Orange: Correspondence Analysis - Revision history

Onnowpurbo: /* Contoh */

2020-02-22T22:45:40Z

Contoh

← Older revision		Revision as of 22:45, 22 February 2020
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			'''WARNING:''' Scatter Plot yang baru tidak bisa untuk diskrit data. Scatter Plot yang baru hanya untuk data kontinyu.

	==Referensi==		==Referensi==

Onnowpurbo at 22:00, 22 February 2020

2020-02-22T22:00:45Z

← Older revision		Revision as of 22:00, 22 February 2020
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	Correspondence analysis ~~for~~ categorical multivariate data.		Correspondence analysis untuk categorical multivariate data.

	==Input==		==Input==
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	Coordinates: coordinates of all components		Coordinates: coordinates of all components

	Correspondence Analysis (CA) ~~computes the~~ CA ~~linear transformation of the~~ input ~~data~~. ~~While it is similar to~~ PCA, CA ~~computes~~ linear ~~transformation on discrete rather than on continuous~~ data.		Correspondence Analysis (CA) menghitung transformasi linear CA dari data input. Meskipun mirip dengan PCA, CA menghitung transformasi linear pada data diskrit bukan pada data kontinu.

	[[File:CorrespondenceAnalysis-stamped.png\|center\|200px\|thumb]]		[[File:CorrespondenceAnalysis-stamped.png\|center\|200px\|thumb]]


	* Select the variables you want to see plotted.		* Select the variables you want to see plotted.
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	==Contoh==		==Contoh==

	~~Below~~, ~~is a simple comparison between the~~ Correspondence Analysis ~~and~~ Scatter Plot ~~widgets on the~~ Titanic ~~dataset~~. ~~While the~~ Scatter Plot ~~shows fairly well which class and sex had a good survival rate and which one didn’t~~, Correspondence Analysis ~~can plot several variables in a~~ 2-D ~~graph~~, ~~thus making it easy to see the relations between variable values~~. ~~It is clear from the graph that~~ “no”, “male” ~~and~~ “crew” ~~are related to each other~~. ~~The same goes for~~ “yes”, “female” ~~and~~ “first”.		Di bawah, adalah perbandingan sederhana antara widget Correspondence Analysis dan Scatter Plot pada dataset Titanic. Sementara Scatter Plot menunjukkan dengan cukup baik kelas dan jenis kelamin mana yang memiliki tingkat kelangsungan hidup yang baik dan mana yang tidak, Correspondence Analysis dapat memplot beberapa variabel dalam grafik 2-D, sehingga memudahkan untuk melihat hubungan antara nilai-nilai variabel. Jelas dari grafik bahwa “no”, “male” dan “crew” terkait satu sama lain. Hal yang sama berlaku untuk “yes”, “female” dan “first”.

	[[File:CorrespondenceAnalysis-Example.png\|center\|200px\|thumb]]		[[File:CorrespondenceAnalysis-Example.png\|center\|200px\|thumb]]

Onnowpurbo at 01:07, 29 January 2020

2020-01-29T01:07:02Z

← Older revision		Revision as of 01:07, 29 January 2020
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	Correspondence analysis for categorical multivariate data.		Correspondence analysis for categorical multivariate data.

	~~Inputs~~		==Input==

	Data: input dataset		Data: input dataset

	~~Outputs~~		==Output==

	Coordinates: coordinates of all components		Coordinates: coordinates of all components

	Correspondence Analysis (CA) computes the CA linear transformation of the input data. While it is similar to PCA, CA computes linear transformation on discrete rather than on continuous data.		Correspondence Analysis (CA) computes the CA linear transformation of the input data. While it is similar to PCA, CA computes linear transformation on discrete rather than on continuous data.
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	Select the variables you want to see plotted.		* Select the variables you want to see plotted.
	Select the component for each axis.		* Select the component for each axis.
	Inertia values (percentage of independence from transformation, i.e. variables are in the same dimension).		* Inertia values (percentage of independence from transformation, i.e. variables are in the same dimension).
	Produce a report.		* Produce a report.

	==Contoh==		==Contoh==

Onnowpurbo at 00:42, 24 January 2020

2020-01-24T00:42:09Z

← Older revision		Revision as of 00:42, 24 January 2020
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	Correspondence Analysis (CA) computes the CA linear transformation of the input data. While it is similar to PCA, CA computes linear transformation on discrete rather than on continuous data.		Correspondence Analysis (CA) computes the CA linear transformation of the input data. While it is similar to PCA, CA computes linear transformation on discrete rather than on continuous data.

	~~../../_images/~~CorrespondenceAnalysis-stamped.png		[[File:CorrespondenceAnalysis-stamped.png\|center\|200px\|thumb]]


	Select the variables you want to see plotted.		Select the variables you want to see plotted.

	Select the component for each axis.		Select the component for each axis.

	Inertia values (percentage of independence from transformation, i.e. variables are in the same dimension).		Inertia values (percentage of independence from transformation, i.e. variables are in the same dimension).

	Produce a report.		Produce a report.

	~~Example~~		==Contoh==

	Below, is a simple comparison between the Correspondence Analysis and Scatter Plot widgets on the Titanic dataset. While the Scatter Plot shows fairly well which class and sex had a good survival rate and which one didn’t, Correspondence Analysis can plot several variables in a 2-D graph, thus making it easy to see the relations between variable values. It is clear from the graph that “no”, “male” and “crew” are related to each other. The same goes for “yes”, “female” and “first”.		Below, is a simple comparison between the Correspondence Analysis and Scatter Plot widgets on the Titanic dataset. While the Scatter Plot shows fairly well which class and sex had a good survival rate and which one didn’t, Correspondence Analysis can plot several variables in a 2-D graph, thus making it easy to see the relations between variable values. It is clear from the graph that “no”, “male” and “crew” are related to each other. The same goes for “yes”, “female” and “first”.

	~~../../_images/~~CorrespondenceAnalysis-Example.png		[[File:CorrespondenceAnalysis-Example.png\|center\|200px\|thumb]]

Onnowpurbo: Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/correspondenceanalysis.html Correspondence analysis for categorical multivariate data. Inputs..."

2020-01-16T23:51:29Z

Created page with "Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/correspondenceanalysis.html Correspondence analysis for categorical multivariate data. Inputs..."

New page

Sumber: https://docs.biolab.si//3/visual-programming/widgets/unsupervised/correspondenceanalysis.html

Correspondence analysis for categorical multivariate data.

Inputs

Data: input dataset

Outputs

Coordinates: coordinates of all components

Correspondence Analysis (CA) computes the CA linear transformation of the input data. While it is similar to PCA, CA computes linear transformation on discrete rather than on continuous data.

../../_images/CorrespondenceAnalysis-stamped.png

Select the variables you want to see plotted.

Select the component for each axis.

Inertia values (percentage of independence from transformation, i.e. variables are in the same dimension).

Produce a report.

Example

Below, is a simple comparison between the Correspondence Analysis and Scatter Plot widgets on the Titanic dataset. While the Scatter Plot shows fairly well which class and sex had a good survival rate and which one didn’t, Correspondence Analysis can plot several variables in a 2-D graph, thus making it easy to see the relations between variable values. It is clear from the graph that “no”, “male” and “crew” are related to each other. The same goes for “yes”, “female” and “first”.

../../_images/CorrespondenceAnalysis-Example.png

==Referensi==

* https://docs.biolab.si//3/visual-programming/widgets/unsupervised/correspondenceanalysis.html

==Pranala Menarik==

* [[Orange]]