similar to: help please: predict error code

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

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

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

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

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

> 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

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,

Does lme prediction work correctly with poly() terms? In the following simulated example, the predictions are wildly off. Or am I doing something daft? Milk yield for five cows is measured weekly for 45 weeks. Yield is simulated as cubic function of weekno + random cow effect (on intercept) + residual error. I want to recover an estimate of the fixed curve. ############### library(nlme)

> 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

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

Is there any way that I can see the step by step code for functions in the base package? For instance the dexp function. I am a student working on writing my own function for something that is similar to this dexp function and I would like to see the step by step code. Brittany Laine GTA WVU Statistics Department 331 Hodges

> 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

On Thu, 2015-01-08 at 09:35 -0700, Warren Young wrote: > Once a thing becomes reliable, it stops being technology. Oh No. Just because something works well it does not stop being "technology" unless the USA people, who have decimated my language (English), have a new definition for "technology". Warren are you serious that things that do not work well are

Dear all, The following problem just has been submitted to me by an accountant. In his new job, he has to close some old accounts. He has yearly amounts, and a list of products that have been bought over the years, at certain prices for which he has an exhaustive record. The problem is: He does not know what product was bought this or that year (don't ask). He does not want to find back

Dear useRs, French reading people among you might be interested by the following book: Noel, Y. (2013). Psychologie statistique avec R [Statistical psychology with R, in French], coll. PratiqueR, Paris: Springer. http://www.springer.com/psychology/book/978-2-8178-0424-8 This book provides a detailed presentation of all basics of statistical inference for psychologists, both in a fisherian

> 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>

Hello,   I am fairly new to R and having a problem with the lme command. I have searched on forums, read the Fox 2002 chapter, and R help, but the suggestions that I have tried have not helped me.   My data file is called Acsdata.   This is my script:   Acsdata.1 <- lme(Acsdata$gsi ~ Acsdata$asitotal + Acsdata$famstrto + as.factor(Acsdata$GEN) + Acsdata$asxfs + Acsdata$asxpar + Acsdata$asxgp +

Hello everyone. I would like to create many objects with R. Does R support objects? The number of objects needed is not predetermined and it is a parameter specified by the user. If the user selects to create many objects like 100, would it be possible to handle each one by some index? I would like to thank you in advance for your help. Best Regards Alex [[alternative HTML version