Re: [R] expand a matrix
You can specify multiple indexes to replace at once, so you can avoid
a for loop entirely like this:
M <- matrix(nrow = 10, ncol = 10)
M[1:4, 5: 6] <- M[5: 6, 1:4] <- m[1, 2]
M[1:4,7] <- M[ 7, 1:4] <- m[1, 3]
M[1:4, 8:10] <- M[8:10, 1:4] <- m[1, 4]
M[5:6,7] <- M[ 7, 5:6] <- m[2, 3]
M[5:6, 8:10] <- M[8:10, 5:6] <- m[2, 4]
M[ 7, 8:10] <- M[8:10, 7] <- m[3, 4]
M
You could also specify it directly with function 'matrix', it is
somewhat ugly though:
M <- matrix(c(
NA, NA, NA, NA, m[1,2], m[1,2], m[1,3], m[1,4],
m[1,4], m[1,4],
NA, NA, NA, NA, m[1,2], m[1,2], m[1,3], m[1,4],
m[1,4], m[1,4],
NA, NA, NA, NA, m[1,2], m[1,2], m[1,3], m[1,4],
m[1,4], m[1,4],
NA, NA, NA, NA, m[1,2], m[1,2], m[1,3], m[1,4],
m[1,4], m[1,4],
m[1,2], m[1,2], m[1,2], m[1,2], NA, NA, m[2,3], m[2,4],
m[2,4], m[2,4],
m[1,2], m[1,2], m[1,2], m[1,2], NA, NA, m[2,3], m[2,4],
m[2,4], m[2,4],
m[1,3], m[1,3], m[1,3], m[1,3], m[2,3], m[2,3], NA, m[3,4],
m[3,4], m[3,4],
m[1,4], m[1,4], m[1,4], m[1,4], m[2,4], m[2,4], m[3,4], NA,
NA, NA,
m[1,4], m[1,4], m[1,4], m[1,4], m[2,4], m[2,4], m[3,4], NA,
NA, NA,
m[1,4], m[1,4], m[1,4], m[1,4], m[2,4], m[2,4], m[3,4], NA,
NA, NA
), nrow = 10, ncol = 10, byrow = TRUE)
M
If it interests you, you could take a look at package 'Matrix', it
contains classes for specific types of matrices, including upper
triangular matrices. This package helps to improve speed of linear
algebra computations and reduce the memory size of matrices.
On Mon, Sep 5, 2022 at 10:46 PM Jinsong Zhao wrote:
>
> Hi there,
>
> I have a matrix likes:
> > m
> [,1] [,2] [,3] [,4]
> [1,] NA123
> [2,]1 NA65
> [3,]26 NA4
> [4,]354 NA
>
> I hope to expand it to 10 by 10 matrix, M, likes:
> > M
>[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
> [1,] NA NA NA NA11233 3
> [2,] NA NA NA NA11233 3
> [3,] NA NA NA NA11233 3
> [4,] NA NA NA NA11233 3
> [5,]1111 NA NA655 5
> [6,]1111 NA NA655 5
> [7,]222266 NA44 4
> [8,]3333554 NA NANA
> [9,]3333554 NA NANA
> [10,]3333554 NA NANA
>
> I use the following code:
>
> M <- matrix(NA, 10, 10)
>
> for (i in 1:10) {
> for (j in 1:10) {
>if (i %in% 1:4 & j %in% 5:6) M[i,j] <- M[j,i] <- m[1,2]
>if (i %in% 1:4 & j == 7) M[i,j] <- M[j,i] <-m[1,3]
>if (i %in% 1:4 & j %in% 8:10) M[i,j] <- M[j,i] <-m[1,4]
>if (i %in% 5:6 & j == 7) M[i,j] <- M[j,i] <-m[2,3]
>if (i %in% 5:6 & j %in% 8:10) M[i,j] <- M[j,i] <-m[2,4]
>if (i == 7 & j %in% 8:10) M[i,j] <- M[j,i] <-m[3,4]
> }
> }
>
> Is there any convenience way to do it? Thanks!
>
> Best,
> Jinsong
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
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> and provide commented, minimal, self-contained, reproducible code.
