transLS: Location-Scale transition probabilities
In TPmsm: Estimation of Transition Probabilities in Multistate Models

transLS

R Documentation

Location-Scale transition probabilities

Description

Provides estimates for the transition probabilities based on the Location-Scale estimator, LS.

Usage

transLS(object, s, t, h, nh=40, ncv=10, window="normal", state.names=c("1", "2", "3"),
conf=FALSE, n.boot=1000, conf.level=0.95, method.boot="percentile", boot.cv=FALSE,
cv.full=TRUE)

Arguments

`object`	An object of class ‘survTP’.
`s`	The first time for obtaining estimates for the transition probabilities. If missing, 0 will be used.
`t`	The second time for obtaining estimates for the transition probabilities. If missing, the maximum of `Stime` will be used.
`h`	A vector with 1 up to 4 values, indicating the minimum and maximum bandwidths to test by cross-validation.
`nh`	The number of bandwidth values to test by cross-validation. Defaults to 40.
`ncv`	The number of cross-validation samples. Defaults to 10.
`window`	A character string specifying the desired kernel. Possible options are “normal”, “epanech”, “biweight”, “triweight”, “box”, “tricube”, “triangular” or “cosine”. Defaults to “normal” where the gaussian density kernel will be used.
`state.names`	A vector of characters giving the state names.
`conf`	Provides pointwise confidence bands. Defaults to `FALSE`.
`n.boot`	The number of bootstrap samples. Defaults to 1000 samples.
`conf.level`	Level of confidence. Defaults to 0.95 (corresponding to 95%).
`method.boot`	The method used to compute bootstrap confidence bands. Possible options are “percentile” and “basic”. Defaults to “percentile”.
`boot.cv`	If `TRUE` the bandwidth is computed by cross-validation for each bootstrap sample. If `FALSE` the bandwidth used to compute the estimates is used to compute each bootstrap estimate. Defaults to `FALSE`.
`cv.full`	If `TRUE` the bandwidth is computed by cross-validation for both the location and scale functions. If `FALSE` the bandwidth is computed by cross-validation only for the location function. And the bandwidth for the scale function is taken to be equal to the location one. Defaults to `TRUE`.

Value

An object of class ‘TPmsm’. There are methods for contour, image, print and plot. ‘TPmsm’ objects are implemented as a list with elements:

`method`	A string indicating the type of estimator used in the computation.
`est`	A matrix with transition probability estimates. The rows being the event times and the columns the 5 possible transitions.
`inf`	A matrix with the lower transition probabilities of the confidence band. The rows being the event times and the columns the 5 possible transitions.
`sup`	A matrix with the upper transition probabilities of the confidence band. The rows being the event times and the columns the 5 possible transitions.
`time`	Vector of times where the transition probabilities are computed.
`s`	Start of the time interval.
`t`	End of the time interval.
`h`	The bandwidth used. If the estimator doesn't require a bandwidth, it's set to `NULL`.
`state.names`	A vector of characters giving the states names.
`n.boot`	Number of bootstrap samples used in the computation of the confidence band.
`conf.level`	Level of confidence used to compute the confidence band.

Author(s)

Artur Araújo, Javier Roca-Pardiñas and Luís Meira-Machado

References

Araújo A, Meira-Machado L, Roca-Pardiñas J (2014). TPmsm: Estimation of the Transition Probabilities in 3-State Models. Journal of Statistical Software, 62(4), 1-29. doi: 10.18637/jss.v062.i04

Meira-Machado L., Roca-Pardiñas J., Van Keilegom I., Cadarso-Suárez C. (2013). Bandwidth Selection for the Estimation of Transition Probabilities in the Location-Scale Progressive Three-State Model. Computational Statistics, 28(5), 2185-2210. doi: 10.1007/s00180-013-0402-0

Meira-Machado L., Roca-Pardiñas J., Van Keilegom I., Cadarso-Suárez C. (2010). Estimation of transition probabilities in a non-Markov model with successive survival times. https://sites.uclouvain.be/IAP-Stat-Phase-V-VI/ISBApub/dp2010/DP1053.pdf

Van Keilegom I., de Uña-Álvarez J., Meira-Machado L. (2011). Nonparametric location-scale models for successive survival times under dependent censoring. Journal of Statistical Planning and Inference, 141(3), 1118-1131. doi: 10.1016/j.jspi.2010.09.010

Davison, A. C., Hinkley, D. V. (1997). Bootstrap Methods and their Application, Chapter 5, Cambridge University Press.

Examples

# Set the number of threads
nth <- setThreadsTP(2)

# Create survTP object
data(bladderTP)
bladderTP_obj <- with(bladderTP, survTP(time1, event1, Stime, event))

# Compute transition probabilities
LS0 <- transLS(object=bladderTP_obj, s=5, t=59, h=c(0.25, 2.5), nh=25, ncv=50, conf=FALSE)
print(LS0)

# Compute transition probabilities with confidence band
h <- with( LS0, c( rep(h[1], 2), rep(h[2], 2) ) )
transLS(object=bladderTP_obj, s=5, t=59, h=h, conf=TRUE,
conf.level=0.95, method.boot="percentile", boot.cv=FALSE)

# Restore the number of threads
setThreadsTP(nth)

TPmsm documentation built on Jan. 14, 2023, 1:17 a.m.