search for: dimentions

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,

Dear Dr. Liaw Andy: I have a few more questions about your heatmap function. actually heatmap is what I am looking for. heatmap(x, Rowv, Colv, distfun = dist, hclustfun = hclust, add.expr, scale=c("row", "column", "none"), na.rm = TRUE, ...) my data is a XNEW, > dim(XNEW) [1] 554 335 554 genes, 335 samples. now I want to use 1-CORR as a distance

...,] 1 1 1 [9,] 1 1 1 > apply(a,2, mean) [1] 11 14 17 > apply(a,2, sd) [,1] [,2] [,3] [1,] 1 1 1 [2,] 1 1 1 [3,] 1 1 1 I could not figure out from the documentation how MARGIN argument of function "apply" works in case of arrays with dimentions larger than 2, so I created the above test code. I still do not know how it suppose to work but I should not get the results with different dimentions, while calculating var and sd. Hope this helps, Jarek

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,

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],

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))

Dear R users I have the matrix of the centres of some clusters, e.g. 20 clusters each with 100 dimentions, so this matrix contains 20 rows * 100 columns numeric values. I have collected new data (each with 100 numeric values) and would like to keep the above 20 centres fixed/'unmoved' whilst just see how my new data fit in this grouping system, e.g. if the data is close to cluster 1 than lable...

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]]

...ot;M" does not match for the original and reduced form. I need to get answer of the following questions: 1. Is there any fault in the "for" loop in the Reduced form? 2. To get exactly same result M in the next run, where should i put set.seed()? 3. How to plot such plots in R having dimentions G, K and vectorized M? In matlab, mesh(K,G,M) does the trick. scatterplot3d(K,G,M) gives me error message. Thank you for your time. Thanks in advance. Mohammad Ehsanul Karim wildscop at yahoo dot com Institute of Statistical Research and Training University of Dhaka ###########################...

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 Wash...

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

Art (and group), I'm doing this as a form of missing value analysis. Approximately 30% of the cases are missing data for one variable. To impute values for those cases, I'd like to match those cases that are missing the variable to all other cases and then take an average of those to infill. I realize there are many methods for imputing data. I'm not well versed on any in

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

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

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