Linear regression models have a special related measure called R2 (R-squared). R2 is a value between 0 and 1 that tells us how well a linear regression model fits the data. When people talk about correlations being strong, they often mean that the R2 value was large....
## Residual standard error: 9.259 on 37 degrees of freedom ## Multiple R-squared: 0.1757, Adjusted R-squared: 0.1311 ## F-statistic: 3.942 on 2 and 37 DF, p-value: 0.02805
temp')R2_adj <- c()p_value <- c()for (i in env) { fit_stat <- summary(lm(dat[['fish']]~dat[[i]])) #一元线性回归 R2_adj <- c(R2_adj, fit_stat$adj.r.squared) #提取校正后 R2 p_value <- c(p_value, fit_stat$coefficients[2,4]) #提取显著性 p 值}env...
Multiple R-squared: 0.0148, Adjusted R-squared: -0.0468 F-statistic: 0.24 on 1 and 16 DF, p-value: 0.631 AIC(model.1) [1] 99.133 summary(model.2) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -9.00e+02 2.70e+02 -3.33 0.0046 ** Length 5.86e+00 1.75e+00 3.35...
Multiple / Adjusted R-Square: For one variable, the distinction doesn’t really matter. R-squared shows the amount of variance explained by the model. Adjusted R-Square takes into account the number of variables and is most useful for multiple-regression.然后是R方和调整的R方，R方为这个模型能...
Multiple / Adjusted R-Square: For one variable, the distinction doesn’t really matter. R-squared shows the amount of variance explained by the model. Adjusted R-Square takes into account the number of variables and is most useful for multiple-regression. ...
Multiple / Adjusted R-Square: For one variable, the distinction doesn’t really matter. R-squared shows the amount of variance explained by the model. Adjusted R-Square takes into account the number of variables and is most useful for multiple-regression. ...
Multiple R-squared: 0.2798, Adjusted R-squared: 0.2461 F-statistic: 8.289 on 3 and 64 DF, p-value: 9.717e-05 方差分析 Anova Table (Type II tests) Response: Longnose Sum Sq Df F value Pr(>F) Acerage 14652 1 8.6904 0.004461 ** ...
Multiple R-squared: 0.9722, Adjusted R-squared: 0.9718 F-statistic: 2588 on 3 and 222 DF, p-value: < 2.2e-16 从调整后的结果来看,效果还不错。不过,也并没有比最初的模型有所提高。 对于模型调整的过程,如果我们手动调整测试时,一般都会基于业务知识来操作。如果是按照数据指标来计算,我们可以用R语...
Multiple R-squared: 0.2798, Adjusted R-squared: 0.2461 F-statistic: 8.289 on 3 and 64 DF, p-value: 9.717e-05 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 方差分析 Anova Table (Type II tests) ...