lineals: Linearizing bivariate regressions

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

This function performs optimal scaling in order to achieve linearizing transformations for each bivariate regression.

Usage

1
lineals(data, level = "nominal", itmax = 100, eps = 1e-06)

Arguments

data

Data frame or matrix

level

Vector with scale level of the variables ("nominal" or "ordinal"). If all variables have the same scale level, only one value can be provided

itmax

Maximum number of iterations

eps

Convergence criterion

Details

This function can be used as a preprocessing tool for categorical and ordinal data for subsequent factor analytical techniques such as structural equation models (SEM) using the resulting correlation matrix based on the transformed data. The estimates of the corresponding structural parameters are consistent if all bivariate regressions can be linearized.

Value

loss

Final value of the loss function

catscores

Resulting category scores (after optimal scaling)

cormat

Correlation matrix based on the scores

cor.rat

Matrix with correlation ratios

indmat

Indicator matrix (dummy coded)

scoremat

Transformed data matrix (i.e with category scores resulting from optimal scaling)

burtmat

Burt matrix

niter

Number of iterations

Author(s)

Jan de Leeuw, Patrick Mair

References

Mair, P., & de Leeuw, J. (2008). Scaling variables by optimizing correlational and non-correlational aspects in R. Journal of Statistical Software, Volume 32, Issue 9..

de Leeuw, J. (1988). Multivariate analysis with linearizable regressions. Psychometrika, 53, 437-454.

See Also

corAspect

Examples

1
2
3
data(galo)
res.lin <- lineals(galo)
summary(res.lin)

Example output

Correlation matrix of the scaled data:
            gender          IQ     advice         SES      School
gender  1.00000000 -0.04874627  0.0587721  0.44464385  0.11812726
IQ     -0.04874627  1.00000000 -0.3785085 -0.02835839 -0.04516452
advice  0.05877210 -0.37850852  1.0000000  0.10432693  0.10179781
SES     0.44464385 -0.02835839  0.1043269  1.00000000  0.21574025
School  0.11812726 -0.04516452  0.1017978  0.21574025  1.00000000


Correlation ratios:
            gender          IQ     advice         SES     School
gender 1.000000000 0.062646808 0.04382425 0.211611346 0.02876474
IQ     0.002374223 1.000000000 0.14797968 0.002707828 0.02368946
advice 0.003453644 0.151868265 1.00000000 0.014426019 0.03786227
SES    0.197709488 0.015776525 0.02439980 1.000000000 0.06305761
School 0.013954884 0.005686305 0.01462881 0.047306820 1.00000000

Category scores:
gender:
         score
F  0.02827736
M -0.02741393

IQ:
         score
1  0.01493307
2  0.04974990
3  0.03738258
4 -0.01845816
5 -0.01950283
6  0.03631026
7  0.00945805
8 -0.04985012
9 -0.05793540

advice:
            score
Agr   0.03773377
Ext  -0.03560291
Gen  -0.02665911
Grls -0.04475325
Man   0.02311238
None -0.02683326
Uni   0.03177932

SES:
              score
LoWC   0.035301554
MidWC -0.004592743
Prof   0.023233137
Shop  -0.029013033
Skil   0.034270873
Unsk  -0.058979048

School:
           score
1   0.033547729
2  -0.055892479
3  -0.030851601
4   0.022145217
5   0.006617672
6  -0.016776551
7  -0.027050022
8   0.046316419
9   0.006755903
10  0.007598514
11 -0.023818143
12 -0.029238985
13 -0.023741831
14 -0.001763256
15  0.060965074
16  0.004110105
17 -0.004537087
18 -0.010765472
19  0.041463536
20 -0.008007485
21  0.035683000
22  0.014749997
23  0.003369739
24 -0.056624313
25  0.034450939
26 -0.017516501
27  0.040176603
28  0.010139095
29  0.057634029
30 -0.049383121
31  0.017509168
32  0.007637931
33  0.013166794
34 -0.033436307
35  0.021174944
36 -0.013521375
37 -0.008761477

aspect documentation built on May 2, 2019, 2:15 p.m.

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