| Title: | Create and Evaluate NONMEM Models in a Project Context |
|---|---|
| Description: | Systematically creates and modifies NONMEM(R) control streams. Harvests NONMEM output, builds run logs, creates derivative data, generates diagnostics. NONMEM (ICON Development Solutions <https://www.iconplc.com/>) is software for nonlinear mixed effects modeling. See 'package?nonmemica'. |
| Authors: | Tim Bergsma [aut, cre] |
| Maintainer: | Tim Bergsma <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.11 |
| Built: | 2024-10-26 03:36:56 UTC |
| Source: | CRAN |
Checks if file path is absolute.
absolute(x)absolute(x)
x |
character (a file path) |
logical; TRUE if x starts with / or .: (e.g. C:)
Coerces character to 'nms_canonical'.
as_nms_canonical(x, ...)as_nms_canonical(x, ...)
x |
character |
... |
ignored |
Other nms:
as_nms_nonmem(),
as_nms_pmx(),
as_nms_psn(),
nms_canonical(),
nms_canonical.character(),
nms_canonical.model(),
nms_canonical.nms_nonmem(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.character(),
nms_nonmem.model(),
nms_nonmem.nms_canonical(),
nms_nonmem.nms_pmx(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_canonical(),
nms_pmx.nms_nonmem(),
nms_psn(),
nms_psn.character(),
nms_psn.model(),
nms_psn.numeric()
Coerces character to 'nms_nonmem'.
as_nms_nonmem(x, ...)as_nms_nonmem(x, ...)
x |
character |
... |
ignored |
Other nms:
as_nms_canonical(),
as_nms_pmx(),
as_nms_psn(),
nms_canonical(),
nms_canonical.character(),
nms_canonical.model(),
nms_canonical.nms_nonmem(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.character(),
nms_nonmem.model(),
nms_nonmem.nms_canonical(),
nms_nonmem.nms_pmx(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_canonical(),
nms_pmx.nms_nonmem(),
nms_psn(),
nms_psn.character(),
nms_psn.model(),
nms_psn.numeric()
Coerces character to 'nms_pmx'.
as_nms_pmx(x, ...)as_nms_pmx(x, ...)
x |
character |
... |
ignored |
Other nms:
as_nms_canonical(),
as_nms_nonmem(),
as_nms_psn(),
nms_canonical(),
nms_canonical.character(),
nms_canonical.model(),
nms_canonical.nms_nonmem(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.character(),
nms_nonmem.model(),
nms_nonmem.nms_canonical(),
nms_nonmem.nms_pmx(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_canonical(),
nms_pmx.nms_nonmem(),
nms_psn(),
nms_psn.character(),
nms_psn.model(),
nms_psn.numeric()
Coerces character to 'nms_psn'.
as_nms_psn(x, ...)as_nms_psn(x, ...)
x |
character |
... |
ignored |
Other nms:
as_nms_canonical(),
as_nms_nonmem(),
as_nms_pmx(),
nms_canonical(),
nms_canonical.character(),
nms_canonical.model(),
nms_canonical.nms_nonmem(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.character(),
nms_nonmem.model(),
nms_nonmem.nms_canonical(),
nms_nonmem.nms_pmx(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_canonical(),
nms_pmx.nms_nonmem(),
nms_psn(),
nms_psn.character(),
nms_psn.model(),
nms_psn.numeric()
Convert vector to best of numeric or character.
## Default S3 method: as.best(x, prefix = "", na.strings = c(".", "NA", ""), ...)## Default S3 method: as.best(x, prefix = "", na.strings = c(".", "NA", ""), ...)
x |
default |
prefix |
prefix for viewing numerics |
na.strings |
strings that should be treated as NA |
... |
ignored |
Converts vector to numeric if doing so creates no new NA; otherwise to character. Number-like vectors that are nevertheless character are prefixed by default to make this explicit when viewing only a few rows.
Other util:
%contains%(),
as.best(),
enclose(),
locf(),
padded(),
parens(),
pool(),
runhead(),
text2decimal()
Creates bootstrap from bootstrap.
## S3 method for class 'bootstrap' as.bootstrap(x, ...)## S3 method for class 'bootstrap' as.bootstrap(x, ...)
x |
bootstrap |
... |
ignored |
data.frame
Other as.bootstrap:
as.bootstrap(),
as.bootstrap.numeric()
Coerces to half matrix. Treats x as halfmatrix, coerces to matrix and takes half.
## Default S3 method: as.halfmatrix(x, ...)## Default S3 method: as.halfmatrix(x, ...)
x |
object |
... |
passed arguments |
Other halfmatrix:
as.data.frame.halfmatrix(),
as.halfmatrix(),
as.halfmatrix.halfmatrix(),
as.matrix.halfmatrix(),
half(),
half.matrix(),
is.square(),
is.square.matrix(),
offdiag(),
offdiag.halfmatrix(),
ord(),
ord.halfmatrix(),
ord.matrix(),
print.halfmatrix()
Coerces init to init
## S3 method for class 'init' as.init(x = numeric(0), fixed = FALSE, comment = character(0), ...)## S3 method for class 'init' as.init(x = numeric(0), fixed = FALSE, comment = character(0), ...)
x |
init |
fixed |
logical |
comment |
character |
... |
passed arguments |
init
Other as.init:
as.init(),
as.init.numeric()
Coerces list to inits.
## S3 method for class 'list' as.inits(x, comment = character(0), ...)## S3 method for class 'list' as.inits(x, comment = character(0), ...)
x |
list |
comment |
character |
... |
passed arguments |
inits
Other as.inits:
as.inits(),
as.inits.numeric()
Coerces half matrix to matrix.
## S3 method for class 'halfmatrix' as.matrix(x, ...)## S3 method for class 'halfmatrix' as.matrix(x, ...)
x |
object |
... |
passed arguments |
Other halfmatrix:
as.data.frame.halfmatrix(),
as.halfmatrix(),
as.halfmatrix.default(),
as.halfmatrix.halfmatrix(),
half(),
half.matrix(),
is.square(),
is.square.matrix(),
offdiag(),
offdiag.halfmatrix(),
ord(),
ord.halfmatrix(),
ord.matrix(),
print.halfmatrix()
Coerces chacter to model.
## S3 method for class 'character' as.model( x, pattern = "^\\s*\\$(\\S+)(\\s.*)?$", head = "\\1", tail = "\\2", parse = TRUE, ... )## S3 method for class 'character' as.model( x, pattern = "^\\s*\\$(\\S+)(\\s.*)?$", head = "\\1", tail = "\\2", parse = TRUE, ... )
x |
character |
pattern |
pattern to identify record declarations |
head |
subpattern to identify declaration type |
tail |
subpattern remaining |
parse |
whether to convert thetas omegas and sigmas to inits, tables to items, and runrecords to fields |
... |
ignored |
list
Other as.model:
[.model(),
[[.model(),
as.model(),
as.model.numeric(),
read.model(),
write.model()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.modellibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.model
Creates an xml_document in a project context.
Coerces xml_document to xml_document
as.xml_document(x, ...) ## S3 method for class 'xml_document' as.xml_document(x, ...)as.xml_document(x, ...) ## S3 method for class 'xml_document' as.xml_document(x, ...)
x |
xml_document |
... |
ignored |
xml_document
xml_document
as.xml_document(xml_document): xml_document method
Other xpath:
as.xml_document.character(),
as.xml_document.numeric(),
xpath(),
xpath.default(),
xpath.xml_document()
Other xpath:
as.xml_document.character(),
as.xml_document.numeric(),
xpath(),
xpath.default(),
xpath.xml_document()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.xml_documentlibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.xml_document
Creates an xml_document from character (modelname or filepath).
## S3 method for class 'character' as.xml_document(x, strip.namespace = TRUE, ...)## S3 method for class 'character' as.xml_document(x, strip.namespace = TRUE, ...)
x |
file path or run name |
strip.namespace |
whether to strip e.g. nm: from xml elements |
... |
passed to modelpath() |
xml_document
Other xpath:
as.xml_document(),
as.xml_document.numeric(),
xpath(),
xpath.default(),
xpath.xml_document()
Extracts comments from inits.
## S3 method for class 'inits' comments(x, type, prior, ...)## S3 method for class 'inits' comments(x, type, prior, ...)
x |
inits |
type |
item type: theta, omega, sigma (tables give items not inits) |
prior |
number of prior items of this type (maybe imporant for numbering) |
... |
ignored |
data.frame
Other comments:
comments(),
comments.items(),
comments.model()
Extracts comments from items.
## S3 method for class 'items' comments(x, ...)## S3 method for class 'items' comments(x, ...)
x |
items |
... |
ignored |
data.frame
Other comments:
comments(),
comments.inits(),
comments.model()
Extracts comments from model.
