X, y = np.random.multivariate_normal(mean, covariance, 3000).T ,Ĭovariance = covariance # must be symmetric # generate normally distributed points to plot # make the plot reproducible by setting the seed Let’s plot a few random clusters of points to see what this problem looks like in practice. This is a useful way to visualize the data, but the plot’s legend will use the same marker sizes by default and it can be quite difficult to discern the color of a single point in isolation. The last low-level plotting function that we’ll go over in detail is legend() which adds a legend to a plot.I frequently find myself plotting clusters of points in Matplotlib with relatively small marker sizes. For example, legend = c("Males, "Females") will create two groups with names Males and Females.Īdditional arguments specifying symbol types ( pch), line types ( lty), line widths ( lwd), background color of symbol types 21 through 25 ( pt.bg) and several other optional arguments. For example, "bottomright" will always put the legend at the bottom right corner of the plot.Ī string vector specifying the text in the legend. Alternatively, you can enter a string indicating where to put the legend (i.e. 18.5 Chapter 8: Matrices and Dataframesġ1.7.7 legend() Table 11.12: Arguments to legend() ArgumentĬoordinates of the legend - for example, x = 0, y = 0 will put the text at the coordinates (0, 0).18.4 Chapter 7: Indexing vectors with.17.4 Loops over multiple indices with a design matrix.17.3 Updating a container object with a loop.17.2 Creating multiple plots with a loop.17.1.2 Adding the integers from 1 to 100.16.4.4 Storing and loading your functions to and from a function file with source().16.4.2 Using stop() to completely stop a function and print an error.16.3 Using if, then statements in functions.16.2.3 Including default values for arguments.16.2 The structure of a custom function.16.1 Why would you want to write your own function?.15.5.2 Transforming skewed variables prior to standard regression.15.5.1 Adding a regression line to a plot.15.5 Logistic regression with glm(family = "binomial".15.4 Regression on non-Normal data with glm().15.3 Comparing regression models with anova().15.2.6 Getting an ANOVA from a regression model with aov().15.2.5 Center variables before computing interactions!.15.2.4 Including interactions in models: y ~ x1 * x2.15.2.3 Using predict() to predict new data from a model.15.2.2 Getting model fits with fitted.values.15.2.1 Estimating the value of diamonds with lm().14.7 Repeated measures ANOVA using the lme4 package.14.6 Getting additional information from ANOVA objects.14.5 Type I, Type II, and Type III ANOVAs.14.1 Full-factorial between-subjects ANOVA.13.5.1 Getting APA-style conclusions with the apa function.13.1 A short introduction to hypothesis tests.12.3.1 Complex plot layouts with layout().12.3 Arranging plots with par(mfrow) and layout().11.10 Test your R might! Purdy pictures.11.8 Saving plots to a file with pdf(), jpeg() and png().11.7.5 Combining text and numbers with paste().10.6 Test your R might!: Mmmmm…caffeine.9.6.3 Reading files directly from a web URL.9.1.1 Why object and file management is so important.8.7 Test your R might! Pirates and superheroes.7.3.1 Ex: Fixing invalid responses to a Happiness survey.7.2.2 Counts and percentages from logical vectors.6.2.3 Sample statistics from random samples.6.2.2 Additional numeric vector functions.4.4.4 Example: Pirates of The Caribbean.4.3.1 Commenting code with the # (pound) sign.4.3 A brief style guide: Commenting and spacing.4.2.1 Send code from an source to the console.1.5.2 Getting R help and inspiration online.
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