similar to: matrix weirdness

I think my problems are coused by a fundamental R incompatibility in how matrices are stored and the usual way of specifying Cartesian coordinates. When I do data<-read.table("~/r/rt/data/unif/6cbcif2d.out",header=TRUE) x<-unique(data$lag1) y<-unique(data$lag2) z<-matrix(data$cif2d,length(y),length(x)) This z matrix is printed apparently correctly from a Cartesian point of

I am using R 63.0. Now let's try this simple image plot. Here is the data file: ============================ lag1 lag2 cif2d 1 1 11 1 2 12 1 3 13 2 1 21 2 2 22 2 3 23 3 1 31 3 2 32 3 3 33 ==================== data<-read.table("~/r/rt/data/unif/junk.out",header=TRUE) x<-unique(data$lag1) y<-unique(data$lag2) z<-matrix(data$cif2d,length(y),length(x)) At this point, see

I include the argument pty="s" to image, but still when I make an image by the method > postscript("rstuff/test.ps") > tauseq<-seq(0,1,.5) > cif2d.image(x,,y,tauseq) > dev.off() the image plot created is NOT square. I thought pty="s" would make it square. Generally it would be nice if the image saved to disk were like the one we see while in R

I have a binary file(a) with size of (360 720 )for the globe.I wrote the code given below to read and extract an area (south america)from that file. when I use summary for the whole file I got: summary(a, na.rm=FALSE) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's 0.00 1.00 3.00 4.15 7.00 20.00 200083 . But when I used summary for the region(b) which I extracted, I got

My data is called xc and has more than 15 variables. When I used summary(xc) it gave me the detail description of each variable. Summary(xc) Y1 x1 x2 x3 .. Min. :0.0000 Min. : 1.000 Min. : 1.000 Min. : 1.000 1st Qu. :0.0000 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 2.000 Median :1.0000 Median : 1.000

I think you are not using the best function for what your intentions are. Try: > by(data=mtcars, INDICES=list(as.factor(mtcars$am)), FUN=colMeans) : 0 mpg cyl disp hp drat wt qsec vs 17.1473684 6.9473684 290.3789474 160.2631579 3.2863158 3.7688947 18.1831579 0.3684211 am gear carb 0.0000000

> On Apr 15, 2016, at 1:16 AM, Akhilesh Singh <akhileshsingh.igkv at gmail.com> wrote: > > Dear All, > > Thanks for your help. However, I would like to draw your attention to the > following: > > Actually, I was replicating the Example 2.3, using the dataset > "brainsize.txt" given in Section 2.3.3 ("Summarize by group") at page 55, > of a

I suspect the weights argument is not having any effect. Package: Matrix Version: 0.995-2 Date: 2006-01-19 Beginning with this: Browse[1]> resp.lmer <- lmer(SensSSC ~ Block + Season + (1 | Plot) + (1 | Ma) + (1 | Pa) + + (1 | MaPa), weights = SensSSC.N, data = xx) I group the output into a table with my ran.eff function and get this:

> On Apr 16, 2016, at 2:03 AM, Akhilesh Singh <akhileshsingh.igkv at gmail.com> wrote: > > Dear All, > > I have got your core message, that it is my responsibility to determine whether any particular function in my version of R satisfies the language requirements at the time of your use. Jim Albert and Maria Rizzo must have used their code, which was permitted in the R-code

Dear Sirs, I am Professor at Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh, India. While taking classes, I found the *by() *function producing following error when I use FUN=mean or median and some other functions, however, FUN=summary works. Given below is the output of the example I used on a built-in dataset "mtcars", along with error message reproduced herewith: >

Dear All, I have got your core message, that it is my responsibility to determine whether any particular function in my version of R satisfies the language requirements at the time of your use. Jim Albert and Maria Rizzo must have used their code, which was permitted in the R-code of their time (2012). Therefore, I have now modified my R-code, as per R-3..2.4 version, according to my requirement

Hi, I wanted to ask it on elli_dbergs''s thread ''problem with xm create'', but it seems that my message was never gonna be published (waited for 24 hours til i figured it was taking way too much time). So i''m working with Xen unstable, installed dom0 over Ubuntu 8.10, no problem so far apparently, reboot on Xen, then I go to /etc/xen to get some example config

When I load a table from a data source and run summary() on it, the summary gives me basic summary statistics I'm looking for, and it also discriminates between quantitative and qualitative data and summarizes them accordingly. For example, if I do this: mydata <- read.table("data.txt") summary(mydata) I would get output like this: > summary(mydata) County

Dear All, Thanks for your help. However, I would like to draw your attention to the following: Actually, I was replicating the Example 2.3, using the dataset "brainsize.txt" given in Section 2.3.3 ("Summarize by group") at page 55, of a famous book "R by Example" written by "Jim Albert and Maria Rizzo" published in Springers (2012) in a Use R! Series. The

I was unsure of what suitable goodness-of-fit tests existed in R for logistic regression. After searching the R-help archive I found that using the Design models and resid, could be used to calculate this as follows: d <- datadist(mydataframe) options(datadist = 'd') fit <- lrm(response ~ predictor1 + predictor2..., data=mydataframe, x =T, y=T) resid(fit, 'gof'). I set up a

Hi guys My data is Tasmania txt There are *N *= 16 samples, consisting of 8 replicate cores (taken from different areas across the sandflat) from each of 2 natural 'treatments' (either disturbed "D" or undisturbed "U" by soldier crab burrowing activity. The abundances of each of *p *= 56 species were recorded from each core (variables 1 to 39 in the file are

Hi, I've two questions. Please help me. Thanks! --- Question (1) --- When I type: print("School Name") the output is: [1] "School Name" How to get rid of the '[1]' and the double quotes "" and make the output like this: School Name --- Question (2) --- I got the following output by typing "summary(data)". dstuser dstmethod

Hi I have a data frame like this: V1 V2 V3 V4 Min. :0.01146 Min. :0.0006714 Min. :0.004912 Min. : 0 1st Qu.:0.03938 1st Qu.:0.0072805 1st Qu.:0.052719 1st Qu.:1150 Median :0.04224 Median :0.0077581 Median :0.056388 Median :1150 Mean :0.04010 Mean :0.0074669 Mean :0.052602 Mean :1173 3rd

I'm using R 1.8.1 on Red Hat Linux 9. I've worked out the linear model decomposition by hand. Using "aov" with "Error" to fit a split-plot model, I get very different results depending on whether I use the "data" argument for "aov" or not. When I use the "data" argument, the ANOVA table from "summary" is correct but the

--l4Siqd0eqV Content-Type: text/plain; charset=us-ascii Content-Transfer-Encoding: 7bit Hello, I run the following job. Please, compare the results of summary and table concerning berufl. From similar SPSS/PSPP runs, the result of table is correct. Did I misunderstand anything or is there a bug? What does the difference come from? What does '(other)' mean? What about the strange