Calculate matrix means and compile across matrices

I've only been using R for about a week (converting long-time SAS user). I'm running a model that generates multiple identical matrices (representing different parameter scenarios) similarly to the simplified example below:

    library(matrixStats)

    len<-50

    wid<-10

    mat1 <- matrix(nrow=len, ncol=wid)

    mat2 <- matrix(nrow=len, ncol=wid)

    mat3 <- matrix(nrow=len, ncol=wid)

set.seed(123)    

for (i in 1:wid){

      for (n in 1:len){

        mat1[n,i] <- runif(1)

        mat2[n,i] <- runif(1)

        mat3[n,i] <- runif(1)

    }}

I want to calculate the mean and standard deviation of the column sums for each matrix independently, similar to:

a <- colSums(mat1)

mean(a)

sd(a)

repeated for each matrix and then compile those values into a single dataset with a matrix identifier, mean, and standard deviation, similar to:

mat1_ID    24.42858    2.198454

mat2_ID    25.67452    1.677669

mat3_ID    24.31933    1.572029

I have no idea how to do this in R. Any help would be greatly appreciated.

edited Nov 21 '18 at 16:01

asked Nov 21 '18 at 15:55

Jack Handy

FYI, the code displayed here does not make use of the 'matrixStats' package, so no need to use library(matrixStats) here. The colSums() function comes from the 'base' package.
– HenrikB
yesterday

add a comment |

    library(matrixStats)

    len<-50

    wid<-10

    mat1 <- matrix(nrow=len, ncol=wid)

    mat2 <- matrix(nrow=len, ncol=wid)

    mat3 <- matrix(nrow=len, ncol=wid)

set.seed(123)    

for (i in 1:wid){

      for (n in 1:len){

        mat1[n,i] <- runif(1)

        mat2[n,i] <- runif(1)

        mat3[n,i] <- runif(1)

    }}

I want to calculate the mean and standard deviation of the column sums for each matrix independently, similar to:

a <- colSums(mat1)

mean(a)

sd(a)

repeated for each matrix and then compile those values into a single dataset with a matrix identifier, mean, and standard deviation, similar to:

mat1_ID    24.42858    2.198454

mat2_ID    25.67452    1.677669

mat3_ID    24.31933    1.572029

I have no idea how to do this in R. Any help would be greatly appreciated.

edited Nov 21 '18 at 16:01

asked Nov 21 '18 at 15:55

Jack Handy

FYI, the code displayed here does not make use of the 'matrixStats' package, so no need to use library(matrixStats) here. The colSums() function comes from the 'base' package.
– HenrikB
yesterday

add a comment |

    library(matrixStats)

    len<-50

    wid<-10

    mat1 <- matrix(nrow=len, ncol=wid)

    mat2 <- matrix(nrow=len, ncol=wid)

    mat3 <- matrix(nrow=len, ncol=wid)

set.seed(123)    

for (i in 1:wid){

      for (n in 1:len){

        mat1[n,i] <- runif(1)

        mat2[n,i] <- runif(1)

        mat3[n,i] <- runif(1)

    }}

I want to calculate the mean and standard deviation of the column sums for each matrix independently, similar to:

a <- colSums(mat1)

mean(a)

sd(a)

repeated for each matrix and then compile those values into a single dataset with a matrix identifier, mean, and standard deviation, similar to:

mat1_ID    24.42858    2.198454

mat2_ID    25.67452    1.677669

mat3_ID    24.31933    1.572029

I have no idea how to do this in R. Any help would be greatly appreciated.

edited Nov 21 '18 at 16:01

asked Nov 21 '18 at 15:55

Jack Handy

    library(matrixStats)

    len<-50

    wid<-10

    mat1 <- matrix(nrow=len, ncol=wid)

    mat2 <- matrix(nrow=len, ncol=wid)

    mat3 <- matrix(nrow=len, ncol=wid)

set.seed(123)    

for (i in 1:wid){

      for (n in 1:len){

        mat1[n,i] <- runif(1)

        mat2[n,i] <- runif(1)

        mat3[n,i] <- runif(1)

    }}

I want to calculate the mean and standard deviation of the column sums for each matrix independently, similar to:

a <- colSums(mat1)

mean(a)

sd(a)

repeated for each matrix and then compile those values into a single dataset with a matrix identifier, mean, and standard deviation, similar to:

mat1_ID    24.42858    2.198454

mat2_ID    25.67452    1.677669

mat3_ID    24.31933    1.572029

I have no idea how to do this in R. Any help would be greatly appreciated.

