TRUE,
fmt = "%5.2f",
params = c(`\\alpha` = "alpha", `log(\\beta)` = "beta"),
preamble = "The prior for θ is given by\\n",
asis = TRUE
)
## S3 method for class 'LogisticNormalMixture'
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)
## S3 method for class 'LogisticNormalFixedMixture'
knit_print(x, ..., asis = TRUE, use_values = TRUE, fmt = "%5.2f", units = NA)
## S3 method for class 'OneParLogNormalPrior'
knit_print(
x,
...,
tox_label = "toxicity",
asis = TRUE,
use_values = TRUE,
fmt = "%5.2f"
)
## S3 method for class 'OneParExpPrior'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'LogisticLogNormalGrouped'
knit_print(
x,
...,
use_values = TRUE,
fmt = "%5.2f",
params = c(`\\alpha` = "alpha", `\\beta` = "beta", `log(\\delta_0)` = "delta_0",
`log(\\delta_1)` = "delta_1"),
preamble = "The prior for θ is given by\\n",
asis = TRUE
)
## S3 method for class 'LogisticLogNormalOrdinal'
knit_print(
x,
...,
use_values = TRUE,
fmt = "%5.2f",
params = NA,
preamble = "The prior for θ is given by\\n",
asis = TRUE
)
## S3 method for class 'LogisticIndepBeta'
knit_print(
x,
...,
use_values = TRUE,
fmt = "%5.2f",
params = NA,
tox_label = "DLAE",
preamble = "The prior for θ is given by\\n",
asis = TRUE
)
## S3 method for class 'Effloglog'
knit_print(
x,
...,
use_values = TRUE,
fmt = "%5.2f",
params = NA,
tox_label = "DLAE",
eff_label = "efficacy",
label = "participant",
preamble = "The prior for θ is given by\\n",
asis = TRUE
)
## S3 method for class 'IncrementsRelative'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'IncrementsRelativeDLT'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'IncrementsDoseLevels'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'IncrementsHSRBeta'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'IncrementsMin'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'IncrementsOrdinal'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'IncrementsRelativeParts'
knit_print(x, ..., asis = TRUE, tox_label = c("toxicity", "toxicities"))
## S3 method for class 'IncrementsRelativeDLTCurrent'
knit_print(x, ..., asis = TRUE, tox_label = c("DLT", "DLTs"))
## S3 method for class 'NextBestMTD'
knit_print(
x,
...,
target_label = "the 25th centile",
tox_label = "toxicity",
asis = TRUE
)
## S3 method for class 'NextBestNCRM'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
## S3 method for class 'NextBestThreePlusThree'
knit_print(
x,
...,
tox_label = c("toxicity", "toxicities"),
label = "participant",
asis = TRUE
)
## S3 method for class 'NextBestDualEndpoint'
knit_print(
x,
...,
tox_label = "toxicity",
biomarker_label = "the biomarker",
biomarker_units = ifelse(x@target_relative, "%", ""),
asis = TRUE
)
## S3 method for class 'NextBestMinDist'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
## S3 method for class 'NextBestInfTheory'
knit_print(
x,
...,
tox_label = "toxicity",
citation_text = "Mozgunov & Jaki (2019)",
citation_link = "https://doi.org/10.1002/sim.8450",
asis = TRUE
)
## S3 method for class 'NextBestTD'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
## S3 method for class 'NextBestMaxGain'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
## S3 method for class 'NextBestProbMTDLTE'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
## S3 method for class 'NextBestProbMTDMinDist'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
## S3 method for class 'NextBestNCRMLoss'
knit_print(
x,
...,
tox_label = "toxicity",
asis = TRUE,
format_func = function(x) {
kableExtra::kable_styling(x, bootstrap_options =
c("striped", "hover", "condensed"))
}
)
## S3 method for class 'NextBestTDsamples'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
## S3 method for class 'NextBestMaxGainSamples'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
## S3 method for class 'NextBestOrdinal'
knit_print(x, ..., tox_label = "toxicity", asis = TRUE)
## S3 method for class 'SafetyWindow'
knit_print(x, ..., asis = TRUE, time_unit = "day", label = "participant")
## S3 method for class 'SafetyWindowConst'
knit_print(
x,
...,
asis = TRUE,
label = "participant",
ordinals = c("first", "second", "third", "fourth", "fifth", "sixth", "seventh",
"eighth", "ninth", "tenth"),
time_unit = "day"
)
## S3 method for class 'SafetyWindowSize'
knit_print(
x,
...,
asis = TRUE,
ordinals = c("first", "second", "third", "fourth", "fifth", "sixth", "seventh",
"eighth", "ninth", "tenth"),
label = "participant",
time_unit = "day",
level = 2L
)
## S3 method for class 'StoppingOrdinal'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'StoppingMaxGainCIRatio'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'StoppingList'
knit_print(x, ..., preamble, indent = 0L, asis = TRUE)
## S3 method for class 'StoppingAny'
knit_print(x, ..., preamble, asis = TRUE)
## S3 method for class 'StoppingAll'
knit_print(x, ..., preamble, asis = TRUE)
## S3 method for class 'StoppingTDCIRatio'
knit_print(
x,
...,
dose_label = "the next best dose",
