predict.homals: Classification tables for homals solutions
In homals: Gifi Methods for Optimal Scaling
Description
Usage
Arguments
Details
Value
See Also
Examples
Description
Given a multiple homals solution we can reconstruct the indicator
matrix by assigning each object to the closest category point of
the variable. We can then find out how well we have reconstructed the
original data in terms of a classification table.
Usage
1
2
Arguments
object
Object of class "homals"
...
Further arguments ignored
Details
For variables with rank restrictions we first project the objects on the
hyperplane spanned by the category quantifications, and then compute distances in that plane.
If there are sets of variables, the percentage correctly classified
for a variable is usually not the best thing to look at; except in some
special cases, in which some sets consist of a single variable (such as
regression and discriminant analysis). In any case a square table with observed vs.
predicted values is computed.
Value
cl.table
List of classification tables for each variable.
cr.vec
Vector with classification rates.
See Also
Examples
1
2
3
4
5
Example output
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE
3: .onUnload failed in unloadNamespace() for 'rgl', details:
call: fun(...)
error: object 'rgl_quit' not found
Classification rate:
Variable Cl. Rate %Cl. Rate
1 gender 0.7023 70.23
2 IQ 0.5147 51.47
3 advice 0.6481 64.81
4 SES 0.4760 47.60
5 School 0.0302 3.02
Classification Tables (observed vs. predicted):
Variable: gender
pre
obs F M
F 344 291
M 93 562
Variable: IQ
pre
obs 1 2 3 4 5 6 7 8 9
1 4 0 0 0 1 0 0 0 0
2 0 15 2 0 0 0 0 0 0
3 19 23 69 9 1 0 0 0 0
4 89 4 19 75 37 5 5 0 0
5 0 0 10 118 168 77 17 0 0
6 0 0 0 0 42 178 24 0 0
7 0 0 0 1 7 52 97 40 0
8 0 0 0 0 0 0 6 42 15
9 0 0 0 0 0 0 0 3 16
Variable: advice
pre
obs Agr Ext Gen Grls Man None Uni
Agr 11 0 1 0 0 0 0
Ext 11 93 0 0 24 32 1
Gen 45 0 166 166 29 0 1
Grls 0 0 5 13 0 0 0
Man 30 49 10 9 409 2 0
None 0 3 0 0 0 8 0
Uni 36 0 0 0 0 0 136
Variable: SES
pre
obs LoWC MidWC Prof Shop Skil Unsk
LoWC 7 9 12 0 12 3
MidWC 1 117 50 45 29 1
Prof 14 51 78 0 19 1
Shop 10 125 26 235 60 45
Skil 7 51 18 54 163 33
Unsk 0 0 0 0 0 14
Variable: School
pre
obs 11 13 15 16 17 18 2 20 23 24 25 26 27 28 29 3 30 32 34 35 36 37 4 5 6
1 0 0 0 0 1 1 3 1 0 6 2 0 0 2 1 0 6 0 0 0 0 0 1 4 0
10 0 1 0 2 1 0 4 1 0 6 3 0 0 5 0 1 1 1 0 1 0 0 0 4 0
11 0 0 0 2 0 0 4 1 0 6 3 0 0 2 0 1 5 0 0 0 0 0 0 6 0
