similar to: k-means: should columns in dataset be in same scale?

Hi, I'm having a mental block as to how I can automatically assign filenames to the output of the following code. I am wishing to create a separate .png file for every image created, each of them having a sequential filename ie "sourcefile_index.png" so that I can create a movie from them. Please could someone tell me where I am going wrong? the following code works fine and

--rwEMma7ioTxnRzrJ Content-Type: text/plain; charset=us-ascii Content-Disposition: inline R, like recent versions of S-Plus, sometimes - but not always - loses names when subscripting objects with "[". (Earlier versions of S and S-Plus had the correct, name-preserving behavior.) This seems bad, it would be better to remove names only by explicit request, not as an accidental

Hi, I need to run a PCoA (PCO) for a data set wich has both positive and negative values for variables. I  could not find any distancecoefficient other than euclidean distace running for the data set. Are there any other coefficient works with negtive values.Also I cannot get summary out put (the eigen values) for PCO as for PCA.   Thanks. Dilshan [[alternative HTML version deleted]]

Patrick, ## Run the following script an notice the different values of the dataframe "data" in each instance. # I understand you have done something like the following: data <- data.frame(COL1 = c("6/1/14", "7/1/14"), COL2 = c("5/1/15", "5/1/15"), stringsAsFactors = FALSE) data$Date_Flag <- ifelse(data$COL2 >

If I perform PCA on the 'eurodist' data, should I get an accurate geographic layout of the cities with biplot? (barring inversions, i.e. their is no way to define north.. but you get the idea...) I have a complex distance matrix, and I am thinking about how to cluster it and how to visualize the quality of the resulting clusters. If I could 'see' the clusters in space I could

I have a dataset of 3 ?columns? and 5 ?rows?. temp<-data.frame(col1=c(5,10,14,56,7),col2=c(4,2,8,3,34),col3=c(28,4,52,34,67)) I wish to convert this to a single ?column?, with column 1 on ?top? and column 3 on ?bottom?. i.e. 5 10 14 56 7 4 2 8 3 34 28 4 52 34 67 Are there any functions that do this, and that will work well on much larger datasets (e.g. 1000 rows, 6000 columns)?

> On Jun 21, 2017, at 9:11 AM, Evan Cooch <evan.cooch at gmail.com> wrote: > > Suppose I have the following sort of dataframe, where each column name has a common structure: prefix, followed by a number (for this example, col1, col2, col3 and col4): > > d = data.frame( col1=runif(10), col2=runif(10), col3=runif(10),col4=runif(10)) > > What I haven't been able to

I have a data frame like this: col1 col2 r1 2 1 r2 4 3 r3 6 5 r4 8 7 r5 10 9 r6 12 11 r7 14 13 r8 16 15 r9 18 17 r10 20 19 I want to modify this data frame, for example, assign every row in column col1 and col2 to -1 if the values in col1 is less than 12 and values in col2 is greater than 10. The result should look like this: col1

Hi: Can anyone advice me on how to loop and perform a calculation through all the columns. here's my data xd<- c(2.2024,2.4216,1.4672,1.4817,1.4957,1.4431,1.5676) pd<- c(0.017046,0.018504,0.012157,0.012253,0.012348,0.011997,0.012825) td<- c(160524,163565,143973,111956,89677,95269,81558) mydf<-data.frame(xd,pd,td) trans<-t(mydf) trans I have these values that I need to

In applying multidimensional scaling, it seems to me that sometimes the underlying dimensionality of the matrix is 1. However I found a case where cmdscale failed when I tried k=1. Here it is: m<-matrix( c(.5,.81,.23,.47,.61, .19,.5,.06,.17,.28, .77,.94,.5,.74,.85, .53,.83,.26,.5,.64, .39,.72,.15,.36,.5), nrow=5) # BTW I think cmdscale uses only the lower triangle--how to enter only # that

Suppose I have the following sort of dataframe, where each column name has a common structure: prefix, followed by a number (for this example, col1, col2, col3 and col4): d = data.frame( col1=runif(10), col2=runif(10), col3=runif(10),col4=runif(10)) What I haven't been able to suss out is how to efficiently 'extract/manipulate/play with' columns from the data frame, making use

Hi your code is wrong. I get > test<-read.table("clipboard", header=T) > str(test) 'data.frame': 2 obs. of 2 variables: $ COL1: Factor w/ 2 levels "6/1/14","7/1/14": 1 2 $ COL2: Factor w/ 1 level "5/1/15": 1 1 > test$COL2<- as.Date(as.character(test$COL2, format="%y/%m/%d")) > test$COL1<-

Thanks. But when I apply your codes I get all NA instead of TRUE and FALSE ________________________________ From: PIKAL Petr <petr.pikal at precheza.cz> Sent: Wednesday, August 23, 2017 11:20:00 AM To: Patrick Casimir; r-help at r-project.org Subject: RE: Comparing 2 dale columns Hi your code is wrong. I get > test<-read.table("clipboard", header=T) > str(test)

http://r.789695.n4.nabble.com/file/n3031344/RSV.Rdata RSV.Rdata I want to split my dataset to training set and test set using kennard-stone(KS) algorithm, it is lucky there is R packages soil.spec to implement it. but when I used it to my dataset, it does not work, who can help me, how reasons is it, below, it is my code, and my data in the attachment.

Hi, I'm trying to build a thesaurus that will sensible values for rare words. I suspect the best algorithm to use is k-means although I'm not sure about that -- I would have preferred a k dimensional space with a binary cluster in each dimension so a word can belong to 0..k clusters, but I digress... I can measure the strength of correlation between words fairly easily by counting

Dear R fellows, I created a new column Date_flag to compare the dates of COL1 and COL2 using the code below. But it showed that 5/1/15 is greater than 6/1/2014 and 5/1/2015 greater than 7/1/2014 despite the year is greater. How do I fix that? I did try to format as %y/%m/%d but it does not fix that. data$Date_Flag <- ifelse(data$COL2 > data$COL1, 0,1) COL1 COL2 6/1/14

Dear all, I was looking for methods in R that allow assessing the number of significant principal coordinates. Unfortunatly I was not very successful. I expanded my search to the web and Current Contents, however, the information I found is very limited. Therefore, I tried to write code for doing a randomization. I would highly appriciate if somebody could comment on the following approach.

I have a dataframe with 3 columns. The first column stores an index. I would like to calculate the mean of the numbers stored in each of the rest of the columns. So, here is my data matrix: col1 col2 col3 1 23 34 2 45 56 3 23 56 4 34 68 For each row I would like to calculate the means of the numbers stored in col2 and col3. How can this be done in R? TIA, Anjan -- =============================

# I can not draw a 45 degree line on a qqnorm() plot, jj <- sample(c(1:100), 10) qqnorm(jj) abline() don't work. Thank you.

Hey Parth, Thanks for the reply. I am considering implementing a cosine distance metric too, along with euclidian distance because of the dimensionality issue that comes in with K-Means and euclidian distance metric. That does help when we deal with sparse vectors for documents. The particular problem I'm having is representing centroids in an efficient way. For example, when we find the mean