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)