r - 从matlab代码到R代码的矩阵

Question

创建 matlab 中的代码是为了确定生态系统因生态系统中物种的丧失而发挥功能的可能性。现在，这段代码必须翻译成 R。但是我在翻译 matlab 中的矩阵操作时遇到了问题。

在 Matlab 中，这是我试图翻译成 R 代码的代码:

for j=1:N+1
multi_matrix4(:,j)=matrix(:,1);
end

在 R 中，我将这段代码放在 for 循环中:

+ multi.matrix4 <- matrix[,1,drop=FALSE]
+ multi.matrix4 <- multi.matrix4[,j,drop=FALSE]
+ class(multi.matrix4)

这是来自 R 的消息，位于 for 循环之下:

Error: subscript out of bounds

我的问题是:如何使用 R 进行这种矩阵操作？？？？？

没有最后一张图的 matlab 代码是:

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'

R 代码如下所示:

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)

Answer 1

虽然我手边没有 Matlab 和 R，但我怀疑这是导致问题的原因:

在 R 中，您尝试分配到矩阵中不存在的位置，结果:失败

在 Matlab 中，您试图分配到矩阵中不存在的位置，结果:它原谅了您的奇怪选择，扩展了您的矩阵并成功了。

假设这是问题所在，解决方案很简单:

When creating the matrix in R, make sure that it is big enough to contain all the things you want to add to it later.

这称为初始化，在大多数情况下是最佳实践。即使在 Matlab 中，通常也建议尽可能提前正确初始化您的变量，而不是让它们随着您的增长而增长。