Description Usage Arguments Details Value Author(s) References See Also Examples
Customised plotting of estimated curve parameter values
from single or multiple PM plates using
parallelplot
from the lattice package with
some adaptations likely to be useful for
OmniLog(R) data.
parallelplot
is an alias of parallel_plot
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | ## S4 method for signature 'MOPMX,ANY'
parallel_plot(x, data, ...)
## S4 method for signature 'MOPMX,XOPMX'
parallel_plot(x, data, ...)
## S4 method for signature 'MOPMX,missing'
parallel_plot(x, data, ...)
## S4 method for signature 'NULL,XOPMX'
parallel_plot(x, data, ...)
## S4 method for signature 'OPMX,ANY'
parallel_plot(x, data, groups = 1L,
panel.var = NULL, pnames = param_names(), col = opm_opt("colors"),
strip.fmt = list(), striptext.fmt = list(), legend.fmt = list(),
legend.sep = " ", draw.legend = TRUE, space = "top", ...)
## S4 method for signature 'OPMX,XOPMX'
parallel_plot(x, data, ...)
## S4 method for signature 'OPMX,missing'
parallel_plot(x, data, ...)
## S4 method for signature 'formula,XOPMX'
parallel_plot(x, data, ...)
## S4 method for signature 'missing,XOPMX'
parallel_plot(x, data, ...)
## S4 method for signature 'vector,XOPMX'
parallel_plot(x, data, ...)
## S4 method for signature 'MOPMX,ANY'
parallelplot(x, data, ...)
## S4 method for signature 'MOPMX,XOPMX'
parallelplot(x, data, ...)
## S4 method for signature 'MOPMX,missing'
parallelplot(x, data, ...)
## S4 method for signature 'NULL,XOPMX'
parallelplot(x, data, ...)
## S4 method for signature 'OPMX,ANY'
parallelplot(x, data, ...)
## S4 method for signature 'OPMX,XOPMX'
parallelplot(x, data, ...)
## S4 method for signature 'OPMX,missing'
parallelplot(x, data, ...)
## S4 method for signature 'formula,XOPMX'
parallelplot(x, data, ...)
## S4 method for signature 'missing,XOPMX'
parallelplot(x, data, ...)
## S4 method for signature 'vector,XOPMX'
parallelplot(x, data, ...)
|
x |
An |
data |
Any kind of object that can be used for
selecting Most flexibility is available if |
groups |
Character or numerical scalar determining
which metadata entry or other information, such as the
well indexes, (see the examples) is used for assigning
colours to the curves. If a numeric scalar, it refers to
the position of the (potentially merged) metadata entry
within |
panel.var |
Character or numeric vector indicating
which metadata entry or other information, such as the
well indexes, (see the examples) is used for creating
sub-panels. If a numeric vector, it refers to the
position of the (potentially merged) metadata entry
within |
pnames |
Character vector or formula to select the
curve parameters for plotting. It has to comprise at
least two of the names given by
|
col |
Character or numerical scalar or vector. This
and the following arguments work like the eponymous
arguments of |
strip.fmt |
List. |
striptext.fmt |
List. |
legend.fmt |
List. |
legend.sep |
Character scalar. |
draw.legend |
Logical scalar. |
space |
Character scalar. |
... |
Optional arguments passed to
|
The main application of this function is to include all four estimated curve parameters into a single comprehensive overview. This assists in addressing questions such as
Are there any consistent patterns of individual curves that may be explained by specific class membership? For instance, which curve parameter best reflects the origin of the tested strains?
Are there any patterns of individual curves with unexpected deviations? For instance, do differences between experimental repetitions occur?
An object of class ‘trellis’ or list of such
objects. See xyplot
from the lattice package
for details.
Lea A.I. Vaas
Sarkar, D. 2008 Lattice: Multivariate Data Visualization with R. New York: Springer, 265 p.
lattice::xyplot lattice::parallelplot
Other plotting-functions: ci_plot
,
heat_map
, level_plot
,
radial_plot
, summary
,
xy_plot
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 | if ("package:lattice" %in% search())
detach("package:lattice") # only necessary for knitr
## OPM objects
parallelplot(vaas_1)
parallelplot(vaas_1, data = list("Species", "Strain"))
# ... no effect on selection but on header
# value of 'groups' not found in the data: per default no metadata are used
x <- try(parallelplot(vaas_1, groups = "Species"), silent = TRUE)
stopifnot(inherits(x, "try-error"))
# same problem: metadata selected but 'groups' is not contained
x <- try(parallelplot(vaas_1, data = list("Species", "Strain"),
groups = "missing"), silent = TRUE)
stopifnot(inherits(x, "try-error"))
# ... thus it is safer to use a positional 'groups' argument
## OPMS objects
# per default metadata are ignored
parallelplot(vaas_4[, , 1:10])
# otherwise selecting metadata is as usual
parallelplot(vaas_4[, , 1:10], data = ~ J(Species, Strain))
parallelplot(vaas_4[, , 1:10], data = list("Species", "Strain"))
# value of 'groups' not found in the data: per default no metadata are used
x <- try(parallelplot(vaas_4[, , 1:10], groups = "Species"), silent = TRUE)
stopifnot(inherits(x, "try-error"))
# now 'groups' is all present but not a character scalar
x <- try(parallelplot(vaas_4[, , 1:10], data = list("Species", "Strain"),
groups = c("Strain", "Species")), silent = TRUE)
stopifnot(inherits(x, "try-error"))
# here 'groups' is positional but beyond the last element
x <- try(parallelplot(vaas_4[, , 1:10], data = list("Species", "Strain"),
groups = 3), silent = TRUE)
stopifnot(inherits(x, "try-error"))
# 'groups' and 'panel.var' arguments that work
parallelplot(vaas_4[, , 1:10], data = ~ J(Species, Strain),
panel.var = "Species", groups = "Strain")
parallelplot(vaas_4[, , 1:10], data = "Species", panel.var = "Species",
groups = NULL)
parallelplot(vaas_4[, , 1:10], data = list("Species", "Strain"),
panel.var = "Species")
# use of non-metadata information: here the names of the wells
parallelplot(vaas_4[, , 1:10], data = "Species", panel.var = "Well",
groups = "Species")
# selection of parameters via 'pnames'
parallelplot(vaas_4[, , 1:10], pnames = ~ A + AUC + mu,
data = ~ Species + Strain, panel.var = "Species",
col = c("black", "red"), groups = "Species")
x <- try(parallelplot(vaas_4[, , 1:10], pnames = "A",
data = ~ Species + Strain, panel.var = "Species",
col = c("black", "red"), groups = "Species"), silent = TRUE)
stopifnot(inherits(x, "try-error")) # => at least two 'pnames' needed
# selecting the parameters via the left side of a 'data' formula
parallelplot(vaas_4[, , 1:10], data = A + AUC ~ J(Species, Strain))
parallelplot(vaas_4[, , 1:10], data = A + AUC ~ J(Species, Strain),
groups = "Species")
# 'pnames' explicitly given => left side of formula ignored
parallelplot(vaas_4[, , 1:10], data = A + AUC ~ J(Species, Strain),
pnames = c("A", "mu", "AUC"), groups = "Species")
# again: at least two 'pnames' needed
x <- try(parallelplot(vaas_4[, , 1:10], data = AUC ~ J(Species, Strain),
groups = "Species"), silent = TRUE)
stopifnot(inherits(x, "try-error"))
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