Shulou(Shulou.com)06/03 Report--

In this issue, the editor will bring you an example analysis of the influencePlot () function to find the strong influence points of outliers. The article is rich in content and analyzes and describes it from a professional point of view. I hope you can get something after reading this article.

After fitting the regression model with the lm () function, we can use the influencePlot () function of the car package to observe and find the outliers that affect the regression.

The characteristic of the influencePlot () function is that it draws the information of outliers, high leverage points and strong influence points that we are concerned about in one graph, and the efficiency of reading the graph is high.

Suppose we fit the multiple regression model based on lm (). The murder.step,influencePlot () function will directly extract the residual data and sample size data from murder.step to draw statistical graphs.

Grammar demonstration:

InfluencePlot (murder.step,id.method= "identify", main= "Influent Plot", sub= "Circle size is proportional to Cook's distance")

Text result:

The specific abnormal data information is given, including student union residual value, hat value and Cook distance value.

Graphic results:

Interpretation of the picture:

Scattered points with ordinates more than + 2 or less than-2 can be considered outliers.

Scatter points with horizontal axis more than 0.2 or 0.3 have high leverage value.

The circle size is proportional to the influence, and the large point of the circle may be the strong influence point of the disproportionate influence on the estimation of the model parameters.

The above is the example of the influencePlot () function shared by Xiaobian to find the strong influence points of outliers. If you happen to have similar doubts, you might as well refer to the above analysis to understand. If you want to know more about it, you are welcome to follow the industry information channel.

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