| aggregated {opm} | R Documentation |
Get the aggregated kinetic data or the aggregation
settings used. (See do_aggr for generating
aggregated data.)
## S4 method for signature 'MOPMX'
aggr_settings(object, join = NULL)
## S4 method for signature 'OPMA'
aggr_settings(object, join = NULL)
## S4 method for signature 'OPMS'
aggr_settings(object, join = NULL)
## S4 method for signature 'MOPMX'
aggregated(object, ...)
## S4 method for signature 'OPMA'
aggregated(object, subset = NULL, ci = TRUE,
trim = c("no", "full", "medium"), full = FALSE, in.parens = TRUE,
max = opm_opt("max.chars"), ...)
## S4 method for signature 'OPMS'
aggregated(object, ...)
object |
|
subset |
Character vector. If not |
ci |
Logical scalar. Include the estimates of confidence intervals (CIs) in the output? |
trim |
Character scalar. Parameter estimates from intrinsically negative reactions (i.e., no respiration) are sometimes biologically unreasonable because they are too large or too small, and some corrections might be appropriate.
Currently the other parameters are not checked, and all
|
full |
Logical scalar passed to |
in.parens |
Logical scalar also passed to that function. |
max |
Numeric scalar also passed to that function. |
join |
Empty or character scalar. If empty, a list
is returned; a nested list in the case of
All other
values of |
... |
Optional arguments passed between the methods
or to |
Note that the conversion of the settings list to a matrix
or data frame might not work for all combinations of
object and join, mainly because the options
entry can hold arbitrary content. For similar conversions
of the metadata, see the OPMX methods of
to_metadata.
aggregated yields a numeric matrix of aggregated
values (a.k.a. the curve parameters). If bootstrapping
was used, their CIs are included. The columns
represent the wells, the rows the estimated parameters
and their CIs.
aggr_settings returns a named list if join
is empty. Other values yield a matrix or data frame (or
an error). See the description of the argument above and
the examples below for further details.
Other getter-functions: anyDuplicated,
anyNA, contains,
csv_data, dim,
disc_settings, discretized,
duplicated, has_aggr,
has_disc, hours,
max, measurements,
minmax, seq,
subset, thin_out,
well
# 'OPMA' methods
# Get full matrix
(x <- aggregated(vaas_1))[, 1:3]
## A01 A02 A03
## mu 4.070829 7.5624410 16.4848300
## lambda 18.523010 0.5628715 1.7110130
## A 123.455800 248.1809000 284.0994000
## AUC 8918.137000 18391.5900000 21960.0800000
## mu CI95 low 3.971357 6.4975400 15.1024400
## lambda CI95 low -7.194295 -33.9691600 -0.7143524
## A CI95 low 122.886200 247.4527000 283.8384000
## AUC CI95 low 8846.238000 18255.3400000 21808.5600000
## mu CI95 high 10.581020 16.4906500 21.9867000
## lambda CI95 high 82.911920 73.6234100 2.6739110
## A CI95 high 125.714400 249.5763000 285.3661000
## AUC CI95 high 8944.920000 18453.3300000 22040.5700000
stopifnot(is.matrix(x), dim(x) == c(12, 96))
(y <- aggregated(vaas_1, full = TRUE))[, 1:3] # full names
## A01 (Negative Control) A02 (Dextrin) A03 (D-Maltose)
## mu 4.070829 7.5624410 16.4848300
## lambda 18.523010 0.5628715 1.7110130
## A 123.455800 248.1809000 284.0994000
## AUC 8918.137000 18391.5900000 21960.0800000
## mu CI95 low 3.971357 6.4975400 15.1024400
## lambda CI95 low -7.194295 -33.9691600 -0.7143524
## A CI95 low 122.886200 247.4527000 283.8384000
## AUC CI95 low 8846.238000 18255.3400000 21808.5600000
## mu CI95 high 10.581020 16.4906500 21.9867000
## lambda CI95 high 82.911920 73.6234100 2.6739110
## A CI95 high 125.714400 249.5763000 285.3661000
## AUC CI95 high 8944.920000 18453.3300000 22040.5700000
stopifnot(x == y, nchar(colnames(x)) < nchar(colnames(y)))
# Subsetting
(x <- aggregated(vaas_1, "lambda"))[, 1:3]
## A01 A02 A03
## lambda 18.523010 0.5628715 1.7110130
## lambda CI95 low -7.194295 -33.9691600 -0.7143524
## lambda CI95 high 82.911920 73.6234100 2.6739110
stopifnot(is.matrix(x), dim(x) == c(3, 96), any(x < 0))