## S3 method for class 'model' comments( x, fields = c("symbol", "unit", "label"), expected = character(0), na = NA_character_, tables = TRUE, ... )## S3 method for class 'model' comments( x, fields = c("symbol", "unit", "label"), expected = character(0), na = NA_character_, tables = TRUE, ... )
x |
model |
fields |
data items to scavenge from control stream comments |
expected |
parameters known from NONMEM output |
na |
string to use for NA values when writing default metafile |
tables |
whether to include table comments |
... |
passed arguments |
data.frame
Other comments:
comments(),
comments.inits(),
comments.items()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.model %>% commentslibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.model %>% comments
Checks whether text contains pattern.
contains(pattern, text, ...)contains(pattern, text, ...)
pattern |
regular expression |
text |
character vector to check |
... |
arguments to methods |
logical
Identifies the datafile used by a model. Expresses it relative to current working directory.
## S3 method for class 'character' datafile(x, ...)## S3 method for class 'character' datafile(x, ...)
x |
the model name or path to a control stream |
... |
ext can be passed to modelfile, etc. |
character
Other path:
datafile(),
datafile.numeric(),
modeldir(),
modelfile(),
modelpath(),
modelpath.character(),
modelpath.numeric(),
psn_options(),
specfile(),
specfile.character(),
specfile.numeric()
library(spec) source <- system.file(package = 'nonmemica','project') target <- tempdir() target <- gsub('\\\\','/',target) # for windows file.copy(source,target,recursive = TRUE) project <- file.path(target,'project','model') options(project = project) library(magrittr) 1001 %>% datafile datafile(1001) %matches% specfile(1001) 1001 %>% specfile 1001 %>% specfile %>% read.speclibrary(spec) source <- system.file(package = 'nonmemica','project') target <- tempdir() target <- gsub('\\\\','/',target) # for windows file.copy(source,target,recursive = TRUE) project <- file.path(target,'project','model') options(project = project) library(magrittr) 1001 %>% datafile datafile(1001) %matches% specfile(1001) 1001 %>% specfile 1001 %>% specfile %>% read.spec
Havests model item definitions.
Create Item Definitions from Model Name
definitions(x, ...) ## S3 method for class 'character' definitions( x, verbose = FALSE, ctlfile = modelfile(x, ...), metafile = modelpath(x, "def", ...), fields = getOption("fields", default = c("symbol", "label", "unit")), read = length(metafile) == 1, write = FALSE, ... )definitions(x, ...) ## S3 method for class 'character' definitions( x, verbose = FALSE, ctlfile = modelfile(x, ...), metafile = modelpath(x, "def", ...), fields = getOption("fields", default = c("symbol", "label", "unit")), read = length(metafile) == 1, write = FALSE, ... )
x |
object of dispatch |
... |
arguments to methods |
verbose |
set FALSE to suppress messages |
ctlfile |
path to control stream (pass length-zero argument to ignore) |
metafile |
path to definitions file (pass length-zero argument to ignore) |
fields |
metadata fields to read from control stream if no metafile |
read |
whether to read the definitions file |
write |
whether to write the definitions file |
x can be numeric or character model name, assuming project is identified by argument or option.
Creates item definitions from a model name. Scavenges definitions optionally from the control stream and optionally from the definitions file. Optionally writes the result to the definitions file. Always returns a data.frame with at least the column 'item' but possibly no rows.
object of class definitions, or path to metafile if write = TRUE.
definitions(character): character method
Other definitions:
definitions.definitions(),
definitions.numeric()
Other definitions:
definitions.definitions(),
definitions.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% definitionslibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% definitions
Creates a model item definitions from a definitions object.
## S3 method for class 'definitions' definitions(x, ...)## S3 method for class 'definitions' definitions(x, ...)
x |
definitions |
... |
ignored |
Just returns the object unmodified.
Other definitions:
definitions(),
definitions.numeric()
Identify those models in the lineage of models in x.
## Default S3 method: depends(x, ...)## Default S3 method: depends(x, ...)
x |
object |
... |
passed arguments |
character
Other depends:
depends()
Gets model asymptotic standard errors in canonical order, treating character as model names.
See parameters for a less formal interface.
## S3 method for class 'character' errors( x, xmlfile = modelpath(x, ext = "xml", ...), strip.namespace = TRUE, digits = 3, ... )## S3 method for class 'character' errors( x, xmlfile = modelpath(x, ext = "xml", ...), strip.namespace = TRUE, digits = 3, ... )
x |
character (modelname) |
xmlfile |
path to xml file |
strip.namespace |
whether to strip e.g. nm: from xml elements for easier xpath syntax |
digits |
passed to signif |
... |
dots |
numeric
nms_canonical errors
Other errors:
errors(),
errors.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% errorslibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% errors
Gets model parameter estimates in canonical order, treating character as model names.
See parameters for a less formal interface.
## S3 method for class 'character' estimates( x, xmlfile = modelpath(x, ext = "xml", ...), strip.namespace = TRUE, digits = 3, ... )## S3 method for class 'character' estimates( x, xmlfile = modelpath(x, ext = "xml", ...), strip.namespace = TRUE, digits = 3, ... )
x |
character (modelname) |
xmlfile |
path to xml file |
strip.namespace |
whether to strip e.g. nm: from xml elements for easier xpath syntax |
digits |
passed to signif |
... |
dots |
numeric
nms_canonical errors
Other estimates:
estimates(),
estimates.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% estimateslibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% estimates
Checks if init is fixed.
## S3 method for class 'init' fixed(x, ...)## S3 method for class 'init' fixed(x, ...)
x |
init |
... |
ignored |
logical
Other fixed:
fixed(),
fixed.model(),
fixed<-(),
fixed<-.init(),
fixed<-.inits(),
fixed<-.model()
Checks if model is fixed. Returns a logical vector with element for each init, in canonical order.
## S3 method for class 'model' fixed(x, ...)## S3 method for class 'model' fixed(x, ...)
x |
inits |
... |
ignored |
logical
Other fixed:
fixed(),
fixed.init(),
fixed<-(),
fixed<-.init(),
fixed<-.inits(),
fixed<-.model()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.model %>% fixedlibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.model %>% fixed
#Generalize a nonmissing value. If there is only one such among zero or more NA, impute that value for all NA.
generalize(x, ...)generalize(x, ...)
x |
vector |
... |
ignored |
Other superset:
ignored(),
meta(),
meta.character(),
meta.numeric(),
metaplot.character(),
metaplot.numeric(),
metaplot_character(),
metasuperset(),
ninput(),
ninput.character(),
ninput.numeric(),
shuffle(),
superset(),
superset.character(),
superset.numeric(),
superspec(),
superspec.character(),
superspec.numeric()
Extracts index for ignored records, given a NONMEM control stream.
ignored( x, read.input = list(read.csv, header = TRUE, as.is = TRUE), ext = getOption("modex", "ctl"), project = getOption("project", getwd()), nested = getOption("nested", TRUE), ... )ignored( x, read.input = list(read.csv, header = TRUE, as.is = TRUE), ext = getOption("modex", "ctl"), project = getOption("project", getwd()), nested = getOption("nested", TRUE), ... )
x |
length-one character: a model name |
read.input |
list of arguments representing a methodology for acquiring the data of interest: the first argument is not named and will be passed to match.fun(); the other arguments will be passed to the result, and must include an argument named 'header'. |
ext |
model file extension, e.g. 'mod' or 'ctl' |
project |
project directory (can be expression) |
nested |
whether model files are nested in eponymous directories |
... |
passed to |
logical
Other superset:
generalize(),
meta(),
meta.character(),
meta.numeric(),
metaplot.character(),
metaplot.numeric(),
metaplot_character(),
metasuperset(),
ninput(),
ninput.character(),
ninput.numeric(),
shuffle(),
superset(),
superset.character(),
superset.numeric(),
superspec(),
superspec.character(),
superspec.numeric()
Gets model initial estimates.
## S3 method for class 'model' initial(x, ...)## S3 method for class 'model' initial(x, ...)
x |
model |
... |
dots |
Other initial:
initial(),
initial<-(),
initial<-.model()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.model %>% initiallibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.model %>% initial
Sets upper bounds for model initial estimates.
## S3 replacement method for class 'model' initial(x) <- value## S3 replacement method for class 'model' initial(x) <- value
x |
model |
value |
numeric |
Other initial:
initial(),
initial.model(),
initial<-()
Makes a copy of a model in a corresponding directory. Problem statement is updated to reflect that the model is LIKE the reference model BUT different in some fundamental way.
likebut( x, but = "better", y = NULL, project = getOption("project", getwd()), nested = getOption("nested", TRUE), overwrite = FALSE, ext = getOption("modex", "ctl"), include = "\\.def$", update = FALSE, ... )likebut( x, but = "better", y = NULL, project = getOption("project", getwd()), nested = getOption("nested", TRUE), overwrite = FALSE, ext = getOption("modex", "ctl"), include = "\\.def$", update = FALSE, ... )
x |
a model name, presumably interpretable as numeric |
but |
a short description of the characteristic difference from x |
y |
optional name for model to be created, auto-incremented by default |
project |
project directory (can be expression) |
nested |
model files nested in run-specific directories |
overwrite |
whether to overwrite y if it exists |
ext |
extension for the model file |
include |
regular expressions for files to copy to new directory |
update |
use final estimates of x as initial estimates of y |
... |
passed arguments, including PsN runrecord elements (experimental) |
the value of y
# Create a working project. source <- system.file(package = 'nonmemica','project') target <- tempdir() target <- gsub('\\\\','/',target) # for windows source target file.copy(source,target,recursive = TRUE) project <- file.path(target,'project','model') # Point project option at working project options(project = project) library(magrittr) # Derive models. 1001 %>% likebut('revised',y = 1002, overwrite=TRUE ) # At this point, edit 1002.ctl to match whatever 'revised' means. # Then run it with NONMEM.# Create a working project. source <- system.file(package = 'nonmemica','project') target <- tempdir() target <- gsub('\\\\','/',target) # for windows source target file.copy(source,target,recursive = TRUE) project <- file.path(target,'project','model') # Point project option at working project options(project = project) library(magrittr) # Derive models. 1001 %>% likebut('revised',y = 1002, overwrite=TRUE ) # At this point, edit 1002.ctl to match whatever 'revised' means. # Then run it with NONMEM.
