relweights（）

#https://blog.csdn.net/oldboys/article/details/78474957
#relweights()函数输入为回归分析模型，可以输出模型中自变量的重要性
relweights <-
  function(fit,...){                        
    R <- cor(fit$model)  
    nvar <- ncol(R)         
    rxx <- R[2:nvar, 2:nvar]
    rxy <- R[2:nvar, 1]     
    svd <- eigen(rxx)       
    evec <- svd$vectors                          
    ev <- svd$values        
    delta <- diag(sqrt(ev)) 
    lambda <- evec %*% delta %*% t(evec)       
    lambdasq <- lambda ^ 2  
    beta <- solve(lambda) %*% rxy          
    rsquare <- colSums(beta ^ 2)                  
    rawwgt <- lambdasq %*% beta ^ 2   
    import <- (rawwgt / rsquare) * 100
    lbls <- names(fit$model[2:nvar])  
    rownames(import) <- lbls
    colnames(import) <- "Weights"
    #import<-import[order(import["Weights"],decreasing=TRUE)]
    View(import)
    barplot(t(import),names.arg=lbls,
            ylab="% 重要性",
            xlab="预测变量",
            width=0.2,
            cex.axis = 0.8,
            cex.names = 0.6,
            las=2,
            main="预测变量的相对重要性",
            sub=paste("R-Square=", round(rsquare, digits=3)),
            ...) 
    return(import)
  }