sliceplot | R Documentation |
Plot time slices within a tidal object to view response variable observations, predictions, and normalized results at regular annual intervals.
sliceplot(dat_in, ...)
## S3 method for class 'tidal'
sliceplot(
dat_in,
slices = c(1, 7),
tau = NULL,
dt_rng = NULL,
col_vec = NULL,
predicted = TRUE,
logspace = TRUE,
grids = TRUE,
pretty = TRUE,
lwd = 1,
size = 2,
alpha = 1,
...
)
## S3 method for class 'tidalmean'
sliceplot(
dat_in,
slices = c(1, 7),
predicted = TRUE,
dt_rng = NULL,
col_vec = NULL,
logspace = TRUE,
grids = TRUE,
pretty = TRUE,
lwd = 1,
size = 2,
alpha = 1,
...
)
dat_in |
input tidal or tidalmean object |
... |
arguments passed to other methods |
slices |
numeric vector of calender months to plot, i.e., 1 - 12 |
tau |
numeric vector of quantile to plot. The function will plot the 'middle' quantile if none is specified, e.g., if 0.2, 0.3, and 0.4 are present in the fitted model object then 0.3 will be plotted. |
dt_rng |
Optional chr string indicating the date range of the plot. Must be two values in the format 'YYYY-mm-dd' which is passed to |
col_vec |
chr string of plot colors to use, passed to |
predicted |
logical indicating if standard predicted values are plotted, default |
logspace |
logical indicating if plots are in log space |
grids |
logical indicating if grid lines are present |
pretty |
logical indicating if my subjective idea of plot aesthetics is applied, otherwise the |
lwd |
numeric value indicating width of lines |
size |
numeric value indicating size of points |
alpha |
numeric value indicating transparency of points or lines |
This is a modification of fitplot
that can be used to plot selected time slices from the results of a fitted tidal
object. For example, all results for a particular month across all years can be viewed. This is useful for evaluating between-year differences in results for constant season. Only one quantile fit can be shown per plot because the grouping variable is mapped to the slices.
A ggplot
object that can be further modified
fitplot
, prdnrmplot
## load a fitted tidal object
data(tidfit)
# plot using defaults
sliceplot(tidfit)
# get different months - march and september
sliceplot(tidfit, slices = c(3, 9))
# normalized predictions, 10th percentile
sliceplot(tidfit, tau = 0.1, predicted = FALSE)
# normalized values all months, change line aesthetics, log-space, 90th
# add title
library(ggplot2)
sliceplot(tidfit,
slices = 1:12,
size = 1.5,
tau = 0.9,
alpha = 0.6,
predicted = FALSE,
logspace = TRUE
) +
ggtitle('Normalized predictions for all months, 90th percentile')
## plot a tidalmean object
data(tidfitmean)
sliceplot(tidfitmean)
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