transLS {TPmsm} | R Documentation |
Provides estimates for the transition probabilities based on the Location-Scale estimator, LS.
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)
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 |
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 |
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 |
cv.full |
If |
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 |
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. |
Artur Araújo, Javier Roca-Pardiñas and Luís Meira-Machado
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.
Meira-Machado L., Roca-Pardiñas J., Van Keilegom I., Cadarso-Suárez C. Estimation of transition probabilities in a non-Markov model with successive survival times. Discussion paper 2010. This file can be downloaded from: http://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.
Davison, A. C., Hinkley, D. V. (1997) Bootstrap Methods and their Application Chapter 5, Cambridge University Press.
transAJ
,
transIPCW
,
transKMPW
,
transKMW
,
transLIN
,
transPAJ
.
# 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)