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matlab中的代码是为了使生态系统中的物种在生态系统中丢失而发挥作用的可能性而创建的。现在，这段代码必须翻译成R.但是我有翻译matlab中进行的矩阵操作的问题。将matlab代码中的矩阵转换为R代码
在Matlab中，这是我试图转换成R代码代码：
for j=1:N+1
multi_matrix4(:,j)=matrix(:,1);
end
在R，我已经把这个代码中的for循环：
+ multi.matrix4 <- matrix[,1,drop=FALSE]
+ multi.matrix4 <- multi.matrix4[,j,drop=FALSE]
+ class(multi.matrix4)
这是从R中的信息，附带下方的for循环：
Error: subscript out of bounds
我的问题是： 如何使用R进行这种矩阵操作??????
没有最后图表中的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'
的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)
你可以翻译成'just R'，或者你可以翻译成[Armadillo]（http://arma.sf.net），它可以通过[RcppArmadillo]（http：//dirk.eddelbuettel）从R中使用。 COM /代码/ rcpp.armadillo.html）。 Armadillo的设计目标之一是为Matlab用户提供这种类型的转换。我已经完成了昂贵的仿真问题，取得了巨大的成功，并且获得了非常显着的速度提升。 –
也许如果你用'for'循环发布R代码，我们可以帮助你更好一点。只是猜测，但我怀疑'multi.matrix4'中只有'N'列，并且当'j'命中'N + 1'时循环失败。 – nograpes
是的，这是正确的。现在，R代码已发布。 – user1842171