Variance inflation factors(VIF) give a measure of the extent ofmulticollinearityin the predictors of a regression. If the VIF of a predictor is high, it indicates that that predictor is highly correlated with other predictors, it contains little or no unique information, and there is redundancy ...
首先建立全量回归模型 lm1 <- lm(GNP.deflator ~ ., data = longley) summary(lm1) ## ## Call: ## lm(formula = GNP.deflator ~ ., data = longley) ## ## Residuals: ## Min 1Q Median 3Q Max ## -2.009 -0.515 0.113 0.423 1.550 ## ## Coefficients: ## Estimate Std. Error t value...
exog_idx):'''variance inflation factor, VIF, for one exogenous variableThe variance inflation factor is a measure for the increase of thevariance of the parameter estimates if an additional variable, given byexog_idx is added to the linear regression. It is a measure formulticollinearity of t...
Multiple regression is an extension of the bivariate linear regression presented in Chapter 18. Although dummy variables (nominal variables coded 0,1) may be used, all other variables must be interval or ratio. The generalized equation is shown in the slide. The regression coefficients are stated...
We need one key piece of output from this regression, and that's the R-Sq value: In this case, the R-sq value is .1707. Then we use the following formula to calculate: By the formula, 1/(1-.1707) = 1.21, our VIF. If you take the square root of the variance inflation factor...
而太少的特征会导致模型的拟合不足。将模型优化为足够复杂以使其性能可推广，但又足够简单易于训练、维护和解释是特征选择的主要工作。 “特征选择”意味着可以保留一些特征并放弃其他一些特征。本文的目的是概述一些特征选择策略： 删除未使用的列 删除具有缺失值的列 不相关的特征 低方差特征 多重共线性 特 ...
Finally this worked (see the complete solution in Answers if you still get an error): vif doesn't work on classification models so convert dependent variable to numeric and run linear regression on it and then vif model_iris4 <- train(as.numeric(Species) ~ ., data = iris_train, method...
Then, calculate the VIF factor forwith the following formula: whereR2iis thecoefficient of determinationof the regression equation in step one. Step three Analyze the magnitude ofmulticollinearityby considering the size of the . A common rule of thumbis that if then multicollinearityis high. Also ...
Variance inflation factor (VIF) is used to detect the severity of multicollinearity in the ordinary least square (OLS) regression analysis.
A variance inflation factor (VIF) is a measure of the amount of multicollinearity in regression analysis.Multicollinearityexists when there is a correlation between multiple independent variables in a multiple regression model. This can adversely affect theregressionresults. Thus, the variance inflation fa...