Thanks Erin
Quite relevant. Yes now +sd and -sd are the same values. However they are about +/- 5 and not the values received by the simple code here. I still think it is as the length of y differs. Simple statistics > mean(MS2020[MS2020$Bio=="1",]$QI_A, na.rm=TRUE) [1] 26.81225 > sd(MS2020[MS2020$Bio=="1",]$QI_A, na.rm=TRUE) [1] 21.12419 > mean(MS2020[MS2020$Bio=="0",]$QI_A, na.rm=TRUE) [1] 15.86196 > sd(MS2020[MS2020$Bio=="0",]$QI_A, na.rm=TRUE) [1] 15.00405 Kind regards Sibylle From: Erin Hodgess <erinm.hodg...@gmail.com> Sent: Sunday, August 11, 2024 6:30 PM To: sibylle.stoec...@gmx.ch Cc: R-help@r-project.org Subject: Re: [R] geom_smooth with sd 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.hodg...@gmail.com <mailto:erinm.hodg...@gmail.com> On Sun, Aug 11, 2024 at 10:10 AM SIBYLLE STÖCKLI via R-help <r-help@r-project.org <mailto:r-help@r-project.org> > wrote: 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(axis.text=element_text(size=18))+ theme(axis.title=element_text(size=20))+ ylab("Anteil BFF an LN [%]") +xlab("Jahr")+ scale_color_manual(values=c("red","darkgreen"), labels=c("ÖLN", "BIO"))+ scale_fill_manual(values=c("red","darkgreen"), labels= c("ÖLN", "BIO"))+ theme(legend.title = element_blank())+ theme(legend.text=element_text(size=20))+ scale_linetype_manual(values=c("dashed", "solid"), labels=c("ÖLN", "BIO")) p1<-p1 + expand_limits(y=c(0, 30)) When comparing the plots to the simple statistics the standard deviation do not match. I assume it is because of the na.rm=TRUE which does not match length(y) in the after_stat code. However I was not able to adapt the code using NA values? Simple statistics > mean(MS2020[MS2020$Bio=="1",]$QI_A, na.rm=TRUE) [1] 26.81225 > sd(MS2020[MS2020$Bio=="1",]$QI_A, na.rm=TRUE) [1] 21.12419 > mean(MS2020[MS2020$Bio=="0",]$QI_A, na.rm=TRUE) [1] 15.86196 > sd(MS2020[MS2020$Bio=="0",]$QI_A, na.rm=TRUE) [1] 15.00405 Kind regards Sibylle [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org <mailto:R-help@r-project.org> mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
