search for: vials

...87998 12 38799 1 2 50 10.566150 13 38424 1 2 60 10.556438 14 35240 1 2 70 10.469937 15 46427 1 3 10 10.745636 16 46418 1 3 20 10.745443 17 42095 1 3 30 10.647684 ...... There are 5 columns of data, three levels of "Site", 10 "Vials" per site, and measurements were taken at 10 min intervals from 10-70.. I am primarily interested in the relationship between "Time" and "lnRFU" to calculate the rate at which lnRFU declines over time. I have a nice plot using a ggplot2 code that looks like this p<-ggpl...

...col="transparent"), box.dot=list(cex=0.1, col=1), box.umbrella=list(col=1,lty=1), box.rectangle=list(col= 1)) lset(my.theme) bwplot(paste(METHOD,VIAL)~RESPONSE|STD, data=mydf) as a (fictitious) experiment on a determination of a substance in 3 vials, which was quantified with an external (or internal) standard, with two different methods (A or B) with 3 injections per vial (replicates) I would like to stress the difference between A and B (Method) in the bwplot, so I imagine I could distantiate the boxplots, or colour them according to the &q...

Hi all, Say I have a data frame something like the one below with different sample vials, measured before(B) and after(A) some process, with a value recorded at each measurement point vial measure value 1 B 26 1 A 12 2 B 45 2 A 30 3 B 32 3 A 27 4...

...VIAL = factor(1:4), STD = c("EXT","INT"), SEQUENCE = factor(1:2))) representing the outcome of an HPLC analysis (PPM) as from 2 sessions (SEQUENCE) in which 2 methods were used (with EXTernal or INTernal STanDards) 4 VIALS were analyzed by 3 INJection each I need to calculate some values for each of the combinations of the factors like aggregate(My.df[1], by = list(SEQ=My.df$SEQUENCE, STD=My.df$STD), function(x){mean(x, na.rm=T)}) Up to now, nothing new to me, I can manage it, I've gone eve...

Hello, I am not entirely sure the subject line captures what I am trying to do, but hopefully this description of the problem will help folks to see my challenge and hopefully offer constructive assistance. I have an experimental setup where I measure the decrease in oxygen in small vials as an organism, such as an oyster, consumes the oxygen. Each vial is calibrated before the experiment and these calibrations are used to convert the raw data after the experiment into oxygen values. I end up with two dataframes. One has the calibration data and for example could look like this via...

##I am trying to test for fixed factor main effects in an unbalanced mixed effects model but when I fit the reduced model for "mic" factor effects, the extra degrees of freedom are being allocated to a nested term rather than the residuals. The model has inc, mic and spp are independent variables and vial nested within spp. inc and spp are already coded as factors since they were

I hope that the mailing list is the correct forum for the question below. I have trouble calling functions nlsList and nlme from another function. Any help would be greatly appreciated. Jens Praestgaard Human Genome Sciences Rockville MD. I have a data set v with two components, v$mixeddat and v$init. They are listed below: > v$mixeddat conc result rep sample z 11 20.00000000

hi! I'm studing samba as PDC (with ldap backend) and I would know: - gid and uid are useful in samba? in other words: if pdc admin knows users' uids, he can recovery some wrong situations ? Example: if a user was cancelled and then readded, if his uid changes implies some troubles with shared files? NT mantains, after deletion, association between shared file and uid user and so if

Dear All, I would like to ask a couple of questions on a LME model. I tested 4 selection lines at 4 food concentrations against a standard competitor stock. I had 3 replicate cages per selection line. In each cage I have 10 vials. I counted the number of wild type flies and competitor stock emerging in each vial. My main question is: is there any difference between selection lines? I did fit the following model: mod1<-lme(wt~selection*food, random=~1|c1/food, competition) The quantile plot is straight, the plot of res...

Hallo, I would like to add the marginal distributions along the X and the Y axis to a scatter plot. Can anybody help me, please? Thank you, Paolo -- Paolo Bulla Istituto di Metodi Quantitativi Universit?? "L. Bocconi" viale Isonzo 25 20136 Milano paolo.bulla at unibocconi.it

Hi! I'm looking for a way to disable a directory shared. I think that I can set null valid users parameter but I would know if a boolean parameter exists. Thanks, Fabio -- Dott. Fabio Marcone 2T srl Telefono +39 - 0871- 540154 Fax +39 - 0871- 571594 Indirizzo Viale B. Croce 573, 66013 Chieti Scalo (CH)

Hello anyone, I'm trying to install R on Mandriva 2006 distribution via rpm file with the line urpmi R-2.0.0-1mdk.i586.rpm but I got an error message saying that the file is not accessible due to some info problem. I also tried with a source code in R-2.1.1.tar but I think that there is some problem concerning the new version of gcc (4.x), so I downgraded it to gcc 3.4.5 but it does

dear R-users, I am facing a quite regular and basic problem when it comes to dealing with datasets, but I cannot find any satisfying answer so far. I have a messy dataset of galaxies like that : And XVIII 000214.5+450520 0.69 17 9 0.00 -8.7 26.8 6.44 6.78 < 6.65 -44 0.5 MESSIER031 0.6 1.54 PAndAS-03 000356.4+405319 0.10 17 0.00 -3.6 27.8 4.38

dear all, This is a probably a silly question. If I type > grep("x",c("a.x" ,"b.x","a.xx"),value=TRUE) [1] "a.x" "b.x" "a.xx" Instead, I would like to obtain only "a.x" "b.x" How is it possible to get this result with grep()? many thanks for your attention, best, vito --

Greetings ~ I need some assistance determining an appropriate approach to analyzing multivariate binary response data, and how to do it in R. The setting: Data from an assay, wherein 6-hours-post-fertilization zebrafish embryos (n=20, say) are exposed in a vial to a chemical (replicated 3 times, say), and 5 days later observed for the presence/absence (1/0) of defects in several organ systems

Hallo, yesterday I was puzzled when I discovered that I probabliy miss something in the interepretation of intercept in two-way lm models. I thought that the intercept, using the default contr.treatment contrasts, represents the mean of the group of observations having zero in all column of the model.matrix. It turns out not to be case To be more more clear I am attaching a short example:

Is this a fixed width format? If so, read.fwf() in base, or read_fwf() in the readr package will solve the problem. You may need to trim trailing spaces though. B. > On Oct 5, 2017, at 10:12 AM, jean-philippe <jeanphilippe.fontaine at gssi.infn.it> wrote: > > dear R-users, > > > I am facing a quite regular and basic problem when it comes to dealing with datasets,

Dear all, It appears that MASS::polr() and Design::lrm() return the same point estimates but different st.errs when fitting proportional odds models, grade<-c(4,4,2,4,3,2,3,1,3,3,2,2,3,3,2,4,2,4,5,2,1,4,1,2,5,3,4,2,2,1) score<-c(525,533,545,582,581,576,572,609,559,543,576,525,574,582,574,471,595, 557,557,584,599,517,649,584,463,591,488,563,553,549) library(MASS) library(Design)

Hello, I am planning a study with the main point to evaluate the interaction of two treatments, but for ethical reasons one cell is empty, that with patients receaving no treatment at all Treatment B

Dear contributors I have tried this experiment: x<-c() for (i in 1:12){ x[i]<-list(cbind(x1[i],x2[i])) #this is a list of 12 couples of time series I am using to perform a test } # that compares them 2 by 2 # ################# #trace statistic test<-data.frame() cval<-array( , dim=c(2,3,12)) for (i in 2:12){ for (k in 1:2){ for (j in 1:3){ result[k,j,i]<-