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.
# S4 method for RPPAFit
coef(object, ...)
# S4 method for RPPAFit
coefficients(object, ...)
# S4 method for RPPAFit
fitted(object,
type=c("Y", "y", "X", "x"),
...)
# S4 method for RPPAFit
hist(x,
type=c("Residuals", "StdRes", "ResidualsR2"),
xlab=NULL,
main=NULL,
...)
# S4 method for RPPAFit
image(x,
measure=c("Residuals", "ResidualsR2", "StdRes", "X", "Y"),
main,
...)
# S4 method for 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 RPPAFit
resid(object,
type=c("raw", "standardized", "r2"),
...)
# S4 method for RPPAFit
residuals(object,
type=c("raw", "standardized", "r2"),
...)
# S4 method for RPPAFit
summary(object, ...)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.
object of class RPPAFit
object of class RPPAFit
character string describing the type of fitted values, residuals, images, histograms, or plots
character string specifying measure to compute from fit
graphics parameter specifying how the x-axis should be labeled
graphics parameter specifying how the y-axis should be labeled
character string specifying title for the plot
function to transform the raw data associated with the
measure for the plot. If NULL, no transformation occurs.
not used
graphics parameter, used only if type='series',
to color the lines connecting different dilution series.
Eight default colors are used if the argument is NULL.
extra arguments for generic or plotting routines
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
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
noise:numeric vector of estimated relative background concentrations for noise for use in calculating qc values for positive control dilution series with Spot.Types designated as posCtrl-Noise or Noise.
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 RPPASPACE 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 \(X_i = X - \delta_i\).
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 coombes.3@osu.edu, P. Roebuck paul_roebuck@comcast.net, James M. Melott jmmelott@mdanderson.org
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 \(\delta_i\), which represent the
(log) concentration of the protein present in different dilution series.
RPPA,
RPPADesignParams,
RPPAFit,
hist