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用中文解释下

这个输出是广义线性模型（GLM），包括平滑项。 "Parametric coefficients" 部分显示了截距和任何包含作为参数项的预测变量的估计系数。"Approximate significance of smooth terms" 部分显示了每个平滑项的估计自由度（edf）、参考自由度（Ref.df）、F统计量和p值。"Signif. codes" 表示每个项的显著性水平。"R-sq.(adj)" 是调整后的R-squared值，表示在考虑到所包含的预测变量数量后，模型解释的方差比例。"Deviance explained" 是模型解释的响应变量偏差的百分比。"GCV" 是广义交叉验证分数，是预测精度的一种度量。"Scale est." 是模型的估计尺度参数，表示响应变量未被预测变量解释的变异性。"n" 是样本大小。

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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

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.

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运行输的结果图进行解释

这个输出结果是一个GAM（广义加性模型）的结果，它包含了模型的参数估计、标准误差、t值、p值等信息。

Parametric coefficients部分给出了每个自变量的估计系数。在这个例子中，只有截距项的系数是显著的（即Intercept），其他自变量的系数不显著。

Approximate significance of smooth terms部分给出了每个平滑项的平滑度（edf）、参考自由度（Ref.df）、F值和p值。在这个例子中，X1、X2、X4和X5的平滑项是显著的（即p值小于0.05），而X3的平滑项不显著。

R-sq.(adj)表示调整后的R方，这个模型可以解释94.3%的响应变量的方差。Deviance explained表示这个模型可以解释95.4%的总离差平方和。GCV是广义交叉验证误差，用于评估模型的预测性。Scale est.表示模型的标准差估计值。n是样本量。

综上所述，这个GAM模型的结果表明，截距项对响应变量的影响是显著的，而其他自变量对响应变量的影响不显著。同时，X1、X2、X4和X5的平滑项对响应变量的影响是显著的，而X3的平滑项对响应变量的影响不显著。