Use non-parametric methods to calculate time series trends
smoothTrend(mydata, pollutant = "nox", deseason = FALSE, type = "default",
statistic = "mean", avg.time = "month", percentile = NA,
data.thresh = 0, simulate = FALSE, n = 200, autocor = FALSE,
cols = "brewer1", shade = "grey95", xlab = "year",
y.relation = "same", ref.x = NULL, ref.y = NULL,
key.columns = length(percentile), name.pol = pollutant, ci = TRUE,
alpha = 0.2, date.breaks = 7, auto.text = TRUE, k = NULL, ...)A data frame containing the field date and at
least one other parameter for which a trend test is required;
typically (but not necessarily) a pollutant.
The parameter for which a trend test is required. Mandatory.
Should the data be de-deasonalized first? If
TRUE the function stl is used (seasonal trend
decomposition using loess). Note that if TRUE missing
data are first linearly interpolated because stl cannot
handle missing data.
type determines how the data are split i.e.
conditioned, and then plotted. The default is will produce a
single plot using the entire data. Type can be one of the
built-in types as detailed in cutData e.g.
“season”, “year”, “weekday” and so on. For
example, type = "season" will produce four plots --- one
for each season.
It is also possible to choose type as another variable in
the data frame. If that variable is numeric, then the data will
be split into four quantiles (if possible) and labelled
accordingly. If type is an existing character or factor
variable, then those categories/levels will be used directly.
This offers great flexibility for understanding the variation of
different variables and how they depend on one another.
Type can be up length two e.g. type = c("season",
"weekday") will produce a 2x2 plot split by season and day of
the week. Note, when two types are provided the first forms the
columns and the second the rows.
Statistic used for calculating monthly values.
Default is “mean”, but can also be “percentile”.
See timeAverage for more details.
Can be “month” (the default), “season” or “year”. Determines the time over which data should be averaged. Note that for “year”, six or more years are required. For “season” the data are plit up into spring: March, April, May etc. Note that December is considered as belonging to winter of the following year.
Percentile value(s) to use if statistic =
"percentile" is chosen. Can be a vector of numbers e.g.
percentile = c(5, 50, 95) will plot the 5th, 50th and
95th percentile values together on the same plot.
The data capture threshold to use (
aggregating the data using avg.time. A value of zero
means that all available data will be used in a particular
period regardless if of the number of values available.
Conversely, a value of 100 will mean that all data will need to
be present for the average to be calculated, else it is recorded
as NA. Not used if avg.time = "default".
Should simulations be carried out to determine the
Mann-Kendall tau and p-value. The default is FALSE. If
TRUE, bootstrap simulations are undertaken, which also
account for autocorrelation.
Number of bootstrap simulations if simulate =
TRUE.
Should autocorrelation be considered in the trend
uncertainty estimates? The default is FALSE. Generally,
accounting for autocorrelation increases the uncertainty of the
trend estimate sometimes by a large amount.
Colours to use. Can be a vector of colours e.g.
cols = c("black", "green") or pre-defined openair colours
--- see openColours for more details.
The colour used for marking alternate years. Use “white” or “transparent” to remove shading.
x-axis label, by default “year”.
This determines how the y-axis scale is plotted.
"same" ensures all panels use the same scale and "free" will use
panel-specfic scales. The latter is a useful setting when
plotting data with very different values. ref.x See
ref.y for details. In this case the correct date format
should be used for a vertical line e.g. ref.x = list(v =
as.POSIXct("2000-06-15"), lty = 5).
See ref.y.
A list with details of the horizontal lines to be
added representing reference line(s). For example, ref.y =
list(h = 50, lty = 5) will add a dashed horizontal line at 50.
Several lines can be plotted e.g. ref.y = list(h = c(50,
100), lty = c(1, 5), col = c("green", "blue")). See
panel.abline in the lattice package for more
details on adding/controlling lines.
Number of columns used if a key is drawn when
using the option statistic = "percentile".
