pool {mice} | R Documentation |
Pools the results of m repeated complete data analysis
pool(object, method="smallsample")
object |
An object of class |
method |
A string describing the method to compute the degrees of freedom. The default value is "smallsample", which specifies the is Barnard-Rubin adjusted degrees of freedom (Barnard and Rubin, 1999) for small samples. Specifying a different string produces the conventional degrees of freedom as in Rubin (1987). |
The function averages the estimates of the complete data model, computes the total variance over the repeated analyses, and computes the relative increase in variance due to nonresponse and the fraction of missing information. The function relies on the availability of
the estimates of the model, typically present as 'coefficients' in the fit object
an appropriate estimate of the variance-covariance matrix of the
estimates per analyses (estimated by vcov
.
The function pools also estimates obtained with lme()
and lmer()
,
BUT only the fixed part of the model.
An object of class mipo
, which stands for 'multiple imputation pooled outcome'. The object
is a list containing the following items:
call |
The call to the pool function. |
call1 |
The original call how the mira object was calculated. |
call2 |
The original call to the mice function to calculate the underlying midsobject. |
formula |
The formula that is used in call1. |
nmis |
The number of missing entries for each variable. |
m |
The number of imputations |
qhat |
A matrix, containing the estimated coeffients of the m repeated complete data analyses |
u |
The corresponding m variancematrices of the estimates in an three dimensional array. |
qbar |
The pooled estimate, formula (3.1.2) Rubin (1987). |
ubar |
The mean of the variances, formula (3.1.3), Rubin (1987). |
b |
The within imputation variance, formula (3.1.4), Rubin (1987). |
t |
Total variance of the pooled estimates, formula (3.1.5), Rubin (1987). |
r |
Relative increase in variance due to nonresponse, formula (3.1.7), Rubin (1987). |
dfcom |
Degrees of freedom for estimates in the complete data analysis. |
df |
Degrees of freedom for t reference distribution, calculated according to the article of Barnard and Rubin (1999). |
fmi |
Fraction missing information due to nonresponse, formula (3.1.10), Rubin (1987). |
gamma |
Proportion of the total variance explained by the imputations. |
Stef van Buuren, Karin Groothuis-Oudshoorn, 2009
Barnard, J. and Rubin, D.B. (1999). Small sample degrees of freedom with multiple imputation. Biometrika, 86, 948-955.
Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley and Sons.
van Buuren S and Groothuis-Oudshoorn K (2011).
mice
: Multivariate Imputation by Chained Equations in R
.
Journal of Statistical Software, 45(3), 1-67.
http://www.jstatsoft.org/v45/i03/
Pinheiro, J.C. and Bates, D.M. (2000). Mixed-Effects Models in S and S-PLUS. Berlin: Springer.
# which vcov methods can R find methods(vcov) # imp <- mice(nhanes) fit <- with(data=imp,exp=lm(bmi~hyp+chl)) pool(fit) #Call: pool(object = fit) # #Pooled coefficients: #(Intercept) hyp chl # 22.01313 -1.45578 0.03459 # #Fraction of information about the coefficients missing due to nonresponse: #(Intercept) hyp chl # 0.29571 0.05639 0.38759 #> summary(pool(fit)) # est se t df Pr(>|t|) lo 95 hi 95 missing #(Intercept) 22.01313 4.94086 4.4553 12.016 0.000783 11.24954 32.77673 NA #hyp -1.45578 2.26789 -0.6419 20.613 0.528006 -6.17752 3.26596 8 #chl 0.03459 0.02829 1.2228 9.347 0.251332 -0.02904 0.09822 10 # fmi #(Intercept) 0.29571 #hyp 0.05639 #chl 0.38759 #