fixed url, submitted again

vignettes/forecast-evaluation.Rmd

@@ -89,7 +89,7 @@ knit_hooks$set(chunk = function(x, options) {
 
 ```
 
-[onlineforecasting]: https://onlineforecasting.org/articles/onlineforecasting.pdf
+[onlineforecasting]: https://arxiv.org/abs/2109.12915
 [building heat load forecasting]: https://onlineforecasting.org/examples/building-heat-load-forecasting.html
 [onlineforecasting.org]: https://onlineforecasting.org
 <!--shared-init-end-->

vignettes/model-selection.Rmd

@@ -89,7 +89,7 @@ knit_hooks$set(chunk = function(x, options) {
 
 ```
 
-[onlineforecasting]: https://onlineforecasting.org/articles/onlineforecasting.pdf
+[onlineforecasting]: https://arxiv.org/abs/2109.12915
 [building heat load forecasting]: https://onlineforecasting.org/examples/building-heat-load-forecasting.html
 [onlineforecasting.org]: https://onlineforecasting.org
 <!--shared-init-end-->

vignettes/online-updating.Rmd

@@ -88,7 +88,7 @@ knit_hooks$set(chunk = function(x, options) {
 })
 ```
 
-[onlineforecasting]: https://onlineforecasting.org/articles/onlineforecasting.pdf
+[onlineforecasting]: https://arxiv.org/abs/2109.12915
 [building heat load forecasting]: https://onlineforecasting.org/examples/building-heat-load-forecasting.html
 [onlineforecasting.org]: https://onlineforecasting.org
 <!--shared-init-end-->

vignettes/setup-and-use-model.Rmd

@@ -89,7 +89,7 @@ knit_hooks$set(chunk = function(x, options) {
 
 ```
 
-[onlineforecasting]: https://onlineforecasting.org/articles/onlineforecasting.pdf
+[onlineforecasting]: https://arxiv.org/abs/2109.12915
 [building heat load forecasting]: https://onlineforecasting.org/examples/building-heat-load-forecasting.html
 [onlineforecasting.org]: https://onlineforecasting.org
 <!--shared-init-end-->

vignettes/setup-data.Rmd

@@ -90,7 +90,7 @@ knit_hooks$set(chunk = function(x, options) {
 
 ```
 
-[onlineforecasting]: https://onlineforecasting.org/articles/onlineforecasting.pdf
+[onlineforecasting]: https://arxiv.org/abs/2109.12915
 [building heat load forecasting]: https://onlineforecasting.org/examples/building-heat-load-forecasting.html
 [onlineforecasting.org]: https://onlineforecasting.org
 <!--shared-init-end-->
@@ -107,7 +107,7 @@ available [here](setup-data.R). More information on [onlineforecasting.org].
 
 First load the package:
 ```{r}
-## Load the package
+# Load the package
 library(onlineforecast)
 ```
 
@@ -118,13 +118,13 @@ heat load forecasting in the building-heat-load-forecasting vignette.
 When the package is loaded the data is also loaded, so we can access it
 directly. Let's start out by:
 ```{r}
-## Keep it in D to simplify notation
+# Keep it in D to simplify notation
 D <- Dbuilding
 ```
 
 The class is 'data.ĺist':
 ```{r}
-## The class of D
+# The class of D
 class(D)
 ```
 
@@ -134,7 +134,7 @@ order to have functions for the particular format of data - the format is explai
 It consists of vectors of time, vectors of observations (model output) and
 data.frames of forecasts (model input):
 ```{r}
-## Print the names to see the variables in the data
+# Print the names to see the variables in the data
 names(D)
 ```
 
@@ -157,7 +157,7 @@ then the check of the variables format is passed. See the help with
 
 First, lets have a look at `D$t`, which is the vector of time points:
 ```{r}
-## The time
+# The time
 class(D$t)
 head(D$t)
 tail(D$t)
@@ -188,9 +188,9 @@ operations can be done with:
 A helper function is provided with the `ct` function which can be called using `?`, or `?ct`. See example below:
 
 ```{r}
-## Convert from a time stamp (tz="GMT" per default)
+# Convert from a time stamp (tz="GMT" per default)
 ct("2019-01-01 11:00")
-## Convert from unix time
+# Convert from unix time
 ct(3840928387)
 ```
 Note that for all functions where a time value as a character is given, the time
@@ -220,7 +220,7 @@ str(D$heatload)
 
