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Hi
On 15 Jun 2004, Kelly Gallagher wrote:
> I would like to use imputation, given the following scenario:
>=20
> The data I possess are not independent as they quantify the degree of
> genetic relatedness for PAIRS of individuals sampled from a
> population.
>=20
> For example:
>=20
> individual_1 individual_2 relatedness
>=20
> 1 1 1.00
> 1 2 0.34
> 1 3 0.00
> 1 4 0.79
> 2 2 1.00
> 2 3 ?
> 3 4 0.50
> 4 4 1.00
>=20
=2E..
> I have tried to use Joe Schafer's NORM software, declaring the
> variables "individual_1" and "individual_2" as dummy variables. In
> doing so, am I using the appropriate model? Alternatively, should I be
> stratifying the
> categorical variables?
I just tried a crude regression approach (perhaps what NORM
does?) and got a result of .66. The SPSS analysis appears below,
although most of stats are not computable because of too few df.
=A0
data list free / i1 i2 rel.
begin data
1 1 1.00
1 2 0.34
1 3 0.00
1 4 0.79
2 2 1.00
2 3 9
3 4 0.50
4 4 1.00
end data.
missing values rel (9).
recode i1 (1 =3D -3) (2 3 4 =3D 1) into i1a.
recode i1 (1 =3D 0) (2 =3D -2) (3 4 =3D 1) into i1b.
recode i1 (1 2 =3D 0) (3 =3D -1) (4 =3D 1) into i1c.
recode i2 (1 =3D -3) (2 3 4 =3D 1) into i2a.
recode i2 (1 =3D 0) (2 =3D -2) (3 4 =3D 1) into i2b.
recode i2 (1 2 =3D 0) (3 =3D -1) (4 =3D 1) into i2c.
regres /vari =3D rel i1a to i2c /dep =3D rel /enter
/save pred(prd).
=A0=A0
=A0
Regression
=A0
Warnings
=A0
=20
For the final model with dependent variable REL, the
variance-covariance matrix is singular. Influence statistics
cannot be computed.
=20
=A0
Variables Entered/Removed(b)
=20
Model Variables Variables Method=20
Entered Removed =20
=20
1 I2C, I1C, . Enter =20
I2A, I2B, =20
I1A, I1B(a) =20
=20
=A2
a All requested variables entered.
b Dependent Variable: REL
=A0
Model Summary(b)
Model R R Square Adjusted R Std. Error =20
Square of the =20
Estimate =20
1 1.000(a) 1.000 1.000 . =20
=20
=A2
a Predictors: (Constant), I2C, I1C, I2A, I2B, I1A, I1B
b Dependent Variable: REL
=A0
ANOVA(b)
Model Sum of df Mean Square F Sig.=20
Squares =20
=20
1 Regression .927 6 .155 . .(a)=20
=20
Residual .000 0 . =20
=20
Total .927 6 =20
=20
=A2
a Predictors: (Constant), I2C, I1C, I2A, I2B, I1A, I1B
b Dependent Variable: REL
=A0
Coefficients(a)
Unstandardized Standardized t Sig.=20
Coefficients Coefficients =20
=20
Model B Std. Error Beta =20
=20
1 (Constant) .678 .000 . . =20
I1A 4.833E-02 .000 .263 . . =20
I1B -.233 .000 -.594 . . =20
I1C .250 .000 .367 . . =20
I2A -.156 .000 -.599 . . =20
I2B 1.833E-02 .000 .066 . . =20
I2C .395 .000 .760 . . =20
=20
a Dependent Variable: REL
=A0
Residuals Statistics(a)
Minimum Maximum Mean Std. N=20
Deviation =20
Predicted .000000 1.000000 .661429 .3931254 7=20
Value =20
Residual .000000 .000000 .000000 .0000000 7=20
Std. -1.682 .861 .000 1.000 7=20
Predicted =20
Value =20
Std. . . . . 0=20
Residual =20
=20
a Dependent Variable: REL
=A0
list i1 i2 rel prd.
=A0
List
I1 I2 REL PRD
1.0000 1.0000 1.0000 1.00000
1.0000 2.0000 .3400 .34000
1.0000 3.0000 .0000 .00000
1.0000 4.0000 .7900 .79000
2.0000 2.0000 1.0000 1.00000
2.0000 3.0000 9.0000 .66000
3.0000 4.0000 .5000 .50000
4.0000 4.0000 1.0000 1.00000
Best wishes
Jim
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=3D
James M. Clark=09=09=09=09(204) 786-9757
Department of Psychology=09=09(204) 774-4134 Fax
University of Winnipeg=09=09=094L05D
Winnipeg, Manitoba R3B [EMAIL PROTECTED]
CANADA=09=09=09=09=09http://www.uwinnipeg.ca/~clark
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