similar to: extract fit values from geom_smooth

?s 05:17 de 12/08/2023, Thomas Subia via R-help escreveu: > Colleagues, > > Here is my reproducible code for a graph using geom_smooth > set.seed(55) > scatter_data <- tibble(x_var = runif(100, min = 0, max = 25) > ?????????????????????? ,y_var = log2(x_var) + rnorm(100)) > > library(ggplot2) > library(cowplot) > > ggplot(scatter_data,aes(x=x_var,y=y_var))+

Colleagues, Here is my reproducible code for a graph using geom_smooth set.seed(55) scatter_data <- tibble(x_var = runif(100, min = 0, max = 25) ?????????????????????? ,y_var = log2(x_var) + rnorm(100)) library(ggplot2) library(cowplot) ggplot(scatter_data,aes(x=x_var,y=y_var))+ ? geom_point()+ ? geom_smooth(se=TRUE,fill="blue",color="black",linetype="dashed")+

G'day Thomas, On Sat, 12 Aug 2023 04:17:42 +0000 (UTC) Thomas Subia via R-help <r-help at r-project.org> wrote: > Here is my reproducible code for a graph using geom_smooth The call "library(tidyverse)" was missing. :) > I'd like to add a black boundary around the shaded area. I suspect > this can be done with geom_ribbon but I cannot figure this out. Some >

> On Jul 20, 2016, at 10:01 AM, Tom Subia <tgs77m at gmail.com> wrote: > > Default level = 0.95. > Does this mean +/- 0.025 from estimate? > > [[alternative HTML version deleted]] I would have guessed that it meant something along the lines of localized (or one might say "loess-ized") mean +/- 2* similarly localized standard error of the estimate. To find out

Hi all, Is that possible to remove the grey colour in the legend key that goes with the geom_smooth? In my case it doesn't ease the reading of the legend. http://www.4shared.com/file/125864977/e10644f8/desorb.html Cordialement / Regards ------------------------------------------- Benoit Boulinguiez Ecole de Chimie de Rennes (ENSCR) Bureau 1.20 Equipe CIP UMR CNRS 6226 "Sciences

Dear community Using after_stat() I was able to visualise ggplot with standard deviations instead of a confidence interval as seen in the R help. p1<-ggplot(data = MS1, aes(x= Jahr, y= QI_A,color=Bio, linetype=Bio)) + geom_smooth(aes(fill=Bio, ymax=after_stat(y+se*sqrt(length(y))), ymin=after_stat(y-se*sqrt(y))) , method = "lm" , formula = y ~ x +

Hi! This is probably completely off base, but your ymin and y max setup lines are different. One uses sqrt(y), while the second uses sqrt(length(y)). Could that play a part, please? Thank you Erin Hodgess, PhD mailto: erinm.hodgess at gmail.com On Sun, Aug 11, 2024 at 10:10?AM SIBYLLE ST?CKLI via R-help < r-help at r-project.org> wrote: > Dear community > > > > Using

Hello, I am attempting to fit a non-linear model (Von Bertalanffy growth model) to fish length-at-age data with the purpose of determining if any of the three parameters differ between male and female fish. I believe that I can successfully accomplish this goal assuming an additive error structure (illustrated in section 1 below). My trouble begins when I attempt this analysis using a model

I am using the "predict" function on a support vector machine (svm) object, and I don't understand why I can't predict on a dataset with more observations than the training dataset. I think this problem is a generic "predict" problem, but I'm not sure. The original svm was fit on 50 observations.

I'm learning ggplot and am a little confused. Sometimes discrete scales work like I'd expect, and sometimes they don't. For example... This works exactly like one would expect: df<-data.frame(names=c("Bob","Mary","Joe","Bob","Bob")) ggplot(df,aes(names))+geom_histogram() But this yields an error:

Default level = 0.95. Does this mean +/- 0.025 from estimate? [[alternative HTML version deleted]]

Dear R user, Is there any well-developed R package for pre-processing MALDI-TOF or SELDI-TOF data besides PROcess? The package should be able to perform denoising, baseline correction, normalization, spectrum alignment/calibration, peak identification and binning. Thank you. Deming Mi

Dear Friends. I tried to use nls.control() to change the 'minFactor' in nls( ), but it does not seem to work. I used nls( ) function and encountered error message "step factor 0.000488281 reduced below 'minFactor' of 0.000976563". I then tried the following: 1) Put "nls.control(minFactor = 1/(4096*128))" inside the brackets of nls, but the same error message

Hi When I run my script using ggplot and geom_smooth I get messages that I would like to suppress: p <- ggplot(dataSubset) p <- p + aes(x = as.Date(factor(key),format="%Y%m%d")) + geom_line() p <- p + geom_smooth(span=0.2,se=FALSE,size=0.7) The messages look like this: geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method

Icecast 1.3.12 IceS 0.3 Linshout 2.0/2.1 tried Linux 2.6.7 VIA C3 architechure Hi. I have found a very interesting problem with the above setup. Icecast starts up fine, but when I try to connect with iceS I get no response. I've straced iceS and the last word comes from icecast, but waiting for more. Icecast mentions nothing during this process, but show a quick connect/lost connection when

Hello all-- I'm attempting to produce overlaid histograms with partially transparent columns. Whether this display will end up being useful, I can't say. But I do want to get it right. I've already got one solution (shown below), but I tried some other versions and had questions about my results. (Note: I'm using a quartz device, so transparency shows up correctly. You might

Hi I want to change a control parameter for an nls () as I am getting an error message "step factor 0.000488281 reduced below 'minFactor' of 0.000976562". Despite all tries, it seems that the control parameter of the nls, does not seem to get handed down to the function itself, or the error message is using a different one. Below system info and an example highlighting the

Dear All, A couple of questions about the nls package. 1. I'm trying to run a nonlinear least squares regression but the routine gives me the following error message: step factor 0.000488281 reduced below `minFactor' of 0.000976563 even though I previously wrote the following command: nls.control(minFactor = 1/4096), which should set the minFactor to a lower level than the default

When I run gnls I get the error: Error in nls(y ~ cbind(1, 1/(1 + exp((xmid - x)/exp(lscal)))), data = xy, : step factor 0.000488281 reduced below 'minFactor' of 0.000976563 My first thought was to decrease minFactor but gnlsControl does not contain minFactor nor nlsMinFactor (see below). It does however contain nlsMaxIter and nlsTol which I assume are the analogs of

Dear all, I am trying to fit a non linear regression model to time series data. If I do this: reg.logis = nls(myVar~SSlogis(myTime,Asym,xmid,scal)) I get this error message (translated to English from French): Erreur in nls(y ~ 1/(1 + exp((xmid - x)/scal)), data = xy, start = list(xmid = aux[1], : le pas 0.000488281 became inferior to 'minFactor' of 0.000976562 I then tried to set