WebJan 20, 2016 · Again, the plot of the data points looks just fine, but the geom_smooth lines won't plot, returning instead the following Warning message: Warning messages: 1: Computation failed in stat_smooth(): parameters without starting value in 'data': Cornet_corr, dS 2: Computation failed in stat_smooth(): WebYou can control the size of the bins and the summary functions. stat_summary_bin () can produce y, ymin and ymax aesthetics, also making it useful for displaying measures of spread. See the docs for more details. You’ll learn more about how geoms and stats interact in …
5 Statistical summaries ggplot2
WebSep 15, 2016 · This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain. WebAccording to documentation to ggplot2 (and book in comment below): stat_smooth is a confidence interval of the smooth shown in grey. If you want to turn the confidence interval off, use se = FALSE. Share Cite Improve this answer Follow edited Jan 17, 2014 at 19:28 Andy W 15.6k 8 75 197 answered Jan 17, 2014 at 15:24 Ladislav Naďo 2,282 4 22 45 1 father figure paternity wear
geom_smooth: Smoothed conditional means in ggplot2: Create …
WebError: stat_smooth requires the following missing aesthetics: x, y. Why didn’t that work? This is because when we specfy aesthetics inside a call to geomtery it only applies for that layer (only geom_point got the x and y values). The only information that gets passed to all geometery calls is aethetics specified in the initial creation of ... WebThere are three options: If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot (). A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data … WebTo plot your smooth line you will have to extract the model fit. This should do the trick: plot (y~x, data = dat, cex = .1) G1pred <- predict (G1) I1 <- order (dat$y) lines (dat$x, G1pred) You can also adjust k manually, and see what number of k brings you closest to the k value set automatically by GCV. Share Cite Improve this answer Follow freshwater fish and saltwater fish