Parametric coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.52314 0.01395 37.5 <2e-16 *** --- Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1 Approximate significance of smooth terms: edf Ref.df F p-value s(X1) 1.976 1.999 11.034 0.000196 *** s(X2) 1.000 1.000 22.669 3.73e-05 *** s(X3) 1.434 1.670 2.187 0.097375 . s(X4) 1.000 1.000 17.832 0.000178 *** s(X5) 1.875 1.974 6.487 0.007730 ** --- Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1 R-sq.(adj) = 0.943 Deviance explained = 95.4% GCV = 0.01 Scale est. = 0.0079798 n = 41解释下
时间: 2024-04-28 14:21:28 浏览: 12
This output is from a generalized linear model (GLM) with smooth terms included. The "Parametric coefficients" section shows the estimated coefficients for the intercept and any predictor variables that were included as parametric terms. The "Approximate significance of smooth terms" section shows the estimated degrees of freedom (edf), reference degrees of freedom (Ref.df), F-statistic, and p-value for each smooth term included in the model. The "Signif. codes" indicate the level of significance for each term. The "R-sq.(adj)" is the adjusted R-squared value, which indicates the proportion of variance explained by the model after adjusting for the number of predictors included. The "Deviance explained" is the percentage of deviance in the response variable that is explained by the model. The "GCV" is the generalized cross-validation score, which is a measure of predictive accuracy. The "Scale est." is the estimated scale parameter for the model, which represents the variability of the response variable that is not explained by the predictors. The "n" is the sample size.