43:, is a graphical visualization of data from two or more qualitative variables. It is the multidimensional extension of spineplots, which graphically display the same information for only one variable. It gives an overview of the data and makes it possible to recognize relationships between different variables. For example, independence is shown when the boxes across categories all have the same areas. Mosaic plots were introduced by Hartigan and Kleiner in 1981 and expanded on by Friendly in 1994. Mosaic plots are also called
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293:, it is not possible for the mosaic plot to plot a confidence interval. However, the tiles can be colored according to the standardized residual from a model of independence, so that cells with excessively large or small deviations are shaded to show those that are 'significant' and the pattern of association can be discerned.
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survival probability. The survival probability for females is seen to have been higher than that for men (marginalised over all classes). Similarly, a marginalization over gender identifies first-class passengers as most probable to survive. Overall, about 1/3 of all people survived (proportion of light gray areas).
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At the left edge of the first variable we first plot "Gender," meaning that we divide the data vertically in two blocks: the bottom blocks corresponds to females, while the upper (much larger) one to males. One immediately sees that roughly a quarter of the passengers were female and the remaining
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The categorical variables are first put in order. Then, each variable is assigned to an axis. In the table to the right, sequence and classification is presented for this data set. Another ordering will result in a different mosaic plot, i.e., the order of the variables is significant as for all
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The last variable ("Survived") is finally applied, this time along the left edge with the result highlighted by shade: dark grey rectangles represent people that did not survive the disaster, light grey ones people that did. Women in the first class are immediately seen to have had the highest
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One then applies the second variable "Class" to the top edge. The four vertical columns therefore mark the four values of that variable (1st, 2nd, 3rd, and crew). These columns are of variable thickness, because column width indicates the relative proportion of the corresponding value on the
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prints. However, in statistical applications, mosaic plots can be colored and shaded according to deviations from independence, whereas
Marimekko charts are colored according to the category levels, as in the image.
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population. Crew plainly represents the largest male group, whereas third-class passengers are the largest female group. The number of female crew members is also seen to have been marginal.
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The areas of the rectangular tiles that are available for a combination of features are proportional to the number of observations that have this combination of features.
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Mosaic plot showing cross-sectional distribution through time of different musical themes in the
Guardian's list of "1000 songs to hear before you die".
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The mosaic plot has been criticised for making the data hard to perceive and to compare visually, because the values correspond to areas.
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and spineplots, the area of the tiles, also known as the bin size, is proportional to the number of observations within that category.
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The plot is of at least two variables. There is no upper limit, but too many variables may be confusing in graphic form.
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Discrete Data
Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data
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The number of observations is not limited, but not read in the image.
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An example of mosaic plots uses data from the passengers on the
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Interactive
Graphics for Data Analysis: Principles and Examples
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The displayed variables are categorical or ordinal scales.
600:Journal of Computational and Graphical Statistics
87:did this person survive the sinking (yes / no)?
527:Martin Theus; Simon Urbanek (23 March 2011).
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448:Michael Friendly & David Meyer (2016).
84:the class (1st, 2nd and 3rd class, or crew)
373:. IOS Press. 1 January 1997. p. 254.
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81:the gender of the person (male / female)
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341:Sandra D. Schlotzhauer (1 April 2007).
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596:A Brief History of the Mosaic Display
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397:SAS System for Statistical Graphics
394:Michael Friendly (1 January 1991).
421:SAS Institute (6 September 2013).
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556:"Design Example: Marimekko Chart"
475:"How to apply Marimekko to data"
473:Smith, Alan (6 September 2017).
427:. SAS Institute. pp. 251–.
400:. SAS Institute. pp. 512–.
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616:Statistical charts and diagrams
344:Elementary Statistics Using JMP
585:Mosaics for contingency tables
347:. SAS Institute. p. 407.
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583:John Hartigan, Beat Kleiner:
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452:. Chapman & Hall/CRC.
285:Unlike, for example, the
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190:Mosaic plot construction
41:percent stacked bar plot
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424:JMP 11 Basic Analysis
247:three quarters male.
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602:, 2002, 11, 89–107.
243:multivariate plots.
591:. 1981, S. 268–273.
594:Michael Friendly:
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16:Data visualization
540:978-1-4200-1106-7
503:"Marimekko Chart"
459:978-1-4987-2583-5
434:978-1-61290-684-3
407:978-1-55544-441-9
380:978-90-5199-326-4
354:978-1-59994-428-9
322:Contingency table
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484:27 September
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512:13 December
225:Horizontal
37:Mekko chart
29:mosaic plot
565:30 October
328:References
268:Properties
106:3rd Class
103:2nd Class
100:1st Class
61:bar charts
298:Criticism
236:Vertical
214:Vertical
97:Survived
53:Marimekko
45:Marimekko
610:Category
312:Heat map
306:See also
233:Survived
200:Variable
59:As with
317:Treemap
291:QQ plot
287:boxplot
151:Female
94:Gender
74:Titanic
67:Example
587:. In:
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377:
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211:Gender
222:Class
203:Axis
197:Order
114:Male
109:Crew
598:In:
567:2011
535:ISBN
514:2017
486:2019
454:ISBN
429:ISBN
402:ISBN
375:ISBN
349:ISBN
174:141
171:Yes
163:106
146:192
134:Yes
129:670
126:422
123:154
120:118
289:or
183:20
180:90
177:93
160:13
154:No
143:88
140:25
137:62
117:No
47:or
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Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
