本文介绍了R:如何在两个列表上运行函数?的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧! 问题描述 函数(Z,p){ imp < - as.vector(cbind(imp = rowSums(Z))) exp x = p + imp ac = p + imp - exp einsdurchx = 1 / as.vector(x) einsdurchx [is.infinite(einsdurchx)] A = Z% *%diag(einsdurchx) R = solve(diag(length(p)) - A)%*%diag(p) C = ac * einsdurchx R_bar = diag(as。向量(C))%*%R rR_bar = round(R_bar) return(rR_bar)} 在矩阵和一个向量上工作正常。不过,我需要在矩阵列表和矢量列表中运行此函数。我尝试了 lapply / mapply 以下这个示例,请参阅下文。这里有一些示例数据显示了我的数据结构: Z nrow = 4,ncol = 4,byrow = T),112.2012=矩阵(c (10,90,0,30,10,90,0,10,200,50,10,350,150,100,200,10), nrow = 4,ncol = 4,byrow = T))p 这里我试过的 lapply 代码(我改变了X和Y函数中的所有Z和p,不知道是否需要) : lapply(X = Z,Y = p,函数(Z,p){ imp as.vector(cbind(imp = rowSums(X))) exp x = Y + imp ac = Y + imp - exp einsdurchx = 1 / as.vector(x) einsdurchx [is.infinite(einsdurchx)] A = X%*%diag (einsdurchx) R = solve(diag(length(Y)) - A)%*% diag(Y) C = ac * einsdurchx R_bar = diag(as.vector(C))%*%R rR_bar = round(R_bar) return(rR_bar) }) 我似乎有一个索引列表对象的问题,但是对于列表来说我相对较新。你有什么想法我做错了吗?此外,对象(Z和p)需要按名称匹配,因为列表中有超过1000个对象(Info:两个列表具有相同的对象/项目长度,并且Z中矩阵的行/列具有与p中的矢量长度相同)。 这里我预期的结果: $'112.2012' [,1] [,2] [,3] [,4] [1,] 174 191 31 4 [2,] 0 450 0 0 [3,] 11 188 49 1 [4,] 14 171 20 5 $'111.2012' [,1] [,2] [,3] [,4] [1,] 45 14 0 1 [2,] 8 670 0 2 [3,] 190 157 44 59 [4,] 57 59 6 38 我非常感谢您的想法。 解决方案您可以使用 mapply ,这是 lapply 的多变量版本, / b> fun imp exp< - as.vector(t(cbind(exp = colSums(Z))))x = p + imp ac = p + imp - exp einsdurchx = 1 / as.vector(x) einsdurchx [is.infinite(einsdurchx)] A = Z%*%diag(einsdurchx) R = solve(diag(length(p )) - A)%*%diag(p) C = ac * einsdurchx R_bar = diag(as.vector(C))%*%R rR_bar = round(R_bar) return(rR_bar)} Z nrow = 4,ncol = 4,byrow = T),112.2012=矩阵(c(10,90,0,30,10,90,0) ,10,200,50,10,350,150,100,200,10), nrow = 4,ncol = 4,byrow = T))p 112.2012= c(300,900,50,100)) mapply(fun,Z,p,SIMPLIFY = FALSE) ## $`111.2012` ## [,1] [,2] [,3] [,4] ## [1,] 174 191 31 4 ## [2,] 0 450 0 0 ## [3,] 11 188 49 1 ## [4,] 14 171 20 5 ## $`112.2012` ## [,1] [,2] [,3] [,4] ## [1,] 45 14 0 1 ## [2,] 8 670 0 2 ## [3,] 190 157 44 59 ## [4,] 57 59 6 38 I want to run the following function on two lists:function(Z, p) { imp <- as.vector(cbind(imp=rowSums(Z))) exp <- as.vector(t(cbind(exp=colSums(Z)))) x = p + imp ac = p + imp - exp einsdurchx = 1/as.vector(x) einsdurchx[is.infinite(einsdurchx)] <- 0 A = Z %*% diag(einsdurchx) R = solve(diag(length(p))-A) %*% diag(p) C = ac * einsdurchx R_bar = diag(as.vector(C)) %*% R rR_bar = round(R_bar) return(rR_bar)}which works fine on a matrix and a vector. However, I need to run this function on a list of matrices and a list