similar to: Obtain gradient at multiple values for exponetial decay model

This smells like homework, which the Posting Guide indicates is off topic. I am not aware of "the function" that will solve this, but if you know what a gradient is analytically then you should be able to put together a solution very similar to the code you already have with the addition of using the coef function. -- Sent from my phone. Please excuse my brevity. On April 5, 2018

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,

> 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

> Sent: Thursday, April 05, 2018 at 4:40 PM > From: "Jeff Newmiller" <jdnewmil at dcn.davis.ca.us> > > the coef function. > For the benefit of other novices, used the following command to read the documentation: ?coef Then tried and obtained: > cvalue100<-coef(graphmodelp~100) > cvalue100 NULL Then looked at the model values which of course correspond

> 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:

Try coef( graphmodeld ) And you don't need to approximate if you use the newdata argument to the predict function. I think reading the "Introduction to R" that comes with R would help. -- Sent from my phone. Please excuse my brevity. On April 5, 2018 2:00:45 PM PDT, g l <gnulinux at gmx.com> wrote: >> Sent: Thursday, April 05, 2018 at 4:40 PM >> From:

> 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

> 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

> On Apr 5, 2018, at 2:00 PM, g l <gnulinux at gmx.com> wrote: > >> Sent: Thursday, April 05, 2018 at 4:40 PM >> From: "Jeff Newmiller" <jdnewmil at dcn.davis.ca.us> >> >> the coef function. >> > > For the benefit of other novices, used the following command to read the documentation: > > ?coef > > Then tried and

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>

> 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

Hello, I am searching for the best method to plot two variables with points whose output color depends on the size of a third variable. For example, the darkness of the x-y point would increase incrementally based on the size of the z value, similar to the colramp parameter in geneplotter. This would be analagous to symbols(), except changing the selection from the color gradient rather than the

Hello, I am using R, through rsruby, to create a graph and best fit line for a set of data points, regarding data collected in a Chemistry class. The problem is that although the graph functions perfectly properly, the best fit line will not work. I initially used code I pretty much copied from a website with a tutorial on this, which was: graphData.png("/code/Beer's-Law

Hello everyone, I''m new here. I''ve been working with prototype and plotr for about a month now, off and on, and I have pretty much hit the wall on using the data returned by Ajax.Request. I''m using some php code to return a string: {''foo'': [[0,0.0865334429075127], [1,0.0828179861705063], [2,0.0828173042602942], [3,0.0841707718624196]]} But I keep

Hey guys, I'd really appreciate any help. I have a multivariate analysis done, the output of which is: > GraphData <-read.table("eigen.coa") > GraphData V1 V2 V3 V4 1 1 0.371970 0.8552 0.8552 2 2 0.061785 0.1420 0.9972 3 3 0.001211 0.0028 1.0000 4 4 0.000000 0.0000 1.0000 > summary(GraphData) V1 V2 V3

Hello,   I am trying to calculate predicted values derived from one dataset into a hypothetical dataset. I tried this line of code:   graphdata$fmgpredvalues <- predict(Acs250.3.4, graphdata)   and received the following error message:   ERROR: ZXend[1], drop = FALSE] %*%lmeFit$beta   I have made sure all variable names are the same between the two datasets and all factors are appropriately

Hello all: I would like to test whether there are treatment effects on decomposition rate, and I would like to inquire about the best, most appropriate means using R. I have plant decomposition data that is generally considered to follow an exponential decay model as follows: Wt = Wi * exp(-k * t) Where Wt and Wi are the weights of the plant material at time t and 0, respectively. k is a

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

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