Gets lower bounds for model initial estimates.
## S3 method for class 'model' lower(x, ...)## S3 method for class 'model' lower(x, ...)
x |
model |
... |
dots |
Other lower:
lower(),
lower<-(),
lower<-.model()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.model %>% lowerlibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.model %>% lower
Sets lower bounds for model initial estimates.
## S3 replacement method for class 'model' lower(x) <- value## S3 replacement method for class 'model' lower(x) <- value
x |
model |
value |
numeric |
Other lower:
lower(),
lower.model(),
lower<-()
Gets metadata for character, treating it as a model name. Blends metadata from specfile with metadata from control stream, removing both exact duplicates as well as redefined values (with warning).
## S3 method for class 'character' meta(x, simplify = TRUE, ...)## S3 method for class 'character' meta(x, simplify = TRUE, ...)
x |
object |
simplify |
logical: remove range information from guide text |
... |
passed arguments |
data.frame
Other superset:
generalize(),
ignored(),
meta(),
meta.numeric(),
metaplot.character(),
metaplot.numeric(),
metaplot_character(),
metasuperset(),
ninput(),
ninput.character(),
ninput.numeric(),
shuffle(),
superset(),
superset.character(),
superset.numeric(),
superspec(),
superspec.character(),
superspec.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% metalibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% meta
Plots character by treating as model name. A dataset is constructed by combining the model input with a the model output and calling metaplot with the result.
metaplot_character(x, groups, meta = NULL, subset, var, ...)metaplot_character(x, groups, meta = NULL, subset, var, ...)
x |
object |
groups |
columns by which to group the dataset |
meta |
metadata; meta(x) by default |
subset |
a condition for filtering data |
var |
variables to plot |
... |
passed arguments |
Other superset:
generalize(),
ignored(),
meta(),
meta.character(),
meta.numeric(),
metaplot.character(),
metaplot.numeric(),
metasuperset(),
ninput(),
ninput.character(),
ninput.numeric(),
shuffle(),
superset(),
superset.character(),
superset.numeric(),
superspec(),
superspec.character(),
superspec.numeric()
Plots character by treating as model name. A dataset is constructed by combining the meta version of the model input with a meta version of the model output and calling metaplot with the result.
## S3 method for class 'character' metaplot(x, ..., groups, meta = match.fun("meta")(x), subset)## S3 method for class 'character' metaplot(x, ..., groups, meta = match.fun("meta")(x), subset)
x |
object |
... |
unquoted names of variables to plot, or other named arguments (passed) |
groups |
columns by which to group the dataset |
meta |
metadata; meta(x) by default |
subset |
a condition for filtering data |
Other superset:
generalize(),
ignored(),
meta(),
meta.character(),
meta.numeric(),
metaplot.numeric(),
metaplot_character(),
metasuperset(),
ninput(),
ninput.character(),
ninput.numeric(),
shuffle(),
superset(),
superset.character(),
superset.numeric(),
superspec(),
superspec.character(),
superspec.numeric()
library(magrittr) library(metaplot) options(project = system.file('project/model',package='nonmemica')) ## Not run: 1001 %>% metaplot( CWRESI, TAD, SEX, groups = c('ID','TIME'), subset = 'MDV == 0', yref = 0, ysmooth = TRUE ) ## End(Not run)library(magrittr) library(metaplot) options(project = system.file('project/model',package='nonmemica')) ## Not run: 1001 %>% metaplot( CWRESI, TAD, SEX, groups = c('ID','TIME'), subset = 'MDV == 0', yref = 0, ysmooth = TRUE ) ## End(Not run)
Retrieves model outputs with metadata.
metasuperset( x, groups, meta = match.fun("meta")(x, ...), subset = getOption("metasuperset_subset", NULL), ... )metasuperset( x, groups, meta = match.fun("meta")(x, ...), subset = getOption("metasuperset_subset", NULL), ... )
x |
model name |
groups |
vector of key column names in superset, e.g. USUBJID, TIME |
meta |
metadata with column 'item' and possibly attributes such as 'label' and 'guide' |
subset |
length-one character: a condition for filtering results, e.g. 'EVID == 0' |
... |
passed arguments |
data.frame
Other superset:
generalize(),
ignored(),
meta(),
meta.character(),
meta.numeric(),
metaplot.character(),
metaplot.numeric(),
metaplot_character(),
ninput(),
ninput.character(),
ninput.numeric(),
shuffle(),
superset(),
superset.character(),
superset.numeric(),
superspec(),
superspec.character(),
superspec.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% metasuperset(c('ID','TIME')) %>% headlibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% metasuperset(c('ID','TIME')) %>% head
Identifies the directory used by a model.
modeldir(x, ext, ...)modeldir(x, ext, ...)
x |
the model name |
ext |
model file extension |
... |
passed arguments |
character
Other path:
datafile(),
datafile.character(),
datafile.numeric(),
modelfile(),
modelpath(),
modelpath.character(),
modelpath.numeric(),
psn_options(),
specfile(),
specfile.character(),
specfile.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% modeldirlibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% modeldir
Identifies the modelfile used by a model.
modelfile(x, ext = getOption("modex", "ctl"), ...)modelfile(x, ext = getOption("modex", "ctl"), ...)
x |
the model name |
ext |
model file extension |
... |
passed arguments |
character
Other path:
datafile(),
datafile.character(),
datafile.numeric(),
modeldir(),
modelpath(),
modelpath.character(),
modelpath.numeric(),
psn_options(),
specfile(),
specfile.character(),
specfile.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% modelfile('xml')library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% modelfile('xml')
Resolves a path to a model-related file.
modelpath(x, ...)modelpath(x, ...)
x |
object |
... |
passed arguments |
character
Other path:
datafile(),
datafile.character(),
datafile.numeric(),
modeldir(),
modelfile(),
modelpath.character(),
modelpath.numeric(),
psn_options(),
specfile(),
specfile.character(),
specfile.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% modelpathlibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% modelpath
Resolves a path to a model-related file, treating x as a model name.
By default (ext is NULL) the run directory is returned.
As of version 0.9.2, nested can be a function of ext and ...
That returns logical.
## S3 method for class 'character' modelpath( x, ext = NULL, project = getOption("project", getwd()), nested = getOption("nested", TRUE), ... )## S3 method for class 'character' modelpath( x, ext = NULL, project = getOption("project", getwd()), nested = getOption("nested", TRUE), ... )
x |
object |
ext |
file extension, no leading dot |
project |
project directory (can be expression) |
nested |
whether model files are nested in eponymous directories |
... |
passed arguments |
character
Other path:
datafile(),
datafile.character(),
datafile.numeric(),
modeldir(),
modelfile(),
modelpath(),
modelpath.numeric(),
psn_options(),
specfile(),
specfile.character(),
specfile.numeric()
Calculates number of inputs.
ninput(x, ...)ninput(x, ...)
x |
object |
... |
passed arguments |
Other superset:
generalize(),
ignored(),
meta(),
meta.character(),
meta.numeric(),
metaplot.character(),
metaplot.numeric(),
metaplot_character(),
metasuperset(),
ninput.character(),
ninput.numeric(),
shuffle(),
superset(),
superset.character(),
superset.numeric(),
superspec(),
superspec.character(),
superspec.numeric()
Calculates number of inputs for character by treating as a model name.
## S3 method for class 'character' ninput(x, ...)## S3 method for class 'character' ninput(x, ...)
x |
character |
... |
passed arguments |
integer
Other superset:
generalize(),
ignored(),
meta(),
meta.character(),
meta.numeric(),
metaplot.character(),
metaplot.numeric(),
metaplot_character(),
metasuperset(),
ninput(),
ninput.numeric(),
shuffle(),
superset(),
superset.character(),
superset.numeric(),
superspec(),
superspec.character(),
superspec.numeric()
Calculates number of inputs for numeric by coercing to character.
