8.7 Modelling Roles

# Note the risk variable which measures the severity of the outcome.

risk <- "risk_mm"

# Note the identifiers.

id <- c("date", "location")

# Initialise ignored variables: identifiers.

ignore <- c(risk, id)

# Remove the variables to ignore.

vars <- setdiff(vars, ignore)

# Identify the input variables for modelling.

inputs <- setdiff(vars, target) %T>% print()

##  [1] "rain_today"      "temp_3pm"        "temp_9am"        "cloud_3pm"      
##  [5] "cloud_9am"       "pressure_3pm"    "pressure_9am"    "humidity_3pm"   
##  [9] "humidity_9am"    "wind_speed_3pm"  "wind_speed_9am"  "wind_dir_3pm"   
## [13] "wind_dir_9am"    "wind_gust_speed" "wind_gust_dir"   "sunshine"       
## [17] "evaporation"     "rainfall"        "max_temp"        "min_temp"

# Also record them by indicies.

inputi <- 
  inputs %>%
  sapply(function(x) which(x == names(ds)), USE.NAMES=FALSE) %T>%
  print()

##  [1] 22 21 20 19 18 17 16 15 14 13 12 11 10  9  8  7  6  5  4  3

Also create the formula for modelling. Note that the target variable is the final column of the dataset. The stats::formula() function treats the first column as the target so reverse the list here to automatically generate the correct default formula.

form <- formula(ds[rev(vars)])

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