similar to: grofit package

Dear All, I want to use grofit package for biological growth curves. My dataset only includes "age" variable and "size" variable. I want to use logistic, gompertz and richards growth curves to predict age from size. How can I implement this data set to the function in grofit package? Best regards, Deniz [[alternative HTML version deleted]]

Hello, I'm trying to understand grofit's estimation of models, and fairly new to growth models generally. The data used by grofit consists of the vector of "experiments", that is the growth values for a vector of individuals measured at different times. Can I understand correctly that the program estimates parameters for the growth model based on a regression(linear or non

Hi Is it possible to use grofit to get the AIC of several (e.g. two) growth models and compare both these and model parameters? All I can get it to do so far is return parameters for a single model. Cheers Roland [[alternative HTML version deleted]]

Dear list, Hi, I am trying to get the second derivative of a logistic formula, in R summary the model is given as : ### >$nls >Nonlinear regression model >model: data ~ logistic(time, A, mu, lambda, addpar) >data: parent.frame() > A mu lambda >0.53243 0.03741 6.94296 ### but I know the formula used is #

Hello, I would like to ask about the statistic used for initial values for models built with grofit. Is the mean (of the experiments or cases) at t1 used? Also at the other time points? regards, Russell [[alternative HTML version deleted]]

Dear all, I have some questions regarding the validity an implementation of statistical tests based on linear models and loess. I've searched the R-help arhives and found several informative threads that related to my questions, but there are still a few issues I'm not clear about. I'd be grateful for guidance. Background and data set: I wish to compare the growth and metabolism

I have a data frame that looks like this: > gctablechromonly[1:5,] refseq geometry gccontent X60_origin X60_terminus length kingdom 1 NC_009484 cir 0.6799 1790000 773000 3389227 Bacteria 2 NC_009484 cir 0.6799 1790000 773000 3389227 Bacteria 3 NC_009484 cir 0.6799 1790000 773000 3389227 Bacteria 4 NC_009484 cir 0.6799

I am trying to define a general R function that has a function as the output that depends on the user's input arguments (this may make more sense by looking at the toy example below). My real use for this type of code is to allow a user to choose from many parameterizations of the same general model. My "issue" is that when I compile a package with this type of code in it I get a

usage appears to grow gradually, not exponentially. A rsync may take several hours to complete. (I have one running now that started over four hours ago. The filesystem contains 236 GB of data in 2.4 million files. It is currently taking up 1351MB of memory on the mirror server and 646M on the source server.) All filesystems are veritas filesystem, in case that is relevant. I saw someone on

Doug, I have been attempting to learn a little bit about nlme without too much documentation except the online help. The Latex file in the nlme directory looks interesting but uses packages that I do not have so that I have not been able to read it. I have run the example from gnls to compare it with the results I get from my libraries (code below - I have not included output as it is rather

Sorry about this one being long, and I apologise beforehand if there is something obvious here that I have missed. I am new to creating my own functions in R, and I am uncertain of how they work. I have a data set that I have read into a data frame: > gctable[1:5,] refseq geometry X60_origin X60_terminus length kingdom 1 NC_009484 cir 1790000 773000 3389227 Bacteria 2

Dear R-Help, I am trying to determine the growth rate of bacteria under specific conditions using ipolygrowth function `ipg_multisample`. While this worked before, I got some data that give the error: ``` Error in if (tb.result$peak.growth.time == 0) { : missing value where TRUE/FALSE needed In addition: Warning message: In max(pgr[pgr > 0 & Re(x) >= 0 & Re(x) <= max]) :

Granzow, Doug (NCI) [granzowd@mail.nih.gov] writes: > Hmm... I have a filesystem that contains 3,098,119 files. That's > 3,098,119 * 56 bytes or 173,494,664 bytes (about 165 MB). Allowing > for the exponential resizing we end up with space for 4,096,000 > files * 56 bytes = 218 MB. But 'top' tells me the rsync running on > this filesystem is taking up 646 MB, about 3

Granzow, Doug (NCI) [granzowd@mail.nih.gov] writes: > From what I've observed by running top while rsync is running, its memory > usage appears to grow gradually, not exponentially. The exponential portion of the growth is up front when rsync gathers the file listing (it starts with room for 1000 files, then doubles that to 2000, 4000, etc...). So if your rsync has started

Hello, I am new to R and I am having problems trying to model logistic population growth with the deSolve package. I would like to run the model for four populations with the same initial population and carrying capacity but with different growth rates and put the results into a data frame. When I run the following lines of code I get unexpected results from "output" but the format is

Hello All, I am learning mixed effects logistic regression (lmer in lme4), and I am having problems deciphering the goodness of fit function plot.logistic.fit.fnc (languageR package). The only information I can find is that this function plots observed proportions against mean predicted probabilities for some binning of the data. I have explored this function with several datasets, and have had

Hello all, I am facing difficulty in deciding how to go about analysis of a situation explained as follows: I have two variables Y (response) and X (explanatory) (There are several other potential candidate explanatory variables, but right now I am looking at only one variable.) Y is binary taking values 1 and 0. X is a continuous variable. Both variables are observed over a equally spaced

Is the labeling/naming of levels in the documentation for the predict.glmmPQL function "backwards"? The documentation states "Level values increase from outermost to innermost grouping, with level zero corresponding to the population predictions". Taking the sample in the documentation: fit <- glmmPQL(y ~ trt + I(week > 2), random = ~1 | ID, family =

I have written two functions which do useful things with panel data a.k.a. longitudinal data, where one unit of observation (a firm or a person or an animal) is observed on a uniform time grid: - The first function makes lagged values of variables of your choice. - The second function makes growth rates w.r.t. q observations ago, for variables of your choice. These strike me as

Dear all, I have tried to calculate a GLMM fit with lmer (lme4) and glmmPQL (MASS), I also used glm for comparison. I am getting very different results from different functions, and I suspect that the problem is with our dataset rather than the functions, but I would appreciate help in deciding whether my suspicions are right. If indeed we are attempting the wrong type of analysis, some