 It must have the same length as the time vector:
 ```{r}
-## Same length as time
+# Same length as time
 length(D$t)
 length(D$heatload)
 ```
@@ -233,11 +233,11 @@ plot(D$t, D$heatload, type="l", xlab="Time", ylab="Headload (kW)")
 The convention used in all examples is that the time points are always
 set to the time interval end point, e.g.:
 ```{r}
-## The observation
+# The observation
 D$heatload[2]
-## Represents the average load between
+# Represents the average load between
 D$t[1]
-## and
+# and
 D$t[2]
 ```
 The main idea behind setting the time point at the end of the interval is:
@@ -262,18 +262,18 @@ The rules are:
 
 Have a look at the forecasts of the global radiation:
 ```{r}
-## Global radiation forecasts
+# Global radiation forecasts
 head(D$I)
 ```
 
 At the first time point:
 ```{r}
-## First time point
+# First time point
 D$t[1]
 ```
 the available forecast ahead in time is at the first row:
 ```{r}
-## The forecast available ahead in time is in the first row
+# The forecast available ahead in time is in the first row
 D$I[1, ]
 ```
 
@@ -289,10 +289,10 @@ the steps are hourly, is an equi-distant time series. Picking out the
 entire series can be done by `D$I$k8` - hence a plot (together with the
 observations) can be generated by:
 ```{r}
-## Just pick some points by
+# Just pick some points by
 i <- 200:296
 plot(D$t[i], D$I$k8[i], type="l", col=2, xlab="Time", ylab="Global radiation (W/m²)")
-## Add the observations
+# Add the observations
 lines(D$t[i], D$Iobs[i])
 legend("topright", c("8-step forecasts","Observations"), bg="white", lty=1, col=2:1)
 ```
@@ -358,15 +358,15 @@ and note that the forecasts are lagged to be aligned in time. See `?pairs.data.l
 Just as a quick side note: This is the principle used for fitting onlineforecast
 models, simply shift forecasts to align with the observations:
 ```{r, fig.width=fhs, fig.height=fhs, out.width=ows}
-## Lag the 8-step forecasts to be aligned with the observations
+# Lag the 8-step forecasts to be aligned with the observations
 x <- lagvec(D$I$k8, 8)
-## Take a smaller range
+# Take a smaller range
 x <- x[i]
-## Take the observations
+# Take the observations
 y <- D$Iobs[i]
-## Fit a linear regression model
+# Fit a linear regression model
 fit <- lm(y ~ x)
-## Plot the result
+# Plot the result
 plot(x, y, xlab="8-step forecasts (W/m²)", ylab="Obsservations (W/m²)", main="Global radiation")
 abline(fit)
 ```
@@ -389,7 +389,7 @@ Taking a subset of a `data.list` is very useful and it can easily be done in
 different ways using the `subset` function (i.e. it's really the
 `subset.data.list` function called when:
 ```{r}
-## Take the 1 to 4 values of each variable in D
+# Take the 1 to 4 values of each variable in D
 Dsub <- subset(D, 1:4)
 summary(Dsub)
 ```
@@ -430,11 +430,11 @@ class(Df)
 
 After processing it is easily converted back to the `data.list` again by:
 ```{r}
-## Set back to data.frame
+# Set back to data.frame
 setDF(Df)
-## Convert to a data.list
+# Convert to a data.list
 Dsub2 <- as.data.list(Df)
-## Compare it with the original Dsub
+# Compare it with the original Dsub
 summary(Dsub2)
 summary(Dsub)
 ```