## S3 method for class 'numeric' ninput(x, ...)## S3 method for class 'numeric' ninput(x, ...)
x |
numeric |
... |
passed arguments |
Other superset:
generalize(),
ignored(),
meta(),
meta.character(),
meta.numeric(),
metaplot.character(),
metaplot.numeric(),
metaplot_character(),
metasuperset(),
ninput(),
ninput.character(),
shuffle(),
superset(),
superset.character(),
superset.numeric(),
superspec(),
superspec.character(),
superspec.numeric()
Generates canonical names for character by converting to parsed model.
## S3 method for class 'character' nms_canonical(x, ...)## S3 method for class 'character' nms_canonical(x, ...)
x |
object of dispatch |
... |
passed arguments |
Other nms:
as_nms_canonical(),
as_nms_nonmem(),
as_nms_pmx(),
as_nms_psn(),
nms_canonical(),
nms_canonical.model(),
nms_canonical.nms_nonmem(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.character(),
nms_nonmem.model(),
nms_nonmem.nms_canonical(),
nms_nonmem.nms_pmx(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_canonical(),
nms_pmx.nms_nonmem(),
nms_psn(),
nms_psn.character(),
nms_psn.model(),
nms_psn.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% nms_canonicallibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% nms_canonical
Generates canonical names for a NONMEM control stream object. Canonical names indicate all and only the declared model parameters in lower-case conventional order (theta, omega row-major, sigma) with underscores and two-digit (or more) indices. E.g. theta_01, theta_02, omega_01_01, omega_02_01, omega_02_02, omega_01_01.
## S3 method for class 'model' nms_canonical(x, ...)## S3 method for class 'model' nms_canonical(x, ...)
x |
a model designator |
... |
passed arguments |
nms_canonical (character)
as.model
Other nms:
as_nms_canonical(),
as_nms_nonmem(),
as_nms_pmx(),
as_nms_psn(),
nms_canonical(),
nms_canonical.character(),
nms_canonical.nms_nonmem(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.character(),
nms_nonmem.model(),
nms_nonmem.nms_canonical(),
nms_nonmem.nms_pmx(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_canonical(),
nms_pmx.nms_nonmem(),
nms_psn(),
nms_psn.character(),
nms_psn.model(),
nms_psn.numeric()
Converts nms_nonmem to nms_canonical.
## S3 method for class 'nms_nonmem' nms_canonical(x, ...)## S3 method for class 'nms_nonmem' nms_canonical(x, ...)
x |
nms_nonmem |
... |
ignored |
nms_canonical
Other nms:
as_nms_canonical(),
as_nms_nonmem(),
as_nms_pmx(),
as_nms_psn(),
nms_canonical(),
nms_canonical.character(),
nms_canonical.model(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.character(),
nms_nonmem.model(),
nms_nonmem.nms_canonical(),
nms_nonmem.nms_pmx(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_canonical(),
nms_pmx.nms_nonmem(),
nms_psn(),
nms_psn.character(),
nms_psn.model(),
nms_psn.numeric()
Generates NONMEM names for numeric by converting to parsed model.
## S3 method for class 'character' nms_nonmem(x, ...)## S3 method for class 'character' nms_nonmem(x, ...)
x |
object of dispatch |
... |
passed arguments |
Other nms:
as_nms_canonical(),
as_nms_nonmem(),
as_nms_pmx(),
as_nms_psn(),
nms_canonical(),
nms_canonical.character(),
nms_canonical.model(),
nms_canonical.nms_nonmem(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.model(),
nms_nonmem.nms_canonical(),
nms_nonmem.nms_pmx(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_canonical(),
nms_pmx.nms_nonmem(),
nms_psn(),
nms_psn.character(),
nms_psn.model(),
nms_psn.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% nms_nonmemlibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% nms_nonmem
Generates NONMEM names for parameters declared in a NONMEM control stream object.
## S3 method for class 'model' nms_nonmem(x, ...)## S3 method for class 'model' nms_nonmem(x, ...)
x |
a model designator |
... |
passed arguments |
nms_nonmem (character)
as.model
Other nms:
as_nms_canonical(),
as_nms_nonmem(),
as_nms_pmx(),
as_nms_psn(),
nms_canonical(),
nms_canonical.character(),
nms_canonical.model(),
nms_canonical.nms_nonmem(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.character(),
nms_nonmem.nms_canonical(),
nms_nonmem.nms_pmx(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_canonical(),
nms_pmx.nms_nonmem(),
nms_psn(),
nms_psn.character(),
nms_psn.model(),
nms_psn.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.model %>% nms_nonmemlibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.model %>% nms_nonmem
Converts nms_canonical to nms_nonmem.
## S3 method for class 'nms_canonical' nms_nonmem(x, ...)## S3 method for class 'nms_canonical' nms_nonmem(x, ...)
x |
nms_canonical |
... |
ignored |
nms_nonmem
Other nms:
as_nms_canonical(),
as_nms_nonmem(),
as_nms_pmx(),
as_nms_psn(),
nms_canonical(),
nms_canonical.character(),
nms_canonical.model(),
nms_canonical.nms_nonmem(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.character(),
nms_nonmem.model(),
nms_nonmem.nms_pmx(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_canonical(),
nms_pmx.nms_nonmem(),
nms_psn(),
nms_psn.character(),
nms_psn.model(),
nms_psn.numeric()
# example code# example code
Converts nms_pmx to nms_nonmem. I.e., THETA1 stays THETA1, but OM1,1 becomes OMEGA(1,1).
## S3 method for class 'nms_pmx' nms_nonmem(x, ...)## S3 method for class 'nms_pmx' nms_nonmem(x, ...)
x |
nms_pmx |
... |
ignored |
nms_nonmem (character)
Other nms:
as_nms_canonical(),
as_nms_nonmem(),
as_nms_pmx(),
as_nms_psn(),
nms_canonical(),
nms_canonical.character(),
nms_canonical.model(),
nms_canonical.nms_nonmem(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.character(),
nms_nonmem.model(),
nms_nonmem.nms_canonical(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_canonical(),
nms_pmx.nms_nonmem(),
nms_psn(),
nms_psn.character(),
nms_psn.model(),
nms_psn.numeric()
Other nm:
nms_canonical.nms_pmx(),
nms_pmx.nms_nonmem()
library(magrittr) c( 'THETA1', 'OM1,1', 'OM1,2', 'SG1,1', 'SG1,2' ) %>% as_nms_pmx %>% nms_canonicallibrary(magrittr) c( 'THETA1', 'OM1,1', 'OM1,2', 'SG1,1', 'SG1,2' ) %>% as_nms_pmx %>% nms_canonical
Converts nms_canonical to nms_pmx. I.e., theta_01 becomes THETA1, and omega_01_01 becomes OM1,1.
## S3 method for class 'nms_canonical' nms_pmx(x, ...)## S3 method for class 'nms_canonical' nms_pmx(x, ...)
x |
nms_canonical |
... |
ignored |
nms_pmx (character)
Other nms:
as_nms_canonical(),
as_nms_nonmem(),
as_nms_pmx(),
as_nms_psn(),
nms_canonical(),
nms_canonical.character(),
nms_canonical.model(),
nms_canonical.nms_nonmem(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.character(),
nms_nonmem.model(),
nms_nonmem.nms_canonical(),
nms_nonmem.nms_pmx(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_nonmem(),
nms_psn(),
nms_psn.character(),
nms_psn.model(),
nms_psn.numeric()
library(magrittr) c( 'theta_15', 'omega_01_01', 'omega_01_02', 'sigma_01_01', 'sigma_02_01' ) %>% as_nms_canonical %>% nms_pmxlibrary(magrittr) c( 'theta_15', 'omega_01_01', 'omega_01_02', 'sigma_01_01', 'sigma_02_01' ) %>% as_nms_canonical %>% nms_pmx
Converts nms_nonmem to nms_pmx. I.e., THETA1 stays THETA1, but OMEGA(1,1) becomes OM1,1.
## S3 method for class 'nms_nonmem' nms_pmx(x, ...)## S3 method for class 'nms_nonmem' nms_pmx(x, ...)
x |
nms_pmx |
... |
ignored |
nms_nonmem (character)
Other nms:
as_nms_canonical(),
as_nms_nonmem(),
as_nms_pmx(),
as_nms_psn(),
nms_canonical(),
nms_canonical.character(),
nms_canonical.model(),
nms_canonical.nms_nonmem(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.character(),
nms_nonmem.model(),
nms_nonmem.nms_canonical(),
nms_nonmem.nms_pmx(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_canonical(),
nms_psn(),
nms_psn.character(),
nms_psn.model(),
nms_psn.numeric()
Other nm:
nms_canonical.nms_pmx(),
nms_nonmem.nms_pmx()
library(magrittr) c( 'THETA1', 'OMEGA(1,1)', 'OMEGA(1,2)', 'SIGMA(1,1)', 'SIGMA(1,2)' ) %>% as_nms_pmx %>% nms_canonicallibrary(magrittr) c( 'THETA1', 'OMEGA(1,1)', 'OMEGA(1,2)', 'SIGMA(1,1)', 'SIGMA(1,2)' ) %>% as_nms_pmx %>% nms_canonical
Generates PsN-style names for numeric by converting to parsed model.
