ERRci: Profile likelihood based confidence intervals
In linERR: Linear Excess Relative Risk Model
Description
Usage
Arguments
Value
Author(s)
References
See Also
Examples
Description
The standard procedure for computing a confidence interval for a parameter β (Wald-type CI), based on \hat{β} \pm z_{1-\frac{α}{2}} SE(\hat{β}) may work poorly if the distribution of the parameter estimator is markedly skewed or if the standard error is a poor
estimate of the standard deviation of the estimator. Profile likelihood confidence intervals doesn't assume normality of
the estimator and perform better for small sample sizes or skewed estimates than Wald-type confidence intervals.
Usage
1
ERRci(object, prob=0.95)
Arguments
object
An object of class fit.linERR.
prob
Level of confidence, defaults to 0.95.
Value
A numeric vector containing the prob profile likelihood based confidence interval.
Author(s)
David Moriña, ISGlobal, Centre for Research in Environmental Epidemiology (CREAL)
References
B. Langholz and D. B. Richardson. Fitting general relative risk models for survival time and matched
case-control analysis. American journal of epidemiology, 171(3):377-383, 2010.
D. L. Preston, J. H. Lubin, D. A. Pierce, and M. E. McConney. Epicure: User's Guide. HiroSoft
International Corporation, Seattle, WA, 1993.
E. Ron, J. H. Lubin, R. E. Shore, K. Mabuchi, B. Modan, L. M. Pottern, A. B. Schneider, M. A.
Tucker, and J. D. Boice Jr. Thyroid Cancer after Exposure to External Radiation: A Pooled Analysis
of Seven Studies. Radiation Research, 141(3):259-277, 1995.
See Also
Examples
1
2
3
4
5
6
7
data(cohort1)
fit.1 <- fit.linERR(Surv(entryage, exitage, leu)~sex|dose1+dose2+dose3+dose4+dose5+dose6+
dose7+dose8+dose9+dose10+dose11+dose12+dose13+dose14+dose15+dose16+
dose17+dose18+dose19+dose20+dose21+dose22+dose23+dose24+dose25+dose26+
dose27+dose28+dose29+dose30+dose31+dose32, data=cohort1, beta=NULL,
ages=cohort1[, 7:38], lag=2)
ERRci(fit.1, prob=0.9)
linERR documentation built on May 1, 2019, 7:59 p.m.
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Description Usage Arguments Value Author(s) References See Also Examples
Description
The standard procedure for computing a confidence interval for a parameter β (Wald-type CI), based on \hat{β} \pm z_{1-\frac{α}{2}} SE(\hat{β}) may work poorly if the distribution of the parameter estimator is markedly skewed or if the standard error is a poor estimate of the standard deviation of the estimator. Profile likelihood confidence intervals doesn't assume normality of the estimator and perform better for small sample sizes or skewed estimates than Wald-type confidence intervals.
Usage
1 | ERRci(object, prob=0.95)
|
Arguments
object |
An object of class |
prob |
Level of confidence, defaults to 0.95. |
Value
A numeric vector containing the prob profile likelihood based confidence interval.
Author(s)
David Moriña, ISGlobal, Centre for Research in Environmental Epidemiology (CREAL)
References
B. Langholz and D. B. Richardson. Fitting general relative risk models for survival time and matched case-control analysis. American journal of epidemiology, 171(3):377-383, 2010. D. L. Preston, J. H. Lubin, D. A. Pierce, and M. E. McConney. Epicure: User's Guide. HiroSoft International Corporation, Seattle, WA, 1993. E. Ron, J. H. Lubin, R. E. Shore, K. Mabuchi, B. Modan, L. M. Pottern, A. B. Schneider, M. A. Tucker, and J. D. Boice Jr. Thyroid Cancer after Exposure to External Radiation: A Pooled Analysis of Seven Studies. Radiation Research, 141(3):259-277, 1995.
See Also
Examples
1 2 3 4 5 6 7 | data(cohort1)
fit.1 <- fit.linERR(Surv(entryage, exitage, leu)~sex|dose1+dose2+dose3+dose4+dose5+dose6+
dose7+dose8+dose9+dose10+dose11+dose12+dose13+dose14+dose15+dose16+
dose17+dose18+dose19+dose20+dose21+dose22+dose23+dose24+dose25+dose26+
dose27+dose28+dose29+dose30+dose31+dose32, data=cohort1, beta=NULL,
ages=cohort1[, 7:38], lag=2)
ERRci(fit.1, prob=0.9)
|
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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