transLS {TPmsm}

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.

See Also

transAJ, transIPCW, transKMPW, transKMW, transLIN, transPAJ.

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);