me.variance.estimate: Estimates measurement error variance given replicate data.
In Rsurrogate: Robust Estimation of the Proportion of Treatment Effect Explained by Surrogate Marker Information
View source: R/Functions_Rsurrogate.R
me.variance.estimate R Documentation
Estimates measurement error variance given replicate data.
Description
Estimates measurement error variance given replicate data using a simple components of variance analysis.
Usage
me.variance.estimate(replicates)
Arguments
replicates
matrix of data where each row indicates a subject and each column is a replicated measurement; columns can have NAs when subjects have different numbers of measurements.
Details
Estimates measurement error variance given replicate data using a simple components of variance analysis.
Value
estimate of measurement error variance
Author(s)
Layla Parast
References
Carroll, R. J., Ruppert, D., Crainiceanu, C. M., and Stefanski, L. A. (2006). Measurement
error in nonlinear models: a modern perspective. Chapman and Hall/CRC.
Parast, L., Garcia, T. P., Prentice, R. L., & Carroll, R. J. (2022). Robust methods to correct for measurement error when evaluating a surrogate marker. Biometrics, 78(1), 9-23.
Rsurrogate documentation built on May 29, 2024, 11 a.m.
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View source: R/Functions_Rsurrogate.R
| me.variance.estimate | R Documentation |
Estimates measurement error variance given replicate data.
Description
Estimates measurement error variance given replicate data using a simple components of variance analysis.
Usage
me.variance.estimate(replicates)
Arguments
replicates |
matrix of data where each row indicates a subject and each column is a replicated measurement; columns can have NAs when subjects have different numbers of measurements. |
Details
Estimates measurement error variance given replicate data using a simple components of variance analysis.
Value
estimate of measurement error variance
Author(s)
Layla Parast
References
Carroll, R. J., Ruppert, D., Crainiceanu, C. M., and Stefanski, L. A. (2006). Measurement error in nonlinear models: a modern perspective. Chapman and Hall/CRC.
Parast, L., Garcia, T. P., Prentice, R. L., & Carroll, R. J. (2022). Robust methods to correct for measurement error when evaluating a surrogate marker. Biometrics, 78(1), 9-23.
R Package Documentation
Browse R Packages
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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