predict matrix of predictors X

Predict matrix of predictors x

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theta.predict <- function(fit,x){cbind(1,x)%*%fit$coef} # matrix of predictors X <- as.matrix(mydata[c("x1","x2","x3")]) # vector of predicted values y <- as.matrix(mydata[c("y")]) results <- crossval(X,y,theta.fit,theta.predict,ngroup=10) cor(y, fit$fitted.values)**2 # raw R2 cor(y,results$cv.fit)**2 # cross-validated R2 Variable Selection Selecting a subset of predictor variables from a larger set (e.g., stepwise selection) is a controversial topic. You can perform stepwise selection (forward, backward, both) using the stepAIC( ) function from the MASS package. stepAIC( ) performs stepwise model selection by exact AIC. # Stepwise Regression library(MASS) fit <- lm(y~x1+x2+x3,data=mydata) step <- stepAIC(fit, direction="both") step$anova # display results Alternatively, you can perform all-subsets regression using the leaps( ) function from the leaps package. In the following code nbest indicates the number of subsets of each size to report. Here, the ten best models will be reported for each subset size (1 predictor, 2 predictors, etc.). # All Subsets Regression library(leaps) attach(mydata) leaps<-regsubsets(y~x1+x2+x3+x4,data=mydata,nbest=10) # view results summary(leaps) # plot a table of models showing variables in each model. # models are ordered by the selection statistic. plot(leaps,scale="r2") # plot statistic by subset size library(car) subsets(leaps, statistic="rsq") click to view Other options for plot( ) are bic, Cp, and adjr2. Other options for plotting with subset( ) are bic, cp, adjr2, and rss. Relative Importance The relaimpo package provides measures of relative importance for each of the predictors in the model. See help(calc.relimp) for details on the four measures of relative importance provided. # Calculate Relative Importance for Each Predictor library(relaimpo) calc.relimp(fit,type=c("lmg","last","first","pratt"), rela=TRUE) # Bootstrap Measures of Relative Importance (1000 samples) boot <- boot.relimp(fit, b = 1000, type = c("lmg", "last", "first", "pratt"), rank = TRUE, diff = TRUE, rela = TRUE) booteval.relimp(boot) # print result plot(booteval.relimp(boot,sort=TRUE)) # plot result click to view Graphic Enhancements The car package offers a wide variety of plots for regression, including added variable plots, and enhanced diagnostic and scatter plots . Going Further Nonlinear Regression The nls package provides functions for nonlinear regression. See John Fox's Nonlinear Regression and Nonlinear Least Squares for an overview. Huet and colleagues' Statistical Tools for Nonlinear Regression: A Practical Guide with S-PLUS and R Examples is a valuable reference book. Robust Regression There are many functions in R to aid with robust regression. For example, you can perform robust regression with the rlm( ) function in the MASS package. John Fox's (who else?) Robust Regression provides a good starting overview. The UCLA Statistical Computing website has Robust Regression Examples .

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