r matrix

edited Nov 21 '18 at 16:01

asked Nov 21 '18 at 15:55

Jack Handy

edited Nov 21 '18 at 16:01

asked Nov 21 '18 at 15:55

Jack Handy

edited Nov 21 '18 at 16:01

asked Nov 21 '18 at 15:55

Jack Handy

asked Nov 21 '18 at 15:55

Jack Handy

asked Nov 21 '18 at 15:55

Jack Handy

FYI, the code displayed here does not make use of the 'matrixStats' package, so no need to use library(matrixStats) here. The colSums() function comes from the 'base' package.
– HenrikB
yesterday

add a comment |

FYI, the code displayed here does not make use of the 'matrixStats' package, so no need to use library(matrixStats) here. The colSums() function comes from the 'base' package.
– HenrikB
yesterday

FYI, the code displayed here does not make use of the 'matrixStats' package, so no need to use library(matrixStats) here. The colSums() function comes from the 'base' package.
– HenrikB
yesterday

add a comment |

2 Answers
2

active

oldest

votes

mats <- paste0("mat", 1:3)

t(sapply(mats, function(x) {

  cs <- colSums(get(x))

  c(mean(cs), sd(cs))

}))



         [,1]     [,2]

mat1 24.42858 2.198454

mat2 25.67452 1.677669

mat3 24.31933 1.572029

answered Nov 21 '18 at 16:06

snoram

6,399831

Another (possibly confusing) option funs <- list(sd=sd, mean=mean); sapply(funs, mapply, lapply(mget(paste0("mat", 1:3)), colSums))
– markus
Nov 21 '18 at 16:16

Thanks...this worked great! I particularly like the paste0 function, as I have many matrices with sequentially increasing file names and this will keep me from having to type them all individually. If I had sets of matrices with different file prefixes (e.g, mat1, mat2, mat3, test1, test2, test3) could I similarly combine them all using this function?
– Jack Handy
Nov 21 '18 at 17:50

@JackHandy: Sure, for example: paste0(c("mat", "test"), rep(1:3, each = 2))
– snoram
Nov 22 '18 at 7:31

add a comment |

If you want speed you can try my way. I will use package Rfast. Since you are new in R I will show step by step my example.

install.packages("Rfast") # download package Rfast

I will use the matrices from your example.

mats <- list(mat1=mat1,mat2=mat2,mat3=mat3) # "mat=mat" store matrix mat and add name

result <- sapply(mats,function(x){

             s <- Rfast::colsums(x) # you can use also the parallel version using argument "parallel"

             c(sd=Rfast::Var(s,std=TRUE),mean=mean(s))

          })



result # this will print the matrix    

      mat1      mat2      mat3

sd    1.677669  1.677669  1.572029

mean 25.674519 25.674519 24.319328

You can transpose the matrix to get the result as you wish

t(result)

           sd     mean

mat1 1.677669 25.67452

mat2 1.677669 25.67452

mat3 1.572029 24.31933

edited Nov 21 '18 at 19:28

answered Nov 21 '18 at 17:03

Csd

29819

1

The code wouldn't work for me. Is there something missing in the mean command?
– Jack Handy
Nov 21 '18 at 17:47

Yes and sorry for that. There was a type but I have edited my answer and test again.
– Csd
Nov 21 '18 at 17:54

Great...thanks! I found two other typos: in the list statement, it should be mat1=mat1 (which is why the mat1 and mat2 result values were identical in your example), and there's a right parentheses missing after the mean command (there should be 3). I'm not sure what the speed difference is between the two methods with this simple example, but I'll see what happens with my larger matrices.
– Jack Handy
Nov 21 '18 at 19:00

Thanks for you comments. I have edit again the answer. Yes, my answer is more for the speed part because you might need it if your matrices are large. Especially for var which might crash for large matrices.
– Csd
Nov 21 '18 at 19:33

add a comment |

Your Answer

StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");

StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});

function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});

}
});

draft saved

draft discarded

Sign up or log in

StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});

Post as a guest

Name

Required, but never shown

StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53415885%2fcalculate-matrix-means-and-compile-across-matrices%23new-answer', 'question_page');
}
);

Post as a guest

Name

Required, but never shown

2 Answers
2

active

oldest

votes

2 Answers
2

active

oldest

votes

mats <- paste0("mat", 1:3)

t(sapply(mats, function(x) {

  cs <- colSums(get(x))

  c(mean(cs), sd(cs))

}))



         [,1]     [,2]

mat1 24.42858 2.198454

mat2 25.67452 1.677669

mat3 24.31933 1.572029

answered Nov 21 '18 at 16:06

snoram

6,399831

Another (possibly confusing) option funs <- list(sd=sd, mean=mean); sapply(funs, mapply, lapply(mget(paste0("mat", 1:3)), colSums))
– markus
Nov 21 '18 at 16:16

