title: Extract only the forecasted values from forecast()
body: <p>I have a dataframe that looks like this:</p>
  
  <pre><code>&gt; head(forecasts)
  $`1_1`
           Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
  Dec 2016       7.370299 7.335176 7.405422 7.316583 7.424015
  
  $`1_10`
           Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
  Dec 2016       7.396656 7.359845 7.433467 7.340359 7.452953
  
  $`1_2`
           Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
  Dec 2016       7.780033 7.752462 7.807605 7.737866 7.822201
  
  $`1_3`
           Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
  Dec 2016       7.216894 7.178896 7.254892 7.158781 7.275007
  
  $`1_4`
           Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
  Dec 2016       7.501195 7.465049 7.537341 7.445915 7.556475
  
  $`1_5`
           Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
  Dec 2016       7.455131 7.424918 7.485345 7.408924 7.501339
  </code></pre>
  
  <p>I would like to extract only the <code>Point Forecast</code></p>
  
  <p>A call to <code>str(forecasts)</code> returns a lot of output, this is the output for just one of the 89 lists in the 'forecasts' variable:</p>
  
  <pre><code>$ 9_9  :List of 10
    ..$ method   : chr "ARIMA(0,0,0)(0,1,0)[12] with drift"
    ..$ model    :List of 19
    .. ..$ coef     : Named num 0.00965
    .. .. ..- attr(*, "names")= chr "drift"
    .. ..$ sigma2   : num 0.0047
    .. ..$ var.coef : num [1, 1] 1.24e-06
    .. .. ..- attr(*, "dimnames")=List of 2
    .. .. .. ..$ : chr "drift"
    .. .. .. ..$ : chr "drift"
    .. ..$ mask     : logi TRUE
    .. ..$ loglik   : num 33.4
    .. ..$ aic      : num -62.7
    .. ..$ arma     : int [1:7] 0 0 0 0 12 0 1
    .. ..$ residuals: Time-Series [1:38] from 2014 to 2017: 0.00546 0.00583 0.006 0.00564 0.00563 ...
    .. ..$ call     : language .f(y = .x[[i]], x = list(x = c(5.4677292870219, 5.85045765518954, 6.02852764863892, 5.67941181324485,  5.67526620| __truncated__ ...
    .. ..$ series   : chr ".x[[i]]"
    .. ..$ code     : int 0
    .. ..$ n.cond   : int 0
    .. ..$ nobs     : int 26
    .. ..$ model    :List of 10
    .. .. ..$ phi  : num(0) 
    .. .. ..$ theta: num(0) 
    .. .. ..$ Delta: num [1:12] 0 0 0 0 0 0 0 0 0 0 ...
    .. .. ..$ Z    : num [1:13] 1 0 0 0 0 0 0 0 0 0 ...
    .. .. ..$ a    : num [1:13] 0.0677 5.6916 5.7073 5.692 5.7108 ...
    .. .. ..$ P    : num [1:13, 1:13] 0 0 0 0 0 0 0 0 0 0 ...
    .. .. ..$ T    : num [1:13, 1:13] 0 1 0 0 0 0 0 0 0 0 ...
    .. .. ..$ V    : num [1:13, 1:13] 1 0 0 0 0 0 0 0 0 0 ...
    .. .. ..$ h    : num 0
    .. .. ..$ Pn   : num [1:13, 1:13] 1 0 0 0 0 0 0 0 0 0 ...
    .. ..$ xreg     : int [1:38, 1] 1 2 3 4 5 6 7 8 9 10 ...
    .. .. ..- attr(*, "dimnames")=List of 2
    .. .. .. ..$ : NULL
    .. .. .. ..$ : chr "drift"
    .. ..$ bic      : num -60.2
    .. ..$ aicc     : num -62.2
    .. ..$ x        : Time-Series [1:38] from 2014 to 2017: 5.47 5.85 6.03 5.68 5.68 ...
    .. ..$ fitted   : Time-Series [1:38] from 2014 to 2017: 5.46 5.84 6.02 5.67 5.67 ...
    .. ..- attr(*, "class")= chr [1:2] "ARIMA" "Arima"
    ..$ level    : num [1:2] 80 95
    ..$ mean     : Time-Series [1:1] from 2017 to 2017: 6.32
    ..$ lower    : Time-Series [1, 1:2] from 2017 to 2017: 6.23 6.18
    .. ..- attr(*, "dimnames")=List of 2
    .. .. ..$ : NULL
    .. .. ..$ : chr [1:2] "80%" "95%"
    ..$ upper    : Time-Series [1, 1:2] from 2017 to 2017: 6.4 6.45
    .. ..- attr(*, "dimnames")=List of 2
    .. .. ..$ : NULL
    .. .. ..$ : chr [1:2] "80%" "95%"
    ..$ x        : Time-Series [1:38] from 2014 to 2017: 5.47 5.85 6.03 5.68 5.68 ...
    ..$ series   : chr ".x[[i]]"
    ..$ fitted   : Time-Series [1:38] from 2014 to 2017: 5.46 5.84 6.02 5.67 5.67 ...
    ..$ residuals: Time-Series [1:38] from 2014 to 2017: 0.00546 0.00583 0.006 0.00564 0.00563 ...
    ..- attr(*, "class")= chr "forecast"
  </code></pre>