R: Calculate nonparametric smooth trends

smoothTrend {openair} | R Documentation

## Calculate nonparametric smooth trends

### Description

Use non-parametric methods to calculate time series trends

### Usage

    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,
      ...
    )

### Arguments

`mydata` | 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.
`pollutant` | The parameter for which a trend test is required. Mandatory.
`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.
`type` | `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` | Statistic used for calculating monthly values. Default is “mean”, but can also be “percentile”. See `timeAverage` for more details.
`avg.time` | 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` | 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.
`data.thresh` | 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"`.
`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.
`cols` | 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.
`shade` | The colour used for marking alternate years. Use “white” or “transparent” to remove shading.
`xlab` | x-axis label, by default “year”.
`y.relation` | 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)`.
`ref.x` | See `ref.y`.
`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.
`key.columns` | Number of columns used if a key is drawn when using the option `statistic = "percentile"`.
`name.pol` | 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.
`ci` | Should confidence intervals be plotted? The default is `FALSE`.
`alpha` | The alpha transparency of shaded confidence intervals - if plotted. A value of 0 is fully transparent and 1 is fully opaque.
`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. 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.
`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.
`k` | 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.

### 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 `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 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

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 `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`.

### Author(s)

David Carslaw

### See Also

`TheilSen` for an alternative method of calculating trends.

### Examples

    # 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)")

    # several pollutants, no plotting symbol
    ## Not run: smoothTrend(mydata, pollutant = c("no2", "o3", "pm10", "pm25"), pch = NA)

    # percentiles
    ## Not run: smoothTrend(mydata, pollutant = "o3", statistic = "percentile",
    percentile = 95)
    ## End(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))
    ## End(Not run)

[Package openair version 2.8-5 Index]