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matlab中的代碼是爲了使生態系統中的物種在生態系統中丟失而發揮作用的可能性而創建的。現在，這段代碼必須翻譯成R.但是我有翻譯matlab中進行的矩陣操作的問題。將matlab代碼中的矩陣轉換爲R代碼
在Matlab中，這是我試圖轉換成R代碼代碼：
for j=1:N+1
multi_matrix4(:,j)=matrix(:,1);
end
在R，我已經把這個代碼中的for循環：
+ multi.matrix4 <- matrix[,1,drop=FALSE]
+ multi.matrix4 <- multi.matrix4[,j,drop=FALSE]
+ class(multi.matrix4)
這是從R中的信息，附帶下方的for循環：
Error: subscript out of bounds
我的問題是： 如何使用R進行這種矩陣操作??????
沒有最後圖表中的MATLAB代碼是：
clear all
% No of permutations
sim=1000;
% Total No of ecosystem functions
N=3;
%Total dimensions
J=3;
% Total No of species in pool
total_species=4;
% No of species drawn from pool
species=4;
multi_matrix=zeros(total_species,N);
% "Threshold"
t=.5;
result=zeros(sim,J);
for i=1:sim
% %Uniformly increasing trait values
for j=1:N
matrix=rand(total_species,2);
matrix(:,1)=linspace(0,1,total_species);
matrix=sortrows(matrix,2);
multi_matrix4(:,j)=matrix(:,1);
end
%Complete covariance
matrix=rand(total_species,2);
matrix(:,1)=linspace(0,1,total_species);
matrix=sortrows(matrix,2);
for j=1:N+1
multi_matrix4(:,j)=matrix(:,1);
end
% Excess of high trait values
for j=1:N
matrix=rand(total_species,2);
X=1:total_species;X=X';
matrix(:,1)=1-exp(-0.02*X.^2);
matrix=sortrows(matrix,2);
multi_matrix4(:,j)=matrix(:,1);
end
% Deficiency of high trait values
for j=1:N
matrix=rand(total_species,2);
X=1:total_species;X=X';
% matrix(:,1)=exp((X./22.6).^3)-1;
matrix(:,1)=exp((X./13.55).^3)-1;
matrix=sortrows(matrix,2);
multi_matrix4(:,j)=matrix(:,1);
end
% Reading empirical data
warning off
% [NUMERIC,txt]=xlsread('Plant_6.xls','Sheet1');
Exp07_2 = [ 0 0.72 0.70 ; 1 1 0 ; 0.62 0 1 ; 0.36 0.69 0.61]
multi_matrix(1:total_species,1:N)=Exp07_2;
random=rand(1,N);
multi_matrix(total_species+1,1:N)=random;
multi_matrix2=sortrows(multi_matrix',total_species+1);
multi_matrix3=multi_matrix2';
multi_matrix4=multi_matrix3(1:total_species,:);
warning on
% adding a sorting column
random2=rand(total_species,1);
multi_matrix4(:,N+1)=random2;
sort_multi_matrix=sortrows(multi_matrix4,N+1);
% loop adding one function at a time
for j=1:J
loss_matrix=sort_multi_matrix(1:species,1:j);
max_value=loss_matrix>=t;
B=any(max_value',2);
C=all(B);
result(i,j)=sum(C);
end
end
% reporting
res=mean(result);
res'
的R-代碼如下所示：
rm()
#No of permutation
sims <- 1000;
#Total number of ecosystem functions
N <- 3
#Total dimensions
J <- 3
#Total number of species in pool
total.species <- 4
#No of species drawn from pool
species <- 4
multi.matrix <- matrix(0, nrow=total.species, ncol=N)
class(multi.matrix)
# $Threshold$
t <- .5;
# The results are to be put in a matrix
result <- matrix(0, nrow=sims, ncol=J)
for (i in 1 : sims)
{
#Uniformly increasing trait values
for (j in 1 : N)
{
matrix <- matrix(runif(total.species*2),total.species)
class(matrix)
matrix[,1] <- seq(0,1, len=total.species) # test 2
class(matrix)
matrix <- matrix[order(matrix(,2)),]
class(matrix)
# multi.matrix4[,j,drop=FALSE] = matrix[,1,drop=FALSE]
multi.matrix4 <- matrix[,1,drop=FALSE]