12 0 0 0 2 0 0 11 0 1 2 5 0 0 2 0 0 4 2 0 1 0 0 1 5 0
13 0 1 0 1 1 0 15 1 0 7 2 0 0 5 0 2 4 5 0 0 0 1 1 6 0
14 0 0 0 2 0 0 5 1 0 5 0 0 0 2 0 5 1 6 0 0 0 0 0 2 0
15 0 0 0 1 0 0 5 0 0 3 6 0 0 6 0 3 1 4 0 0 0 0 1 3 0
16 1 0 0 3 1 0 13 7 0 1 0 0 0 1 0 8 4 20 0 0 0 0 3 3 0
17 0 0 2 1 2 0 13 2 0 8 2 0 0 0 1 4 2 14 0 0 1 0 4 0 0
18 0 0 0 1 0 0 4 1 0 4 5 0 0 1 1 1 1 3 0 0 0 1 1 5 1
19 0 0 0 0 0 1 3 0 0 5 2 0 0 2 0 4 2 1 0 0 0 0 1 5 0
2 0 0 0 0 0 0 15 2 0 0 0 1 0 0 1 2 3 7 0 0 0 0 0 2 0
20 0 0 1 1 0 0 7 1 0 5 0 0 0 1 1 2 2 0 0 0 0 1 0 5 0
21 0 0 0 1 1 0 3 0 0 5 0 0 0 4 1 1 3 0 0 0 0 0 0 3 0
22 0 0 0 0 0 0 4 1 0 3 1 0 0 1 1 0 6 4 0 0 0 0 2 4 0
23 0 0 1 1 0 0 3 4 0 4 1 0 1 2 1 3 1 2 0 0 0 0 1 1 0
24 0 0 1 0 0 0 3 1 0 9 7 0 0 1 0 1 2 1 0 1 0 2 0 7 0
25 0 0 2 0 0 0 0 0 0 0 5 0 0 3 0 0 0 1 0 0 0 0 0 0 0
26 0 0 1 2 1 0 8 0 0 5 1 0 0 1 0 1 3 1 0 0 0 0 0 3 0
27 0 0 0 2 1 0 3 4 0 0 1 0 0 0 1 0 1 1 0 0 0 0 1 0 0
28 0 0 0 0 0 0 1 2 0 2 3 0 0 4 0 2 1 2 0 0 0 2 2 6 0
29 0 1 0 3 1 0 4 1 0 0 1 0 0 1 0 1 1 1 0 0 0 0 0 4 0
3 0 0 0 2 0 0 12 2 0 2 0 0 0 1 0 1 4 3 0 0 0 0 1 3 0
30 0 0 0 0 0 0 7 0 0 6 3 0 0 0 0 5 5 0 0 0 0 1 1 4 0
31 0 0 0 2 1 0 12 2 0 2 1 0 0 4 2 2 5 15 0 0 0 0 0 1 0
32 0 0 5 2 1 0 11 0 0 3 2 2 0 0 0 2 1 24 0 0 0 2 0 2 0
33 0 0 0 2 1 0 13 2 0 1 1 0 0 4 1 1 3 6 0 0 0 0 0 2 0
34 0 0 0 2 0 0 14 1 0 1 0 0 0 2 0 2 5 10 0 0 0 0 0 2 0
35 0 0 0 0 0 0 6 2 0 5 3 0 0 4 0 5 1 2 0 1 0 1 1 4 0
36 0 0 1 1 1 0 6 0 0 3 0 0 0 1 1 0 2 0 0 1 0 0 0 10 0
37 0 0 0 0 0 0 1 0 0 3 3 0 0 0 0 2 1 1 0 0 0 0 0 2 0
4 1 1 1 6 0 0 16 3 0 5 0 0 0 1 0 4 0 14 1 0 0 0 10 3 0
5 0 0 0 0 1 1 3 1 0 6 8 0 0 4 0 4 6 0 0 0 0 0 0 5 0
6 1 1 0 2 0 0 5 1 1 5 3 0 0 1 2 5 9 3 0 0 0 1 2 3 0
7 1 0 0 3 1 1 6 1 0 6 3 0 0 0 1 3 3 6 0 0 0 0 2 2 0
8 0 1 0 0 1 0 4 0 0 6 4 0 0 2 0 1 3 4 0 1 0 0 0 4 0
9 0 1 1 2 0 1 17 0 0 1 1 0 0 3 1 4 6 12 0 0 0 0 0 3 0
pre
obs 7 8
1 1 0
10 0 0
11 0 0
12 0 0
13 0 0
14 0 0
15 0 0
16 0 0
17 1 0
18 0 1
19 0 0
2 0 0
20 0 0
21 3 0
22 0 0
23 0 0
24 0 0
25 0 0
26 0 0
27 0 0
28 0 0
29 0 1
3 0 0
30 0 0
31 0 0
32 0 0
33 0 0
34 0 0
35 0 0
36 1 0
37 3 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
9 0 0
homals documentation built on May 2, 2019, 6:51 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value See Also Examples
Description
Given a multiple homals solution we can reconstruct the indicator matrix by assigning each object to the closest category point of the variable. We can then find out how well we have reconstructed the original data in terms of a classification table.
