R-sq.(adj) = 0.976 Deviance explained = 98.7% GCV = 4.9296 Scale est. = 2.697 n = 41
时间: 2024-06-07 21:07:34 浏览: 19
这是一个回归模型的评估结果。R-sq.(adj) 表示调整后的决定系数,它是一个衡量模型拟合优度的指标,值越接近1说明模型对数据的解释能力越好。这里的 R-sq.(adj) 值为 0.976,说明模型对数据的解释能力非常好。
Deviance explained 是一个衡量模型解释方差的指标,它的值越高说明模型的解释能力越强。这里的 Deviance explained 为 98.7%,说明模型解释了数据中大约 98.7% 的方差。
GCV 是广义交叉验证误差,它是用来评估模型的泛化能力的指标,一般用于模型选择。这里的 GCV 值为 4.9296,值越小说明模型的泛化能力越好。
Scale est. 是一个衡量模型误差的指标,它表示模型的标准误差。这里的 Scale est. 值为 2.697。
n = 41 表示数据集中有 41 个样本。
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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。