dl.combine | R Documentation |
Apply several Positioning methods to the original data frame.
dl.combine(...)
... |
Several Positioning Methods. |
A Positioning Method that returns the combined data frame after applying each specified Positioning Method.
Toby Dylan Hocking
## Simple example: label the start and endpoints library(nlme) library(lattice) ratplot <- xyplot( weight~Time|Diet,BodyWeight,groups=Rat,type='l',layout=c(3,1)) both <- dl.combine("first.points","last.points") rat.both <- direct.label(ratplot,"both") print(rat.both) ## same as repeated call to direct.label: rat.repeated <- direct.label(direct.label(ratplot,"last.points"),"first.points") print(rat.repeated) ## same with ggplot2: if(require(ggplot2)){ rp2 <- qplot( Time,weight,data=BodyWeight,geom="line",facets=.~Diet,colour=Rat) print(direct.label(direct.label(rp2,"last.points"),"first.points")) print(direct.label(rp2,"both")) } ## more complex example: first here is a function for computing the ## lasso path. mylars <- function ## Least angle regression algorithm for calculating lasso solutions. (x, ## Matrix of predictor variables. y, ## Vector of responses. epsilon=1e-6 ## If correlation < epsilon, we are done. ){ xscale <- scale(x) # need to work with standardized variables b <- rep(0,ncol(x))# coef vector starts at 0 names(b) <- colnames(x) ycor <- apply(xscale,2,function(xj)sum(xj*y)) j <- which.max(ycor) # variables in active set, starts with most correlated alpha.total <- 0 out <- data.frame() while(1){## lar loop xak <- xscale[,j] # current variables r <- y-xscale%*%b # current residual ## direction of parameter evolution delta <- solve(t(xak)%*%xak)%*%t(xak)%*%r ## Current correlations (actually dot product) intercept <- apply(xscale,2,function(xk)sum(r*xk)) ## current rate of change of correlations z <- xak%*%delta slope <- apply(xscale,2,function(xk)-sum(z*xk)) ## store current values of parameters and correlation out <- rbind(out,data.frame(variable=colnames(x), coef=b, corr=abs(intercept), alpha=alpha.total, arclength=sum(abs(b)), coef.unscaled=b/attr(xscale,"scaled:scale"))) if(sum(abs(intercept)) < epsilon)#corr==0 so we are done return(transform(out,s=arclength/max(arclength))) ## If there are more variables we can enter into the regression, ## then see which one will cross the highest correlation line ## first, and record the alpha value of where the lines cross. d <- data.frame(slope,intercept) d[d$intercept<0,] <- d[d$intercept<0,]*-1 d0 <- data.frame(d[j[1],])# highest correlation line d2 <- data.frame(rbind(d,-d),variable=names(slope))#reflected lines ## Calculation of alpha for where lines cross for each variable d2$alpha <- (d0$intercept-d2$intercept)/(d2$slope-d0$slope) subd <- d2[(!d2$variable%in%colnames(x)[j])&d2$alpha>epsilon,] subd <- subd[which.min(subd$alpha),] nextvar <- subd$variable alpha <- if(nrow(subd))subd$alpha else 1 ## If one of the coefficients would hit 0 at a smaller alpha ## value, take it out of the regression and continue. hit0 <- xor(b[j]>0,delta>0)&b[j]!=0 alpha0 <- -b[j][hit0]/delta[hit0] takeout <- length(alpha0)&&min(alpha0) < alpha if(takeout){ i <- which.min(alpha0) alpha <- alpha0[i] } b[j] <- b[j]+alpha*delta ## evolve parameters alpha.total <- alpha.total+alpha ## add or remove a variable from the active set j <- if(takeout)j[j!=which(names(i)==colnames(x))] else c(j,which(nextvar==colnames(x))) } } ## Calculate lasso path, plot labels at two points: (1) where the ## variable enters the path, and (2) at the end of the path. if(require(lars)){ data(diabetes,envir=environment()) dres <- with(diabetes,mylars(x,y)) P <- xyplot(coef~arclength,dres,groups=variable,type="l") mylasso <- dl.combine("lasso.labels", "last.qp") plot(direct.label(P,"mylasso")) }
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