## S3 method for class 'character' nms_psn(x, ...)## S3 method for class 'character' nms_psn(x, ...)
x |
object of dispatch |
... |
passed arguments |
Other nms:
as_nms_canonical(),
as_nms_nonmem(),
as_nms_pmx(),
as_nms_psn(),
nms_canonical(),
nms_canonical.character(),
nms_canonical.model(),
nms_canonical.nms_nonmem(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.character(),
nms_nonmem.model(),
nms_nonmem.nms_canonical(),
nms_nonmem.nms_pmx(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_canonical(),
nms_pmx.nms_nonmem(),
nms_psn(),
nms_psn.model(),
nms_psn.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% nms_psnlibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% nms_psn
Generates PsN-style names for parameters declared in a NONMEM control stream object. PsN uses NONMEM-style names, substituting a comment, if any: everything after the first semicolon, up to the second semicolon if present, without leading/trailing spaces/tabs.
## S3 method for class 'model' nms_psn(x, ...)## S3 method for class 'model' nms_psn(x, ...)
x |
a model designator |
... |
passed arguments |
nms_psn (character)
as.model
Other nms:
as_nms_canonical(),
as_nms_nonmem(),
as_nms_pmx(),
as_nms_psn(),
nms_canonical(),
nms_canonical.character(),
nms_canonical.model(),
nms_canonical.nms_nonmem(),
nms_canonical.nms_pmx(),
nms_canonical.numeric(),
nms_nonmem(),
nms_nonmem.character(),
nms_nonmem.model(),
nms_nonmem.nms_canonical(),
nms_nonmem.nms_pmx(),
nms_nonmem.numeric(),
nms_pmx(),
nms_pmx.nms_canonical(),
nms_pmx.nms_nonmem(),
nms_psn(),
nms_psn.character(),
nms_psn.numeric()
Nonmemica (emphasis like 'America') creates and evaluates NONMEM models in a project context.
NONMEM (ICON Development Solutions) is software for nonlinear mixed effects modeling. The fundamental interface is a text file (control stream, typ. *.mod or *.ctl) that specifies model input, structure, and output. There are many add-on interfaces for NONMEM (see references for a few examples). However, much day-to-day modeling, even for R users, involves substantial manual interventions.
Nonmemica streamlines interactions with NONMEM. It adopts some established conventions and techniques (e.g. from PsN and metrumrg), but introduces others that may be useful. Principally, it parses existing control streams for systematic analysis and alteration. Relatively simple, single-problem control streams are supported; see the example.
Of course, NONMEM itself is licensed software that must be installed independently. Nonmemica is largely indifferent to how NONMEM is installed or invoked. However, several features depend on the *.xml output that NONMEM creates; make sure it is available. Also, the best-supported directory structure is that which has numbers for model names, with all model-specific files in eponymous subdirectories of a "project" directory. An example is given below.
Nonmemica adopts three control stream encoding conventions that merit special mention. First, the problem statement is encoded in the form //like/x//but/y// where x is a reference model name and y is a feature difference from the reference model (see likebut() ). This allows any given model to be described by chaining together its legacy of features (use runlog(depenencies = TRUE, ...) ), which generally works better than trying to describe it exhaustively in the model name. As of version 0.9.2, experimental support is available for natural-language problem statements of the form "like run1001 but fixed additive error".
Second, Nonmemica only needs a single output table ($TABLE record). Be sure to use ONEHEADER but avoid FIRSTONLY. Nonmemica will integrate model inputs and outputs, regardless of table counts, into one data.frame (see superset() ).
Third, Nonmemica supports integrated metadata. With respect to model inputs, use package spec to store column metadata in a companion file (a data specification, e.g. *.spec). Keep the data file and data specification in a central location, not copied to the model directory. For model outputs (tabled items) supply column metadata directly in the control stream (or a *.def file; see example and help).
Nonmemica supports three global options: 'project' (default getwd() ) is the parent directory of model-specific files or directories; 'nested' (default TRUE) tells whether model-specific files are nested within eponymous directories; 'modex' (default 'ctl') gives the file extension for control streams. In many cases you can pass these options to the relevant functions; but since they likely won't change for the scope of a given project, it saves effort to set them as global options (if they differ from the defaults) using e.g. options(project=).
Numbers make good names for models because it is never hard for you or the software to think of a new one. That said, model names are typically processed as character in Nonmemica. There are many generic functions with both numeric and character methods that simply assume the (length-one) argument you supply is a model name.
Maintainer: Tim Bergsma [email protected]
# Create a working project. source <- system.file(package = 'nonmemica','project') target <- tempdir() target <- gsub('\\\\','/',target) # for windows source target file.copy(source,target,recursive = TRUE) project <- file.path(target,'project','model') # Point project option at working project options(project = project) # Load some packages library(magrittr) library(metaplot) library(wrangle) library(spec) library(dplyr,warn.conflicts = FALSE) # Identify features of a model. 1001 %>% modelpath 1001 %>% modeldir 1001 %>% modelfile 1001 %>% modelpath('xml') 1001 %>% datafile datafile(1001) %matches% specfile(1001) 1001 %>% specfile 1001 %>% specfile %>% read.spec 1001 %>% as.model 1001 %>% as.model %>% comments 1001 %>% definitions 1001 %>% runlog(TRUE) 1001 %>% runlog 1001 %>% partab 1001 %>% num_parameters 1001 %>% nms_canonical 1001 %>% nms_psn 1001 %>% nms_nonmem 1001 %>% parameters 1001 %>% errors 1001 %>% as.model %>% initial 1001 %>% as.model %>% lower 1001 %>% as.model %>% upper 1001 %>% as.model %>% fixed 1001 %>% meta %>% class 1001 %>% meta # Derive datasets. 1001 %>% superset %>% head 1001 %>% superset %>% filter(VISIBLE == 1) %>% group_by(ID,TIME) %>% status 1001 %>% metasuperset(c('ID','TIME')) %>% head 1001 %>% metasuperset(c('ID','TIME')) %>% sapply(attr,'label') # Make diagnostic plots. 1001 %>% metaplot( CWRESI, TAD, SEX, groups = c('ID','TIME'), subset = 'MDV == 0', yref=0, ysmooth = TRUE ) 1001 %>% metaplot( ETA1, SEX, ref = 0, groups = c('ID','TIME'), subset = 'MDV == 0' ) 1001 %>% metaplot( SEX, ETA1, ref = 0, groups = c('ID','TIME'), subset = 'MDV == 0' ) 1001 %>% metaplot( ETA1, ETA2, ETA3, groups = c('ID','TIME'), subset = 'MDV == 0' ) # Derive models. 1001 %>% likebut('revised',y = 1002, overwrite=TRUE ) # At this point, edit 1002.ctl to match whatever 'revised' means. # Then run it with NONMEM and post-process results as above. # Make ten new models with slightly different initial estimates. 1001 %>% tweak# Create a working project. source <- system.file(package = 'nonmemica','project') target <- tempdir() target <- gsub('\\\\','/',target) # for windows source target file.copy(source,target,recursive = TRUE) project <- file.path(target,'project','model') # Point project option at working project options(project = project) # Load some packages library(magrittr) library(metaplot) library(wrangle) library(spec) library(dplyr,warn.conflicts = FALSE) # Identify features of a model. 1001 %>% modelpath 1001 %>% modeldir 1001 %>% modelfile 1001 %>% modelpath('xml') 1001 %>% datafile datafile(1001) %matches% specfile(1001) 1001 %>% specfile 1001 %>% specfile %>% read.spec 1001 %>% as.model 1001 %>% as.model %>% comments 1001 %>% definitions 1001 %>% runlog(TRUE) 1001 %>% runlog 1001 %>% partab 1001 %>% num_parameters 1001 %>% nms_canonical 1001 %>% nms_psn 1001 %>% nms_nonmem 1001 %>% parameters 1001 %>% errors 1001 %>% as.model %>% initial 1001 %>% as.model %>% lower 1001 %>% as.model %>% upper 1001 %>% as.model %>% fixed 1001 %>% meta %>% class 1001 %>% meta # Derive datasets. 1001 %>% superset %>% head 1001 %>% superset %>% filter(VISIBLE == 1) %>% group_by(ID,TIME) %>% status 1001 %>% metasuperset(c('ID','TIME')) %>% head 1001 %>% metasuperset(c('ID','TIME')) %>% sapply(attr,'label') # Make diagnostic plots. 1001 %>% metaplot( CWRESI, TAD, SEX, groups = c('ID','TIME'), subset = 'MDV == 0', yref=0, ysmooth = TRUE ) 1001 %>% metaplot( ETA1, SEX, ref = 0, groups = c('ID','TIME'), subset = 'MDV == 0' ) 1001 %>% metaplot( SEX, ETA1, ref = 0, groups = c('ID','TIME'), subset = 'MDV == 0' ) 1001 %>% metaplot( ETA1, ETA2, ETA3, groups = c('ID','TIME'), subset = 'MDV == 0' ) # Derive models. 1001 %>% likebut('revised',y = 1002, overwrite=TRUE ) # At this point, edit 1002.ctl to match whatever 'revised' means. # Then run it with NONMEM and post-process results as above. # Make ten new models with slightly different initial estimates. 1001 %>% tweak
Isolates off-diagonal of halfmatrix.
