smoothTrend: Calculate nonparametric smooth trends

In openair: Tools for the Analysis of Air Pollution Data

Calculate nonparametric smooth trends

Description

Use non-parametric methods to calculate time series trends

Usage

smoothTrend(
  mydata,
  pollutant = "nox",
  avg.time = "month",
  data.thresh = 0,
  statistic = "mean",
  percentile = NA,
  k = NULL,
  deseason = FALSE,
  simulate = FALSE,
  n = 200,
  autocor = FALSE,
  type = "default",
  cols = "brewer1",
  x.relation = "same",
  y.relation = "same",
  ref.x = NULL,
  ref.y = NULL,
  key.columns = 1,
  key.position = "bottom",
  strip.position = "top",
  name.pol = NULL,
  date.breaks = 7,
  date.format = NULL,
  auto.text = TRUE,
  ci = TRUE,
  alpha = 0.2,
  progress = TRUE,
  plot = TRUE,
  key = NULL,
  ...
)

Arguments

mydata	A data frame of time series. Must include a date field and at least one variable to plot.
pollutant	Name of variable to plot. Two or more pollutants can be plotted, in which case a form like pollutant = c("nox", "co") should be used.
avg.time	This defines the time period to average to. Can be "sec", "min", "hour", "day", "DSTday", "week", "month", "quarter" or "year". For much increased flexibility a number can precede these options followed by a space. For example, an average of 2 months would be avg.time = "2 month". In addition, avg.time can equal "season", in which case 3-month seasonal values are calculated with spring defined as March, April, May and so on. Note that avg.time can be less than the time interval of the original series, in which case the series is expanded to the new time interval. This is useful, for example, for calculating a 15-minute time series from an hourly one where an hourly value is repeated for each new 15-minute period. Note that when expanding data in this way it is necessary to ensure that the time interval of the original series is an exact multiple of avg.time e.g. hour to 10 minutes, day to hour. Also, the input time series must have consistent time gaps between successive intervals so that timeAverage() can work out how much 'padding' to apply. To pad-out data in this way choose fill = TRUE.
data.thresh	The data capture threshold to use (%). 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. See also interval, start.date and end.date to see whether it is advisable to set these other options.
statistic	Statistic used for calculating monthly values. Default is "mean", but can also be "percentile". See timeAverage() for more details.
percentile	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.
k	This is the smoothing parameter used by the mgcv::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.
deseason	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 imputed using a Kalman filter and Kalman smooth.
simulate	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.
n	Number of bootstrap simulations if simulate = TRUE.
autocor	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.
type	Character string(s) defining how data should be split/conditioned before plotting. "default" produces a single panel using the entire dataset. Any other options will split the plot into different panels - a roughly square grid of panels if one type is given, or a 2D matrix of panels if two types are given. type is always passed to cutData(), and can therefore be any of: A built-in type defined in cutData() (e.g., "season", "year", "weekday", etc.). For example, type = "season" will split the plot into four panels, one for each season. The name of a numeric column in mydata, which will be split into n.levels quantiles (defaulting to 4). The name of a character or factor column in mydata, which will be used as-is. Commonly this could be a variable like "site" to ensure data from different monitoring sites are handled and presented separately. It could equally be any arbitrary column created by the user (e.g., whether a nearby possible pollutant source is active or not). Most openair plotting functions can take two type arguments. If two are given, the first is used for the columns and the second for the rows.
cols	Colours to use for plotting. Can be a pre-set palette (e.g., "turbo", "viridis", "tol", "Dark2", etc.) or a user-defined vector of R colours (e.g., c("yellow", "green", "blue", "black") - see colours() for a full list) or hex-codes (e.g., c("#30123B", "#9CF649", "#7A0403")). See openColours() for more details.
x.relation, y.relation	This determines how the x- and y-axis scales are plotted. "same" ensures all panels use the same scale and "free" will use panel-specific 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).
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")).
key.columns	Number of columns to be used in a categorical legend. With many categories a single column can make to key too wide. The user can thus choose to use several columns by setting key.columns to be less than the number of categories.
key.position	Location where the legend is to be placed. Allowed arguments include "top", "right", "bottom", "left" and "none", the last of which removes the legend entirely.
strip.position	Location where the facet 'strips' are located when using type. When one type is provided, can be one of "left", "right", "bottom" or "top". When two types are provided, this argument defines whether the strips are "switched" and can take either "x", "y", or "both". For example, "x" will switch the 'top' strip locations to the bottom of the plot.
name.pol	This option can be used to give alternative names for the variables plotted. Instead of taking the column headings as names, the user can supply replacements. For example, if a column had the name "nox" and the user wanted a different description, then setting name.pol = "nox before change" can be used. If more than one pollutant is plotted then use c e.g. name.pol = c("nox here", "o3 there").
date.breaks	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. The user can override this behaviour by adjusting the value of date.breaks up or down.
date.format	This option controls the date format on the x-axis. A sensible format is chosen by default, but the user can set date.format to override this. For format types see strptime(). For example, to format the date like "Jan-2012" set date.format = "\%b-\%Y".
auto.text	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". Passed to quickText().
ci	Should confidence intervals be plotted? The default is TRUE.
alpha	The alpha transparency of shaded confidence intervals - if plotted. A value of 0 is fully transparent and 1 is fully opaque.
progress	Show a progress bar when many groups make up type? Defaults to TRUE.
plot	When openair plots are created they are automatically printed to the active graphics device. plot = FALSE deactivates this behaviour. This may be useful when the plot data is of more interest, or the plot is required to appear later (e.g., later in a Quarto document, or to be saved to a file).
key	Deprecated; please use key.position. If FALSE, sets key.position to "none".
...	Addition options are passed on to cutData() for type handling. Some additional arguments are also available: xlab, ylab and main override the x-axis label, y-axis label, and plot title. ylim and xlim control axis limits. layout sets the layout of facets - e.g., layout(2, 5) will have 2 columns and 5 rows. fontsize overrides the overall font size of the plot. cex, lwd, lty, alpha, and pch control various graphical parameters.

Details

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 mgcv::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% confidence intervals are shown by shading. Bootstrap estimates 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.

Value

an openair object

Author(s)

David Carslaw

See Also

Other time series and trend functions: TheilSen(), calendarPlot(), timePlot(), timeProp(), timeVariation()

Examples

# 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)")

# several pollutants, no plotting symbol
smoothTrend(mydata, pollutant = c("no2", "o3", "pm10", "pm25"), pch = NA)

# percentiles
smoothTrend(mydata,
  pollutant = "o3", statistic = "percentile",
  percentile = 95
)

# several percentiles with control over lines used
smoothTrend(mydata,
  pollutant = "o3", statistic = "percentile",
  percentile = c(5, 50, 95), lwd = c(1, 2, 1), lty = c(5, 1, 5)
)

## End(Not run)

openair documentation built on April 2, 2026, 9:07 a.m.