search for: dimentional

Suppose there are 4 variables d is a function of a , b and c I want to know how a, b and c change will make d change It will be straightforward to see it if we can graph the d surface if d is only a function of a and b, I can use 'persp' to see the surface of d. I can easily see at what values of a and b, d will get the maxium or minium or multiple modes, etc But for 4 dimention graph,

...is there any way for me to define these two in heatmap options? thanks. lp ----- Original Message ----- From: "Liaw, Andy" <andy_liaw at merck.com> To: "'liping'" <liping66 at hotmail.com> Sent: Monday, August 11, 2003 10:35 AM Subject: RE: [R] re: two dimentional hierarchical clustering algorithm > Check out the heatmap() function in the 'mva' package (came with R) and see > if that does what you want. > > Andy > > > -----Original Message----- > > From: liping [mailto:liping66 at hotmail.com] > > Sent: Monday, Augu...

Full_Name: jarek tuszynski Version: 1.8.1 OS: windows 2000 Submission from: (NULL) (198.151.13.10) Example code: > a=array(1:27, c(3,3,3)) > apply(a,2, var) [,1] [,2] [,3] [1,] 1 1 1 [2,] 1 1 1 [3,] 1 1 1 [4,] 1 1 1 [5,] 1 1 1 [6,] 1 1 1 [7,] 1 1 1 [8,] 1 1 1 [9,] 1 1 1 > apply(a,2, mean) [1]

Hi All. I have a mysql statement that I would really really like to call from my Ruby program which goes like this: SELECT a, b, DAYOFWEEK(date_time) as DOW, HOUR(date_time) at hr, AVG(x/y) FROM records; This is possible by creating a 3-dimentional array of a, b, date_time containing x/y, and then finding averages and putting it into a 4-dimensional array of a, b, dow, and hr. _3-dim_array_ a b date_time _4-dim_array_ a b dow hr My reasons for not wanting to do it this way is that there could be 100,000+ records. Also, look at ho...

I''ve been using file_column for a while and it''s the best thing since sliced bread. But there''s one thing that it doesn''t do that seems like it''d be a pretty useful feature. For an app I''m working on I really need to know the dimensions of the store image in my view (and in general it''s always good to have the dimensions in your

Hi: I have a large dataset mydata, of 1000 rows and 1000 columns. The rows have gene names and columns have condition names (cond1, cond2, cond3, etc). mydata<- read.table(file="c:/file1.mtx", header=TRUE, sep="") I applied PCA as follows: data_after_pca<- prcomp(mydata, retx=TRUE, center=TRUE, scale.=TRUE); Now i get 1000 PCs and i choose first three PCs and make a

Dear all R users, I am wondering if there is any way to define a 3 dimentional or 4 dimentional array in R. Sincerely yours, thanks in advance Send instant messages to your online friends http://in.messenger.yahoo.com Stay connected with your friends even when away from PC. Link: http://in.mobile.yahoo.com/new/messenger/ [[alternative HTML version deleted]...

Hi, I need to do several matrix multiplications with the corresponding matrices forming two 3-dimentional arrays. To illustrate my problem, let's say I have the following 3-dimensional arrays: array1 <- array(1:30,dim=c(3,2,5)) array2 <- array(1:20,dim=c(2,2,5)) I know that I can get what I want with the following computation : result <- array(dim=c(dim(array1)[1], dim(array2)[2], dim(...

It is frustrating to see the labels I want in the dimensions of a list but not be able to extract those labels into titles for plots generated from component objects. If someone could set me straight, I would appreciate it. For your amusement, I have provided an example of the Byzantine code I am currently using to avoid loops: # Simulate ANOVA type test data sex<-c(rep(1,8),rep(0,8))

...Centre) I wondered if there is other R packages out there can keep the centres fixed and lable each observations of my new data? Or I have to write my own function? To illustrate my task using a simpler example: I have Centre<- matrix(c(0,1,0,1), nrow=2) # the two created centres in a two dimentional case are Centre [,1] [,2] [1,] 0 0 [2,] 1 1 NewData<-rbind(matrix(rnorm(100, sd = 0.3), ncol = 2), matrix(rnorm(100, mean = 1, sd = 0.3), ncol = 2)) NewData1<-cbind(c1:100), NewData) colnames(NewData1)<-c("ID","x","y") # my dat...