## S3 method for class 'halfmatrix' offdiag(x, ...)## S3 method for class 'halfmatrix' offdiag(x, ...)
x |
object |
... |
passed arguments |
Other halfmatrix:
as.data.frame.halfmatrix(),
as.halfmatrix(),
as.halfmatrix.default(),
as.halfmatrix.halfmatrix(),
as.matrix.halfmatrix(),
half(),
half.matrix(),
is.square(),
is.square.matrix(),
offdiag(),
ord(),
ord.halfmatrix(),
ord.matrix(),
print.halfmatrix()
Gets parameters, treating character as model names. If x is length one,
slightly more details are returned such as datafile, reference model, and feature.
Otherwise results are bound together, one model per column.
See estimates and errors for a more formal interface to model estimates and asymptotic standard errors.
## S3 method for class 'character' parameters(x, simplify = FALSE, ...)## S3 method for class 'character' parameters(x, simplify = FALSE, ...)
x |
object |
simplify |
if x is length one and simplify is TRUE, return a named vector |
... |
passed arguments |
data.frame
Other parameters:
parameters(),
parameters.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% parameterslibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% parameters
Creates a parameter table.
Creates a model parameter table from a partab object.
partab(x, ...) ## S3 method for class 'partab' partab(x, ...)partab(x, ...) ## S3 method for class 'partab' partab(x, ...)
x |
partab |
... |
ignored |
x can be numeric or character model name, assuming project is identified by argument or option.
Just returns the object unmodified.
partab(partab): partab method
Other partab:
partab.character(),
partab.numeric()
Other partab:
partab.character(),
partab.numeric()
Creates a parameter table from a model name. Pass the project argument or set the project option.
## S3 method for class 'character' partab( x, verbose = FALSE, lo = "5", hi = "95", metafile = modelpath(x, "def", ...), xmlfile = modelpath(x, "xml", ...), ctlfile = modelfile(x, ...), bootcsv, strip.namespace = TRUE, skip = 28, check.names = FALSE, digits = 3, ci = TRUE, open = "(", close = ")", sep = ", ", format = TRUE, fields = getOption("fields", default = c("symbol", "label", "unit")), relative = TRUE, percent = relative, nonzero = TRUE, shrinkage = FALSE, correlation = FALSE, ... )## S3 method for class 'character' partab( x, verbose = FALSE, lo = "5", hi = "95", metafile = modelpath(x, "def", ...), xmlfile = modelpath(x, "xml", ...), ctlfile = modelfile(x, ...), bootcsv, strip.namespace = TRUE, skip = 28, check.names = FALSE, digits = 3, ci = TRUE, open = "(", close = ")", sep = ", ", format = TRUE, fields = getOption("fields", default = c("symbol", "label", "unit")), relative = TRUE, percent = relative, nonzero = TRUE, shrinkage = FALSE, correlation = FALSE, ... )
x |
a model name (numeric or character) |
verbose |
set FALSE to suppress messages |
lo |
the PsN bootstrap lower confidence limit (%) |
hi |
the PsN bootstrap upper confidence limit (%) |
metafile |
optional metadata for parameter table (see also: fields) |
xmlfile |
path to xml file |
ctlfile |
path to control stream |
bootcsv |
path to PsN bootstrap_results.csv |
strip.namespace |
whether to strip e.g. nm: from xml elements for easier xpath syntax |
skip |
number of lines to skip in bootstrap_results.csv |
check.names |
passed to bootstrap reader |
digits |
limits numerics to significant digits (use NULL to suppress) |
ci |
combine bootstrap lo and hi into an enclosed interval |
open |
first character for bootstrap interval |
close |
last character for bootstrap interval |
sep |
separator for bootstrap interval |
format |
format numerics as character |
fields |
metadata fields to read from control stream. See details. |
relative |
transform standard errors to relative standard errors: rse replaces se |
percent |
if relative is true, express as percent (else ignore): prse replaces se |
nonzero |
limit random effects to those with nonzero estimates |
shrinkage |
whether to include percent shrinkage on random effects |
correlation |
whether to include correlation of random effects (as percent if |
... |
passed to other functions |
Normally you can just call the generic. Suitable defaults are supplied, but much customization is supported by means of arguments documented here and in called functions.
Metadata can be added to the parameter table two ways: as markup in the control stream, and as a *.def file in the model directory. See vignette('parameter-table') for details.
object of class partab, data.frame
Other partab:
partab(),
partab.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% partab 1001 %>% partab(shrinkage = TRUE, correlation = TRUE)library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% partab 1001 %>% partab(shrinkage = TRUE, correlation = TRUE)
Identifies the model problem statement for character (model name).
## S3 method for class 'character' problem(x, ...)## S3 method for class 'character' problem(x, ...)
x |
object |
... |
passed arguments |
character
Other problem:
as.problem(),
problem(),
problem.numeric(),
problem_()
Check whether a particular file extension corresponds to a file that is nested within a subdirectory using default PsN conventions.
psn_nested(x, ...)psn_nested(x, ...)
x |
character, a file extension, without dot. |
... |
ignored |
logical
psn_nested('mod')psn_nested('mod')
Sets PsN-style directory and control stream options. Supports control streams with semicolon-delimited metadata including symbol, unit, transform, and label. Expects model files to be found in nested directory, except for *.mod and *.lst.
psn_options( project = "NONMEM", modex = "mod", fields = c("symbol", "unit", "transform", "label"), nested = psn_nested, ... )psn_options( project = "NONMEM", modex = "mod", fields = c("symbol", "unit", "transform", "label"), nested = psn_nested, ... )
project |
character, path to project directory |
modex |
character, extension for model control stream (no dot) |
fields |
character |
nested |
logical, or function of file extension returning logical |
... |
ignored |
used for side-effects (sets options 'fields' and 'nested')
Other path:
datafile(),
datafile.character(),
datafile.numeric(),
modeldir(),
modelfile(),
modelpath(),
modelpath.character(),
modelpath.numeric(),
specfile(),
specfile.character(),
specfile.numeric()
## Not run: psn_options() ## End(Not run)## Not run: psn_options() ## End(Not run)
Relativizes a path.
relativizePath(x, dir = getwd(), sep = "/", ...)relativizePath(x, dir = getwd(), sep = "/", ...)
x |
a file path |
dir |
a reference directory |
sep |
path separator |
... |
ignored arguments |
x and dir are first normalized, then x is expressed relative to dir. If x and dir are on different drives (i.e. C:/ D:/) x is returned as an absolute path.
Resolves a file path. Returns the path if absolute. If relative, concatenates the directory and file.
resolve(file, dir)resolve(file, dir)
file |
path to a file |
dir |
reference directory for a relative file path |
character
Translate among various idioms for expressing parameter names
library(magrittr) options(project = system.file('project/model',package='nonmemica')) # we generate some nonmem parameter names in nonmem format 1001 %>% nms_nonmem -> nonmem nonmem # from these we generate canonical and pmxTools equivalents canonical <- nms_canonical(nonmem) pmxtools <- nms_pmx(nonmem) canonical pmxtools # We demonstrate equivalence stopifnot(identical(nonmem, nms_nonmem(canonical))) stopifnot(identical(nonmem, nms_nonmem(pmxtools))) stopifnot(identical(canonical, nms_canonical(nonmem))) stopifnot(identical(canonical, nms_canonical(pmxtools))) stopifnot(identical(pmxtools, nms_pmx(nonmem))) stopifnot(identical(pmxtools, nms_pmx(canonical))) # on-the-fly conversions nonmem %>% class nonmem %>% nms_pmx nonmem %>% nms_canonical nonmem %>% nms_canonical %>% nms_pmx nonmem %>% nms_canonical %>% nms_pmx %>% nms_nonmem # comparison data.frame( can = I(canonical), non = I(nonmem), pmx = I(pmxtools), psn = I(nms_psn(1001)) )library(magrittr) options(project = system.file('project/model',package='nonmemica')) # we generate some nonmem parameter names in nonmem format 1001 %>% nms_nonmem -> nonmem nonmem # from these we generate canonical and pmxTools equivalents canonical <- nms_canonical(nonmem) pmxtools <- nms_pmx(nonmem) canonical pmxtools # We demonstrate equivalence stopifnot(identical(nonmem, nms_nonmem(canonical))) stopifnot(identical(nonmem, nms_nonmem(pmxtools))) stopifnot(identical(canonical, nms_canonical(nonmem))) stopifnot(identical(canonical, nms_canonical(pmxtools))) stopifnot(identical(pmxtools, nms_pmx(nonmem))) stopifnot(identical(pmxtools, nms_pmx(canonical))) # on-the-fly conversions nonmem %>% class nonmem %>% nms_pmx nonmem %>% nms_canonical nonmem %>% nms_canonical %>% nms_pmx nonmem %>% nms_canonical %>% nms_pmx %>% nms_nonmem # comparison data.frame( can = I(canonical), non = I(nonmem), pmx = I(pmxtools), psn = I(nms_psn(1001)) )
Creates a Runlog for character by treating x as modelname(s).
