spml.reg: Circular or angular regression(s)

In Rfast: A Collection of Efficient and Extremely Fast R Functions

Circular or angular regression(s)

Description

Regression with circular dependent variable and Euclidean or categorical independent variables.

Usage

spml.reg(y, x, tol = 1e-07, seb = FALSE, maxiters = 100)
spml.regs(y, x, tol = 1e-07, logged = FALSE, maxiters = 100, parallel = FALSE)

Arguments

y	The dependent variable, it can be a numerical vector with data expressed in radians or it can be a matrix with two columns, the cosinus and the sinus of the circular data. The benefit of the matrix is that if the function is to be called multiple times with the same response, there is no need to transform the vector every time into a matrix.
x	The independent variable(s) of the single regression model. Can be Euclidean or categorical (factor variables). For the many regression models, a matrix with continuous variables.
tol	The tolerance value to terminatate the Newton-Raphson algorithm.
seb	Do you want the standard error of the estimates to be returned? TRUE or FALSE.
logged	Do you want the logarithm of the p-value be returned? TRUE or FALSE.
maxiters	The maximum number of iterations to implement.
parallel	Do you want the regression models to run in parallel? Default value is FALSE.

Details

The Newton-Raphson algorithm is fitted in this regression as described in Presnell et al. (1998).

For the case of spml.regs(), for each colum of x a circual regression model is fitted and the hypothesis testing of no association between y and this variable is performed.

Value

For the spml.reg() a list including:

iters	The number of iterations required until convergence of the EM algorithm.
be	The regression coefficients.
seb	The standard errors of the coefficients.
loglik	The value of the maximised log-likelihood.
seb	The covariance matrix of the beta values.

For the spml.regs() a matrix with two columns, the test statistics and their associated (log) p-values.

Author(s)

Michail Tsagris and Stefanos Fafalios.

R implementation and documentation: Michail Tsagris <[e-mail]> and Stefanos Fafalios <[e-mail]>.

References

Presnell Brett, Morrison Scott P. and Littell Ramon C. (1998). Projected multivariate linear models for directional data. Journal of the American Statistical Association, 93(443): 1068-1077.

See Also

spml.mle, iag.mle, acg.mle

Examples

x <- rnorm(100)
z <- cbind(3 + 2 * x, 1 -3 * x)
y <- cbind( rnorm(100,z[ ,1], 1), rnorm(100, z[ ,2], 1) )
y <- y / sqrt( rowsums(y^2) )
a1 <- spml.reg(y, x)
y <- atan( y[, 2] / y[, 1] ) + pi * I(y[, 1] < 0) 
a2 <- spml.reg(y, x)

x <- matrnorm(100, 5)
a3 <- spml.regs(y, x)

Rfast documentation built on April 3, 2025, 11:34 p.m.