search for: 0.217

Hello I have a data set like below: plate.id well.id Group HYB rlt1 1 P1 A1 Control SKOV3hyb 0.190 2 P1 A2 Control SKOV3hyb 0.210 3 P1 A3 Control SKOV3hyb 0.205 4 P1 A4 Control SKOV3hyb 0.206 5 P1 A5 Control SKOV3hyb 0.184 385 P1 A1 ovca SKOV3hyb 0.184 386 P1 A2 ovca SKOV3hyb 0.229 387

I am trying to investigate the interaction term in the below. The paradigm in aquatic systems is that algal production is either nitrogen (TIN) or Phosphorus limited, and I am trying to investigate this- what is the best way to go about investigating the interaction term. I have some thoughts on the above, but I will withhold them to see what others think. Thanks for your help. d <-

I've data that look like: Outcome V1_AA V1_EU V1_NA V2_AA V2_EU V2_NA 0 0.046 1.001 0.954 0.045 1.001 0.954 0 0.007 1 0.993 0.007 1 0.993 1 1.774 0.217 0.009 1.774 0.217 0.009 1 0.004 1.996 0 0.004 1.996 0 1

Dear Sirs: I've been working with several variables in a dataframe that serve as part of a calculation that I need to perform in a different way depending on its value. Let me explain: The main dataframe is called llmcc llmcc : 'data.frame': 283 obs. of 11 variables: $ Area : num 308.8 105.6 51.4 51.4 52.9 ... $ mFondo : num 30.1 10 10.2 10.2 40.4 ... $ mFachada :

Dear fellow R users, I can't figure out how to do a simple thing properly: apply an operation to matrix columns on a selected subset of rows. Things go wrong when only one row is being selected. I am sure there's a way to do this properly. Here's an example: # define a 3-by-4 matrix x > x <- matrix(runif(12),ncol=4) > str(x) num [1:3, 1:4] 0.568 0.217 0.309 0.859

I have pushed out klibc-1.0 and linked it into the Stable directory; it's functionally the same as 0.217. With klcc having been introduced and stabilized, it seemed to be time to give it a nonzero major number. -hpa

I have some data with two categorises plus/minus (p53) and a particular time (Time) and the outcome is a continuous vairable (Result). I set up a maximum model. ancova <- lm(Result~Time*p53) > summary(ancova) .. Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.05919 0.55646 0.106 0.916 Time -0.02134 0.01785 -1.195 0.241 p53plus

Hi, I hope some one can help. I need to compute Thurston's case 5 on a large set of data. I have gotten as far as computing the proportional preference matrix but the next math is beyond me. Here us my matrix 0.500 0.472 0.486 0.587 0.366 0.483 0.496 0.434 0.528 0.500 0.708 0.578 0.633 0.554 0.395 0.620 0.514 0.292 0.500 0.370 0.557 0.580 0.615 0.329 0.413 0.422 0.630 0.500 0.783 0.641 0.731

Full_Name: Rick Stahlhut Version: 2.9.1 OS: os x 10.5.7 Submission from: (NULL) (128.151.71.23) I upgraded to 2.9.1 today from 2.9.0. I work with large CDC (center for disease control) datasets and start, frequently, with a series of 23 large-ish merges to create the final dataset I work on. I do this each time because (a) R is fast. why not? and b) the datasets occasionally get updated by

Hello everybody, I am sorry if my questions are too simple or not easily understandable. I’m not a native English speaker and this is my first analysis using this function. I have a problem with a cross correlation function and I would like to understand how I have to perform it in R. I have yearly data of an independent variable (x) from 1982 to 2010, and I also have yearly data of a variable

Hi, I am using R to do a principal components analysis for a class which is generally using SPSS - so some of my question relates to SPSS output (and this might not be the right place). I have scoured the mailing list and the web but can't get a feel for this. It is annoying because they will be marking to the SPSS output. Basically I'm getting different values for the component

------------------------------ On Fri, Dec 13, 2013 16:29 GMT David Winsemius wrote: > >On Dec 11, 2013, at 7:30 PM, Hin-Tak Leung wrote: > >> Here is a rather long discussion etc about freetype 2.5.2, problem with the survival package, and build R 2.15.x with gcc 4.8.x. Please feel free to skip forward. >> >> - freetype 2.5.2: >> >> the fix to cope with

Dear R-help list, I would like to perform multiple comparisons for lme. Can you report to me if my way to is correct or not? Please, note that I am not nor a statistician nor a mathematician, so, some understandings are sometimes quite hard for me. According to the previous helps on the topic in R-help list May 2003 (please, see Torsten Hothorn advices) and books such as Venables &

Hi,. We have 2 values (first formant F1, second formant F2) for a given phoneme for six languages. We want to see whether the languages are significantly different one from another for this given phoneme. We have done a manova on our data and it works well, but we doesn't allow us to see which pair of languages are different. If we have only one formant for the phoneme, we would use

I've been struggling with this for a while and cannot get it to work. Is there a simple way to add grid lines ---- I'd like to only add the horizontal ones --- when using the groups argument in xyplot? I can get the grid lines to plot, or the points/lines, but not both. Here is a simple example of the sort of thing that I'm working with. Thanks, in advance. >all est STE

Sorry, some things got garbled in that request for help that might make my request unintelligible, probably it being too late at night when I posted, here is the corrected function call for which I would like to learn how to add horizontal grid lines. Any suggestions would be appreciated. Apologies for the posting error xyplot(est ~ STE, data = all, groups = AREA, type = "b",

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

How do you get the design matrix R used when calculating ANOVA? -- View this message in context: http://www.nabble.com/how-to-get-design-matrix--tp23464638p23464638.html Sent from the R help mailing list archive at Nabble.com.

Hello, I have been trying for weeks to compile a 64-bit Rblas. I started with ATLAS where I have had success in the past, but 64 bit was not behaving, and as each compilation takes between 9 and 12 hours, "test, check, and revise" was not going to be really viable. I therefore switched to OpenBLas (OPBL). I was successful in compiling R-3.0.1 and an OPBL-based BLAS for Windows 64bit

Hello. I apologize in advance for the VERY lengthy e-mail. I endeavor to include enough detail. I have a question about survival curves I have been battling off and on for a few months. No one local seems to be able to help, so I turn here. The issue seems to either be how R calculates Kaplan-Meier Plots, or something with the underlying statistic itself that I am misunderstanding. Basically,