I have three column vectors (X1, X2, X3).          X1  X2  X3        20   25  40        100 90  80 I want to put them as one matrix of dimention 2 by 3,  but remove headers(X1,X2,X3) from the matrix. I wrote as follows U<-cbind (X1,X2,X3) the headers are there. I need help please. Thanks Dereje [[alternative HTML version deleted]]

Dear List, This is just a programming problem which i cannot seem to figure out. I am trying to get a set of power from a test (say, kolmogorov smirnov) out of a distribution (say, G-K distribution) as follows. I am trying to reduce to pain of writing the whole set of data points (p# below) using "for" loop. However, I seem to have some problem in it as the output "M" does not

Hi friends in PHP there is a direct function GetImageSize(), with it I can easily determine the dimentions of an image file. How can I do in RoR. Please help me out Thanks in advance -------------- next part -------------- An HTML attachment was scrubbed... URL: http://wrath.rubyonrails.org/pipermail/rails/attachments/20060505/13ba1640/attachment.html

Hi, there. I have two matrix with identical dimentions. matrix A contains information of 0 and 1, and matrix B contains data. I only want the data in matrix B where matrix A has 1's. the places where matrix A has 0's, I want NA's in matrix B. How do I do that?? Thank you very much. Lei Jiang Department of Chemsitry University of Washington Box 351700 Seattle, WA 98195 Phone:

Hi, I deal with 3D array say: , , 1 [,1] [,2] [,3] [,4] [,5] [1,] 1 4 7 10 13 [2,] 2 5 8 11 14 [3,] 3 6 9 12 15 , , 2 [,1] [,2] [,3] [,4] [,5] [1,] 16 19 22 25 28 [2,] 17 20 23 26 29 [3,] 18 21 24 27 30 , , 3 [,1] [,2] [,3] [,4] [,5] [1,] 31 34 37 40 43 [2,] 32 35 38 41 44 [3,] 33 36

...verage of those to infill. I realize there are many methods for imputing data. I'm not well versed on any in particular (expect regression and cluster analysis). That said, given that I have an extensive data set already with most variables populated, I can find the closest observations in N-dimentional space and impute the value that way - by focusing on the best matches. If there are any other thoughts on how to do this (relatively easily), I'm open to suggestions and being educated. Thanks, Danny > From: Art Kendall <Art at DrKendall.org> > Date: 2004/02/13 Fri PM 02:47:00 E...

Hi, there: I am wondering if I can get some general help or source about canonical discriminant analysis in R. My idea is trying to linearly "combine" 300 variables supervisely (according to the class lables to the observations". I think it is kinda PCA to do some decreasing dimentionality work, but w/ considering the class and I used SAS to do CDA proc before. But I read the introduction from sas on this proc and found the following statement: "The process of extracting canonical variables can be repeated until the number of canonical variables equals the number of origina...

Dear List, Is there any function in R to generate multivariate F distribution with given correlation/covariance matrix? Actually, I just want to generate some 2-dimentional non-normal data sets (skewed) for low (may be around 0.3 cor coeff.) negatively and also positively correlated variables ? Thanks in advance. Anna

This should be simple but I am struggling. I like to easily switch in xyplot between a linear or logarithmic y-axis by setting a logical flag logY to False or True. This switch changes the scales argument of xyplot. I found out that the original two-dimentional data (Conc vs Time in my case) are converted to log10(Conc) if log=TRUE in scales, but it appears that functions like panel.curve need to provide the y values in log10 form (if there is an automatic method, I would like to know). I therefore like to pass on the value logY to my custom panel.curve f...

Dear R users: I'm trying to generate a output file with fixed format using function "sprintf" in R. However, the execution time in R is very long even the toy data (smaller size df) seems to work fine. The syntax that I used is as follows: df.fmt <- sprintf("%2s%2s%2.4f", df$v1, df$v2, df$v3) write.table(df.fmt, output.name,...) The actual dataset is a df with the