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Easy-to-use packages for estimating rank and spline parameters

Author

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  • Roger Newson

    (National Heart and Lung Institute, Imperial College London)

Abstract

So-called non-parametric methods are in fact based on estimating and testing parameters, usually either rank parameters or spline parameters. Two comprehensive packages for estimating these are somersd (for rank parameters) and bspline (for spline parameters). Both of these estimate a wide range of parameters, but both are frequently found to be difficult to use by casual users. This presentation introduces rcentile, an easy-to-use front end for somersd, and polyspline, an easy-to-use front end for bspline. rcentile estimates percentiles with confidence limits, optionally allowing for clustered sampling and sampling-probability weights. The confidence intervals are saved in a Stata matrix, with one row per percentile, which the user can save to a resultsset using the xsvmat package. polyspline inputs an X-variable and a user-defined list of reference points and outputs a basis of variables for a polynomial or for another unrestricted spline. This basis can be included in the covariate list for an estimation command, and the corresponding parameters will be values of the polynomial or spline at the reference points, or differences between these values. By default, the spline will simply be a polynomial, with a degree one less than the number of reference points. However, if the user specifies a lower degree, then the spline will have knots interpolated sensibly between the reference points.

Suggested Citation

  • Roger Newson, 2014. "Easy-to-use packages for estimating rank and spline parameters," United Kingdom Stata Users' Group Meetings 2014 01, Stata Users Group.
    • Handle: RePEc:boc:usug14:01
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    File URL: http://repec.org/usug2014/newson_uksug14.pdf
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    File URL: http://repec.org/usug2014/newson_uksug14.examples1.do
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    References listed on IDEAS

    as
    1. Roger B. Newson, 2012. "Sensible parameters for univariate and multivariate splines," Stata Journal, StataCorp LP, vol. 12(3), pages 479-504, September.
    2. Roger Newson, 2006. "Confidence intervals for rank statistics: Somers' D and extensions," Stata Journal, StataCorp LP, vol. 6(3), pages 309-334, September.
    3. Roger Newson, 2011. "Sensible parameters for polynomials and other splines," United Kingdom Stata Users' Group Meetings 2011 01, Stata Users Group.
    4. Roger Newson, 2006. "Confidence intervals for rank statistics: Percentile slopes, differences, and ratios," Stata Journal, StataCorp LP, vol. 6(4), pages 497-520, December.
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    Cited by:

    1. Roger Newson, 2017. "Ridit splines with applications to propensity weighting," United Kingdom Stata Users' Group Meetings 2017 01, Stata Users Group.
    2. Roger Newson, 2021. "Ridits right, left, center, native and foreign," London Stata Conference 2021 1, Stata Users Group.
    3. Roger Newson, 2019. "Bland–Altman plots, rank parameters, and calibration ridit splines," London Stata Conference 2019 01, Stata Users Group.

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