The model fit can be seen as how much a model explains of the variance of the analyzed matrix or matrices. The explained variance is expressed as the ratio between the residual and the total variance. R2X is the explained variance of the X matrix and R2Y is the explained variance of the Y matrix.

For example, the explained variance in X can be expressed as:

where R2X is derived from the total and the residual variances of matrix X. When the residual variance approaches zero, the explained variance will reach its maximum value of one unit. An overview of R2X can be obtained by plotting the cumulative variances, R2X_cum, of the components of a model.