The Platinum Age of Television: From I Love Lucy to the Walking Dead, How TV Became Terrific
by David Bianculli · 15 Nov 2016 · 676pp · 203,386 words
an anchor with a dolphin are registered trademarks of Penguin Random House LLC. Cover design by Michael J. Windsor Cover images: digital glitch © Igorstevanovic / Shutterstock; error bars © Admirolas / Shutterstock; T.V. snow © Shutterstock; test pattern © Donald Sawvel / Shutterstock All photos are from the author’s personal collection. LIBRARY OF CONGRESS CATALOGING-IN
Halting State
by Charles Stross · 9 Jul 2011 · 350pp · 107,834 words
, but no. They haven’t been in all day.” He frowns pensively. “That’s odd, now you mention it.” He’s green-lit within the error bars, all the way: telling the truth again. How inconvenient. “They were running some sort of database trawl overnight, I think. They demanded access to a
Leviathan Wakes
by James S. A. Corey · 14 Jun 2011 · 648pp · 170,770 words
dangers out of his mind and did the work. For ten nervous minutes, his suit smelled of overheating plastic. All the diagnostics showed within the error bars, and by the time the recyclers cleared it, his air supply still looked good. Another little mystery he wasn’t going to solve. The abyss
A Half-Built Garden
by Ruthanna Emrys · 25 Jul 2022 · 431pp · 127,720 words
.” * * * I was, in fact, scared. I wasn’t stupid and I didn’t trust experts promising absolutes—“undetectable” was like a sensor reading with no error bars, and Bet members tended to dismiss any personal risk short of certain death. And for all that we held sway over the corporations in anything
The Milky Way: An Autobiography of Our Galaxy
by Moiya McTier · 14 Aug 2022 · 194pp · 63,798 words
uncertainty to the equation. But astronomers are used to being met with uncertainty. Then they spend the rest of their careers trying to drive those error bars down. Under special circumstances, your astronomers can also use something they call “standard sirens” to determine distances. On the surface, these work in much the
First Light: Switching on Stars at the Dawn of Time
by Emma Chapman · 23 Feb 2021 · 265pp · 79,944 words
Internet how many stars there are in the Milky Way it says there are 250 billion – plus or minus 150 billion. That is a big error bar by all accounts. Counting the number of stars in the Milky Way is hard because of the large volume of faint stars that we cannot
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
by Wes McKinney · 25 Sep 2017 · 1,829pp · 135,521 words
Dinner 4 0.172069 In [86]: sns.barplot(x='tip_pct', y='day', data=tips, orient='h') Figure 9-19. Tipping percentage by day with error bars Plotting functions in seaborn take a data argument, which can be a pandas DataFrame. The other arguments refer to column names. Because there are multiple
Python Data Analytics: With Pandas, NumPy, and Matplotlib
by Fabio Nelli · 27 Sep 2018 · 688pp · 107,867 words
is usually combined with another kwarg called error_kw, which, in turn, accepts other kwargs specialized for representing error bars. Two very specific kwargs used in this case are eColor, which specifies the color of the error bars, and capsize, which defines the width of the transverse lines that mark the ends of the
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error bars. Another kwarg that you can use is alpha, which indicates the degree of transparency of the colored bar. Alpha
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you should use a kwarg called label to identify the series that you are representing. At the end you will get a bar chart with error bars, as shown in Figure 7-36.In [ ]: import numpy as np ...: index = np.arange(5) ...: values1 = [5,7,3,4,6] ...: std1 = [0.8,1
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},alpha=0.7,label='First') ...: plt.xticks(index+0.4,['A','B','C','D','E']) ...: plt.legend(loc=2) Figure 7-36A bar chart with error bars Horizontal Bar Charts So far you have seen the bar chart oriented vertically. There are also bar chart oriented horizontally. This mode is implemented by
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networks edges hidden layer input and output layer multi layer perceptron nodes schematization of SLP ( see Single layer perceptron (SLP)) weight B Bar chart 3D error bars horizontal matplotlib multiserial multiseries stacked bar pandas DataFrame representations stacked bar charts x-axis xticks() function Bayesian methods Big Data Bigrams Biological neural networks Blending
Sunfall
by Jim Al-Khalili · 17 Apr 2019 · 381pp · 120,361 words
numbers into the accelerator design in advance?’ ‘Because then we would have no control. The energy and luminosity of the beams will always have tiny error bars and so all parameters have to be constantly adjusted. Remember, for the run to work, all eight beams need to coincide, and these particles are
Beginning R: The Statistical Programming Language
by Mark Gardener · 13 Jun 2012
Lines Confidence Intervals on Fitted Lines Summarizing Regression Models Diagnostic Plots Summary of Fit Summary Chapter 11: More About Graphs Adding Elements to Existing Plots Error Bars Adding Legends to Graphs Adding Text to Graphs Adding Points to an Existing Graph Adding Various Sorts of Lines to Graphs Matrix Plots (Multiple Series
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() command on an lm() result object to produce diagnostic plots. Chapter 11 More About Graphs What You Will Learn In This Chapter: How to add error bars to existing graphs How to add legends to plots How to add text to graphs, including superscripts and subscripts How to add mathematical symbols to
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fine-tuning your plots, adding new elements, and generally increasing your graphical capabilities. You start by looking at adding various elements to existing plots, like error bars, legends, and marginal text. Later you see how to create a new type of graph, enabling you to plot multiple series on one chart. Adding
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to add extra things to it to help the reader understand the situation better. These extra things may be simple axis labels or more complex error bars and legends. Generally speaking, the graphical commands you have seen so far produce adequate graphs, but the addition of a few tweaks can render them