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[R] expand a matrix
Hi there,
I have a matrix likes:
> m
[,1] [,2] [,3] [,4]
[1,] NA123
[2,]1 NA65
[3,]26 NA4
[4,]354 NA
I hope to expand it to 10 by 10 matrix, M, likes:
> M
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] NA NA NA NA11233 3
[2,] NA NA NA NA11233 3
[3,] NA NA NA NA11233 3
[4,] NA NA NA NA11233 3
[5,]1111 NA NA655 5
[6,]1111 NA NA655 5
[7,]222266 NA44 4
[8,]3333554 NA NANA
[9,]3333554 NA NANA
[10,]3333554 NA NANA
I use the following code:
M <- matrix(NA, 10, 10)
for (i in 1:10) {
for (j in 1:10) {
if (i %in% 1:4 & j %in% 5:6) M[i,j] <- M[j,i] <- m[1,2]
if (i %in% 1:4 & j == 7) M[i,j] <- M[j,i] <-m[1,3]
if (i %in% 1:4 & j %in% 8:10) M[i,j] <- M[j,i] <-m[1,4]
if (i %in% 5:6 & j == 7) M[i,j] <- M[j,i] <-m[2,3]
if (i %in% 5:6 & j %in% 8:10) M[i,j] <- M[j,i] <-m[2,4]
if (i == 7 & j %in% 8:10) M[i,j] <- M[j,i] <-m[3,4]
}
}
Is there any convenience way to do it? Thanks!
Best,
Jinsong
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Re: [R] Clustering of datasets
Hello,
I am not at all sure that the following answers the question.
The code below ries to find the optimal number of clusters. One of the
changes I have made to your call to kmeans is to subset DMs not dropping
the dim attribute.
library(cluster)
max_clust <- 10
wss <- numeric(max_clust)
for(k in 1:max_clust) {
km <- kmeans(DMs[,2], centers = k, nstart = 25)
wss[k] <- km$tot.withinss
}
plot(wss, type = "b")
dm <- DMs[, 2, drop = FALSE]
# Where is the elbow, at 2 or at 4?
factoextra::fviz_nbclust(dm, kmeans, method = "wss")
factoextra::fviz_nbclust(dm, kmeans, method = "silhouette")
k2 <- kmeans(dm, centers = 2, nstart = 25)
k3 <- kmeans(dm, centers = 3, nstart = 25)
k4 <- kmeans(dm, centers = 4, nstart = 25)
main2 <- paste(length(k2$centers), "clusters")
main3 <- paste(length(k3$centers), "clusters")
main4 <- paste(length(k4$centers), "clusters")
old_par <- par(mfcol = c(1, 3))
plot(DMs[,2], col = k2$cluster, pch = 19, main = main2)
plot(DMs[,2], col = k3$cluster, pch = 19, main = main3)
plot(DMs[,2], col = k4$cluster, pch = 19, main = main4)
par(old_par)
Hope this helps,
Rui Barradas
Às 12:31 de 05/09/2022, Subhamitra Patra escreveu:
Dear all,
I am about to cluster my datasets by using K-mean clustering techniques in
R, but getting some type of scattered results. Herewith I pasted my code
below. Please suggest to me where I am lacking in my code. I was pasting my
data before applying the K-mean method as follows.
DMs<-read.table(text="Country DATA
IS -0.0092
BA -0.0235
HK -0.0239
JA -0.0333
KU -0.0022
OM -0.0963
QA -0.0706
SK -0.0322
SA -0.1233
SI -0.0141
TA -0.0142
UAE -0.0656
AUS -0.0230
BEL -0.0006
CYP -0.0085
CR -0.0398
DEN -0.0423
EST -0.0604
FIN -0.0227
FRA -0.0085
GER -0.0272
GrE -0.3519
ICE -0.0210
IRE -0.0057
LAT -0.0595
LITH -0.0451
LUXE -0.0023
MAL -0.0351
NETH -0.0048
NOR -0.0495
POL -0.0081
PORT -0.0044
SLOVA -0.1210
SLOVE -0.0031
SPA -0.0213
SWE -0.0106
SWIT -0.0152
UK -0.0030
HUNG -0.0086
CAN -0.0144
CHIL -0.0078
USA -0.0042
BERM -0.0035
AUST -0.0211
NEWZ -0.0538" ,
header = TRUE,stringsAsFactors=FALSE)
library(cluster)
k1<-kmeans(DMs[,2],centers=2,nstart=25)
plot(DMs[,2],col=k1$cluster,pch=19,xlim=c(1,46), ylim=c(-0.12,0))
text(1:46,DMs[,2],DMs[,1],col=k1$cluster)
legend(10,1,c("cluster 1: Highly Integrated","cluster 2: Less Integrated"),
col=1:2,pch=19)
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Re: [R] Clustering of datasets
Hi Subhamitra,
I think the fact that you are passing a vector of values rather than a
matrix is part of the problem. As you have only one value for each
country, The points plotted will be the index on the x-axis and the
value for each country on the y-axis. Passing a value for ylim= means
that you are cutting off the lowest points. Here is an example that
will give you two clusters and show the values for the centers in the
middle of the plot. Perhaps this is all you need, but I suspect there
is more work to be done.
