Beta in ggplot

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By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Using the dataset Lahman::Batting I've estimated parameters for the beta distribution. Now I want to plot this empirically derived beta distribution onto the histogram that I estimated it from. Now that I have parameters alpha0 and beta0how do I plot the beta distribution so that I obtain something like this:. This question is based on a post I'm reading here. All code, including the code for the plots, can be found here. The following code is used to get the requested plot:. How are we doing? Please help us improve Stack Overflow. Take our short survey. Learn more. Plot beta distribution in R Ask Question. Asked 2 years, 8 months ago. Active 2 years, 8 months ago. Viewed 4k times. Rich Pauloo Rich Pauloo 3, 2 2 gold badges 15 15 silver badges 41 41 bronze badges. Perhaps, you might want to look at this answer by BenBolker. Active Oldest Votes. Florian Florian Thanks Florian--this works! I see you've also been through this post on empirical Bayes estimation.

Creating animations from ggplot2 plots


All ggplot2 plots begin with a call to ggplotsupplying default data and aesthethic mappings, specified by aes. To save a plot to disk, use ggsave. A layer combines data, aesthetic mapping, a geom geometric objecta stat statistical transformationand a position adjustment. All layers have a position adjustment that resolves overlapping geoms. They are used to add fixed reference data to plots. The following help topics give a broad overview of some of the ways you can use each aesthetic. Scales control the details of how data values are translated to visual properties. Override the default scales to tweak details like the axis labels or legend keys, or to use a completely different translation from data to aesthetic. The guides the axes and legends help readers interpret your plots. Guides are mostly controlled via the scale e. Facetting generates small multiples, each displaying a different subset of the data. Facets are an alternative to aesthetics for displaying additional discrete variables. The coordinate system determines how the x and y aesthetics combine to position elements in the plot. Themes control the display of all non-data elements of the plot. These functions provides tools to help you program with ggplot2, creating functions and for-loops that generate plots for you. Start by reading vignette "extending-ggplot2" then consult these functions for more details.

Graphics with ggplot2


R in Action 2nd ed significantly expands upon this material. The ggplot2 package, created by Hadley Wickham, offers a powerful graphics language for creating elegant and complex plots. Its popularity in the R community has exploded in recent years. Origianlly based on Leland Wilkinson's The Grammar of Graphicsggplot2 allows you to create graphs that represent both univariate and multivariate numerical and categorical data in a straightforward manner. Grouping can be represented by color, symbol, size, and transparency. The creation of trellis plots i. Mastering the ggplot2 language can be challenging see the Going Further section below for helpful resources. There is a helper function called qplot for quick plot that can hide much of this complexity when creating standard graphs. The qplot function can be used to create the most common graph types. While it does not expose ggplot 's full power, it can create a very wide range of useful plots. The format is:. Here are some examples using automotive data car mileage, weight, number of gears, number of cylinders, etc. Unlike base R graphs, the ggplot2 graphs are not effected by many of the options set in the par function. They can be modified using the theme function, and by adding graphic parameters within the qplot function. For greater control, use ggplot and other functions provided by the package. We have only scratched the surface here. To learn more, see the ggplot reference siteand Winston Chang's excellent Cookbook for R site. Though slightly out of date, ggplot2: Elegant Graphics for Data Anaysis is still the definative book on this subject. Try the free first chapter of this interactive tutorial on ggplot2. Kabacoff, Ph. Graphics with ggplot2 The ggplot2 package, created by Hadley Wickham, offers a powerful graphics language for creating elegant and complex plots. For line plots, color associates levels of a variable with line color. For density and box plots, fill associates fill colors with a variable. Legends are drawn automatically. The geom option is expressed as a character vector with one or more entries. When the number of observations is greater than 1, a more efficient smoothing algorithm is employed. Methods include "lm" for regression, "gam" for generalized additive models, and "rlm" for robust regression. The formula parameter gives the form of the fit. Note that the formula uses the letters x and y, not the names of the variables. For univariate plots for example, histogramsomit y xlab, ylab Character vectors specifying horizontal and vertical axis labels xlim,ylim Two-element numeric vectors giving the minimum and maximum values for the horizontal and vertical axes, respectively Notes: At present, ggplot2 cannot be used to create 3D graphs or mosaic plots. Use I value to indicate a specific value.

