Sib-pair Command: impute


ClassAnalysis and data manipulation command
Nameimpute
Arguments <ytrait>
<ytrait> on <loc1>...[to]...<locN> [complete_obs]

If a single argument is given, replace missing quantitative trait values with the predicted values from a regression on the spouse, sibling and offspring observed values. Designed mainly for imputing missing age or date of birth.

If covariates are given, then replace missing quantitative trait values with the predicted value from the multivariate regression on the list of predictors (which may include the average allele length of a marker locus). The com option means only individuals with no missing values for any of the listed predictor traits will be updated. Otherwise, missing values are replaced with the sample mean for that phenotype when calculating the predicted value.

Example:

>> impute DOB

Relation       N    Intercept          Sex         Beta
Offspr     22780  -10631.6323     700.7983       0.9357
Siblings   44283      14.0954     -26.4170       0.9999
Spouses    18893   -1161.2528    1456.5714       0.9617

Outliers (>3 SDs)

Regression   ID                      Observed       Expected    StdRes  Relatives-Mean
---------- ---------------------- -------------- -------------- ------- --------------
 Offspring 0002.00.009-00.009.013    7305.000000  -28005.154070    3.67  -18567.000000
 Offspring 0002.01.004-01.004.012    7305.000000  -28403.503637    3.71  -18992.714286
    Spouse 0004.02.004-02.004.017  -23741.000000   10832.702214   -3.59   10957.000000

Imputed   9673 missing values.

>> include williamsex.in
>> impute adjChol = ldl CHD age

------------------------------------------------
Linear regression analysis of trait "adjChol"
------------------------------------------------

    Variable         Beta    Stand Error        t-Value
  -----------------------------------------------------
  Intercept      239.4865        44.2594         5.4110 ***
  ldl*2          120.8932        36.1504         3.3442 **
  CHD             27.0280       117.2997         0.2304 .
  age             -3.9219         1.6702         2.3481 *

No. usable observations =     27      (  45.0%)
Model Mean Square       =  49742.0342 (df=   3)
Mean Square Error       =  12360.3563 (df=  23)
Multiple R**2           =      0.3442
Akaike Inf. Criterion   =      9.6445

Wrote     14 predicted values to adjChol.


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