偏最小二乘回归(Partial least squares regression, PLS回归)是一种统计学方法,与主成分回归有关系,但不是寻找响应和独立变量之间最小方差的超平面,而是通过投影预测变量和观测变量到一个新空间来寻找一个线性回归模型。 偏最小二乘回归提供一种多对多线性回归建模的方法,特别当两组变量的个数很多,且都存在多重相...
### Method: GCV Optimizer: magic## Smoothing parameter selection converged after 4 iterations.## The RMS GCV score gradient at convergence was 1.107369e-05 .## The Hessian was positive definite.## Model rank = 10 / 10### Basis dimension (k) checking results. Low p-value (k-index<1) ...
Start with Regression analysis basics. Next, work through the Regression Analysis tutorial. This topic will cover the results of your analysis to help you understand the output and diagnostics of OLS. Inputs To run the OLS tool, provide an Input Feature Class with a Unique ID Field, the ...
HARRELL, F. E., JR. 2001. Regression Modeling Strategies, New York, Springer-Verlag New York.
## R-sq.(adj) = 0.876 Deviance explained = 87.9% ## GCV = 211.94 Scale est. = 206.93 n = 300 显示了我们截距的模型系数,所有非光滑参数将在此处显示 每个光滑项的总体含义如下。 这是基于“有效自由度”(edf)的,因为我们使用的样条函数可以扩展为许多参数,但我们也在惩罚它们并减少它们的影响。
##R-sq.(adj)=0.876Deviance explained=87.9%##GCV=211.94Scale est.=206.93n=300 显示了我们截距的模型系数,所有非光滑参数将在此处显示 每个光滑项的总体含义如下。 这是基于“有效自由度”(edf)的,因为我们使用的样条函数可以扩展为许多参数,但我们也在惩罚它们并减少它们的影响。
8. ## Basis dimension (k) checking results. Low p-value (k-index<1) may 9. ## indicate that k is too low, especially if edf is close to k'. 10. ## 11. ## k' edf k-index p-value 12. ## s(X) 9.00 6.09 1.1 0.97 ...
Ordinary Least Squares (OLS) produces the best possible coefficient estimates when your model satisfies the OLS assumptions for linear regression. However, if your model violates the assumptions, you might not be able to trust the results. Learn about th
## R-sq.(adj) = 0.876 Deviance explained = 87.9% ## GCV = 211.94 Scale est. = 206.93 n = 300 显示了我们截距的模型系数,所有非光滑参数将在此处显示 每个光滑项的总体含义如下。 这是基于“有效自由度”(edf)的,因为我们使用的样条函数可以扩展为许多参数,但我们也在惩罚它们并减少它们的影响。
Approximate significance of smooth terms:## edf Ref.df F p-value## s(X) 6.087 7.143 296.3 <2e-16 ***## ---## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1### R-sq.(adj) = 0.876 Deviance explained = 87.9%## GCV = 211.94 Scale est. = 206.93 n...