Usage
1 2 |
Arguments
object |
Object of class |
... |
Further arguments ignored |
Details
For variables with rank restrictions we first project the objects on the hyperplane spanned by the category quantifications, and then compute distances in that plane. If there are sets of variables, the percentage correctly classified for a variable is usually not the best thing to look at; except in some special cases, in which some sets consist of a single variable (such as regression and discriminant analysis). In any case a square table with observed vs. predicted values is computed.
Value
cl.table |
List of classification tables for each variable. |
cr.vec |
Vector with classification rates. |
See Also
Examples
1 2 3 4 5 |
Example output
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE
3: .onUnload failed in unloadNamespace() for 'rgl', details:
call: fun(...)
error: object 'rgl_quit' not found
Classification rate:
Variable Cl. Rate %Cl. Rate
1 gender 0.7023 70.23
2 IQ 0.5147 51.47
3 advice 0.6481 64.81
4 SES 0.4760 47.60
5 School 0.0302 3.02
Classification Tables (observed vs. predicted):
Variable: gender
pre
obs F M
F 344 291
M 93 562
Variable: IQ
pre
obs 1 2 3 4 5 6 7 8 9
1 4 0 0 0 1 0 0 0 0
2 0 15 2 0 0 0 0 0 0
3 19 23 69 9 1 0 0 0 0
4 89 4 19 75 37 5 5 0 0
5 0 0 10 118 168 77 17 0 0
6 0 0 0 0 42 178 24 0 0
7 0 0 0 1 7 52 97 40 0
8 0 0 0 0 0 0 6 42 15
9 0 0 0 0 0 0 0 3 16
Variable: advice
pre
obs Agr Ext Gen Grls Man None Uni
Agr 11 0 1 0 0 0 0
Ext 11 93 0 0 24 32 1
Gen 45 0 166 166 29 0 1
Grls 0 0 5 13 0 0 0
Man 30 49 10 9 409 2 0
None 0 3 0 0 0 8 0
Uni 36 0 0 0 0 0 136
Variable: SES
pre
obs LoWC MidWC Prof Shop Skil Unsk
LoWC 7 9 12 0 12 3
MidWC 1 117 50 45 29 1
Prof 14 51 78 0 19 1
Shop 10 125 26 235 60 45
Skil 7 51 18 54 163 33
Unsk 0 0 0 0 0 14
Variable: School
pre
obs 11 13 15 16 17 18 2 20 23 24 25 26 27 28 29 3 30 32 34 35 36 37 4 5 6
1 0 0 0 0 1 1 3 1 0 6 2 0 0 2 1 0 6 0 0 0 0 0 1 4 0
10 0 1 0 2 1 0 4 1 0 6 3 0 0 5 0 1 1 1 0 1 0 0 0 4 0
11 0 0 0 2 0 0 4 1 0 6 3 0 0 2 0 1 5 0 0 0 0 0 0 6 0
12 0 0 0 2 0 0 11 0 1 2 5 0 0 2 0 0 4 2 0 1 0 0 1 5 0
