km.pmf: Probability Mass Function for the support of the Kaplan-Meier...
In conf: Visualization and Analysis of Statistical Measures of Confidence
km.pmf R Documentation
Probability Mass Function for the support of the Kaplan-Meier product-limit estimator
Description
Generates the probability mass function for the support values
of the Kaplan-Meier product-limit estimator for a particular sample
size n, probability of observing a failure h at the time of interest expressed as the cumulative probability perc associated with X = min(T, C), where T is the failure time and C is the censoring time under a random-censoring scheme.
Usage
km.pmf(n, h, perc, plot, sep, xfrac, cex.lollipop)
Arguments
n
sample size
h
probability of observing a failure, in other words, P(X = T)
perc
cumulative probability associated with X = min(T, C)
plot
option to plot the probability mass function (default is TRUE)
sep
option to show the breakdown of the probability for each support value (see function km.outcomes for details on the breakdown) (default is TRUE)
xfrac
option to label support values on the x-axis as exact fractions (default is TRUE)
cex.lollipop
size of the dots atop the spikes
Details
The Kaplan-Meier product-limit estimator is used to
estimate the survivor function for a data set of
positive values in the presence of right censoring.
The km.pmf function generates the probability mass function for the support values
of the Kaplan-Meier product-limit estimator for a particular sample
size n, probability of observing a failure h at the time of interest expressed as the cumulative probability perc associated with X = min(T, C), where T is the failure time and C is the censoring time under a random-censoring scheme.
The n argument must be a positive integer denoting
the sample size. Allowable limits are from 1 to 23.
Larger values of n are not allowed because of CPU
and memory limitations.
For larger sample size n, it is recommended to set
sep = FALSE, xfrac = FALSE, and
cex.lollipop = 0.01 for a better visual effect.
Value
The km.pmf function returns a dataframe with
2 columns. The column named S stores all the support
values for the Kaplan-Meier product-limit estimator
with sample size n, including NA. The
column named P stores the associated probabilities.
Author(s)
Yuxin Qin (yqin08@wm.edu),
Heather Sasinowska (hdsasinowska@wm.edu),
Larry Leemis (leemis@math.wm.edu)
References
Qin, Y., Sasinowska, H., Leemis, L. (2023), "The Probability Mass
Function of the Kaplan-Meier Product-Limit Estimator",
The American Statistician, Volume 77, Number 1, 102-110.
See Also
survfit
Examples
km.pmf(4, 1/3, 0.75)
km.pmf(8, 1/2, 0.75, sep = FALSE, xfrac = FALSE, cex.lollipop = 0.01)
conf documentation built on Oct. 1, 2023, 1:07 a.m.
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| km.pmf | R Documentation |
Probability Mass Function for the support of the Kaplan-Meier product-limit estimator
Description
Generates the probability mass function for the support values
of the Kaplan-Meier product-limit estimator for a particular sample
size n, probability of observing a failure h at the time of interest expressed as the cumulative probability perc associated with X = min(T, C), where T is the failure time and C is the censoring time under a random-censoring scheme.
Usage
km.pmf(n, h, perc, plot, sep, xfrac, cex.lollipop)
Arguments
n |
sample size |
h |
probability of observing a failure, in other words, |
perc |
cumulative probability associated with |
plot |
option to plot the probability mass function (default is |
sep |
option to show the breakdown of the probability for each support value (see function |
xfrac |
option to label support values on the x-axis as exact fractions (default is |
cex.lollipop |
size of the dots atop the spikes |
Details
The Kaplan-Meier product-limit estimator is used to
estimate the survivor function for a data set of
positive values in the presence of right censoring.
The km.pmf function generates the probability mass function for the support values
of the Kaplan-Meier product-limit estimator for a particular sample
size n, probability of observing a failure h at the time of interest expressed as the cumulative probability perc associated with X = min(T, C), where T is the failure time and C is the censoring time under a random-censoring scheme.
The n argument must be a positive integer denoting
the sample size. Allowable limits are from 1 to 23.
Larger values of n are not allowed because of CPU
and memory limitations.
For larger sample size n, it is recommended to set
sep = FALSE, xfrac = FALSE, and
cex.lollipop = 0.01 for a better visual effect.
Value
The km.pmf function returns a dataframe with
2 columns. The column named S stores all the support
values for the Kaplan-Meier product-limit estimator
with sample size n, including NA. The
column named P stores the associated probabilities.
Author(s)
Yuxin Qin (yqin08@wm.edu), Heather Sasinowska (hdsasinowska@wm.edu), Larry Leemis (leemis@math.wm.edu)
References
Qin, Y., Sasinowska, H., Leemis, L. (2023), "The Probability Mass
Function of the Kaplan-Meier Product-Limit Estimator",
The American Statistician, Volume 77, Number 1, 102-110.
See Also
survfit
Examples
km.pmf(4, 1/3, 0.75)
km.pmf(8, 1/2, 0.75, sep = FALSE, xfrac = FALSE, cex.lollipop = 0.01)
R Package Documentation
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