Thanks...this worked great! I particularly like the paste0 function, as I have many matrices with sequentially increasing file names and this will keep me from having to type them all individually. If I had sets of matrices with different file prefixes (e.g, mat1, mat2, mat3, test1, test2, test3) could I similarly combine them all using this function?
– Jack Handy
Nov 21 '18 at 17:50

@JackHandy: Sure, for example: paste0(c("mat", "test"), rep(1:3, each = 2))
– snoram
Nov 22 '18 at 7:31

add a comment |

mats <- paste0("mat", 1:3)

t(sapply(mats, function(x) {

  cs <- colSums(get(x))

  c(mean(cs), sd(cs))

}))



         [,1]     [,2]

mat1 24.42858 2.198454

mat2 25.67452 1.677669

mat3 24.31933 1.572029

answered Nov 21 '18 at 16:06

snoram

6,399831

Another (possibly confusing) option funs <- list(sd=sd, mean=mean); sapply(funs, mapply, lapply(mget(paste0("mat", 1:3)), colSums))
– markus
Nov 21 '18 at 16:16

Thanks...this worked great! I particularly like the paste0 function, as I have many matrices with sequentially increasing file names and this will keep me from having to type them all individually. If I had sets of matrices with different file prefixes (e.g, mat1, mat2, mat3, test1, test2, test3) could I similarly combine them all using this function?
– Jack Handy
Nov 21 '18 at 17:50

@JackHandy: Sure, for example: paste0(c("mat", "test"), rep(1:3, each = 2))
– snoram
Nov 22 '18 at 7:31

add a comment |

mats <- paste0("mat", 1:3)

t(sapply(mats, function(x) {

  cs <- colSums(get(x))

  c(mean(cs), sd(cs))

}))



         [,1]     [,2]

mat1 24.42858 2.198454

mat2 25.67452 1.677669

mat3 24.31933 1.572029

answered Nov 21 '18 at 16:06

snoram

6,399831

mats <- paste0("mat", 1:3)

t(sapply(mats, function(x) {

  cs <- colSums(get(x))

  c(mean(cs), sd(cs))

}))



         [,1]     [,2]

mat1 24.42858 2.198454

mat2 25.67452 1.677669

mat3 24.31933 1.572029

answered Nov 21 '18 at 16:06

snoram

6,399831

answered Nov 21 '18 at 16:06

snoram

6,399831

answered Nov 21 '18 at 16:06

snoram

6,399831

answered Nov 21 '18 at 16:06

snoram

6,399831

Another (possibly confusing) option funs <- list(sd=sd, mean=mean); sapply(funs, mapply, lapply(mget(paste0("mat", 1:3)), colSums))
– markus
Nov 21 '18 at 16:16

Thanks...this worked great! I particularly like the paste0 function, as I have many matrices with sequentially increasing file names and this will keep me from having to type them all individually. If I had sets of matrices with different file prefixes (e.g, mat1, mat2, mat3, test1, test2, test3) could I similarly combine them all using this function?
– Jack Handy
Nov 21 '18 at 17:50

@JackHandy: Sure, for example: paste0(c("mat", "test"), rep(1:3, each = 2))
– snoram
Nov 22 '18 at 7:31

add a comment |

Another (possibly confusing) option funs <- list(sd=sd, mean=mean); sapply(funs, mapply, lapply(mget(paste0("mat", 1:3)), colSums))
– markus
Nov 21 '18 at 16:16

Thanks...this worked great! I particularly like the paste0 function, as I have many matrices with sequentially increasing file names and this will keep me from having to type them all individually. If I had sets of matrices with different file prefixes (e.g, mat1, mat2, mat3, test1, test2, test3) could I similarly combine them all using this function?
– Jack Handy
Nov 21 '18 at 17:50

@JackHandy: Sure, for example: paste0(c("mat", "test"), rep(1:3, each = 2))
– snoram
Nov 22 '18 at 7:31

Another (possibly confusing) option funs <- list(sd=sd, mean=mean); sapply(funs, mapply, lapply(mget(paste0("mat", 1:3)), colSums))
– markus
Nov 21 '18 at 16:16

Thanks...this worked great! I particularly like the paste0 function, as I have many matrices with sequentially increasing file names and this will keep me from having to type them all individually. If I had sets of matrices with different file prefixes (e.g, mat1, mat2, mat3, test1, test2, test3) could I similarly combine them all using this function?
– Jack Handy
Nov 21 '18 at 17:50

