similar to: How to perform clustering without removing rows where NA is present in R

I am having difficulty getting the dynamic tree cut package to work. Given the data table "myddtable" LengthPlaceColorAge5HRed224ABlue205WGreen243GRed222GBlue236WGreen255ARed194H Blue23 I created a similarity matrix using DAISY and Gower metric and specified Place and Color columns as characters (since they are categorical variables) > dd.daisy<-daisy(myddtable, metric =

Hello, I am running DAISY in R and using the GOWER metric since I am working with mixed variables. I am wondering if there is a way to weight the different variables. I see that there is a weight value for Gower but do not know if this is how to weight the diffrent variables with different weighting values. Please advise if there is a way to weight the different variables. Thank you. -- View

Hi All - I am using the daisy function from the cluster library to create a dissimilarity matrix. I'm going to use that matrix to run a cluster analysis. My participants are identified with the variable, hhid. However, when I try to keep hhid in the dataset that I use to create the dissimilarity matrix, daisy uses it to create the matrix rather than ignoring it as an ID variable. I need to

Hi, I'm trying to create a distance matrix. And it works out somewhat ok. However, I suspect that there are some efficiency issues with my efforts. Plz have a look at this: donor <- matrix(c(3,1,2,3,3,1,4,3,5,1,3,2), ncol=4) receiver <- matrix(c(1,4,3,2,4,3,1,5,1,3,2,1,4,5,3,5,1,3,2,4,5,1,2,3,1,4,5,5,1,2,1,3,4,3,2,5,5,1,4,2,5,4,3,2), ncol=4) The above creates my two matrices. I have

Hi, According to daisy function from cluster documentation, it can compute dissimilarity when NA (missing) value(s) is present. http://stat.ethz.ch/R-manual/R-devel/library/cluster/html/daisy.html But why when I tried this code library(cluster) x <- c(1.115,NA,NA,0.971,NA) y <- c(NA,1.006,NA,NA,0.645) df <- as.data.frame(rbind(x,y)) daisy(df,metric="gower") It gave this

Hi, Could you please help? Heatmap doesn't work with: > heatmap(as.matrix(SPIV2),na.rm = T) Error in hclustfun(distfun(x)) : NA/NaN/Inf in foreign function call (arg 11) There are no 0 data rows or column Thanks a lot Regards --------------------------------------- David

Hello Everyone, Since rebooting my Centos 6.10 Openvz server "daisy" yesterday, I am getting horrible system performance.? /var/log/messages is full of HDIO_GET_IDENTITY failed for /dev/sdb.? The latest entries look like this: Apr 22 08:51:32 daisy kernel: [141224.655699] CT: 1005: stopped Apr 22 08:55:04 daisy ata_id[21513]: HDIO_GET_IDENTITY failed for '/dev/sdb' Apr 22

>> Hello Everyone, >> >> Since rebooting my Centos 6.10 Openvz server "daisy" yesterday, I am >> getting horrible system performance.? /var/log/messages is full of >> HDIO_GET_IDENTITY failed for /dev/sdb.? The latest entries look like >> this: >> >> Apr 22 08:51:32 daisy kernel: [141224.655699] CT: 1005: stopped >> Apr 22 08:55:04

Hi List, I am looking for a variable selection procedure with a forward-backward selection method. Firstly, it is meant to work with the cophenetic correlation coefficient (CPCC) and intended to find the variable combination with the highest cophenetic correlation. Secondly, it is aimed at Gower metric with wards method (though this could be easily extended) aimed at categorical data. What I

Hi the list, I need to compute Gower distance between a specific individual and all the other individual. The function DAISY from package cluster compute all the pairwise dissimilarities of a population. If the population is N individuals, that is arround N^2 distances to compute. I need to compute the distance between a specific individual and all the other individual, that is only N

Hi, I'm trying to use a similarity matrix (triangular) as input for pam() or fanny() clustering algorithms. The problem is that this algorithms can only accept a dissimilarity matrix, normally generated by daisy(). However, daisy only accept 'data matrix or dataframe. Dissimilarities will be computed between the rows of x'. Is there any way to say to that your data are already a

Hello, I would like to use my own dissimilarity matrix in a PAM clustering with method "pam" (cluster package) instead of a dissimilarity matrix created by daisy. I read data from a file containing the dissimilarity values using "read.csv". This creates a matrix (alternatively: an array or vector) which is not accepted by "pam": A call

Dear all; I need to run heatmap. Because my first column in my data is alphanumeric, I can not run as.matrix(scale(my_data)). So I need to make my data readable as in data(mtcars). In *mtcars *data the first column is alphanumeric and has no name. Thanks, Greg [[alternative HTML version deleted]]

I am trying to plot a graph but the points on the graph should be different symbols and colors. It should represent what is in the legend. I tried using the points command but this does not work. Is there another command in R that would allow me to use different symbols and colors for the points? Thank you kindly. data(mtcars) plot(mtcars$wt,mtcars$mpg,xlab= "Weight(lbs/1000)",

I am looking for ways to add points to three different plots in parallel. I generate three scatter plots and name them as s3d1, s3d2 and s3d3 s3d1<-scatterplot3d(mtcars[,3],mtcars[,4],mtcars[,5],main="common",pch=20) s3d2<-scatterplot3d(mtcars[,3],mtcars[,4],mtcars[,5],main="common",pch=20)

I'm sure this exists elsewhere, but, as a trade-off, could you achieve what you want with a separate helper function F(expr) that constructs the function you want to pass to [lsv]apply()? Something that would allow you to write: sapply(split(mtcars, mtcars$cyl), F(summary(lm(mpg ~ wt,.))$r.squared)) Such an F() function would apply elsewhere too. /Henrik On Thu, Apr 16, 2020 at 9:30 AM

Hello, When plotting a barchart with ggplot it drops the levels of the factor for which no counts are available. For example: library(ggplot) mtcars$cyl<-factor(mtcars$cyl) ggplot(mtcars[!mtcars$cyl==4,], aes(cyl))+geom_bar() levels(mtcars[!mtcars$cyl==4,]) This shows my problem. Because no counts are available for factorlevel '4', the label 4 dissapears from the plot. However, I

Dear list, I want the 1st, 2nd, 5th, and 6th columns of mtcars. After copying them, the columns become numeric class rather than data frame. But, when I copy rows, they data frame retains its class. Why is this? I don't see why copying rows vs columns is so different. > class(mtcars) [1] "data.frame" > head(mtcars) mpg cyl disp hp drat wt qsec vs

data(mtcars) mtcars[rownames(mtcars)!="Valiant",] # fails mtcars[list(rownames(mtcars))!="Valiant",] # runs but I am not getting the expected result With the latter statement, I expected all rows except the one where the name is "Valiant". I must have got something simple wrong; what is it? Thanks. [[alternative HTML version deleted]]

http://bugs.freedesktop.org/show_bug.cgi?id=10933 Summary: nouveau defaults to first graphics card in system even when xorg instructed differently Product: xorg Version: 7.2 Platform: Other OS/Version: Linux (All) Status: NEW Severity: normal Priority: medium Component: Driver/nouveau