search for: 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",l...

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 Chi...

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 grou...

?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))+ > ? geom_point()+ > ? geom_smooth(se...

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 > advice would be welcome. This works for me: ggplot(scatter_data,aes(x=x_var,y=y_var,...

Hi R - listers, I am receiving an error. Does anyone know what this means? J ggplot(subset(foo, Rayos != "Rayos.NA"), aes(x=HTL, y=DevelopIndex, colour=TotalEggs)) +geom_point() +geom_jitter() + facet_grid(Aeventexhumed ~ Rayos) + geom_smooth(method="lm", fill=NA) + ylim(c(0, 7)) Error in +geom_smooth(method = "lm", fill = NA) : invalid argument to unary operator [[alternative HTML version deleted]]

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

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), control=nls.control(tol=1E-5, minFactor=1/1024), n = 1000 ) Now I would like to extract some valu...

...ML 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 what the base R version of loess does consult `?predict.loess` and to find out what `geom_smooth` does, you can try to find documentation on the `predictdf` fucntion, but the geom_smooth help pages warns you it is undocumented. -- David Winsemius Alameda, CA, USA 'Any technology distinguishable from magic is insufficiently advanced.' -Gehm's Corollary to Clarke's Third Law

...505 0 2 4.296608 1 2 4.826036 2 2 4.765386"),header=TRUE); closeAllConnections(); I can form two plots, scatter and lines, as follows: p <- ggplot(mydata, aes(x=Est, y=Tri)) p + geom_point(aes(colour=factor(Group),shape=factor(Group))) and p+ geom_smooth(aes(group=factor(Group),color=factor(Group)),method=lm,se=F). However, I am unable to have the plots together. I obtain the following error: > p + geom_point(aes(colour=factor(Group),shape=factor(Group)))+geom_smooth(aes(group=factor(Group),color=factor(Group)),method=lm,se=F) Error in `[.dat...

...e that I think illustrates what I'm trying to do. The commented out sections show what I've tried to far: ## Short example to test segments: w <- tsibble( date = as.Date("2022-01-01") + 0:99, value = rnorm(100) ) ggplot(data = w, mapping = aes(date, value)) + geom_smooth(method = "lm", se = FALSE) + geom_point() ## Below gives error about ignoring data ## geom_abline( data = w$date[25:75] ) ## Gives error ''data' must be in <data.frame>' ## geom_smooth(data = w$date[25:35], ## method = lm,...

Dear Helpers, I wonder whether you might be able to help me. I have a plot composed of ggplot (and a follow on geom_smooth call). I would like to restrict the display range of the y axis to a smaller range, a sort of zooming onto a region. I attempted to use ylim, but it will effect the range (i.e. effect the geom_smooth call). Is there any way that I can save the results up to geom_smooth call and then restrict the pr...

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 + I(x^2),linewidth=1) + theme(panel.background = element_blank())+ theme(axis.line = element_line(colour = "black"))+ theme(...

...: > 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 + I(x^2),linewidth=1) + > > theme(panel.background = element_blank())+ > > theme(axis.line = element_line(colour = &q...

...t I've tried to far: >> >> ## Short example to test segments: >> >> w <- tsibble( >> ????? date = as.Date("2022-01-01") + 0:99, >> ????? value = rnorm(100) >> ) >> >> ggplot(data = w, mapping = aes(date, value)) + >> ????? geom_smooth(method = "lm", se = FALSE) + >> ????? geom_point() >> ????? ## Below gives error about ignoring data >> ????? ## geom_abline( data = w$date[25:75] ) >> ????? ## Gives error ''data' must be in <data.frame>' >> ????? ## geom_smooth(data =...

My question is related to plot( ) in the mgcv package. Before modelling the data, a few predictors were transformed to normalize them. Therefore, the x-axes in the plots show transformed predictor values. How do I back-transform the predictors so that the plots are easier to interpret? Thanks in advance, Suzan -- Suzan Pool Oregon State University Cooperative Institute for Marine

...owing for me? How can I get rid of the blue line in the key in the second plot? ## Create a simple data frame df=data.frame(x=1:1000, y=2*1:1000+rnorm(1000,sd=1000), type=sample(letters[1:2],1000, replace=TRUE)) ## Very nice! Almost what I want qplot(x, y, data=df, colour=factor(type)) + geom_smooth() ## Make a nicer plot, with smaller points ## but why does that add the little blue line with a 1? qplot(x, y, data=df, colour=factor(type), size=1) + geom_smooth() [[alternative HTML version deleted]]

With ggplot2, I can plot the glm stat_smooth for binomial data when the response is binary or a two-level factor as follows: data("Donner", package="vcdExtra") ggplot(Donner, aes(age, survived)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ x, alpha = 0.2, size=2) But how can I

...9;ve tried in the commented out sections: weights %>% group_by(year(Date)) %>% summarize( m_K = mean(K, na.rm = TRUE), m_J = mean(J, na.rm = TRUE), ) %>% ggplot(aes(x = `year(Date)`)) + geom_point(aes(y = m_K, color = "red")) + geom_smooth(aes(y = m_K, color = "red")) + geom_point(aes(y = m_J, color = "blue")) + geom_smooth(aes(y = m_J, color = "blue")) + guides(size = "legend", shape = "legend") ## scale_shape_discrete(name="Person", ##...

...is is showing 2012.5 ; 2015.0 ; 2017.5 ; 2020.0 I would like to see on X-axis only the year (2012 ; 2015 ; 2017 ; 2020). How to do? ######### library(ggplot2) df=data.frame(year= c(2012,2015,2018,2022), score=c(495,493, 495, 474)) ggplot(df, aes(x = year, y = score)) + geom_point() + geom_smooth(method = "lm", formula = y ~ x) + labs(title = "Standard linear regression for France", x = "Year", y = "PISA score in mathematics") + scale_y_continuous(limits=c(470,500),oob=scales::squish) ######### Le lundi 11 d?cembre 2023 ? 23:38:06 UTC+1, Ben...