of vectors. I tried so far lapply/mapply following this example, see below. Here some example data showing the structure of my data:Z <- list("111.2012"= matrix(c(0,0,100,200,0,0,0,0,50,350,0,50,50,200,200,0), nrow = 4, ncol = 4, byrow = T), "112.2012"= matrix(c(10,90,0,30,10,90,0,10,200,50,10,350,150,100,200,10), nrow = 4, ncol = 4, byrow = T))p <- list("111.2012"=c(200, 1000, 100, 10), "112.2012"=c(300, 900, 50, 100))Here the lapply code I tried (I changed all Z and p in the function for X and Y, don't know if needed):lapply(X=Z, Y=p, function(Z, p) { imp <- as.vector(cbind(imp=rowSums(X))) exp <- as.vector(t(cbind(exp=colSums(X)))) x = Y + imp ac = Y + imp - exp einsdurchx = 1/as.vector(x) einsdurchx[is.infinite(einsdurchx)] <- 0 A = X %*% diag(einsdurchx) R = solve(diag(length(Y))-A) %*% diag(Y) C = ac * einsdurchx R_bar = diag(as.vector(C)) %*% R rR_bar = round(R_bar) return(rR_bar)} )I seems that I have a problem indexing the the objects of the list, but I am relatively new with lists. Do you have any ideas what I'm doing wrong? Further the objects (of Z and p) need to be matched by name, as I have more than 1000 objects in the lists (Info: both lists have the same object/item length, and rows/cols of the matrices in Z have the same length as the vectors in p). Here my expected result:$'112.2012' [,1] [,2] [,3] [,4][1,] 174 191 31 4[2,] 0 450 0 0[3,] 11 188 49 1[4,] 14 171 20 5$'111.2012' [,1] [,2] [,3] [,4][1,] 45 14 0 1[2,] 8 670 0 2[3,] 190 157 44 59[4,] 57 59 6 38I really appreciate your ideas. 解决方案 You can use mapply , which is kind of multivariate version of lapply, for this taskfun <- function(Z, p) { imp <- as.vector(cbind(imp=rowSums(Z))) exp <- as.vector(t(cbind(exp=colSums(Z)))) x = p + imp ac = p + imp - exp einsdurchx = 1/as.vector(x) einsdurchx[is.infinite(einsdurchx)] <- 0 A = Z %*% diag(einsdurchx) R = solve(diag(length(p))-A) %*% diag(p) C = ac * einsdurchx R_bar = diag(as.vector(C)) %*% R rR_bar = round(R_bar) return(rR_bar)}Z <- list("111.2012"= matrix(c(0,0,100,200,0,0,0,0,50,350,0,50,50,200,200,0), nrow = 4, ncol = 4, byrow = T), "112.2012"= matrix(c(10,90,0,30,10,90,0,10,200,50,10,350,150,100,200,10), nrow = 4, ncol = 4, byrow = T))p <- list("111.2012"=c(200, 1000, 100, 10), "112.2012"=c(300, 900, 50, 100))mapply(fun, Z, p, SIMPLIFY = FALSE)## $`111.2012`## [,1] [,2] [,3] [,4]## [1,] 174 191 31 4## [2,] 0 450 0 0## [3,] 11 188 49 1## [4,] 14 171 20 5## $`112.2012`## [,1] [,2] [,3] [,4]## [1,] 45 14 0 1## [2,] 8 670 0 2## [3,] 190 157 44 59## [4,] 57 59 6 38 这篇关于R:如何在两个列表上运行函数?的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持! 10-10 05:35