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more effective at communicating the situation to the reader. Error Bars Error bars are an important element in many statistical plots. If you create a box-whisker plot using the boxplot() command, you do not need to add
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, bar charts created using the barplot() command will not show sample variability because each bar is a single value—for example, mean. You can add error bars to show standard deviation, standard error, or indeed any information you like by using the segments() command. Using the segments() Command for
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Error Bars The segments() command enables you to add short sections of a straight line to an existing graph. The basic form of the command is as
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5 NA 9 10 4 NA NA 11 7 NA NA 12 8 NA NA 2. Plot the means of these three samples, and for error bars use standard error. Start by creating an object containing the mean values; you can use the apply() command in this case: > bf.m = apply(bf
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the mean values to make the main bars and the size of the standard errors to create the error bars. However, when you draw your plot you create it from the mean values; the error bars are added afterward. This means that the y-axis may not be tall enough to accommodate both the
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height of the bars and the additional error bars. You should check the maximum value you need, and then use the ylim instruction to ensure the axis is long enough: > bf.m + bf.se
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axis labels later using the title() command. Notice that you gave your plot a name—you will use this to determine the coordinates for the error bars. If you look at the object you created, you see that it is a matrix with three values that correspond to the positions of the
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value that is the mean plus the standard error, and finishing at a value that is the mean minus the standard error. You add the error bars like this: > segments(bp, bf.m + bf.se, bp, bf.m - bf.se) The plot now has simple lines representing the
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error bars (see Figure 11-2). The segments() command can also accept additional instructions that are relevant to lines. For example, you can alter the color, width,
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and style using instructions you have seen before (that is, col, lwd, and lty). You can add cross-bars to your error bars using the segments() command like so: > segments(bp - 0.1, bf.m + bf.se, bp + 0.1, bf.m + bf.se) > segments(bp - 0.1
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+ 0.1, bf.m – bf.se) The first line of command added the top cross-bars by starting a bit to the left of each error bar and drawing across to end up a bit to the right of each bar. Here you used a value of ±0.1; this seems to
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like Figure 11-3. Figure 11-1: Figure 11-2: Figure 11-3: You can add additional instructions to your segments() command when drawing the error bars. You might, for example, make the lines wider using the lwd = instruction or alter the color using the col = instruction. Most of the other graphical
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instructions that you have seen will work on the segments() lines. In the following activity you add some error bars for yourself. Try It Out: Create and Add Error Bars to a Bar Chart Use the grass data object from the Beginning.RData file for this activity. It has two columns
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, ylim = c(0, grass.max)) > title(xlab = 'Treatment', ylab = 'Species Richness') 5. Draw in the basic error bars: > segments(bp, grass.m + grass.se, bp, grass.m - grass.se, lwd = 2) 6. Add hats to the error bars: > segments(bp - 0.1, grass.m + grass.se, bp + 0.1, grass.m + grass.se, lwd
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you have a response variable and a predictor variable, you must use the tapply() command to calculate the means and the values required for the error bars. Because you have no NA items, you do not need to modify the command. You can extract the number of replicates using the length() command
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to make the basic plot and the standard error, calculated from the standard deviation and the square root of the number in each sample. The error bars may be “too tall” for the axis, so you need to add the standard error to the mean to work out the limit of the
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the right length. The plot is given a name so that you can determine the x-coordinate of the bars. The segments() command draws the error bars from a point that lies one standard error above the mean to a point one standard error below. You use the lwd instruction to make
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the error bars a bit wider. Finally, you add the hats in two similar commands, first the top and then the bottom. Using the arrows() Command to Add
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Error Bars You can also use the arrows() command to add your error bars; you look at this command a little later in the section on adding various sorts of line to graphs. The
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main difference from the segments() command is that you can add the “hats” to the error bars in one single command rather than separately afterward. Adding Legends to Graphs If you have more than one series of data to plot as a
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˚. x ~ y Leave a space between x and y. For example, x y. Recall the bar chart you created earlier when you created and added error bars using the segments() command. In the following activity you will recreate the graph and add a variety of text items to it. Try It Out
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axis labels to horizontal using the las = 1 instruction. The cex.names = 2 part makes the names larger than normal. The segments() commands add the error bars and their top hats. The titles() commands use the expression() to create the superscript and subscripts. The mean values are taken from the earlier result