Names to be given to the pollutant(s). This is useful if you want to give a fuller description of the variables, maybe also including subscripts etc.
Should confidence intervals be plotted? The default is
FALSE.
The alpha transparency of shaded confidence intervals - if plotted. A value of 0 is fully transparent and 1 is fully opaque.
Number of major x-axis intervals to use. The
function will try and choose a sensible number of dates/times as
well as formatting the date/time appropriately to the range
being considered. This does not always work as desired
automatically. The user can therefore increase or decrease the
number of intervals by adjusting the value of date.breaks
up or down.
Either TRUE (default) or FALSE. If
TRUE titles and axis labels will automatically try and
format pollutant names and units properly e.g. by subscripting
the ‘2’ in NO2.
This is the smoothing parameter used by the gam
function in package mgcv. By default it is not used and
the amount of smoothing is optimised automatically. However,
sometimes it is useful to set the smoothing amount manually
using k.
Other graphical parameters are passed onto
cutData and lattice:xyplot. For example,
smoothTrend passes the option hemisphere =
"southern" on to cutData to provide southern (rather
than default northern) hemisphere handling of type =
"season". Similarly, common graphical arguments, such as
xlim and ylim for plotting ranges and pch
and cex for plot symbol type and size, are passed on
xyplot, although some local modifications may be applied
by openair. For example, axis and title labelling options (such
as xlab, ylab and main) are passed to
xyplot via quickText to handle routine formatting.
One special case here is that many graphical parameters can be
vectors when used with statistic = "percentile" and a
vector of percentile values, see examples below.
As well as generating the plot itself, smoothTrend
also returns an object of class ``openair''. The object includes
three main components: call, the command used to generate
the plot; data, the data frame of summarised information
used to make the plot; and plot, the plot itself. Note
that data is a list of two data frames: data (the
original data) and fit (the smooth fit that has details
of the fit and teh uncertainties). If retained, e.g. using
output <- smoothTrend(mydata, "nox"), this output can be
used to recover the data, reproduce or rework the original plot
or undertake further analysis.
An openair output can be manipulated using a number of generic
operations, including print, plot and
summarise.
The smoothTrend function provides a flexible way of
estimating the trend in the concentration of a pollutant or other
variable. Monthly mean values are calculated from an hourly (or
higher resolution) or daily time series. There is the option to
deseasonalise the data if there is evidence of a seasonal cycle.
smoothTrend uses a Generalized Additive Model (GAM) from
the gam package to find the most appropriate level
of smoothing. The function is particularly suited to situations
where trends are not monotonic (see discussion with
TheilSen for more details on this). The
smoothTrend function is particularly useful as an
exploratory technique e.g. to check how linear or non-linear
trends are.
95
of the confidence intervals are also available through the
simulate option. Residual resampling is used.
Trends can be considered in a very wide range of ways, controlled by
setting type - see examples below.
TheilSen for an alternative method of
calculating trends.
# NOT RUN {
# load example data from package
data(mydata)
# trend plot for nox
smoothTrend(mydata, pollutant = "nox")
# trend plot by each of 8 wind sectors
# }
# NOT RUN {
smoothTrend(mydata, pollutant = "o3", type = "wd", ylab = "o3 (ppb)")
# }
# NOT RUN {
# several pollutants, no plotting symbol
# }
# NOT RUN {
smoothTrend(mydata, pollutant = c("no2", "o3", "pm10", "pm25"), pch = NA)
# }
# NOT RUN {
# percentiles
# }
# NOT RUN {
smoothTrend(mydata, pollutant = "o3", statistic = "percentile",
percentile = 95)
# }
# NOT RUN {
# several percentiles with control over lines used
# }
# NOT RUN {
smoothTrend(mydata, pollutant = "o3", statistic = "percentile",
percentile = c(5, 50, 95), lwd = c(1, 2, 1), lty = c(5, 1, 5))
# }
# NOT RUN {
# }
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