## S3 method for class 'character' runlog(x, dependencies = FALSE, digits = 3, places = 0, ...)## S3 method for class 'character' runlog(x, dependencies = FALSE, digits = 3, places = 0, ...)
x |
object |
dependencies |
whether to log runs in lineage(s) as well |
digits |
significance for parameters |
places |
rounding for objective function |
... |
passed arguments |
data.frame
Other runlog:
runlog(),
runlog.numeric()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) # likebut(2001,'2 cmt', 2002) # edit manually, then ... # likebut(2002,'add. err.', 2003) # edit manually, then ... # likebut(2003,'allo. WT on CL',2004) # edit manually, then ... # likebut(2004,'estimate allometry', 2005) # edit manually, then ... # likebut(2005,'SEX on CL', 2006) # edit manually, then ... # likebut(2006,'full block omega', 2007) # edit manually, then run all 2007 %>% runlog(dependencies = TRUE)library(magrittr) options(project = system.file('project/model',package='nonmemica')) # likebut(2001,'2 cmt', 2002) # edit manually, then ... # likebut(2002,'add. err.', 2003) # edit manually, then ... # likebut(2003,'allo. WT on CL',2004) # edit manually, then ... # likebut(2004,'estimate allometry', 2005) # edit manually, then ... # likebut(2005,'SEX on CL', 2006) # edit manually, then ... # likebut(2006,'full block omega', 2007) # edit manually, then run all 2007 %>% runlog(dependencies = TRUE)
Joins data safely. Generic, with method for data.frame.
safe_join(x, ...)safe_join(x, ...)
x |
object of dispatch |
... |
arguments to methods |
Other safe_join:
safe_join.data.frame()
Joins data frames safely. I.e., a left join that cannot alter row order or number. Supports the case where you only intend to augment existing rows with additional columns and are expecting singular matches. Gives an error if row order or number would have been altered by a left join.
## S3 method for class 'data.frame' safe_join(x, y, ...)## S3 method for class 'data.frame' safe_join(x, y, ...)
x |
data.frame |
y |
data.frame |
... |
passed to dplyr::left_join |
Other safe_join:
safe_join()
library(magrittr) x <- data.frame(code = c('a','b','c'), value = c(1:3)) y <- data.frame(code = c('a','b','c'), roman = c('I','II','III')) x %>% safe_join(y) try( x %>% safe_join(rbind(y,y)) )library(magrittr) x <- data.frame(code = c('a','b','c'), value = c(1:3)) y <- data.frame(code = c('a','b','c'), roman = c('I','II','III')) x %>% safe_join(y) try( x %>% safe_join(rbind(y,y)) )
Move the columns of a data.frame relative to each other.
shuffle(x, who, after = NA, ...)shuffle(x, who, after = NA, ...)
x |
data.frame |
who |
a character vector of column names to move, or a logical vector of length names(x), or a vector of indices |
after |
column after which to put who: may be character, integer, NA, or NULL |
... |
ignored |
data.frame
Other superset:
generalize(),
ignored(),
meta(),
meta.character(),
meta.numeric(),
metaplot.character(),
metaplot.numeric(),
metaplot_character(),
metasuperset(),
ninput(),
ninput.character(),
ninput.numeric(),
superset(),
superset.character(),
superset.numeric(),
superspec(),
superspec.character(),
superspec.numeric()
Identifies the data specification file associated with the datafile used by a model. Locates the datafile specified in the control stream, and substitutes a different extension.
## S3 method for class 'character' specfile(x, find = "\\.csv$", use = ".spec", ...)## S3 method for class 'character' specfile(x, find = "\\.csv$", use = ".spec", ...)
x |
the model name |
find |
file extension to replace |
use |
file extension to use |
... |
pass ext over-ride default file extension in datafile() |
character
datafile
Other path:
datafile(),
datafile.character(),
datafile.numeric(),
modeldir(),
modelfile(),
modelpath(),
modelpath.character(),
modelpath.numeric(),
psn_options(),
specfile(),
specfile.numeric()
library(spec) source <- system.file(package = 'nonmemica','project') target <- tempdir() target <- gsub('\\\\','/',target) # for windows file.copy(source,target,recursive = TRUE) project <- file.path(target,'project','model') options(project = project) library(magrittr) 1001 %>% datafile datafile(1001) %matches% specfile(1001) 1001 %>% specfile 1001 %>% specfile %>% read.speclibrary(spec) source <- system.file(package = 'nonmemica','project') target <- tempdir() target <- gsub('\\\\','/',target) # for windows file.copy(source,target,recursive = TRUE) project <- file.path(target,'project','model') options(project = project) library(magrittr) 1001 %>% datafile datafile(1001) %matches% specfile(1001) 1001 %>% specfile 1001 %>% specfile %>% read.spec
Coerces to superset from character, treating x as a model name.
## S3 method for class 'character' superset( x, read.input = list(read.csv, header = TRUE, as.is = TRUE), read.output = list(read.table, header = TRUE, as.is = TRUE, skip = 1, comment.char = "", check.names = FALSE, na.strings = c("", "\\s", ".", "NA")), include = character(0), exclude = character(0), rename = NULL, digits = 5, visible = "VISIBLE", after = NULL, groups = character(0), imputation = generalize, ... )## S3 method for class 'character' superset( x, read.input = list(read.csv, header = TRUE, as.is = TRUE), read.output = list(read.table, header = TRUE, as.is = TRUE, skip = 1, comment.char = "", check.names = FALSE, na.strings = c("", "\\s", ".", "NA")), include = character(0), exclude = character(0), rename = NULL, digits = 5, visible = "VISIBLE", after = NULL, groups = character(0), imputation = generalize, ... )
x |
object |
read.input |
a methodology for acquiring the input |
read.output |
a methodology for acquiring the output |
include |
column names in output to consider adding |
exclude |
column names in output to reject |
rename |
logical: whether to keep and rename columns with re-used names |
digits |
significant digits for assessing informativeness when exclusive=NULL |
visible |
a name for the flag column indicating visibility |
after |
place new columns after this column; at end by default (NULL); TRUE places them after last model-visible column (see input statement) |
groups |
character vector of groupings within which any imputations will be performed |
imputation |
a list of functions (or arguments to match.fun()) to perform imputations within cells defined by groups: e.g. generalize, forbak, etc (to be tried in succession for new columns only). |
... |
passed arguments |
Given a model name, (project passed or set as global option) superset() figures out the run directory and location of a NONMEM control stream. It reads the control stream to identify the run-time location of input and output files, as well as the "ignore" (and/or "accept") criteria that relate extent of input records to extent of output records. 'read.input' and 'read.output' are lists consisting of functions and arguments appropriate for reading input and output file formats, respectively. The ignore criteria will be reconstructed per row so that output can be mapped unambiguously to input. A column named VISIBLE is bound to the input data, showing 1 where a record was visible to NONMEM, and 0 otherwise.
During integration, naming convention of the input is retained, and output column names are mapped by position, using the control stream input criteria. Output tables are restored to input dimensions using the "ignore" criteria, then checked for length: currently, superset ignores output tables having fewer rows than the input, as well as output tables whose row count is not a multiple of input row count.
Output tables may contain versions of input columns. Disposition depends on the values of include, exclude, and rename. If include has length, other columns are excluded. Then, if exclude has length, these columns are excluded. Then, if rename is FALSE all remaining columns with re-used names will be dropped. If TRUE, such columns will be renamed (*.n, where n is table number). If NULL, only informative columns will be retained and renamed. A column is informative if any element is informative. An element is informative if it is newly generated (not NA and not zero, but original is NA) or if it is an alteration (non-NA, and different from non-NA original). If the column pair can be interpreted as numeric, "different" is determined using only the first digits digits.
Only the first instance of any column among successive output tables is retained.
In the control stream, avoid use of FIRSTONLY, as this alters the number of rows.
superset: a data.frame where row count is a multiple of (typically equal to) input row count.
Other superset:
generalize(),
ignored(),
meta(),
meta.character(),
meta.numeric(),
metaplot.character(),
metaplot.numeric(),
metaplot_character(),
metasuperset(),
ninput(),
ninput.character(),
ninput.numeric(),
shuffle(),
superset(),
superset.numeric(),
superspec(),
superspec.character(),
superspec.numeric()
library(magrittr) library(dplyr) library(wrangle) options(project = system.file('project/model',package='nonmemica')) 1001 %>% superset %>% head 1001 %>% superset %>% filter(VISIBLE == 1) %>% group_by(ID,TIME) %>% status 1001 %>% ignored %>% tablelibrary(magrittr) library(dplyr) library(wrangle) options(project = system.file('project/model',package='nonmemica')) 1001 %>% superset %>% head 1001 %>% superset %>% filter(VISIBLE == 1) %>% group_by(ID,TIME) %>% status 1001 %>% ignored %>% table
Create a specification for the result of superset().
superspec(x, ...)superspec(x, ...)
x |
object |
... |
passed arguments |
Other superset:
generalize(),
ignored(),
meta(),
meta.character(),
meta.numeric(),
metaplot.character(),
metaplot.numeric(),
metaplot_character(),
metasuperset(),
ninput(),
ninput.character(),
ninput.numeric(),
shuffle(),
superset(),
superset.character(),
superset.numeric(),
superspec.character(),
superspec.numeric()
Create a specification for the result of superset() from character by treating as a model name. By default, gives a spec template for superset(x). Tries to supplement with labels and units from parent specification, if it exists. Tries to supplement with any additional labels and units in definitions(x). Defers to actual data if provided. Specify exclusive, visible, and after as for superset.