k2<-kmeans(DMs[,2],centers=2)
plot(DMs[,2],col=k2$cluster,pch=19,xlim=c(1,46))
text(1:46,DMs[,2],DMs[,1],col=k2$cluster)
points(rep(23,2),k2$centers,pch=1:2,cex=2,col=k2$cluster)
legend(10,1,c("cluster 1: Highly Integrated","cluster 2: Less Integrated"),
col=1:2,pch=19)
Jim
On Mon, Sep 5, 2022 at 9:31 PM Subhamitra Patra
wrote:
>
> Dear all,
>
> I am about to cluster my datasets by using K-mean clustering techniques in
> R, but getting some type of scattered results. Herewith I pasted my code
> below. Please suggest to me where I am lacking in my code. I was pasting my
> data before applying the K-mean method as follows.
>
> DMs<-read.table(text="Country DATA
> IS -0.0092
> BA -0.0235
> HK -0.0239
> JA -0.0333
> KU -0.0022
> OM -0.0963
> QA -0.0706
> SK -0.0322
> SA -0.1233
> SI -0.0141
> TA -0.0142
> UAE -0.0656
> AUS -0.0230
> BEL -0.0006
> CYP -0.0085
> CR -0.0398
> DEN -0.0423
> EST -0.0604
> FIN -0.0227
> FRA -0.0085
> GER -0.0272
> GrE -0.3519
> ICE -0.0210
> IRE -0.0057
> LAT -0.0595
> LITH -0.0451
> LUXE -0.0023
> MAL -0.0351
> NETH -0.0048
> NOR -0.0495
> POL -0.0081
> PORT -0.0044
> SLOVA -0.1210
> SLOVE -0.0031
> SPA -0.0213
> SWE -0.0106
> SWIT -0.0152
> UK -0.0030
> HUNG -0.0086
> CAN -0.0144
> CHIL -0.0078
> USA -0.0042
> BERM -0.0035
> AUST -0.0211
> NEWZ -0.0538" ,
> header = TRUE,stringsAsFactors=FALSE)
> library(cluster)
> k1<-kmeans(DMs[,2],centers=2,nstart=25)
> plot(DMs[,2],col=k1$cluster,pch=19,xlim=c(1,46), ylim=c(-0.12,0))
> text(1:46,DMs[,2],DMs[,1],col=k1$cluster)
> legend(10,1,c("cluster 1: Highly Integrated","cluster 2: Less Integrated"),
> col=1:2,pch=19)
>
>
> --
> *Best Regards,*
> *Subhamitra Patra*
> *Phd. Research Scholar*
> *Department of Humanities and Social Sciences*
> *Indian Institute of Technology, Kharagpur*
> *INDIA*
>
> [image: Mailtrack]
>
> Sender
> notified by
> Mailtrack
>
> 09/05/22,
> 04:55:22 PM
>
> [[alternative HTML version deleted]]
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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[R] How to use AUC metric in caret
Hello everyone
I am using nested resampling in caret (5-fold outer and bootstrap inner
resampling) and by default, it shows the "Accuracy" metric. How can I use
it for the ROC/AUC metric?
My code is:
d=readARFF("apns.arff")
index <- createDataPartition(d$isKilled , p = .70,list = FALSE)
tr <- d[index, ]
ts <- d[-index, ]
boot <- trainControl(method = "boot", number=100, search="random",
classProbs = TRUE, summaryFunction = twoClassSummary)
outer_folds <- createFolds(d$isKilled, k = 5)
boot <- trainControl(method = "boot", number=10)
CV1 <- lapply(outer_folds, function(index){
tr <- d[-index, ]
ts <- d[index,]
cart1 <-train(isKilled ~ ., data = tr,
method = "rpart",
tuneLength = 20,
metric = "Accuracy",
preProc = c("center", "scale", "nzv"),
trControl = boot)
postResample(predict(cart1, ts), ts$isKilled)
})
sapply(CV1, function(x) x[3]) -> CV_MAE1
CV_MAE1
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