plot forecasts from fits

plot forecasts from fits

# S3 method for predict_pansim
plot(
  x,
  data = NULL,
  break_dates = NULL,
  dlspace = 1,
  limspace = 10,
  add_tests = FALSE,
  add_ICU_cap = FALSE,
  mult_var = NULL,
  directlabels = TRUE,
  log = TRUE,
  log_lwr = 1,
  ...
)

Arguments

x

a calibrated object (result from calibrate) or a prediction (from predict.fit_pansim)

data

original time series data

break_dates

breakpoints

dlspace

spacing for direct labels (not working)

limspace

extra space (in days) to add to make room for direct labels

add_tests

plot newTests/1000?

add_ICU_cap

include horizontal lines showing ICU capacity?

mult_var

variable in data set indicating multiple forecast types to compare

directlabels

use direct labels?

log

use a log10 scale for the y axis?

log_lwr

lower limit when using log scale

...

extra arguments (unused)

Examples

plot(ont_cal1)
#> Warning: specifying params_timevar with Relative_value is deprecated: auto-converting (reported once per session)
#> Loading required namespace: directlabels
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Removed 17 row(s) containing missing values (geom_path).
#> Warning: Removed 17 rows containing missing values (geom_dl).

ont_trans <- trans_state_vars(ont_all)
plot(ont_cal1,data=ont_trans)
#> Warning: specifying params_timevar with Relative_value is deprecated: auto-converting (reported once per session)
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Removed 17 row(s) containing missing values (geom_path).
#> Warning: Removed 110 rows containing missing values (geom_point).
#> Warning: Removed 97 row(s) containing missing values (geom_path).
#> Warning: Removed 17 rows containing missing values (geom_dl).

plot(ont_cal1,data=ont_trans, add_tests=TRUE)
#> Warning: specifying params_timevar with Relative_value is deprecated: auto-converting (reported once per session)
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Removed 17 row(s) containing missing values (geom_path).
#> Warning: Removed 126 rows containing missing values (geom_point).
#> Warning: Removed 98 row(s) containing missing values (geom_path).
#> Warning: Removed 17 rows containing missing values (geom_dl).

plot(ont_cal1,data=ont_trans, predict_args=list(end_date="2020-07-01"))
#> Warning: specifying params_timevar with Relative_value is deprecated: auto-converting (reported once per session)
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Removed 17 row(s) containing missing values (geom_path).
#> Warning: Removed 110 rows containing missing values (geom_point).
#> Warning: Removed 97 row(s) containing missing values (geom_path).
#> Warning: Removed 17 rows containing missing values (geom_dl).

# \donttest{
## FIXME: don't try these until we have an example where ensemble works
## pp <- predict(ont_cal_2brks, ensemble=TRUE)
## plot(pp)
## plot(pp, data=ont_trans)
# }