Parametric coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.53529 0.03186 16.8 <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) 1.818 2.243 0.687 0.5401 s(X2) 1.729 2.080 2.880 0.0675 . s(X3) 1.740 2.110 0.942 0.4003 s(X4) 1.516 1.828 0.973 0.2865 s(X5) 1.727 2.098 0.011 0.9968 s(X6) 2.063 2.478 0.390 0.7171 s(X7) 1.419 1.682 2.056 0.2184 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 R-sq.(adj) = 0.213 Deviance explained = 42.3% GCV = 0.065102 Scale est. = 0.046688 n = 46拟合质量如何,需要对那些进行修改
时间: 2024-02-17 07:14:35 浏览: 25
根据该拟合结果,模型的拟合质量并不是很高,调整后的R方值为0.213,说明模型只能解释因变量方差的21.3%左右。同时,平滑项中有些p值较高,说明这些平滑项对因变量的影响不显著或者需要更加灵活的模型来描述。
需要对模型进行修改的方面包括:
1. 增加更多的自变量:该模型只包含了七个自变量对因变量的影响,可能需要加入更多的自变量来提高拟合效果。
2. 考虑交互作用:该模型只考虑了每个自变量的单独影响,可能需要考虑不同自变量之间的交互作用来更好地描述变量之间的关系。
3. 更换其他非线性模型:该模型采用了 GAM 模型来描述变量之间的非线性关系,但是该模型可能并不是最适合数据的模型。可以尝试其他的非线性模型,比如神经网络模型、决策树模型等。
4. 增加样本量:该模型只有46个样本,可能需要增加更多的样本来提高模型的拟合效果。
需要综合考虑多个因素来选择合适的模型,比如模型的解释能力、预测能力、复杂度等。
相关问题
Parametric coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.53529 0.03186 16.8 <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) 1.818 2.243 0.687 0.5401 s(X2) 1.729 2.080 2.880 0.0675 . s(X3) 1.740 2.110 0.942 0.4003 s(X4) 1.516 1.828 0.973 0.2865 s(X5) 1.727 2.098 0.011 0.9968 s(X6) 2.063 2.478 0.390 0.7171 s(X7) 1.419 1.682 2.056 0.2184 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 R-sq.(adj) = 0.213 Deviance explained = 42.3% GCV = 0.065102 Scale est. = 0.046688 n = 46这个拟合结果,怎么解释
这个拟合结果是一个 GAM(Generalized Additive Model,广义可加模型)的结果。该模型包含了一个截距项和七个平滑项(s(X1)至s(X7)),其中每个平滑项都是一个自变量对因变量的非线性影响,可以理解为对应自变量的局部回归线。每个平滑项的 edf(estimated degrees of freedom,估计自由度)表示了该平滑项的平滑程度,即该平滑项所对应的局部回归线的灵活性。
拟合结果中还包含了每个参数的估计值(Estimate)、标准误差(Std. Error)、t值(t value)和p值(Pr(>|t|))等信息。其中,t值和p值用于检验每个参数的显著性,p值小于0.05(或0.01、0.001)表示该参数的效应在统计上是显著的。
拟合结果中还包含了模型的拟合优度指标,比如调整后的 R 方值(R-sq.(adj))和解释差异程度(Deviance explained),以及模型的预测误差估计值,比如广义交叉验证误差(GCV)和标准差估计值(Scale est.)等。
需要注意的是,该拟合结果中有些平滑项的p值较大,表明这些平滑项在统计上并不显著。但是,这并不意味着这些自变量对因变量的影响是线性的,而是可能需要更加灵活的非线性模型来描述。
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.
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