# Now with lambda correction
(x <- aggregated(vaas_1, "lambda", trim = "full"))[, 1:3]
## A01 A02 A03
## lambda 18.52301 0.5628715 1.711013
## lambda CI95 low 0.00000 0.0000000 0.000000
## lambda CI95 high 82.91192 73.6234100 2.673911
stopifnot(is.matrix(x), dim(x) == c(3, 96), !any(x < 0))
# settings
(x <- aggr_settings(vaas_1)) # yields named list
## $method
## [1] "grofit"
##
## $options
## $options$neg.nan.act
## [1] FALSE
##
## $options$clean.bootstrap
## [1] TRUE
##
## $options$suppress.messages
## [1] TRUE
##
## $options$fit.opt
## [1] "s"
##
## $options$log.x.gc
## [1] FALSE
##
## $options$log.y.gc
## [1] FALSE
##
## $options$interactive
## [1] FALSE
##
## $options$nboot.gc
## [1] 100
##
## $options$smooth.gc
## NULL
##
## $options$smooth.dr
## NULL
##
## $options$have.atleast
## [1] 6
##
## $options$parameter
## [1] 9
##
## $options$log.x.dr
## [1] FALSE
##
## $options$log.y.dr
## [1] FALSE
##
## $options$nboot.dr
## [1] 0
##
## $options$model.type
## [1] "logistic" "richards" "gompertz" "gompertz.exp"
##
##
## $software
## [1] "opm"
##
## $version
## [1] "0.1-0"
stopifnot(is.list(x), !is.null(names(x)))
(x <- aggr_settings(vaas_1, join = "json")) # yields a matrix
## method
## [1,] "grofit"
## options
## [1,] "{\"neg.nan.act\":false,\"clean.bootstrap\":true,\"suppress.messages\":true,\"fit.opt\":\"s\",\"log.x.gc\":false,\"log.y.gc\":false,\"interactive\":false,\"nboot.gc\":100,\"smooth.gc\":null,\"smooth.dr\":null,\"have.atleast\":6,\"parameter\":9,\"log.x.dr\":false,\"log.y.dr\":false,\"nboot.dr\":0,\"model.type\":[\"logistic\",\"richards\",\"gompertz\",\"gompertz.exp\"]}"
## software version
## [1,] "opm" "0.1-0"
stopifnot(is.matrix(x), is.character(x), nrow(x) == 1)
# 'OPMS' methods
summary(x <- aggregated(vaas_4)) # => one matrix per OPM object
## Length Class Mode
## [1,] 1152 -none- numeric
## [2,] 1152 -none- numeric
## [3,] 1152 -none- numeric
## [4,] 1152 -none- numeric
stopifnot(is.list(x), length(x) == length(vaas_4), sapply(x, is.matrix))
# settings
summary(x <- aggr_settings(vaas_4)) # list of named lists, one per plate
## Length Class Mode
## [1,] 4 -none- list
## [2,] 4 -none- list
## [3,] 4 -none- list
## [4,] 4 -none- list
stopifnot(is.list(x), length(x) == length(vaas_4), sapply(x, is.list))
(x <- aggr_settings(vaas_4, join = "yaml")) # matrix, one row per plate
## method
## [1,] "grofit"
## [2,] "grofit"
## [3,] "grofit"
## [4,] "grofit"
## options
## [1,] "---\nneg.nan.act: no\nclean.bootstrap: yes\nsuppress.messages: yes\nfit.opt: s\nlog.x.gc: no\nlog.y.gc: no\ninteractive: no\nnboot.gc: 100.0\nsmooth.gc: ~\nsmooth.dr: ~\nhave.atleast: 6.0\nparameter: 9.0\nlog.x.dr: no\nlog.y.dr: no\nnboot.dr: 0.0e+00\nmodel.type:\n- logistic\n- richards\n- gompertz\n- gompertz.exp\n\n"
## [2,] "---\nneg.nan.act: no\nclean.bootstrap: yes\nsuppress.messages: yes\nfit.opt: s\nlog.x.gc: no\nlog.y.gc: no\ninteractive: no\nnboot.gc: 100.0\nsmooth.gc: ~\nsmooth.dr: ~\nhave.atleast: 6.0\nparameter: 9.0\nlog.x.dr: no\nlog.y.dr: no\nnboot.dr: 0.0e+00\nmodel.type:\n- logistic\n- richards\n- gompertz\n- gompertz.exp\n\n"
## [3,] "---\nneg.nan.act: no\nclean.bootstrap: yes\nsuppress.messages: yes\nfit.opt: s\nlog.x.gc: no\nlog.y.gc: no\ninteractive: no\nnboot.gc: 100.0\nsmooth.gc: ~\nsmooth.dr: ~\nhave.atleast: 6.0\nparameter: 9.0\nlog.x.dr: no\nlog.y.dr: no\nnboot.dr: 0.0e+00\nmodel.type:\n- logistic\n- richards\n- gompertz\n- gompertz.exp\n\n"
## [4,] "---\nneg.nan.act: no\nclean.bootstrap: yes\nsuppress.messages: yes\nfit.opt: s\nlog.x.gc: no\nlog.y.gc: no\ninteractive: no\nnboot.gc: 100.0\nsmooth.gc: ~\nsmooth.dr: ~\nhave.atleast: 6.0\nparameter: 9.0\nlog.x.dr: no\nlog.y.dr: no\nnboot.dr: 0.0e+00\nmodel.type:\n- logistic\n- richards\n- gompertz\n- gompertz.exp\n\n"
## software version
## [1,] "opm" "0.1-0"
## [2,] "opm" "0.1-0"
## [3,] "opm" "0.1-0"
## [4,] "opm" "0.1-0"
stopifnot(is.matrix(x), is.character(x), nrow(x) == 4)