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创建 matlab 中的代码是为了根据生态系统中物种的丧失来确定生态系统功能的概率。现在，这段代码必须翻译成 R。但我在翻译 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，我将此代码放在 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 循环下方：

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)
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1 回答 1

0

虽然我手头没有 Matlab 和 R，但我怀疑这是导致问题的原因：

在 R 中，您尝试分配给矩阵中不存在的位置，结果：失败

在 Matlab 中，您尝试分配给矩阵中不存在的位置，结果：它原谅了您奇怪的选择，扩展了您的矩阵并成功了。

假设这是问题，解决方案很简单：

在 R 中创建矩阵时，请确保它足够大以包含您以后要添加到其中的所有内容。

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变量，而不是让它们随时间增长。

于 2012-12-04T19:02:11.120 回答