Description Usage Arguments Details Value Objects from the Class Slots Methods Author(s) See Also Examples
Objects of the RPPAFit
class represent the results of fitting a
statistical model of response to the dilution series in a
reverse-phase protein array experiment.
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 | ## S4 method for signature 'RPPAFit'
coef(object, ...)
## S4 method for signature 'RPPAFit'
coefficients(object, ...)
## S4 method for signature 'RPPAFit'
fitted(object,
type=c("Y", "y", "X", "x"),
...)
## S4 method for signature 'RPPAFit'
hist(x,
type=c("Residuals", "StdRes", "ResidualsR2"),
xlab=NULL,
main=NULL,
...)
## S4 method for signature 'RPPAFit'
image(x,
measure=c("Residuals", "ResidualsR2", "StdRes", "X", "Y"),
main,
...)
## S4 method for signature 'RPPAFit,missing'
plot(x, y,
type=c("cloud", "series", "individual", "steps", "resid"),
col=NULL,
main,
xform=NULL,
xlab="Log Concentration",
ylab="Intensity",
...)
## S4 method for signature 'RPPAFit'
resid(object,
type=c("raw", "standardized", "r2"),
...)
## S4 method for signature 'RPPAFit'
residuals(object,
type=c("raw", "standardized", "r2"),
...)
## S4 method for signature 'RPPAFit'
summary(object, ...)
|
object |
object of class |
x |
object of class |
type |
character string describing the type of fitted values, residuals, images, histograms, or plots |
measure |
character string specifying measure to compute from fit |
xlab |
graphics parameter specifying how the x-axis should be labeled |
ylab |
graphics parameter specifying how the y-axis should be labeled |
main |
character string specifying title for the plot |
xform |
function to transform the raw data associated with the
|
y |
not used |
col |
graphics parameter, used only if |
... |
extra arguments for generic or plotting routines |
The RPPAFit
class holds the results of fitting a response model to
all the dilution series on a reverse-phase protein array. For details on
how the model is fit, see the RPPAFit
function. By fitting
a joint model, we assume that the response curve is the same for all
dilution series on the array. The real point of the model, however, is
to be able to draw inferences on the δ_i, which represent the
(log) concentration of the protein present in different dilution series.
The coef
and coefficients
methods return the numeric model
coefficients from objects returned by modeling functions.
The fitted
method returns a numeric vector.
The hist
method returns an object of class histogram
.
The image
method invisibly returns the object x
on which
it was invoked.
The plot
method invisibly returns the object x
on which
it was invoked.
The resid
and residuals
methods return a numeric vector.
The summary
method invisibly returns NULL
.
Objects should be constructed using the RPPAFit
function.
call
:object of class call
specifying the function
call that was used to generate this model fit
rppa
:object of class RPPA
containing the raw data
that was fit
design
:object of class RPPADesign
describing the
layout of the array
measure
:character string containing the name of the measurement column in the raw data that was fit by the model
method
:character string containing the name of the method that was used to estimate the upper and lower limit parameters in the model
trimset
:numeric vector of length 5 containing the low and high intensities, the low and high concentrations that mark the trimming boundaries, and the trim level used
model
:object of class FitClass
unique to the
model that was fit
concentrations
:numeric vector of estimates of the relative log concentration of protein present in each sample
lower
:numeric vector containing the lower bounds on the confidence interval of the log concentration estimates
upper
:numeric vector containing the upper bounds on the confidence interval of the log concentration estimates
conf.width
:numeric scalar specifying width of the confidence interval
intensities
:numeric vector containing the predicted observed intensity at the estimated concentrations for each dilution series
ss.ratio
:numeric vector containing statistic measuring the R^2 for each individual dilution series
warn
:character vector containing any warnings that arose when trying to fit the model to individual dilution series
version
:character string containing the version of SuperCurve that produced the fit
signature(object = "RPPAFit")
:
Extracts model coefficients from objects returned by modeling functions.
signature(object = "RPPAFit")
:
An alias for coef
.
signature(object = "RPPAFit")
:
Extracts the fitted values of the model. This process is more
complicated than it may seem at first, since we are estimating values
on both the X and Y axes. By default, the fitted
values are assumed to be the intensities, Y, which are
obtained using either an uppercase or lowercase 'y' as the
type
argument. The fitted log concentrations are
returned when type
is set to either uppercase or
lowercase 'x'. In the notation used above to describe the model,
these fitted values are given by X_i = X - δ_i.
signature(x = "RPPAFit")
:
Produces a histogram of the residuals. The exact form of the residuals
being displayed depends on the value of the type
argument.
signature(x = "RPPAFit")
:
Produces a 'geographic' plot of either the residuals or the fitted
values, depending on the value of the measure
argument. The
implementation reuses code from the image
method for an
RPPA
object.
signature(x = "RPPAFit", y = "missing")
:
Produces a diagnostic plot of the model fit. The default type
,
'cloud', simply plots the fitted X values against the observed
Y values as a cloud of points around the jointly estimated
sigmoid curve. The 'series' plot uses different colored lines to join
points belonging to the same dilution series. The 'individual' plot
produces separate graphs for each dilution series, laying each one
alongside the jointly fitted sigmoid curve.
signature(object = "RPPAFit")
:
An alias for residuals
.
signature(object = "RPPAFit")
:
Reports the residual errors. The 'raw' residuals are defined
as the difference between the observed intensities and the
fitted intensities, as computed by the fitted
function.
The 'standardized' residuals are obtained by standardizing the
raw residuals.
signature(object = "RPPAFit")
:
Prints a summary of the RPPAFit
object, which reports the
function call used to fit the model and important fitting parameters.
Kevin R. Coombes kcoombes@mdanderson.org, P. Roebuck proebuck@mdanderson.org
RPPA
,
RPPADesign
,
RPPAFit
,
hist
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | extdata.dir <- system.file("extdata", package="SuperCurveSampleData")
txtdir <- file.path(extdata.dir, "rppaTumorData")
erk2 <- RPPA("ERK2.txt", path=txtdir)
design <- RPPADesign(erk2,
grouping="blockSample",
controls=list("neg con", "pos con"))
erk2.fit <- RPPAFit(erk2, design, "Mean.Net")
showMethods('image')
class(erk2.fit)
image(erk2.fit)
image(erk2.fit, measure="Residuals")
plot(erk2.fit, type="cloud")
coef(erk2.fit)
jnk <- RPPA("JNK.txt", path=txtdir)
jnk.fit <- RPPAFit(jnk, design, "Mean.Net")
hist(jnk.fit, type="StdRes")
plot(jnk.fit, type="series")
coef(jnk.fit)
plot(fitted(jnk.fit), resid(jnk.fit))
|
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