forcing uncorrelated random effects

Here is an example. The second has no correlation.

(fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy))
(fm2 <- lmer(Reaction ~ Days + (Days - 1|Subject), sleepstudy))

I don't think that's the correct answer. Model fm2 is also lacking a random 
intercept:

summary(fm2)               

Linear mixed model fit by REML
Formula: Reaction ~ Days + (Days - 1 | Subject)
   Data: sleepstudy
  AIC  BIC logLik deviance REMLdev
 1775 1787 -883.3     1774    1767
Random effects:
 Groups   Name Variance Std.Dev.
 Subject  Days  52.708   7.260
 Residual      842.030  29.018
Number of obs: 180, groups: Subject, 18

Fixed effects:
            Estimate Std. Error t value
(Intercept)  251.405      4.020   62.54
Days          10.467      1.869    5.60

Correlation of Fixed Effects:
     (Intr)
Days -0.340


Compare this to the answer by Daniel Johnson:

(fm2 <- lmer(Reaction ~ Days + (0+Days|Subject) + (1|Subject), sleepstudy))

Linear mixed model fit by REML
Formula: Reaction ~ Days + (0 + Days | Subject) + (1 | Subject)
   Data: sleepstudy
  AIC  BIC logLik deviance REMLdev
 1754 1770 -871.8     1752    1744
Random effects:
 Groups   Name        Variance Std.Dev.
 Subject  Days         35.858   5.9882
 Subject  (Intercept) 627.568  25.0513
 Residual             653.584  25.5653
Number of obs: 180, groups: Subject, 18

Fixed effects:
            Estimate Std. Error t value
(Intercept)  251.405      6.885   36.52
Days          10.467      1.560    6.71

Correlation of Fixed Effects:
     (Intr)
Days -0.184

Claus Wilke
Section of Integrative Biology 
 and Center for Computational Biology and Bioinformatics 
University of Texas at Austin
1 University Station C0930
Austin, TX 78712
cwilke at mail.utexas.edu
512 471 6028