Displaying 6 results from an estimated 6 matches similar to: "ggplot2: geom_smooth and legend"
2012 Oct 04
0
extract fit values from geom_smooth
Dear all,
I have a plot with two gaussian distributions:
ggplot( tofdf, aes( x = tof, y = counts ) ) +
geom_histogram( stat = "identity", position = "stack", fill = 2 ) +
geom_smooth( method = "nls", formula = y ~ (a/b)*exp(-(x-c)^2/(2*b^2)) +
(d/e)*exp(-(x-f)^2/(2*e^2)), se=F,
start=list(a=100, b=1, c=695, d=100, e=1, f=710),
2017 Sep 07
0
Geom_smooth
> 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
2023 Aug 12
2
geom_smooth
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")+
2023 Aug 12
1
geom_smooth
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
>
2016 Jul 20
4
Geom_smooth
Default level = 0.95.
Does this mean +/- 0.025 from estimate?
[[alternative HTML version deleted]]
2023 Aug 12
1
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))+