Description Usage Arguments Details Value Author(s) See Also Examples
Use nonparametric methods to calculate time series trends
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  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,
...
)

mydata 
A data frame containing the field 
pollutant 
The parameter for which a trend test is required. Mandatory. 
deseason 
Should the data be dedeasonalized first? If 
type 
It is also possible to choose Type can be up length two e.g. 
statistic 
Statistic used for calculating monthly values. Default is
“mean”, but can also be “percentile”. See 
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 
data.thresh 
The data capture threshold to use (
the data using 
simulate 
Should simulations be carried out to determine the
MannKendall tau and pvalue. The default is 
n 
Number of bootstrap simulations if 
autocor 
Should autocorrelation be considered in the trend uncertainty
estimates? The default is 
cols 
Colours to use. Can be a vector of colours e.g. 
shade 
The colour used for marking alternate years. Use “white” or “transparent” to remove shading. 
xlab 
xaxis label, by default “year”. 
y.relation 
This determines how the yaxis scale is plotted. "same"
ensures all panels use the same scale and "free" will use panelspecfic
scales. The latter is a useful setting when plotting data with very
different values. ref.x See 
ref.x 
See 
ref.y 
A list with details of the horizontal lines to be added
representing reference line(s). For example, 
key.columns 
Number of columns used if a key is drawn when using the
option 
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

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 xaxis 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

auto.text 
Either 
k 
This is the smoothing parameter used by the 
... 
Other graphical parameters are passed onto 
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 nonlinear 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.
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
.
David Carslaw
TheilSen
for an alternative method of
calculating trends.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  # 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)

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