@JackHandy: Sure, for example: paste0(c("mat", "test"), rep(1:3, each = 2))
– snoram
Nov 22 '18 at 7:31

add a comment |

If you want speed you can try my way. I will use package Rfast. Since you are new in R I will show step by step my example.

install.packages("Rfast") # download package Rfast

I will use the matrices from your example.

mats <- list(mat1=mat1,mat2=mat2,mat3=mat3) # "mat=mat" store matrix mat and add name

result <- sapply(mats,function(x){

             s <- Rfast::colsums(x) # you can use also the parallel version using argument "parallel"

             c(sd=Rfast::Var(s,std=TRUE),mean=mean(s))

          })



result # this will print the matrix    

      mat1      mat2      mat3

sd    1.677669  1.677669  1.572029

mean 25.674519 25.674519 24.319328

You can transpose the matrix to get the result as you wish

t(result)

           sd     mean

mat1 1.677669 25.67452

mat2 1.677669 25.67452

mat3 1.572029 24.31933

edited Nov 21 '18 at 19:28

answered Nov 21 '18 at 17:03

Csd

29819

1

The code wouldn't work for me. Is there something missing in the mean command?
– Jack Handy
Nov 21 '18 at 17:47

Yes and sorry for that. There was a type but I have edited my answer and test again.
– Csd
Nov 21 '18 at 17:54

Great...thanks! I found two other typos: in the list statement, it should be mat1=mat1 (which is why the mat1 and mat2 result values were identical in your example), and there's a right parentheses missing after the mean command (there should be 3). I'm not sure what the speed difference is between the two methods with this simple example, but I'll see what happens with my larger matrices.
– Jack Handy
Nov 21 '18 at 19:00

Thanks for you comments. I have edit again the answer. Yes, my answer is more for the speed part because you might need it if your matrices are large. Especially for var which might crash for large matrices.
– Csd
Nov 21 '18 at 19:33

add a comment |

If you want speed you can try my way. I will use package Rfast. Since you are new in R I will show step by step my example.

install.packages("Rfast") # download package Rfast

I will use the matrices from your example.

mats <- list(mat1=mat1,mat2=mat2,mat3=mat3) # "mat=mat" store matrix mat and add name

result <- sapply(mats,function(x){

             s <- Rfast::colsums(x) # you can use also the parallel version using argument "parallel"

             c(sd=Rfast::Var(s,std=TRUE),mean=mean(s))

          })



result # this will print the matrix    

      mat1      mat2      mat3

sd    1.677669  1.677669  1.572029

mean 25.674519 25.674519 24.319328

You can transpose the matrix to get the result as you wish

t(result)

           sd     mean

mat1 1.677669 25.67452

mat2 1.677669 25.67452

mat3 1.572029 24.31933

edited Nov 21 '18 at 19:28

answered Nov 21 '18 at 17:03

Csd

29819

1

The code wouldn't work for me. Is there something missing in the mean command?
– Jack Handy
Nov 21 '18 at 17:47

Yes and sorry for that. There was a type but I have edited my answer and test again.
– Csd
Nov 21 '18 at 17:54

Great...thanks! I found two other typos: in the list statement, it should be mat1=mat1 (which is why the mat1 and mat2 result values were identical in your example), and there's a right parentheses missing after the mean command (there should be 3). I'm not sure what the speed difference is between the two methods with this simple example, but I'll see what happens with my larger matrices.
– Jack Handy
Nov 21 '18 at 19:00

Thanks for you comments. I have edit again the answer. Yes, my answer is more for the speed part because you might need it if your matrices are large. Especially for var which might crash for large matrices.
– Csd
Nov 21 '18 at 19:33

add a comment |

If you want speed you can try my way. I will use package Rfast. Since you are new in R I will show step by step my example.

install.packages("Rfast") # download package Rfast

I will use the matrices from your example.

mats <- list(mat1=mat1,mat2=mat2,mat3=mat3) # "mat=mat" store matrix mat and add name

result <- sapply(mats,function(x){

             s <- Rfast::colsums(x) # you can use also the parallel version using argument "parallel"

             c(sd=Rfast::Var(s,std=TRUE),mean=mean(s))

          })



result # this will print the matrix    

      mat1      mat2      mat3

sd    1.677669  1.677669  1.572029

mean 25.674519 25.674519 24.319328

You can transpose the matrix to get the result as you wish

t(result)

           sd     mean

mat1 1.677669 25.67452

mat2 1.677669 25.67452

mat3 1.572029 24.31933

edited Nov 21 '18 at 19:28

answered Nov 21 '18 at 17:03

Csd

29819

If you want speed you can try my way. I will use package Rfast. Since you are new in R I will show step by step my example.

