Dear group,
In pheatmap, is it possible to cluster select colmns.
For example, in a matrix of 10 rows and 20 columns. I want to cluster
only those columns 1-10 and cluster rows. Similarly, retaining the
same row clustering resulted from clustering of columns 1-10, I want
to enforce clustering of
Hello,
If I understand correctly, here are two different ways of doing what you
want.
1) base R.
i <- duplicated(df1[[2]])
j <- duplicated(df1[[2]], fromLast = TRUE)
res <- df1[i | j, ]
res[order(res[[2]]), ]# not strictly needed
2) with package dplyr. If you do not want to order by
Do you mean like this?
tmp <- data.frame(v1 = c(1,2,1,4,2), v2 = c(10, 11, 10, 14, 11))
vals <- paste(tmp$v1, tmp$v2, sep ='')
tmp[which(vals %in% vals[duplicated(vals)]),]
On 3/8/19, 12:51 PM, "Silvano Cesar da Costa" wrote:
>Hi,
>
>I have a dataset with ten columns, but I need extract only
This request is quite unclear. Can you make a reproducible example [1][2][3]
and provide data that represents the expected result corresponding to the input
data? The dput function is much much better than tabular form for this purpose.
For one thing, you mention ten columns, but they are not
Hi,
I have a dataset with ten columns, but I need extract only lines that has
common elements.
The columns are:
Animal Mother
[1,] 1143430
[2,] 1144134
[3,] 1146 3
[4,] 1147151
[5,] 1150230
[6,] 1156290
[7,] 1157227
[8,] 1159757
Buenas tardes erreros, tras aplicar randomForest, aplico partialPlots
¿sabéis como estandarizar los PDPs de las distintas variables (para
poder comparar sus efectos), o que me los represente para un mismo
rango de y? Me parece recordar haberlo visto, pero en la documentacion
de
I'm no expert in R internals or floating point computation, however, two things
come to mind.
First, I suspect the exact value is stored. It is just the printing that looks
rounded. That is likely because 0.001 completely dominates the rest. To print
in full precision, you would need over 200
"Is there any way ..."
Two quick answers:
1. using base R functions and data types the answer is 'no' - a double
(i.e. numeric) contains about 15 significant digits.
So 5.678e-100 is fine but 0.01 + 5.678e-100 will keep the
.0100 as the significant digits and "drop" the digits
Aqui adjunto el código que finalmente funcionó, por si alguien pudiera
necesitarlo en el futuro en la lista
func1 <- function(x){
ifelse(x %in% ca, "Central Africa", ifelse(x %in% ea,"East Africa",
ifelse(x%in% sa, "South Africa", ifelse(x%in% wa, "West Africa",
ifelse(x%in% na, "North
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