13 0 1 0 1 1 0 15 1 0 7 2 0 0 5 0 2 4 5 0 0 0 1 1 6 0
14 0 0 0 2 0 0 5 1 0 5 0 0 0 2 0 5 1 6 0 0 0 0 0 2 0
15 0 0 0 1 0 0 5 0 0 3 6 0 0 6 0 3 1 4 0 0 0 0 1 3 0
16 1 0 0 3 1 0 13 7 0 1 0 0 0 1 0 8 4 20 0 0 0 0 3 3 0
17 0 0 2 1 2 0 13 2 0 8 2 0 0 0 1 4 2 14 0 0 1 0 4 0 0
18 0 0 0 1 0 0 4 1 0 4 5 0 0 1 1 1 1 3 0 0 0 1 1 5 1
19 0 0 0 0 0 1 3 0 0 5 2 0 0 2 0 4 2 1 0 0 0 0 1 5 0
2 0 0 0 0 0 0 15 2 0 0 0 1 0 0 1 2 3 7 0 0 0 0 0 2 0
20 0 0 1 1 0 0 7 1 0 5 0 0 0 1 1 2 2 0 0 0 0 1 0 5 0
21 0 0 0 1 1 0 3 0 0 5 0 0 0 4 1 1 3 0 0 0 0 0 0 3 0
22 0 0 0 0 0 0 4 1 0 3 1 0 0 1 1 0 6 4 0 0 0 0 2 4 0
23 0 0 1 1 0 0 3 4 0 4 1 0 1 2 1 3 1 2 0 0 0 0 1 1 0
24 0 0 1 0 0 0 3 1 0 9 7 0 0 1 0 1 2 1 0 1 0 2 0 7 0
25 0 0 2 0 0 0 0 0 0 0 5 0 0 3 0 0 0 1 0 0 0 0 0 0 0
26 0 0 1 2 1 0 8 0 0 5 1 0 0 1 0 1 3 1 0 0 0 0 0 3 0
27 0 0 0 2 1 0 3 4 0 0 1 0 0 0 1 0 1 1 0 0 0 0 1 0 0
28 0 0 0 0 0 0 1 2 0 2 3 0 0 4 0 2 1 2 0 0 0 2 2 6 0
29 0 1 0 3 1 0 4 1 0 0 1 0 0 1 0 1 1 1 0 0 0 0 0 4 0
3 0 0 0 2 0 0 12 2 0 2 0 0 0 1 0 1 4 3 0 0 0 0 1 3 0
30 0 0 0 0 0 0 7 0 0 6 3 0 0 0 0 5 5 0 0 0 0 1 1 4 0
31 0 0 0 2 1 0 12 2 0 2 1 0 0 4 2 2 5 15 0 0 0 0 0 1 0
32 0 0 5 2 1 0 11 0 0 3 2 2 0 0 0 2 1 24 0 0 0 2 0 2 0
33 0 0 0 2 1 0 13 2 0 1 1 0 0 4 1 1 3 6 0 0 0 0 0 2 0
34 0 0 0 2 0 0 14 1 0 1 0 0 0 2 0 2 5 10 0 0 0 0 0 2 0
35 0 0 0 0 0 0 6 2 0 5 3 0 0 4 0 5 1 2 0 1 0 1 1 4 0
36 0 0 1 1 1 0 6 0 0 3 0 0 0 1 1 0 2 0 0 1 0 0 0 10 0
37 0 0 0 0 0 0 1 0 0 3 3 0 0 0 0 2 1 1 0 0 0 0 0 2 0
4 1 1 1 6 0 0 16 3 0 5 0 0 0 1 0 4 0 14 1 0 0 0 10 3 0
5 0 0 0 0 1 1 3 1 0 6 8 0 0 4 0 4 6 0 0 0 0 0 0 5 0
6 1 1 0 2 0 0 5 1 1 5 3 0 0 1 2 5 9 3 0 0 0 1 2 3 0
7 1 0 0 3 1 1 6 1 0 6 3 0 0 0 1 3 3 6 0 0 0 0 2 2 0
8 0 1 0 0 1 0 4 0 0 6 4 0 0 2 0 1 3 4 0 1 0 0 0 4 0
9 0 1 1 2 0 1 17 0 0 1 1 0 0 3 1 4 6 12 0 0 0 0 0 3 0
pre
obs 7 8
1 1 0
10 0 0
11 0 0
12 0 0
13 0 0
14 0 0
15 0 0
16 0 0
17 1 0
18 0 1
19 0 0
2 0 0
20 0 0
21 3 0
22 0 0
23 0 0
24 0 0
25 0 0
26 0 0
27 0 0
28 0 0
29 0 1
3 0 0
30 0 0
31 0 0
32 0 0
33 0 0
34 0 0
35 0 0
36 1 0
37 3 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
9 0 0
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.