gmregress

Geometric Mean Regression (Reduced Major Axis Regression).

Description

Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear relationship between the variables when they both contain error. According to Sokal and Rohlf (1995), the subject of Model II regression is one on which research and controversy are continuing and definitive recommendations are difficult to make.

GMREGRESS is a Model II procedure. It standardize variables before the slope is computed. Each of the two variables is transformed to have a mean of zero and a standard deviation of one. The resulting slope is the geometric mean of the linear regression coefficient of Y on X. Ricker (1973) coined this term and gives an extensive review of Model II regression.

[B,BINTR,BINTJM] = GMREGRESS(X,Y,ALPHA) returns the vector B of regression coefficients in the linear Model II and a matrix BINT of the given confidence intervals for B by the Ricker (1973) and Jolicoeur and Mosimann (1968)-McArdle (1988) procedure.

GMREGRESS treats NaNs in X or Y as missing values, and removes them.

Syntax: function [b,bintr,bintjm] = gmregress(x,y,alpha)

• Required Products: Statistics Toolbox
• MATLAB release: MATLAB 7 (R14)

[Pete]

Well documented. Appears to work as advertised.

I would invite somebody to compare and contrast this script with the scripts by Edward T. Peltzer hosted at: http://www.mbari.org/staff/etp3/regress.htm

(which encouragingly produce identical slopes)

[Holger Goerlitz]

Thank you very much, very well done and works great.

A quick comparison with the rma.m by Edward T. Peltzer (http://web.ics.purdue.edu/~braile/eas309/rma.m) gives identical results.

Except for one error, I believe: gmregress always returns a positive slope, even for data with a negative trend. After correcting the slope by (compare to rma.m):

si = r/abs(r); % sign of correl. coeff.

b = si*b;

negative slopes are negative.

Thanks for the function again,
Holger