tox_label = "toxicity",
fmt_string =
paste0("%sIf, at %s, the ratio of the upper to the lower limit of the posterior ",
"95%% credible interval for %s (targetting %2.0f%%) is less than or equal to "),
asis = TRUE
)
## S3 method for class 'StoppingTargetBiomarker'
knit_print(
x,
...,
dose_label = "the next best dose",
biomarker_label = "the target biomarker",
fmt_string =
paste0("%sIf, at %s, the posterior probability that %s is in the range ",
"(%.2f, %.2f)%s is %.0f%% or more.\n\n"),
asis = TRUE
)
## S3 method for class 'StoppingLowestDoseHSRBeta'
knit_print(
x,
...,
tox_label = "toxicity",
fmt_string =
paste0("%sIf, using a Hard Stopping Rule with a prior of Beta(%.0f, %.0f), the ",
"lowest dose in the dose grid has a posterior probability of %s of ",
"%.0f%% or more.\n\n"),
asis = TRUE
)
## S3 method for class 'StoppingMTDCV'
knit_print(
x,
...,
fmt_string =
paste0("%sIf the posterior estimate of the robust coefficient of variation of ",
"the MTD (targetting %2.0f%%), is than or equal to %.0f%%.\n\n"),
asis = TRUE
)
## S3 method for class 'StoppingMTDdistribution'
knit_print(
x,
...,
fmt_string =
"%sIf the mean posterior probability of %s at %.0f%% of %s is at least %4.2f.\n\n",
dose_label = "the next best dose",
tox_label = "toxicity",
asis = TRUE
)
## S3 method for class 'StoppingHighestDose'
knit_print(
x,
...,
dose_label = "the highest dose in the dose grid",
asis = TRUE
)
## S3 method for class 'StoppingSpecificDose'
knit_print(x, ..., dose_label = as.character(x@dose), asis = TRUE)
## S3 method for class 'StoppingTargetProb'
knit_print(
x,
...,
fmt_string =
paste0("%sIf the probability of %s at %s is in the range [%4.2f, %4.2f] ",
"is at least %4.2f.\n\n"),
dose_label = "the next best dose",
tox_label = "toxicity",
asis = TRUE
)
## S3 method for class 'StoppingMinCohorts'
knit_print(x, ..., asis = TRUE)
## S3 method for class 'StoppingMinPatients'
knit_print(x, ..., label = "participant", asis = TRUE)
## S3 method for class 'StoppingPatientsNearDose'
knit_print(
x,
...,
dose_label = "the next best dose",
label = "participants",
asis = TRUE
)
## S3 method for class 'StoppingCohortsNearDose'
knit_print(x, ..., dose_label = "the next best dose", asis = TRUE)
## S3 method for class 'StoppingMissingDose'
knit_print(x, ..., asis = TRUE)
x |
( |
... |
passed to |
asis |
( |
label |
( |
tox_label |
( |
level |
( |
title |
( |
sections |
( |
full_grid |
( |
summarise |
( |
summarize |
( |
units |
( |
format_func |
( |
use_values |
( |
fmt |
( |
biomarker_label |
( |
params |
( |
preamble |
( |
theta |
( |
eff_label |
( |
target_label |
( |
biomarker_units |
( |
citation_text |
( |
citation_link |
( |
time_unit |
( |
ordinals |
( |
indent |
( |
dose_label |
( |
fmt_string |
( |
a character string that represents the object in markdown.
The markdown representation of the object, as a character string
a character string that represents the object in markdown.
A character string containing a LaTeX rendition of the object.
a character string that represents the object in markdown.
label
describes the trial's participants.
It should be a character vector of length 1 or 2. If of length 2, the first
element describes a cohort_size
of 1 and the second describes all other
cohort_size
s. If of length 1, the character s
is appended to the value
when cohort_size
is not 1.
The default value of col.names
is c("Lower", "Upper", "Cohort size")
and
that of caption
is "Defined by the dose to be used in the next cohort"
.
These values can be overridden by passing col.names
and caption
in the
function call.
The by default, the columns are labelled Lower
, Upper
and Cohort size
.
The table's caption is Defined by the number of <tox_label[2]> so far observed
.
These values can be overridden by passing col.names
and caption
in the
function call.
label
describes the trial's participants.
It should be a character vector of length 1 or 2. If of length 2, the first
element describes a single participant and the second describes all other
situations. If of length 1, the character s
is appended to the value
when the number of participants is not 1.
The default values of col.names
and caption
vary depending on the summary
requested. The default values can be overridden by passing col.names
and
caption
in the function call.
params
must be a character vector of length equal to that of x@mean
(and
x@cov
). Its values represent the parameters of the model as entries in the
vector theta
, on the left-hand side of "~" in the definition of the prior.
If named, names should be valid LaTeX, escaped as usual for R character variables.
For example, "\\alpha"
or "\\beta_0"
. If unnamed, names are constructed by
pre-pending an escaped backslash to each value provided.
The default value of col.names
is c("Min", "Max", "Increment")
and that
of caption
is "Defined by highest dose administered so far"
. These
values can be overridden by passing col.names
and caption
in the function
call.
