Calculate nonparametric smooth trendsSource:
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
dateand 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
stlis used (seasonal trend decomposition using loess). Note that if
TRUEmissing data are first imputed using a Kalman filter and Kalman smooth.
typedetermines 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
cutDatae.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
typeas 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
timeAveragefor 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 ( 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
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
openColoursfor 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.yfor 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).
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
latticepackage 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
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.breaksup or down.
TRUEtitles 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
gamfunction 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
Other graphical parameters are passed onto
lattice:xyplot. For example,
smoothTrendpasses the option
hemisphere = "southern"on to
cutDatato provide southern (rather than default northern) hemisphere handling of
type = "season". Similarly, common graphical arguments, such as
ylimfor plotting ranges and
cexfor 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
main) are passed to
quickTextto handle routine formatting. One special case here is that many graphical parameters can be vectors when used with
statistic = "percentile"and a vector of
percentilevalues, see examples below.
As well as generating the plot itself,
returns an object of class ``openair''. The object includes three main
call, the command used to generate the plot;
data, the data frame of summarised information used to make the
plot, the plot itself. Note that
data is a list of
two data frames:
data (the original data) and
smooth fit that has details of the fit and teh uncertainties). If
retained, e.g. using
output <- smoothTrend(mydata, "nox"), this
output can be
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,
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
smoothTrend function is particularly useful as an
exploratory technique e.g. to check how linear or non-linear trends are.
confidence intervals are also available through the
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
# load example data from package data(mydata) # trend plot for nox smoothTrend(mydata, pollutant = "nox") # trend plot by each of 8 wind sectors if (FALSE) smoothTrend(mydata, pollutant = "o3", type = "wd", ylab = "o3 (ppb)") # several pollutants, no plotting symbol if (FALSE) smoothTrend(mydata, pollutant = c("no2", "o3", "pm10", "pm25"), pch = NA) # percentiles if (FALSE) smoothTrend(mydata, pollutant = "o3", statistic = "percentile", percentile = 95) # several percentiles with control over lines used if (FALSE) smoothTrend(mydata, pollutant = "o3", statistic = "percentile", percentile = c(5, 50, 95), lwd = c(1, 2, 1), lty = c(5, 1, 5))