## S3 method for class 'character' superspec( x, include = character(0), exclude = character(0), rename = NULL, visible = "VISIBLE", after = NULL, data = NULL, ... )## S3 method for class 'character' superspec( x, include = character(0), exclude = character(0), rename = NULL, visible = "VISIBLE", after = NULL, data = NULL, ... )
x |
character |
include |
column names in output to consider adding |
exclude |
column names in output to reject |
rename |
logical: whether to keep and rename columns with re-used names |
visible |
a name for the flag column indicating visibility |
after |
place new columns after this column; at end by default (NULL); TRUE places them after |
data |
an alternative dataset on which to model the specification |
... |
passed arguments |
Other superset:
generalize(),
ignored(),
meta(),
meta.character(),
meta.numeric(),
metaplot.character(),
metaplot.numeric(),
metaplot_character(),
metasuperset(),
ninput(),
ninput.character(),
ninput.numeric(),
shuffle(),
superset(),
superset.character(),
superset.numeric(),
superspec(),
superspec.numeric()
Create a specification for the result of superset() from numeric by coercing to character.
## S3 method for class 'numeric' superspec(x, ...)## S3 method for class 'numeric' superspec(x, ...)
x |
numeric |
... |
passed arguments |
Other superset:
generalize(),
ignored(),
meta(),
meta.character(),
meta.numeric(),
metaplot.character(),
metaplot.numeric(),
metaplot_character(),
metasuperset(),
ninput(),
ninput.character(),
ninput.numeric(),
shuffle(),
superset(),
superset.character(),
superset.numeric(),
superspec(),
superspec.character()
Calculate time since most recent dose. Considers ADDL, but may not work with simultaneous dose records.
tad( x, dose = rep(FALSE, length(x)), addl = rep(0, length(x)), ii = rep(0, length(x)), index = rep(1, length(x)), pre = TRUE, ... )tad( x, dose = rep(FALSE, length(x)), addl = rep(0, length(x)), ii = rep(0, length(x)), index = rep(1, length(x)), pre = TRUE, ... )
x |
a numeric vector of event times |
dose |
length x logical indicating which of x are dose times |
addl |
length x integer: number of additional doses |
ii |
length x numeric: interdose interval for addl |
index |
length x factor (optional) indicating subgroups to evaluate |
pre |
assume that simultaneous sample precedes implied dose |
... |
passed to tod() |
numeric
Other tad:
tod()
data(tad1) x <- tad1 head(x) x$tad <- tad( x = x$TIME, dose = x$EVID %in% c(1,4) & is.na(x$C), addl = x$ADDL, ii = x$II, index = x$ID ) head(x)data(tad1) x <- tad1 head(x) x$tad <- tad( x = x$TIME, dose = x$EVID %in% c(1,4) & is.na(x$C), addl = x$ADDL, ii = x$II, index = x$ID ) head(x)
A dataset showing dose and and observation records for several subjects. Doses are duplicated across compartments 1 and 2 as for mixed absorption modeling.
data(tad1)data(tad1)
C. An exclusion flag, NA by default, or 'C'.
ID. Integer subject identifier.
TIME. Numeric event time (h).
EVID. Event type identifier: observation (0) or dose (1).
CMT. Event compartment: dose (1), central (2) or peripheral (4).
AMT. Amount of dose (mg).
RATE. NONMEM RATE item.
ADDL. Number of additional doses, or NA for observations.
II. Interdose interval for additional doses, or NA for observations.
DV. Observation placeholder.
Calculates time of most recent dose.
tod(x, ref, addl, ii, pre = T, ...)tod(x, ref, addl, ii, pre = T, ...)
x |
a numeric vector of event times |
ref |
length x vector of reference dose times |
addl |
length x integer: number of additional doses |
ii |
length x numeric: interdose interval for addl |
pre |
assume that simultaneous sample precedes implied dose |
... |
ignored |
numeric
Other tad:
tad()
Tweaks a model by jittering initial estimates. Creates a new model directory in project context and places the model there. Copies helper files as well. Expects that x is a number. Assumes nested directory structure (run-specific directories).
## Default S3 method: tweak( x, project = getOption("project", getwd()), ext = getOption("modex", "ctl"), start = NULL, n = 10, include = ".def$", ... )## Default S3 method: tweak( x, project = getOption("project", getwd()), ext = getOption("modex", "ctl"), start = NULL, n = 10, include = ".def$", ... )
x |
object |
project |
project directory (can be expression) |
ext |
file extension for control streams |
start |
a number to use as the first modelname |
n |
the number of variants to generate (named start:n) |
include |
regular expressions for files to copy to new directory |
... |
pass ext to over-ride default model file extension |
character: vector of names for models created
Other tweak:
tweak(),
tweak.init(),
tweak.inits(),
tweak.model()
Tweaks model.
## S3 method for class 'model' tweak(x, sd = 0.13, digits = 3, ...)## S3 method for class 'model' tweak(x, sd = 0.13, digits = 3, ...)
x |
model |
sd |
numeric |
digits |
integer |
... |
passed arguments |
model
Other tweak:
tweak(),
tweak.default(),
tweak.init(),
tweak.inits()
# Create a working project. source <- system.file(package = 'nonmemica','project') target <- tempdir() target <- gsub('\\\\','/',target) # for windows source target file.copy(source,target,recursive = TRUE) project <- file.path(target,'project','model') # Point project option at working project options(project = project) library(magrittr) # Make ten new models with slightly different initial estimates. 1001 %>% tweak# Create a working project. source <- system.file(package = 'nonmemica','project') target <- tempdir() target <- gsub('\\\\','/',target) # for windows source target file.copy(source,target,recursive = TRUE) project <- file.path(target,'project','model') # Point project option at working project options(project = project) library(magrittr) # Make ten new models with slightly different initial estimates. 1001 %>% tweak
Creates the updated version of character by treating as a modelname. Parses the associated control stream and ammends the initial estimates to reflect model results (as per xml file).
## S3 method for class 'character' updated(x, initial = estimates(x, ...), parse = TRUE, verbose = FALSE, ...)## S3 method for class 'character' updated(x, initial = estimates(x, ...), parse = TRUE, verbose = FALSE, ...)
x |
character |
initial |
values to use for initial estimates (numeric) |
parse |
whether to parse the initial estimates, etc. |
verbose |
extended messaging |
... |
passed arguements |
model
Other updated:
updated(),
updated.numeric()
Gets upper bounds for model initial estimates.
## S3 method for class 'model' upper(x, ...)## S3 method for class 'model' upper(x, ...)
x |
model |
... |
dots |
Other upper:
upper(),
upper<-(),
upper<-.model()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.model %>% upperlibrary(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% as.model %>% upper
Sets upper bounds for model initial estimates.
## S3 replacement method for class 'model' upper(x) <- value## S3 replacement method for class 'model' upper(x) <- value
x |
model |
value |
numeric |
Other upper:
upper(),
upper.model(),
upper<-()
Evaluates an xpath expression.
xpath(x, ...)xpath(x, ...)
x |
xml_document |
... |
passed arguments |
The resulting nodeset is scavenged for text, and coerced to best of numeric or character.
Other xpath:
as.xml_document(),
as.xml_document.character(),
as.xml_document.numeric(),
xpath.default(),
xpath.xml_document()
library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% xpath('//etashrink/row/col')library(magrittr) options(project = system.file('project/model',package='nonmemica')) 1001 %>% xpath('//etashrink/row/col')
Coerces x to xml_document and evaluates.
## Default S3 method: xpath(x, ...)## Default S3 method: xpath(x, ...)
x |
default |
... |
passed arguments |
vector
Other xpath:
as.xml_document(),
as.xml_document.character(),
as.xml_document.numeric(),
xpath(),
xpath.xml_document()
Evaluates an xpath expression for a given document.
## S3 method for class 'xml_document' xpath(x, xpath, ...)## S3 method for class 'xml_document' xpath(x, xpath, ...)
x |
xml_document |
xpath |
xpath expression to evaluate |
... |
ignored |
The resulting nodeset is scavenged for text, and coerced to best of numeric or character.
vector
Other xpath:
as.xml_document(),
as.xml_document.character(),
as.xml_document.numeric(),
xpath(),
xpath.default()