title: Model interpretation using timeslice method in CARET
body: <p>Suppose you want to evaluate a simple glm model to forecast an economic data series.
  Consider the following code:</p>
  
  <pre><code>library(caret)
  library(ggplot2)
  data(economics)
  h &lt;- 7
  myTimeControl &lt;- trainControl(method = "timeslice",
                                initialWindow = 24*h,
                                horizon = 12,
                                fixedWindow = TRUE)
  
  fit.glm &lt;- train(unemploy ~ pce + pop + psavert,
                      data = economics,
                      method = "glm",
                      preProc = c("center", "scale","BoxCox"),
                      trControl = myTimeControl)
  </code></pre>
  
  <p>Suppose that the covariates used into the train formula are predictions of values obtained by some other model.
  This simple model gives the following results:</p>
  
  <pre><code>Generalized Linear Model 
  
  574 samples
  3 predictor
  
  Pre-processing: centered (3), scaled (3), Box-Cox transformation (3) 
  Resampling: Rolling Forecasting Origin Resampling (12 held-out with a fixed   
  window) 
  Summary of sample sizes: 168, 168, 168, 168, 168, 168, ... 
  Resampling results:
  
  RMSE      Rsquared 
  1446.335  0.2958317
  </code></pre>
  
  <p>Apart from the bad results obtained (this is only an example).
  I wonder if it is correct:</p>
  
  <ol>
  <li>To consider the above results as results obtained, on the entire dataset, by a GLM trained using only 24*h=24*7 samples and retrained after every horizon=12 samples </li>
  <li>How evaluate RMSE as horizon grows from 1 to 12 (as reported here <a href="http://robjhyndman.com/hyndsight/tscvexample/" rel="nofollow">http://robjhyndman.com/hyndsight/tscvexample/</a> )?</li>
  </ol>
  
  <p>if I show fit.glm summary I obtain:</p>
  
  <pre><code>Call:
  NULL
  
  Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
  -5090.0  -1025.5   -208.1    833.4   4948.4  
  
  Coefficients:
               Estimate Std. Error t value Pr(&gt;|t|)    
  (Intercept)  7771.56      64.93 119.688  &lt; 2e-16 ***
  pce          5750.27    1153.03   4.987 8.15e-07 ***
  pop         -1483.01    1117.06  -1.328    0.185    
  psavert      2932.38     144.56  20.286  &lt; 2e-16 ***
  ---
  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  
  (Dispersion parameter for gaussian family taken to be 2420081)
  
  Null deviance: 3999514594  on 573  degrees of freedom
  Residual deviance: 1379446256  on 570  degrees of freedom
  
  
  AIC: 10072
  
  Number of Fisher Scoring iterations: 2 
  </code></pre>
  
  <p>The parameters showed refer to the last trained GLM or are "average" paramters?
  I hope I've been clear enough.</p>