In my post aboutinterpreting R-squared, I show how evaluating how well a linear regression model fits the data is not as intuitive as you may think. Now, I’ll explore reasons why you need to use adjusted R-squared and predicted R-squared to help you specify a good regression model! Le...
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Harrell. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 我们可以自己指定,比如根据分位数、中位数、平均数等,都可以作为我们的节点。 下面用ggplot2画图。 df.tmp <- data.frame(x=x,y=y) ggplot(df.tmp, aes(x,y))+ geom...
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Understanding Diagnostic Plots for Linear Regression Analysis:data.library.virginia.edu How does plot.lm() determine outliers for residual vs fitted plot?:stackoverflow.com/quest 除了基础包,R中有大量的包(比如car包、olsrr包、plotmo包、HH包、gvlma包)都可以实现对线性回归适用条件的考察。 (1)独立性...
Nagelkerke的R^2是Cox & Snell R-square的一个调整后的版本,它调整统计量的范围,覆盖从0到1的全部范围。 McFadden的R^2是另一种版本,它基于log likelihood的内核,用于intercept-only model和完整的估计模型。 什么构成一个“good”R^2值在不同的应用领域都有所不同。虽然这些统计数据本身是有启发性的,但它们...
But, Logistic Regression employs all different sets of metrics. Here, we deal with probabilities and categorical values. Following are the evaluation metrics used for Logistic Regression:1. Akaike Information Criteria (AIC)You can look at AIC as counterpart of adjusted r square in multiple ...
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To understand what r-square tells us you must understand the word variability. When I say variability, you should think of the word “differs.” Now, I’m going to explain to you what r-squared means. We know that prices of sandwiches vary, or they differ based on the number of ...