modelPlot {glmmSeq} | R Documentation |
Mixed model effects plot
Description
Plot to show differences between groups over time using base graphics.
Usage
modelPlot(
object,
geneName = NULL,
x1var = NULL,
x2var = NULL,
x2shift = NULL,
xlab = NA,
ylab = geneName,
plab = NULL,
title = geneName,
logTransform = is(object, "GlmmSeq"),
shapes = 21,
colours = "grey60",
lineColours = "grey60",
markerSize = 0.5,
fontSize = NULL,
alpha = 0.7,
addModel = TRUE,
addPoints = TRUE,
modelSize = 2,
modelColours = "royalblue",
modelLineSize = 1,
modelLineColours = modelColours,
errorBarLwd = 2.5,
errorBarLength = 0.05,
...
)
Arguments
object |
A glmmSeq/lmmSeq object created by
|
geneName |
The gene/row name to be plotted |
x1var |
The name of the first (inner) x parameter, typically 'time'. This is anticipated to have different values when matched by ID. |
x2var |
The name of an optional second (outer) x parameter, which should be a factor. |
x2shift |
Amount to shift along x axis for each level of |
xlab |
Title for the x axis |
ylab |
Title for the y axis |
plab |
Optional character vector of labels for p-values. These must
align with column names in |
title |
Plot title. If NULL gene name is used |
logTransform |
Whether to perform a log10 transform on the y axis |
shapes |
The marker shapes (default=19) |
colours |
The marker colours (default='red') as vector or named vector |
lineColours |
The line colours (default='grey60') as vector or named vector |
markerSize |
Size of markers (default=2) |
fontSize |
Plot font size |
alpha |
Line and marker opacity (default=0.7) |
addModel |
Whether to add the fit model with markers (default=TRUE) |
addPoints |
Whether to add underlying data points (default=TRUE) |
modelSize |
Size of model points (default=2) |
modelColours |
Colour of model fit markers (default="black") as vector or named vector |
modelLineSize |
Size of model points (default=1) as vector or named vector |
modelLineColours |
Colour of model fit lines. |
errorBarLwd |
Line width of error bars |
errorBarLength |
Head width of error bars |
... |
Other parameters to pass to
|
Value
Returns a paired plot for matched samples
Examples
data(PEAC_minimal_load)
disp <- apply(tpm, 1, function(x){
(var(x, na.rm=TRUE)-mean(x, na.rm=TRUE))/(mean(x, na.rm=TRUE)**2)
})
MS4A1glmm <- glmmSeq(~ Timepoint * EULAR_6m + (1 | PATID),
countdata = tpm[1:2, ],
metadata = metadata,
dispersion = disp)
modelPlot(object=MS4A1glmm,
geneName = 'MS4A1',
x1var = 'Timepoint',
x2var='EULAR_6m')