What Beta Means When Considering a Stock's Risk


For more on data viz, get an introduction to ggplot2 in part 1 or expand your knowledge in part 2! In part 1 of this series, we explored the fundamentals of ggplot2. We learned about the grammar of graphics beginning with data, aesthetics, and geometries. In part 2we extended our understanding of data visaluzation by learning about additional graphical elements including: statistics, coordinates, facets, and themes. We even learned some best practices along the way. In this final chapter, we will explore plots intended for a specialty audience. We will also learn about plots for specific data types such as ternary plots, networks and maps. There are two common types of plots presented to an academic audience: Box plots and Density plots. The Box Plot gives us what Tukey describes as the 5 number summary:. This is advantageous over using the mean and standard deviation for data sets that may not be normally distributed and prone to extreme outliers. The inner quartile range is the difference between the 3rd and 1st quartiles, or what we commonly see as the box in a box plot. The following examples use the movies dataset from the ggplot2movies package. There is a large number of votes for rating. We will need to make some transformations on the data. Be careful as the tranformation will occur differently depending on how you call your stat functions and arguments. It is possible to cut up continuous variables into ordinal variables using the following functions which cut the data. One way of showing this variation is to use the varwidth argument. And just so we can confirm this argument is doing what we expect it to, we can check the math manually. Theoretical density plots use the probability density function PDF to plot the distribution of univariate data. You have certainly seen these types of plots before. They include: normal, t, chi-squared, and F distributions. The kernel function determines the shape of the bumps while the window width, h, determines their width. The KDE calculates a normal distribution for each value in the data. These are known as the bumps. To obtain the true density curve, we simply add up all the y-values for each bump along our x-axis. The following examples use the quakes data from the base r datasets packag. We will be examining the distribution of the magnitudes of quakes measured near Fiji since How do you define largenesss of a data set?

Prepare the data

How should investors assess risk in the stocks that they buy or sell? Analysts use it often when they want to determine a stock's risk profile. However, while beta does say something about price risk, it has its limits for investors looking to determine fundamental risk factors. Beta is a measure of a stock's volatility in relation to the overall market. A stock that swings more than the market over time has a beta above 1. If a stock moves less than the market, the stock's beta is less than 1. High-beta stocks are supposed to be riskier but provide higher return potential; low-beta stocks pose less risk but also lower returns. Beta is a component of the capital asset pricing model CAPMwhich is used to calculate the cost of equity funding. The CAPM formula uses the total average market return and the beta value of the stock to determine the rate of return that shareholders might reasonably expect based on perceived investment risk. In this way, beta can impact a stock's expected rate of return and share valuation. To followers of CAPM, beta is useful. A stock's price variability is important to consider when assessing risk. If you think about risk as the possibility of a stock losing its value, beta has appeal as a proxy for risk. Intuitively, it makes plenty of sense. Think of an early-stage technology stock with a price that bounces up and down more than the market. It's hard not to think that stock will be riskier than, say, a safe-haven utility industry stock with a low beta. Sure, there are variations on beta depending on things such as the market index used and the time period measured. But broadly speaking, the notion of beta is fairly straightforward. It's a convenient measure that can be used to calculate the costs of equity used in a valuation method. For starters, beta doesn't incorporate new information. Consider a utility company: let's call it Company X. Company X has been considered a defensive stock with a low beta. When it entered the merchant energy business and assumed more debt, X's historic beta no longer captured the substantial risks the company took on. At the same time, many technology stocks are relatively new to the market and thus have insufficient price history to establish a reliable beta. Another troubling factor is that past price movement is a poor predictor of the future. Betas are merely rear-view mirrors, reflecting very little of what lies ahead. Furthermore, the beta measure on a single stock tends to flip around over time, which makes it unreliable. Granted, for traders looking to buy and sell stocks within short time periods, beta is a fairly good risk metric. However, for investors with long-term horizons, it's less useful. The well-worn definition of risk is the possibility of suffering a loss. Of course, when investors consider risk, they are thinking about the chance that the stock they buy will decrease in value.

Add Common Legend to Combined ggplot Plots in R (Example) - ggplot2 & gridExtra Package in RStudio



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