multi.matrix4 <- multi.matrix4[,j,drop=FALSE]
class(multi.matrix4)
}
# Complete covariance
matrix <- matrix(runif(total.species*2),total.species)
class(matrix)
matrix[,1] <- seq(0, 1, len=total.species)
class(matrix)
matrix <- matrix[order(matrix(,2)),]
class(matrix)
for (j in 1 : N + 1)
{multi.matrix4 <- matrix[,1,drop=FALSE]
multi.matrix4 <- multi.matrix4[,j,drop=FALSE]
class(multi.matrix4)
}
# Excess of high trait values
for (j in 1 : N)
{matrix <- matrix(runif(total.species*2),total.species)
class(matrix)
X <- 1 : total.species
X <- t(X)
matrix[,1] <- c(1 - exp(-0.02 %*% X^2)) # Hie... p. 8
matrix <- matrix[order(matrix(,2)),]
# multi.matrix4[,j,drop=FALSE] <- matrix[,1,drop=FALSE]
# multi.matrix4[,j,drop=FALSE] <- matrix[,1]
multi.matrix4 <- matrix[,1,drop=FALSE]
multi.matrix4 <- multi.matrix4[,j,drop=FALSE]
class(multi.matrix4)
}
# Deficiency of high trait values
for (j in 1 : N)
{matrix <- matrix(runif(total.species*2),total.species)
class(matrix)
X <- 1 : total.species
X <- t(X)
# matrix[1:4,1] <- c(exp((X/22.6)^3)-1)
matrix[1:4,1] <- c(exp((X/13.55)^3)-1)
class(matrix)
matrix <- matrix[order(matrix(,2))]
class(matrix)
# multi.matrix4[,j,drop=FALSE] <- matrix[,1,drop=FALSE]
# multi.matrix4[,j,drop=FALSE] <- matrix[,1]
# multi.matrix4[,j] <- matrix[,1,drop=FALSE]
# class(multi.matrix4)
multi.matrix4 <- matrix[,1,drop=FALSE]
multi.matrix4 <- multi.matrix4[,j,drop=FALSE]
class(multi.matrix4)
}
# Reading empirical data
Exp_07_2 <- file(description = "Exp_07_2", open = "r", blocking = TRUE, encoding = getOption("encoding"), raw = FALSE)
Exp_07_2 <- matrix(scan(Exp_07_2),nrow=4,byrow=TRUE)
read.matrix <- function(Exp_07_2){
as.matrix(read.table(Exp_07_2))
}
Exp_07_2
class(Exp_07_2)
multi.matrix <- matrix(c(Exp_07_2),ncol=3)
class(multi.matrix)
multi.matrix <- multi.matrix(1:total.species,1:N)
class(multi.matrix)
random <- runif(N)
multi.matrix2 <- t(multi.matrix)[order(t(multi.matrix)[,1], t(multi.matrix)[,2], t(multi.matrix)[,3], t(multi.matrix)[,4]),]
class(multi.matrix2)
multi.matrix3 <- t(multi.matrix2)
class(multi.matrix3)
multi.matrix4 <- multi.matrix3[1:total.species,,drop=FALSE]
class(multi.matrix4)
# Adding a sorting column
random2 <- runif(total.species,1)
random2 <- multi.matrix4[,N+1,drop=FALSE]
sort.multi.matrix <- multi.matrix4(order(multi.matrix4[,1], multi.matrix4[,2], multi.matrix4[,3],multi.matrix4[,4]),N+1,drop=FALSE)
# loop adding one function at a time
for (j in 1 : J)
{loss.matrix <- sort.multi.matrix[nrow=species,ncol=j,drop=FALSE]
class(loss.matrix)
max.value <- loss.matrix >= t
c(B) <- any(t(max.value),2)
c(C) <- all(c(B))
result(i,j) <- c(sum(C))
}
}
# Reporting
res <- mean(result)
res
t(res)
你可以翻譯成'just R'，或者你可以翻譯成[Armadillo]（http://arma.sf.net），它可以通過[RcppArmadillo]（http：//dirk.eddelbuettel）從R中使用。 COM /代碼/ rcpp.armadillo.html）。 Armadillo的設計目標之一是爲Matlab用戶提供這種類型的轉換。我已經完成了昂貴的仿真問題，取得了巨大的成功，並且獲得了非常顯着的速度提升。 –
也許如果你用'for'循環發佈R代碼，我們可以幫助你更好一點。只是猜測，但我懷疑'multi.matrix4'中只有'N'列，並且當'j'命中'N + 1'時循環失敗。 – nograpes
是的，這是正確的。現在，R代碼已發佈。 – user1842171