In the next step, we can draw our data and our circles in a ggplot2 plot: ggplot()+# Draw ggplot2 plot with multiple circlesgeom_point(data=data, aes(x, y))+geom_circle(data=data_circle2, aes(x0=x0, y0=y0, r=r, col=r)) ...
Withgeom_smoothyou can smooth the time series, which can be interesting to analyze trends or patterns. By default,geom_smoothwill add a confidence interval of the smoothed line, but you can remove it by settingse = FALSE. # install.packages("ggplot2")library(ggplot2)# Datadf<-economics[ec...
# since the confidence intervals for the effect sizes are computed using # bootstrapping, important to set a seed for reproducibility set.seed(123) # function call ggstatsplot::ggbetweenstats( data = dplyr::filter(gapminder::gapminder, year == 2007, continent != "Oceania"), x = continent...
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But you can also prefer to show standard errors (or confidence intervals) instead if you want to show how accurately you believe your sample mean represents the population mean. Here I share the code showing the standard errors by error bars per average point. # Required Libraries library(...
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Scalar between0and1. If unspecified, the defaults return95%confidence/credible intervals (0.95). bf.prior A number between0.5and2(default0.707), the prior width to use in calculating Bayes factors and posterior estimates. bf.message Logical that decides whether to display Bayes Factor in favor of...
Additionally, the correlation coefficients (and their confidence intervals) are used as effect sizes- TypeTestCI? Parametric Pearson’s correlation coefficient Yes Non-parametric Spearman’s rank correlation coefficient Yes Robust Percentage bend correlation coefficient Yes Bayes Factor Pearson’s correlation...
ci: An S4 Class to Confidence Intervals circularReplicatedSampling: Replicated Circular-Systematic Sampling circularSampling: Circular Systematic Sampling clear: Clear Memory of All Objects converter: Unit Converter cox.shugart: Cox-Shugart Measure of Proportionality cronbach: Cronbach's Alpha for a matrix...
library(ggplot2) modelplot(mod, draw = FALSE) |> ggplot(aes(x = estimate, y = term, xmin = conf.low, xmax = conf.high)) + geom_pointrange(colour = "red", linewidth = 2, linetype = "dotted") + theme_minimal() + labs( x = "Coefficient estimates and 95% confidence intervals"...