\begin{Schunk}
\begin{Sinput}
 ## The mean-centered model sets the predictors at (x - xmean)
 reg.mod.14 <- meanCenter(reg.mod.11)
 summary(reg.mod.14)
\end{Sinput}
\begin{Soutput}
These variables were mean-centered before any transformations were made on the design matrix.
[1] "agencyc"   "extMotivc"
The centers and scale factors were 
       agencyc extMotivc
mean  2.511814  1.644807
scale 1.000000  1.000000
The summary statistics of the variables in the design matrix (after centering). 
                      mean  std.dev.
posAffect         3.065193 0.6315674
intMotiv          3.022962 0.6607460
agencyc           0.000000 0.5008115
extMotivc         0.000000 0.4760930
agencyc:extMotivc 0.058399 0.2740184

The following results were produced from: 
meanCenter.default(model = reg.mod.11)

Call:
lm(formula = posAffect ~ intMotiv + agencyc * extMotivc, data = stddat)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.88992 -0.35422  0.01966  0.42660  1.17393 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        2.28902    0.15707  14.574  < 2e-16 ***
intMotiv           0.25260    0.05094   4.959 1.08e-06 ***
agencyc            0.24333    0.06990   3.481 0.000559 ***
extMotivc         -0.11198    0.06905  -1.622 0.105687    
agencyc:extMotivc  0.21506    0.11525   1.866 0.062818 .  
---
Signif. codes:  
0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.5858 on 375 degrees of freedom
Multiple R-squared:  0.1488,	Adjusted R-squared:  0.1398 
F-statistic: 16.39 on 4 and 375 DF,  p-value: 2.175e-12
\end{Soutput}
\end{Schunk}
