In the R default plotting, users can change the f index to vary the smoothing level of loess regression (high f means high smooth). I did the loess regression with ggplot2 stat_smooth with the following command: ggplot(data=combine, aes(x=pos/1000000, y=weightedFst, color=group, group=group)) + geom_smooth(method="loess", size=1, formula = y ~ x) + scale_x_continuous(limits=c(0, 302), breaks=c(50, 250)) + xlab("physical position (Mb)") + ylab("Fst") + ggtitle("loess regression of Fst in 10kb non-overlapping windows") + theme_bw()
How can I change the smoothing level of loess regression in ggplot2? I searched around but could not find the answers. Could anyone help here?
In the R default plotting, users can change the f index to vary the smoothing level of loess regression (high f means high smooth). I did the loess regression with ggplot2 stat_smooth with the following command:
ggplot(data=combine, aes(x=pos/1000000, y=weightedFst, color=group, group=group)) + geom_smooth(method="loess", size=1, formula = y ~ x) + scale_x_continuous(limits=c(0, 302), breaks=c(50, 250)) + xlab("physical position (Mb)") + ylab("Fst") + ggtitle("loess regression of Fst in 10kb non-overlapping windows") + theme_bw()How can I change the smoothing level of loess regression in ggplot2? I searched around but could not find the answers. Could anyone help here?
--
--
You received this message because you are subscribed to the ggplot2 mailing list.
Please provide a reproducible example: https://github.com/hadley/devtools/wiki/Reproducibility
To post: email ggp...@googlegroups.com
To unsubscribe: email ggplot2+u...@googlegroups.com
More options: http://groups.google.com/group/ggplot2
---
You received this message because you are subscribed to the Google Groups "ggplot2" group.
To unsubscribe from this group and stop receiving emails from it, send an email to ggplot2+u...@googlegroups.com.
For more options, visit https://groups.google.com/d/optout.
For α < 1, the neighbourhood includes proportion α of the points, and these have tricubic weighting (proportional to (1 - (dist/maxdist)^3)^3). For α > 1, all points are used, with the ‘maximum distance’ assumed to be α^(1/p) times the actual maximum distance for p explanatory variables.