matlab中的r平方是什么意思,R平方：判定系数

R-square: The coefficient of determination

Compute coefficient of determination of data fit model and RMSE

[r2 rmse] = rsquare(y,f)

[r2 rmse] = rsquare(y,f,c)

RSQUARE computes the coefficient of determination (R-square) value from

actual data Y and model data F. The code uses a general version of

R-square, based on comparing the variability of the estimation errors

with the variability of the original values. RSQUARE also outputs the

root mean squared error (RMSE) for the users convenience.

Note: RSQUARE ignores comparisons involving NaN values.

INPUTS

Y : Actual data

F : Model fit

OPTION

C : Constant term in model

R-square may be a questionable measure of fit when no

constant term is included in the model.

[DEFAULT] TRUE : Use traditional R-square computation

FALSE : Uses alternate R-square computation for model

without constant term [R2 = 1 - NORM(Y-F)/NORM(Y)]

OUTPUT

R2 : Coefficient of determination

RMSE : Root mean squared error

EXAMPLE

x = 0:0.1:10;

y = 2.*x + 1 + randn(size(x));

p = polyfit(x,y,1);

f = polyval(p,x);

[r2 rmse] = rsquare(y,f);

figure; plot(x,y,b-);

hold on; plot(x,f,r-);

title(strcat([R2 = num2str(r2) ; RMSE = num2str(rmse)]))

Jered R Wells

11/17/11

jered [dot] wells [at] duke [dot] edu

v1.2 (02/14/2012)

Thanks to John DErrico for useful comments and insight which has helped

to improve this code. His code POLYFITN was consulted in the inclusion of

the C-option (REF. File ID: #34765).

R-square: The coefficient of determination Compute coefficient of determination of data fit model and RMSE [r2 rmse] = rsquare(y,f) [r2 rmse] = rsquare(y,f,c) RSQUARE computes the coefficient of determination (R-square) value from actual data Y and model data F. The code uses a general version of R-square, based on comparing the variability of the estimation errors with the variability of the original values. RSQUARE also outputs the root mean squared error (RMSE) for the users convenience. Note: RSQUARE ignores comparisons involving NaN values. INPUTS Y : Actual data F : Model fit OPTION C : Constant term in model R-square may be a questionable measure of fit when no constant term is included in the model. [DEFAULT] TRUE : Use traditional R-square computation FALSE : Uses alternate R-square computation for model without constant term [R2 = 1 - NORM(Y-F)/NORM(Y)] OUTPUT R2 : Coefficient of determination RMSE : Root mean squared error EXAMPLE x = 0:0.1:10; y = 2.*x + 1 + randn(size(x)); p = polyfit(x,y,1); f = polyval(p,x); [r2 rmse] = rsquare(y,f); figure; plot(x,y,b-); hold on; plot(x,f,r-); title(strcat([R2 = num2str(r2) ; RMSE = num2str(rmse)])) Jered R Wells 11/17/11 jered [dot] wells [at] duke [dot] edu v1.2 (02/14/2012) Thanks to John DErrico for useful comments and insight which has helped to improve this code. His code POLYFITN was consulted in the inclusion of the C-option (REF. File ID: #34765).