Description Usage Arguments Value Author(s) References See Also Examples
View source: R/iso.ci.pack.3rd.R
Calculate untransformed and transforemd Wald-type confidence intervals, bootstrap confidente inverals and boostrap-Wald confidnece intervals for cumulative distribution function of current stuas failure times.
1 | iso.ci(z, d, alpha=0.05, h.opt=0.3, nboots=500, method="wald.tr", seed=1253)
|
z |
a vector of covariate |
d |
a vector of outcome |
alpha |
a test level for 100*(1-alpha) two-sided confidence intervals. |
h.opt |
an optimal bandwidth for estimating g (for "wald.tr" only) |
nboots |
the number of bootstrap iteration (for "bt" and "bt.wald") |
method |
"wald.tr" for untransformed and transformed Wald confidence intervals, "bt" for bootstrap confidence intervals, and "bt.wald" for bootstrap-Wald confidence intervals. |
seed |
seed value for bootstarp and bootstrap-Wald methods |
z |
ordered covariate |
yf |
NPMLE estimate for cumulative distribution function |
wald.lhm |
left side of non-transformed Wald CI |
wald.rhm |
right side of non-transformed Wald CI |
logit.lhm |
left side of logit transformed Wald CI |
logit.rhm |
right side of logit transformed Wald CI |
llog.lhm |
left side of log(-log) transforemd Wald CI |
llog.rhm |
right side of log(-log) transforemd Wald CI |
nbt.lhm |
left side of bootstrap CI |
nbt.rhm |
right side of bootstrap CI |
bt.wald.lhm |
left side of non-transformed boostrap-Wald CI |
bt.wald.rhm |
right side of non-transformed boostrap-Wald CI |
bt.logit.lhm |
left side of logit transformed boostrap-Wald CI |
bt.logit.rhm |
right side of logit transformed boostrap-Wald CI |
bt.llog.lhm |
left side of log(-log) transforemd boostrap-Wald CI |
bt.llog.rhm |
right side of log(-log) transforemd boostrap-Wald CI |
Choi, B. Y., Fine, J. P., and Brookhart, M. A.
Choi, B. Y., Fine, J. P., and Brookhart, M. A. (2013) Practicable confidence intervals for current status data. Statistics in Medicine 32, 1419-1428.
Ghosh, D., Banerjee, M., and Biswas, P. (2008). Inference for Constrained Estimation of Tumor Size Distributions. Biometrics 64, 1009-1017.
Groeneboom, P. and Wellner, J. A. (1992). Information Bounds and Nonparametric Maximum Likelihood Estimation. Boston: Birkhauser.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | # simulating data
n <- 50
z <- rexp(n)
pz <- pexp(z)
d <- rbinom(n,1,pz)
# finding optimal bandwidth for estimationg a density function
h.opt = bandwidth.choose(h.set=seq(0.1,2,len=15),z=z,d=d)
# Untransforemd and transformed Wald-type confidence intervals
fit.wald <- iso.ci(z=z,d=d,h.opt=h.opt$h.opt)
# Bootstrap confidence intervals
## Not run: fit.bt <- iso.ci(z=z,d=d,method="bt",nboots=100)
# Untransforemd and transformed bootstrap-Wald-type confidence intervals
## Not run: fit.bt.wald <- iso.ci(z=z,d=d,method="bt.wald",nboots=100)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.