similar to: Prediction and confidence intervals from predict.drc

R-helpers, I recently submitted a help request for the predict.drm function found in the drc package. I am still having issues with the function and I am submitting reproducible code hoping that somebody can help me figure out what is going on. -------- library(drc) # Fit a 4 parameter logistic model to ryegrass dataset fit <- drm(rootl ~ conc, data = ryegrass, fct = LL.4()) summary(fit) #

Hi R-users, I have this code to uniformise the data using gamma: > length(dp1) [1] 696 > dim(dp1) [1] 58 12 > dim(ahall) [1]  1 12 > dim(bhall) [1]  1 12 > trans_dt <- function(dt,a,b) + { n1 <- ncol(dt) +   n2 <- length(dt) +   trans  <- vector(mode='numeric', length=n2) +   dim(trans) <- dim(dt) +   for (i in 1:n1) +   {  dt[,i] <- as.vector(dt[,i])

Hi Rowland, >>> - Option 1: ( my personal choice, because this keeps thing in sight >>> ) >>> - ( Domain Member settings and/or Stand-Alone installs ) >>> - Configure smb.conf ( make sure you have configured the idmap >>> settings. ) # - You must set a DOMAIN backend configuration, see >>> below idmap config * : backend = tdb >>>

Hi R-users,   I would like to transform my data using gamma, my data are ahll, bhall and dp1 as shown below:   > ahall      ah1  ah2  ah3  ah4  ah5  ah6  ah7  ah8  ah9 ah10 ah11 ah12 [1,] 0.9 1.01 1.24 1.85 1.85 2.45 3.67 3.02 3.06 2.33 1.86 0.96   > bhall        bh1   bh2   bh3   bh4  bh5   bh6   bh7   bh8   bh9  bh10  bh11  bh12 [1,] 48.39 44.26 37.34 29.93 33.5 26.54 20.26 25.73 20.14

Hi, I have to do a four point logistics for a dataset. All I have is the absorbance value for different proteins and need to get the four Point values. I have no idea where to start. Any suggestions would be much helpful. Thanks Ramya -- View this message in context: http://r.789695.n4.nabble.com/Four-paramete-logistics-tp3265251p3265251.html Sent from the R help mailing list archive at

Hi everyone, I wrote up a post on the FreeBSD forums about the issue I am having. It's rather long so I am providing a link to it here: http://forums.freebsd.org/showthread.php?t=39595 In summary, it seems that when the packets are routed in to the gateway from local network hosts, the src and dst addresses are changed to the public IPs of the tunnel -- at least from the perspective of the

Hi Louis, > > At this point i can not recommend to upgrade to 4.9.0 or 4.9.1, a side note on this. > The bug in question why im blocking it for production, does not happen for domain members and AD-DC's but it's still a risk in my opinion. > Because for this bug, your obligated to set the idmap ... : settings or run : net groupmap add sid=S-1-5-32-546 unixgroup=nobody

Hi, I am trying to use drc package to calculate IC50 value. The ED50 calculated in some models (LL4 for example) as a response half-way between the upper and lower limit, which is the definition of the relative IC50 value. Does that mean the ED50 in drc package is IC50? How the ED function in drc package distinguish to estimate ED or IC values? Thanks a lot [[alternative HTML version

Dear R users, I'm a little lost on how to define pmodels for the DRC package. My goals are to produce isoboles of binary toxicity data. any tips? I really just need to know what pmodels refers to. Cheers, Pat -- View this message in context: http://r.789695.n4.nabble.com/pmodels-in-DRC-tp4190567p4190567.html Sent from the R help mailing list archive at Nabble.com.

Hi! I want to use the DRC package in order to calculate the IC50 value of an enzyme inhibition assay. The problem is that the estimated ED50, is always out of the fitted curve. In the example below, I had a ED50 value of 2.2896, But when I predict the response level for this concentration I get a value of 45.71 instead of the expected value of 50. This is my data: #Dose unit is concentration

Dear R Community, I am using the package DRC ( to fit a boltzman model to my data. I can fit the model and extract the lower limit, upper limit, and ED50 (aka V50), but I cannot figure out how to get the slope of the curve at ED50. Is there a simple way to do this? I've searched the mailing list and looked through the package documentation, but could not find anything. I am new to r, and

Hi, I am currently trying to do dose-response curves using weighted 4-parameter model (4PL). The weighting was based on 1/(expected variance) derived from historical data. I tried both drm() from drc package, and nls(), found very different results derived from drm() vs. nls() using "weights=" argument. d1<-read.table("d1.txt",sep='\t',header=T,row.names=1)

Dear all, I can use the very nice drc package (multdrc()) to model and plot a dataframe containing dose and response values. I can also use predict.drc() to yield response values given a dose. I need to do the opposite, estimate a dose given the response. The general predict documentation seems to say that this is possible, but it does not appear that predict.drc has that capability.

Hi every one, I am using the package 'drc' to model root elongation using dose response data. I don't know which model I should use. Though I don't know which model I should use, I tried the following codes given below. But it produced the error messages.Can any one tell me the code in 'drc' package to find out the EC (Effective Concentration) values and Confidence

On Fri, 18 May 2018, Ed Siefker wrote: > I have dose response data I have analyzed with the 'drc' package. > Using plot() works great. I want to arrange my plots and source > data on a single page. I think 'gridExtra' is the usual package for > this. > > I could use plot() and par(mfrow=...), but then I can't put the source > data table on the page. >

I have dose response data I have analyzed with the 'drc' package. Using plot() works great. I want to arrange my plots and source data on a single page. I think 'gridExtra' is the usual package for this. I could use plot() and par(mfrow=...), but then I can't put the source data table on the page. gridExtra provides grid.table() which makes nice graphical tables. It

Hello, We use drc to fit dose-response curves, recently we discovered that there are quite different standard error values returned for the same dataset depending on the drc-version / R-version that was used (not clear which factor is important) On R 2.9.0 using drc_1.6-3 we get an IC50 of 1.27447 and a standard error on the IC50 of 0.43540 Whereas on R 2.7.0 using drc_1.4-2 the IC50 is