install.packages("Rfast") # download package Rfast

I will use the matrices from your example.

mats <- list(mat1=mat1,mat2=mat2,mat3=mat3) # "mat=mat" store matrix mat and add name

result <- sapply(mats,function(x){

             s <- Rfast::colsums(x) # you can use also the parallel version using argument "parallel"

             c(sd=Rfast::Var(s,std=TRUE),mean=mean(s))

          })



result # this will print the matrix    

      mat1      mat2      mat3

sd    1.677669  1.677669  1.572029

mean 25.674519 25.674519 24.319328

You can transpose the matrix to get the result as you wish

t(result)

           sd     mean

mat1 1.677669 25.67452

mat2 1.677669 25.67452

mat3 1.572029 24.31933

edited Nov 21 '18 at 19:28

answered Nov 21 '18 at 17:03

Csd

29819

edited Nov 21 '18 at 19:28

answered Nov 21 '18 at 17:03

Csd

29819

answered Nov 21 '18 at 17:03

Csd

29819

answered Nov 21 '18 at 17:03

Csd

29819

1

The code wouldn't work for me. Is there something missing in the mean command?
– Jack Handy
Nov 21 '18 at 17:47

Yes and sorry for that. There was a type but I have edited my answer and test again.
– Csd
Nov 21 '18 at 17:54

Great...thanks! I found two other typos: in the list statement, it should be mat1=mat1 (which is why the mat1 and mat2 result values were identical in your example), and there's a right parentheses missing after the mean command (there should be 3). I'm not sure what the speed difference is between the two methods with this simple example, but I'll see what happens with my larger matrices.
– Jack Handy
Nov 21 '18 at 19:00

Thanks for you comments. I have edit again the answer. Yes, my answer is more for the speed part because you might need it if your matrices are large. Especially for var which might crash for large matrices.
– Csd
Nov 21 '18 at 19:33

add a comment |

1

The code wouldn't work for me. Is there something missing in the mean command?
– Jack Handy
Nov 21 '18 at 17:47

Yes and sorry for that. There was a type but I have edited my answer and test again.
– Csd
Nov 21 '18 at 17:54

Great...thanks! I found two other typos: in the list statement, it should be mat1=mat1 (which is why the mat1 and mat2 result values were identical in your example), and there's a right parentheses missing after the mean command (there should be 3). I'm not sure what the speed difference is between the two methods with this simple example, but I'll see what happens with my larger matrices.
– Jack Handy
Nov 21 '18 at 19:00

Thanks for you comments. I have edit again the answer. Yes, my answer is more for the speed part because you might need it if your matrices are large. Especially for var which might crash for large matrices.
– Csd
Nov 21 '18 at 19:33

The code wouldn't work for me. Is there something missing in the mean command?
– Jack Handy
Nov 21 '18 at 17:47

Yes and sorry for that. There was a type but I have edited my answer and test again.
– Csd
Nov 21 '18 at 17:54

Great...thanks! I found two other typos: in the list statement, it should be mat1=mat1 (which is why the mat1 and mat2 result values were identical in your example), and there's a right parentheses missing after the mean command (there should be 3). I'm not sure what the speed difference is between the two methods with this simple example, but I'll see what happens with my larger matrices.
– Jack Handy
Nov 21 '18 at 19:00

Thanks for you comments. I have edit again the answer. Yes, my answer is more for the speed part because you might need it if your matrices are large. Especially for var which might crash for large matrices.
– Csd
Nov 21 '18 at 19:33

add a comment |

draft saved

draft discarded

Thanks for contributing an answer to Stack Overflow!

Please be sure to answer the question. Provide details and share your research!

But avoid …

Asking for help, clarification, or responding to other answers.

Making statements based on opinion; back them up with references or personal experience.

To learn more, see our tips on writing great answers.

Some of your past answers have not been well-received, and you're in danger of being blocked from answering.

Please pay close attention to the following guidance:

Please be sure to answer the question. Provide details and share your research!

But avoid …

Asking for help, clarification, or responding to other answers.

Making statements based on opinion; back them up with references or personal experience.

To learn more, see our tips on writing great answers.

draft saved

draft discarded

Sign up or log in

StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});

Post as a guest

Name

Required, but never shown

Post as a guest

Name

Required, but never shown

Sign up or log in

StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});

Post as a guest

Name

Required, but never shown

Sign up or log in

StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});

Post as a guest

Name

Required, but never shown

Sign up or log in

StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});

Post as a guest

Name

Required, but never shown

Name

Required, but never shown

Name

Required, but never shown

This page is only for reference, If you need detailed information, please check here

搜尋此網誌

Ytukyg