galo

<p>The objects (individuals) are 1290 school children in the sixth grade of elementary
school in the city of Groningen (Netherlands) in 1959.</p>

datasets

Performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.

Patrick Mair

homals

Gifi Methods for Optimal Scaling

Jan De Leeuw

galo function

<p>Data frame with the five variables Gender, IQ, Advice, SES and School. IQ (original range 60 to 144) 
has been categorized into 9 ordered categories and the schools are enumerated from 1 to 37. 
SES: LoWC = Lower white collar; MidWC = Middle white collar; Prof = Professional, Managers;
Shop = Shopkeepers; Skil = Schooled labor; Unsk = Unskilled labor. 
Advice: Agr = Agricultural; Ext = Extended primary education; Gen = General; 
Grls = Secondary school for girls; Man = Manual, including housekeeping; 
None = No further education; Uni = Pre-University.</p>

galo: GALO dataset

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Examples