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() command to make them curved. You also saw how to use the segments() command to add sections of straight line; you used these to create error bars. In this section you review some of these methods and add a few extra ways to draw straight and curved lines onto existing graphs. Adding
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to an Existing Plot You can add short segments of line to an existing plot using the segments() command; you saw this before when adding error bars to a bar chart. The basic form of the command is as follows: segments(x0, y0, x1, y1, ...) You provide the starting coordinates and the
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= the front end (that is, x1, y1), and 3 = both ends. One use for the arrows() command is to create error bars with hats. Previously you used the segments() command to add error bars but you used three separate commands, one for the bars and one for each of the hats. You can use
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code = 3 instruction sets heads at both ends. The values for the coordinates are determined from the data; look back at the previous section on error bars for a reminder on how to do this. You can specify other graphical instructions to alter line style, color, and width, for example. In the
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appears. Low resolution and large size make everything very small; as you increase the resolution, everything becomes larger. Figure 11-22: Summary You can add error bars to graphs using segments() or arrows() commands. You can add legends using the legend instruction from some graph commands; for example, barplot() or separately using
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values for the two samples. Alter the aspect ratio of the plot to produce a graph 4 inches wide and 7 inches tall. Now add error bars to show the standard error using the arrows() command. 2. Use the hoglouse data object from the Beginning.RData file for this activity. Make a
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appropriate axis titles and include a subscript to indicate that Length was measured in mm. What You Learned In This Chapter Topic Key Points Error bars: segments () arrows() Error bars can be added using segments() or arrows() commands. Legends: legend () The legend() command can be used to add a legend to an existing plot
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. The following example shows some commands that you might use to make yourself a basic bar chart with error bars (using standard error) and used the # character to organize the information: # Barplot with se error bars. # make copy of data called dat. mn=apply(dat,2,mean,na.rm = TRUE) # set the mean values
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,mx)) # make plot and set y-axis to max value. arrows(bp,mn+se,bp,mn-se,length=0.1,angle=90,code=3) # add error bars. # If y-axis still too short change mx value to a larger one. # END Creating Simple Functions When you use R, you’ll realize that
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a new customized function, which you then save to disk for future use. The script creates a bar chart of mean values and adds standard error bars. The data need to be in column format with one column for the numerical data (the response data) and one column for the grouping (predictor
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It for Future Use In this activity you will create a complicated script that creates a function that will draw a bar plot and add error bars. 1. Start by opening a text editor or use the built-in editor from the File menu. The option you choose depends on your OS
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. Type a couple of comment lines. These are useful to remind you of when you wrote the script and what it does. ## Bar Plot with Error Bars ## Mark Gardener 2011 3. Now start the main function command by giving a name and outlining the required arguments/instructions: barplot.eb = function(y, x
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7. Now enter the commands that will draw the plot and add the error bars: bp = barplot(mean, ylim = c(0, upper), beside = T, ...) # make barplot, fix y-limit to fit largest error bar segments(bp, mean+se, bp, mean-se) # error bars up/down segments(bp-0.1, mean+se, bp+0.1, mean+se
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you to spot the end of blocks of code; here it is not really necessary). The final completed script appears like so: ## Bar Plot with Error Bars ## Mark Gardener 2011 barplot.eb = function(y, x, data, ...) # Parameters # y = y variable # x = x variable # data = data.name { # start function code attach(data) # start
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tidiness mat = rbind(mean, se, len) # make matrix of values upper = round(max(mat[1,]+mat[2,]+0.5),0) # the upper limit to fit error bars on y-axis bp = barplot(mean, ylim = c(0, upper), beside = T, ...) # make barplot, fix y-limit to fit largest
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error bar segments(bp, mean+se, bp, mean-se) # error bars up/down segments(bp-0.1, mean+se, bp+0.1, mean+se) # top hats segments(bp-0.1, mean-se, bp+0.1
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') # summary message print(mat) # show the data summary cat('\n') # newline for tidiness } # end function code ## END The resulting output produces a bar chart with error bars as well as the following text output: > barplot.eb(count, site, data = bfs) Summary stats for bfs Arable Grass Heath mean 10.000000 6.833333
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(bp, hogl.m + hogl.se, bp, hogl.m - hogl.se, length = 0.1, angle = 90, code = 3) The arrows() command is used to make the error bars; here the line style, width, and color are kept as standard but you might like to experiment! Exercise 2 Solution The data are in a
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and axes, and how to make superscript and subscript text and mathematical symbols. You learn how to add legends to plots, and how to add error bars to bar charts or scatter plots. Finally, you learn how to export graphs to disk as high-quality graphics files, suitable for publication. Chapter 12
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