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An S4 class generic function that estimates mean cumulative function (MCF) from a fitted model or computing the sample nonparametric MCF (also called Nelson-Aalen estimator) from data.
mcf(object, ...)# S4 method for formula
mcf(object, data, subset, na.action,
variance = c("Poisson", "LawlessNadeau"), logConfInt = TRUE,
level = 0.95, ...)
# S4 method for rateReg
mcf(object, newdata, groupName, groupLevels, level = 0.95,
na.action, control = list(), ...)
An object used to dispatch a method.
Other arguments for future usage.
An optional data frame, list or environment containing
the variables in the model. If not found in data, the variables are taken
from environment(formula), usually the environment from which
the function is called.
An optional vector specifying a subset of observations to be used in the fitting process.
A function that indicates what should the procedure do
if the data contains NAs. The default is set by the
na.action setting of options.
The "factory-fresh" default is na.omit.
Other possible values inlcude na.fail,
na.exclude, and na.pass.
help(na.fail) for details.
An optional character specifying the variance estimator. The available options are "Poisson" (default) for Poisson process method, and "LawlessNadeau" for Lawless and Nadeau (1995) method. (A simple example is available at Reliawiki, 2012.) Partial matching on the names is allowed.
An optional logical value. If TRUE (default),
the confidence interval of given level will be constucted based on the
normality of logarithm of the MCF function. (Otherwise, the confidence
interval will be constructed based on the normality of MCF function.)
An optional numeric value indicating the confidence level required. The default value is 0.95.
An optional data frame. If specified, the data frame should have the same column names as the covariate names appearing in the formula of original fitting.
An optional length-one charactor vector to specify the
name for grouping each unique row in newdata, such as "gender"
for "male" and "female". The default value is "group".
An optional charactor vector to specify the levels for
each unique row in newdata, such as "treatment" and "control".
The default values are capital letters starting from "A".
An optional list to specify the time grid
where the MCF is estimated.
The availble elements of the control list include
grid, length.out, from and to.
The time grid can be directly specified via element grid.
A dense time grid is suggested.
Element length.out represents the length of grid points.
The dafault value is 1,000.
Element from means the starting point of grid with default 0.
Element to represnts the endpoint of grid
with the right boundary knot as default.
When grid is missing, the grid will be generated
by seq (from package base)
with arguments from, to and length.out.
sampleMcf-class or rateRegMcf-class object.
Their slots include
level: Confidence level specified.
MCF: Mean cumulative function at each time point.
multiGroup: A logical value indicating whether MCF
is estimated for different specified group.
newdata: Given dataset used to estimate MCF.
For the meaning of other slots, see rateReg.
formula: Sample MCF from data.
rateReg: Estimated MCF from a fitted model.
For formula object with Survr object as response,
the covariate specified at the right hand side of the formula
should be either 1 or any "linear" conbination of factor variable
in the data.
The former computes the overall sample MCF.
The latter computes the sample MCF for each level of the combination of
the factor variable(s) specified, respectively.
The sample MCF is also called Nelson-Aalen nonparametric estimator
(Nelson, 2003) and computed on each time point from sample data.
The point estimate of sample MCF at each time point does not
assume any particular underlying model. The variance estimates
at each time point is given by Poisson process method (by default)
or Lawless and Nadeau method (LawLess and Nadeau, 1995).
The approximate confidence intervals are provided as well,
which are constructed based on the asymptotic normality
of logarithm of MCF (by default) or MCF itself directly.
For rateReg-class object,
mcf estimates the baseline MCF and its confidence interval
at each time grid if argument newdata is not specified.
Otherwise, mcf estimates MCF and its confidence interval
for the given newdata based on Delta-method.
Nelson, W. B. (2003). Recurrent events data analysis for product repairs, disease recurrences, and other applications (Vol. 10). SIAM.
Lawless, J. F. and Nadeau, C. (1995). Some Simple Robust Methods for the Analysis of Recurrent Events. Technometrics, 37, 158--168.
ReliaWiki. (2012, March 19). Recurrent Event Data Analysis. Retrieved November 23, 2015, from http://reliawiki.org/index.php/Recurrent_Event_Data_Analysis
rateReg for model fitting;
plot-method for plotting MCF.
# NOT RUN {
library(reda)
### Example 1. valve-seat data
valveMcf <- mcf(Survr(ID, Days, No.) ~ 1, data = valveSeats)
## plot sample MCF
plot(valveMcf, conf.int = TRUE, mark.time = TRUE) + ggplot2::xlab("Days")
### Example 2. sample simulated data
simuMcf <- mcf(Survr(ID, time, event) ~ group + gender,
data = simuDat, ID %in% 1 : 50, logConfInt = FALSE)
## create customized levels in legend
levs <- with(simuDat, expand.grid(levels(group), levels(gender)))
levs <- do.call(paste, c(as.list(levs), sep = " & "))
## plot sample MCF
plot(simuMcf, conf.int = TRUE, lty = 1 : 4,
legendName = "Treatment & Gender", legendLevels = levs)
## For estimated MCF from a fitted model,
## see examples given in function rateReg.
# }
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