similar to: Testing treatment effects on exponential decay models

Hello all: I am confused about the output from a lm() model with an incomplete design/missing level. I have two categorical predictors and a continuous covariate (day) that I am using to model larval mass (l.mass): leaf.species has three levels - map, syc, and oak cond.time has two levels - 30 and 150. There are no response values for Map-150, so that entire, two-way, level is missing.

Hi all: I'm attempting to create a faceted plot with ggplot2 and I'm having issues with a factor's order that is used to define the facet_grid(). The factor (named total.density) has three levels - 8, 16, and 32 - and I would like them presented in that order. Running order(levels(total.density)) yields the incorrect order of the facet grid - 2 3 1, corresponding with 16, 32, and 8.

Hi all: There was a concern raised by reviewers of a manuscript of mine over the proper execution of a Pearson's correlation. In brief, this was undertaken in order to determine the relationship between the extent of wheel running (y axis) and ethanol intake (x axis) across three, separate 10 day periods in 7 animals. In the paper, the correlational plots for each 10 day-period had 70 data

I'm trying to use nls() to fit an exponential decay with an unknown offset in the time (independent variable). (Perhaps this is inherently very difficult?). > decay.pl <- nls (amp ~ expn(b0,b1,tau,t0,t), data = decay, + start = c(b0=1, b1=7.5, tau=3.5, t0=0.1), trace=T) Error in nlsModel(formula, mf, start) : singular gradient matrix at initial parameter estimates

I finally got (mostly) what I wanted. In an attempt to figure out how to get nls to deal with a non-differentiable function, I had (stupidly) 'simplified' the problem until it became singular. Can I do something to make optim() less sensitive to my initial guess? For this example, I get a lousy solution if I make the initial guess for t0 = min(t) = 0.05. Thanks again, -- Robert Merithew

Hello, I have a data set of data which is best fit by an exponential decay function. I would like to use a nonlinear weighted least squares regression. What function should I be using? Thank you! [[alternative HTML version deleted]]

> Sent: Friday, April 06, 2018 at 1:44 PM > From: "Jeff Newmiller" <jdnewmil at dcn.davis.ca.us> > > You did not try my suggestion. You tried David's, which has a leftover mistake from your guesses about what the argument to coef should be. Yes, sorry for the mistake. coef(graphmodeld) (Intercept) graphdata[, 1] 4.513544204 -0.006820623 This corresponds

> Sent: Friday, April 06, 2018 at 4:53 AM > From: "Jeff Newmiller" <jdnewmil at dcn.davis.ca.us> > To: "g l" <gnulinux at gmx.com> > coef( graphmodeld ) > coef(graphmodelp) Error: $ operator is invalid for atomic vectors A quick search engine query revealed primarily references to the dollar sign ($) operator which does not seem relevant to this

You did not try my suggestion. You tried David's, which has a leftover mistake from your guesses about what the argument to coef should be. -- Sent from my phone. Please excuse my brevity. On April 6, 2018 3:30:10 AM PDT, g l <gnulinux at gmx.com> wrote: >> Sent: Friday, April 06, 2018 at 4:53 AM >> From: "Jeff Newmiller" <jdnewmil at dcn.davis.ca.us>

> On Apr 6, 2018, at 3:43 AM, g l <gnulinux at gmx.com> wrote: > >> Sent: Friday, April 06, 2018 at 5:55 AM >> From: "David Winsemius" <dwinsemius at comcast.net> >> >> >> Not correct. You already have `predict`. It is capale of using the `newdata` values to do interpolation with the values of the coefficients in the model. See:

> Sent: Friday, April 06, 2018 at 5:55 AM > From: "David Winsemius" <dwinsemius at comcast.net> > > > Not correct. You already have `predict`. It is capale of using the `newdata` values to do interpolation with the values of the coefficients in the model. See: > > ?predict > The ? details did not mention interpolation explicity; thanks. > The

I have never found the R symbolic differentiation helpful because my functions are typically quite complicated, but was prompted by Steve Ellison's suggestion to try it out in this case: ################# reprex (see reprex package) graphdta <- read.csv( text = "t,c 0,100 40,78 80,59 120,38 160,25 200,21 240,16 280,12 320,10 360,9 400,7 ", header = TRUE ) nd <- c( 100, 250,

Greetings all, So, I''ve finally found a place to play around with the Ajax.PeriodicalUpdater. In looking at the API, I''m liking the decay option -- not necessarily for my current purpose, but just to keep in mind -- and I have a question: can the decay be a function which returns an integer? Basically, why I''m looking for is a logarithmic decay (where the system

Hi, I am new to R. I am trying to plot the decay of a population over time (0-50yrs). I have the initial population value (5000) and the mortality rate (0.26/yr) and I can't figure out how to apply this so I get a remaining population value each year. In excel (ignoring headings) I would put 5000 in A1, in B2 I would enter the formula A1*0.26, and then in A2 (the next years population) I

> On Apr 6, 2018, at 8:03 AM, David Winsemius <dwinsemius at comcast.net> wrote: > > >> On Apr 6, 2018, at 3:43 AM, g l <gnulinux at gmx.com> wrote: >> >>> Sent: Friday, April 06, 2018 at 5:55 AM >>> From: "David Winsemius" <dwinsemius at comcast.net> >>> >>> >>> Not correct. You already have

Hi, I want to use the neural network package AMORE and I don't find in the documentation the weight decay option. Could someone tell if it is possible to add a regularization parameter (also known as a weight decay) to the training method. Is it possible to alter the gradient descent rule for that? Thanks, Ron

Hi there, I'm quite new to R (and statistics), and I like it (both)! But I'm a bit lost in all these packages, so could someone please give me a hint whether there exists a package for fitting exponential curves (of the type t --> \sum_i a_i \exp( - b_i t)) on a noisy signal? In fact monoexponential decay + polynomial growth is what I'd like to try. Thanks in advance,

I just thought of a useful metaphore for the problem I face. I am dealing with a problem in business finance, with two kinds of related events. However, imagine you have a known amount of carbon (so many kilograms), but you do not know what fraction is C14 (and thus radioactive). Only the C14 will give decay events (and once that event has occurred, the atom that decayed will never decay

I thought I was finished, having gotten everything to work as intended. This is a model of risk, and the short term forecasts look very good, given the data collected after the estimates are produced (this model is intended to be executed daily, to give a continuing picture of our risk). But now there is a new requirement. I have weekly samples from a non-autonomous process (i.e. although well

Readers, Data set: t,c 0,100 40,78 80,59 120,38 160,25 200,21 240,16 280,12 320,10 360,9 400,7 graphdata<-read.csv('~/tmp/data.csv') graphmodeld<-lm(log(graphdata[,2])~graphdata[,1]) graphmodelp<-exp(predict(graphmodeld)) plot(graphdata[,2]~graphdata[,1]) lines(graphdata[,1],graphmodelp) Please what is the function and syntax to obtain gradient values for the model curve at