The default value of col.names
is c("Min", "Max", "Increment")
and that
of caption
is "Defined by number of DLTs reported so far"
. These values
can be overridden by passing col.names
and caption
in the function call.
label
defines how toxicities are described.
It should be a character vector of length 1 or 2. If of length 2, the first
element describes a single toxicity and the second describes all other
toxicity counts. If of length 1, the character s
is appended to the value
describing a single toxicity.
The default value of col.names
is c("Min", "Max", "Increment")
and that
of caption
is "Defined by number of DLTs in the current cohort"
. These values
can be overridden by passing col.names
and caption
in the function call.
tox_label
defines how toxicities are described.
It should be a character vector of length 1 or 2. If of length 2, the first
element describes a single toxicity and the second describes all other
toxicity counts. If of length 1, the character s
is appended to the value
describing a single toxicity.
This section describes the use of label
and tox_label
, collectively
referred to as label
s.
A label
should be a scalar or a vector of length 2. If a scalar, it is
converted by adding a second element that is equal to the first, suffixed by s
.
For example, tox_label = "DLT"
becomes tox_label = c("DLT", "DLTs")
. The
first element of the vector is used to describe a count of 1. The second
is used in all other cases.
To use a BibTeX-style citation, specify (for example) citation_text = "@MOZGUNOV", citation_link = ""
.
label
should be a character vector of length 1 or 2. If of length 2, the first
element describes a count of 1 and the second describes all other counts.
If of length 1, the character s
is appended to the value when the count is not 1.
label
and time_unit
are, collectively, labels.
A label should be a character vector of length 1 or 2. If of length 2, the first
element describes a count of 1 and the second describes all other counts.
If of length 1, the character s
is appended to the value when the count is not 1.
label
describes the trial's participants.
It should be a character vector of length 1 or 2. If of length 2, the first
element describes a cohort_size
of 1 and the second describes all other
cohort_size
s. If of length 1, the character s
is appended to the value
when cohort_size
is not 1.
The default value of col.names
is c("Lower", "Upper", "Cohort size")
and
that of caption
is "Defined by the dose to be used in the next cohort"
.
These values can be overridden by passing col.names
and caption
in the
function call.
The by default, the columns are labelled Lower
, Upper
and Cohort size
.
The table's caption is Defined by the number of <tox_label[2]> so far observed
.
These values can be overridden by passing col.names
and caption
in the
function call.
label
describes the trial's participants.
It should be a character vector of length 1 or 2. If of length 2, the first
element describes a single participant and the second describes all other
situations. If of length 1, the character s
is appended to the value
when the number of participants is not 1.
The default values of col.names
and caption
vary depending on the summary
requested. The default values can be overridden by passing col.names
and
caption
in the function call.
params
must be a character vector of length equal to that of x@mean
(and
x@cov
). Its values represent the parameters of the model as entries in the
vector theta
, on the left-hand side of "~" in the definition of the prior.
If named, names should be valid LaTeX, escaped as usual for R character variables.
For example, "\\alpha"
or "\\beta_0"
. If unnamed, names are constructed by
pre-pending an escaped backslash to each value provided.
The default value of col.names
is c("Min", "Max", "Increment")
and that
of caption
is "Defined by highest dose administered so far"
. These
values can be overridden by passing col.names
and caption
in the function
call.
The default value of col.names
is c("Min", "Max", "Increment")
and that
of caption
is "Defined by number of DLTs reported so far"
. These values
can be overridden by passing col.names
and caption
in the function call.
label
defines how toxicities are described.
It should be a character vector of length 1 or 2. If of length 2, the first
element describes a single toxicity and the second describes all other
toxicity counts. If of length 1, the character s
is appended to the value
describing a single toxicity.
The default value of col.names
is c("Min", "Max", "Increment")
and that
of caption
is "Defined by number of DLTs in the current cohort"
. These values
can be overridden by passing col.names
and caption
in the function call.
tox_label
defines how toxicities are described.
It should be a character vector of length 1 or 2. If of length 2, the first
element describes a single toxicity and the second describes all other
toxicity counts. If of length 1, the character s
is appended to the value
describing a single toxicity.
This section describes the use of label
and tox_label
, collectively
referred to as label
s.
A label
should be a scalar or a vector of length 2. If a scalar, it is
converted by adding a second element that is equal to the first, suffixed by s
.
For example, tox_label = "DLT"
becomes tox_label = c("DLT", "DLTs")
. The
first element of the vector is used to describe a count of 1. The second
is used in all other cases.
To use a BibTeX-style citation, specify (for example) citation_text = "@MOZGUNOV", citation_link = ""
.
label
should be a character vector of length 1 or 2. If of length 2, the first
element describes a count of 1 and the second describes all other counts.
If of length 1, the character s
is appended to the value when the count is not 1.
label
and time_unit
are, collectively, labels.
A label should be a character vector of length 1 or 2. If of length 2, the first
element describes a count of 1 and the second describes all other counts.
If of length 1, the character s
is appended to the value when the count is not 1.
knit_print
for more details.
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