Package crossreg. February 19, 2015
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1 Package crossreg February 19, 2015 Type Package Title Confidence intervals for crossover points of two simple regression lines Version 1.0 Date Author Maintainer This package provides functions to calculate confidence intervals for crossover points of two simple linear regression lines using the non-linear regression, the delta method, the Fieller method, and the bootstrap methods. Suggests boot, MASS License GPL-2 NeedsCompilation no Repository CRAN Date/Publication :18:12 R topics documented: crossreg-package BootC DeltaC FiellerC nonlinearc Index 7 1
2 2 BootC crossreg-package Confidence intervals for crossover points Given the linear regression model y = b0 + b1*x1 + b2*x2 + b3*x1*x2, the crossover point of the two simple regression lines implied by the linear regression model can be expressed as C = -b2/b3 (Aiken and West, 1991). This package provides functions to calculate confidence intervals for crossover points of two simple linear regression lines using the non-linear regression, the delta method, the Fieller method, and the bootstrap methods. Package: crossreg Type: Package Version: 1.0 Date: License: GPL-2 Maintainer: <sunboklee@gmail.com> BootC Confidence intervals for crossover points using the bootstrap methods Calculate confidence intervals for crossover points of two simple linear regression lines using the bootstrap BootC()
3 DeltaC 3 The function BootC() calculates confidence intervals for the crossover point C using the boot package in R. Bootstrap confidence intervals include Normal, Basic, Percentile, and BCa confidence intervals. Bollen, K. A., & Stine, R. (1990). Direct and indirect effects: Classical and bootstrap estimate of variability. Sociological Methodology, 20, sim <- data.frame(y=y,x1=x1,x2=x2) # run BootC() library(boot) BootC(sim) DeltaC Confidence intervals for crossover points using the delta method Calculate confidence intervals for crossover points of two simple linear regression lines using the delta method. DeltaC(, order) order a scalar number representing the order of Delta method. 1=1st order delta method and 2=2nd order delta method
4 4 FiellerC Value Given a linear regression model y = b0 + b1*x1 + b2*x2 + b3*x1*x2, the crossover point of two simple regression lines can be directly calculated based on C=-b2/b3. The Delta method can be used to estimate the standard error of C = -b2/b3 based on the standard errors of b2 and b3 which can be obtained from a linear regression. The function DeltaC() calculates the confidence intervals for C based on the standard error of C obtained from the delta method. LowCI UpperCI lower bound of confidence intervals for C based on the delta method upper bound of confidence intervals for C based on the delta method Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Assessing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, sim <- data.frame(y=y,x1=x1,x2=x2) # run DeltaC() DeltaC(sim,2) FiellerC Confidence intervals for crossover points using the Fieller method Calculate confidence intervals for crossover points of two simple linear regression lines using the Fieller method. FiellerC()
5 nonlinearc 5 Fieller (1954) proposed a method for calculating the confidence interval for the ratio of two normally distributed random variables without assuming any particular form for the sampling distribution of the ratio itself. The function FiellerC() calculates confidence intervals for the crossover points of two simple regression lines using the Fieller method. Value LowCI UpperCI lower bound of confidence intervals for C based on the Fieller method upper bound of confidence intervals for C based on the Fieller method Fieller, E. C. (1954). Some problems in interval estimation. Journal of the Royal Statistical Society, Series B: Methodological, 16, sim <- data.frame(y=y,x1=x1,x2=x2) # run FiellerC() FiellerC(sim) nonlinearc Confidence intervals for crossover points using non-linear regression Calculate confidence intervals for crossover points of two simple linear regression lines using nonlinear regression. nonlinearc(, startingvalue)
6 6 nonlinearc startingvalue a list containing starting values for estimating parameters in non-linear regression Value For a crossover point C = -b2/b3 of the two simple regression lines, Widaman et al. (2012) proposed to estimate C using the non-linear regression of the form y = A0 + A1*(x1-C) + A2*(x1-C)*x2. The function nonlinearc() estimates C using the non-linear regression and calculates the confidence intervals for C based on the standard error of C obtained from a non-linear regression. C_Hat SE LowCI UpperCI estimate of C from a non-linear regression standard error of C from a non-linear regression lower bound of confidence intervals for C based on a non-linear regression upper bound of confidence intervals for C based on a non-linear regression Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage Widaman, K. F., Helm, J. L., Castro-Schilo, L., Pluess, M., Stallings, M. C., & Belsky, J. (2012). Distinguishing ordinal and disordinal interactions. Psychological Methods, 17, # set initial values for non-linear regression startingvalue <- list(a0 = 1, B1 = 1, B2 = 1, C = 1) sim <- data.frame(y=y,x1=x1,x2=x2) # run nonlinearc() nonlinearc(sim, startingvalue)
7 Index Topic \textasciitildekwd1 BootC, 2 Topic \textasciitildekwd2 BootC, 2 BootC, 2 crossreg (crossreg-package), 2 crossreg-package, 2 DeltaC, 3 FiellerC, 4 nonlinearc, 5 7
Supplemental material to accompany Preacher and Hayes (2008)
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