4/30/2024 0 Comments Correlation on scatter plot![]() So there is no need to write sm_corr_theme() when using sm_statCorr(). You can report statistical results and plot linear regression from correlation by sm_statCorr(), which also provides sm_corr_theme().For correlation plots, add sm_corr_theme().smplot2 functions can be used to improve ggplot2 visually.You have learned to plot geom_point() and geom_smooth() in the same graph.You have learned to add geom layers such as geom_point(), which shows points, and geom_smooth(), which plots the best-fit function.If aesthetics are specified in aes(), different groups of data will have different looks.If aesthetics (color, shape, etc) are specified outside of aes() function, then there is no group difference.You begin by writing a ggplot() function.If you write xxx.params = list(), even with empty ones, you will remove the defaults of sm_corr_avgErr(), which has default of width = 0 (for errv.params) / height = 0 (for errh.params). Point.params feeds the arguments to geom_point(), such as color, alpha, etc, to plot the average point.Įrrv.params feeds the arguments to geom_errorbar(), such as color, size and width etc, to plot the vertical (y-axis) error bar.Įrrh.params feeds the arguments to geom_errorbarh(), such as color, size and height etc, to plot the horizontal (x-axis) error bar. You can control the aesthetics with a high flexibility using point.params, errh.params and errv.params. Actually these arguments are nearly identical to what you have provided ggplot(data =. ![]() y argument is the variable that is plotted along the y-axis. x argument is the variable that is plotted along the x-axis. The data argument of sm_corr_avgErr requires the variable that stores the data frame that is used to plot the data, which is mtcars. The black point in the middle represents the mean with vertical and horizontal standard errors. Ggplot( data = mtcars, mapping = aes( x = drat, y = mpg)) + geom_point( shape = 21, fill = '#0f993d', color = 'white', size = 3) + sm_corr_avgErr( data = mtcars, x = drat, y = mpg) 15.4 Calculating the five parameters of all subjects, groups and conditions.15.1 Contrast Sensitivity Function Model.15 Understanding the Contrast Sensitivity Function.14.3 Calculating area and R2 of all subjects, groups and conditions.14 Area under Curve, AULCSF and R2 of the CSF.13.3 Facetting the Contrast Sensitivity Functions.13 Plotting the Contrast Sensitivity Function.12.4.1 Annotation using sm_forest_annot().12.3 A Bland Altman plot - sm_bland_altman().12 Slope Charts, Point plots, Bland-Altman, Forests, Rainclouds, Histograms (Part 2).11.8.1 Plotting individual points with unique colors.11.7.1 Plotting individual points with unique colors.11.6.1 Plotting individual points with unique colors.11.5.4 Correlation plot with both regression and reference lines.11 Themes, Colors, Correlations, Boxplots, Violins and Bars (Part 1).10.7.3 Checking the Assumption for Homogeneity of Variance.10.6.1 Issues with post-hoc power analysis.10.2.1 Shapiro-Wilk Test to test for Normality of Data.9.2 Calculating slopes of all subjects, groups and conditions.8.3.3 Figure 3E (modeling in Matlab and plotting in R).8.3.1 Figure 3B (a best-fit line with points and error bars). ![]()
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