similar to: classification by nls and anova

My first Problem is a minor one. Maybe some knows a reson. Pleas give me a hint off-line, as it seems to be something stupid. 1. I sent my first mail to r-help at lists.R-project.org and got Delivery Failure Report Your document: make error R-1.8.0 on SuSE Linux 7.3 (i386) was not delivered to: r-help at lists.R-project.org because: Delivery time expired I already had this problem

Dear help list, I have a timestamp in as a chron object: > (x <- chron(dates = c("12/02/11", "22/11/11"), + times = c("07:30:00", "04:00:00"), + format = c(dates = "d/m/y", times = "h:m:s"))) [1] (12/02/11 07:30:00) (22/11/11 04:00:00) Now I want to shift the timestamp by 06:30 (hh:mm) backwards, to get

Hello everybody! I have 2 >issues< concerning methods applied to missing data. I think they're bugs, but who knows. 1. var(NA) returns Error in var(NA) : missing observations in cov/cor instead of NA. I expanded the summary-function to my.summary including SDev, in order to use it with tapply, which crashes in case of groups with no valid data. 2. is a similar problem. I use

Dear R-users & developers, i'd like to know your opinion on the following suggestion. In short: Wouldn't it be possible to combine the properties of local regression (loess) and resistant regression (lqs) in order to cope with fitting of partially continuous data? I think the lqs-fitting could detect and eliminate the 2 outliers but perserve the discontinuity at t1 in case of the

Hello everyone: I'm a new member of this group. I have a question about "oneway.test". When I use "anova(lm(....))" to analysis the ANOVA, I can get the information about Sum Sq and Mean Sq. (The R code and the results are as follows.)

I have a function in R^2, say f <- function(x,y) { ...skipped } I want to plot this function using contour, persp. wireframe, etc. I know that the function has a global minimum at (x0, y0) The naive approach is to evaluate the function on the outer product of two arrays, like this: sx <- c(seq(-3, x0, len = 100), seq(x0, 3, len = 100)[-1]) sy <- c(seq(-3, y0, len = 100), seq(y0, 3,

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-helpers, I am developing a Mixed-Effects model for a study of immunoassays using 'lme4' library. The study design is as follows: 10 samples were run using 7 different immunoassays, 3 times each, in duplicates. Data attached. I have developed the following model: c.lme <- lmer(Result~SPL + (SPL|Assay/Run) -1, data=data) This model has excellent predictions - the Rsquared of

Using a "by() statement, I am preparing ANOVA's for multiple experiments, and using simint() to generate confidence intervals. This works fine. simint.by.fit <- by(analytes.dfr, list(Assay = analytes.dfr$analyte ), function(data) (simint(value ~ tx, data = data,type='Tukey' ) ) ) I can separately prepare plots of the confidence intervals, and I can prepare separate plots

I used the wrong address. This was meant as a reply to another post. On 10/17/07 10:55, thomas.schwander at mvv.de wrote: > Hi Jonathan, > > I read your post in the R-Help. Did you get rid off the problem? I'm standing inf > ront of the same problem... If you've got an answer to me to drae a curly bracket, > could you please be so kind to tell me who you did? I missed

Pfizer is hiring a statistician supporting early drug discovery for Global Medicinal Chemistry and Pharmacokinetics, Dynamics and Metabolism (PDM). If interested, go to http://pfizercareers.com/ and search for job ID 985944. The location is Groton, Connecticut. Responsibilities The statistician will have a consulting-type role supporting a large pool of scientists in two large platform line

Hi, I need to construct a formula programaticly, and pass it to a function such as the linear mixed model lme. The help says it requires "a two-sided linear formula object describing the fixed-effects part of the model" but I do not know how to create this formula. I have tried various things using formula(x, ...), as.formula(object, env = parent.frame()) and as.Formula(x, ...)

Hi! I'm learning mgcv, and reading Simon Wood's book on GAMs, as recommended to me earlier by some folks on this list. I've run into a question to which I can't find the answer in his book, so I'm hoping somebody here knows. My outcome variable is binary, so I'm doing a binomial fit with gam(). I have five independent variables, all continuous, all uniformly

Dear colleagues, Von Bertalanffy model is commonly adjust to data on fish length (TL) and age (AGE) TL= Linf*(1-exp(-K*(AGE-t0)). Linf, K and t0 are parameters of the model. One main goal of the growth study is the comparison of growth parameter estimates between sexes of the same species, or estimates from different populations. The realibility statistical tests normally applied are highly

Consider the Assay data where block, sample within block and dilut within block is random. This model can be fitted with (where I define Assay2 to get an ordinary data frame rather than a grouped data object): Assay2 <- as.data.frame(Assay) fm2<-lme(logDens~sample*dilut, data=Assay2, random=list(Block = pdBlocked(list(pdIdent(~1), pdIdent(~sample-1),pdIdent(~dilut-1))) )) Now, block

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

Hi all, I'm looking for the algorithm to calculate maximum intrinsic and parameter effects curvature of Bates & Watts (1980). I have Bates & Watts (1980) original article, Bates et al (1983) article, Seber & Wild (1989) and Ratkowsky (1983) books. Those sources show steps and algorithm to get this measures but I don't know translate C code to R language and I've no success

Try this data(Assay) as1 <- lme(logDens~sample*dilut, data=Assay, random=pdBlocked(list( pdIdent(~1), pdIdent(~sample-1), pdIdent(~dilut-1)))) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) I'm

Actually, I re-read the post and think it needs clarification. We may both be right. If the question is "I am building a model and want to know if I should retain this random effect?" (or something like that) then the LRT should be used to compare the fitted model against another model. This would be accomplished via anova(). In other multilevel programs, the variance components are

I'm not sure I'm barking up the right tree here, but would I need to make use of groups to plot two separate datasets within ONE panel in xyplot? The desired end result is a single xy plot of two separate (but similar in values and ranges). Full code follows, xyplot code at bottom #########Determine Frequencies ##########coastal_slope #needs the maptools package to read ESRI grid