interpollen: Interpolation of Missing Data in a Pollen Database by...

Description Usage Arguments Details Value References See Also Examples

Description

Function to simultaneously replace all missing data of an historical database of several pollen types by using different methods of interpolation.

Usage

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interpollen(data, method = "lineal", maxdays = 30, plot = TRUE,
  factor = 2, ndays = 3, spar = 0.5, data2 = NULL, data3 = NULL,
  data4 = NULL, data5 = NULL, mincorr = 0.6, result = "wide")

Arguments

data

A data.frame object including the general database where interpollation must be performed. This data.frame must include a first column in Date format and the rest of columns in numeric format. Each column must contain information of one pollen type. It is not necessary to insert missing gaps; the function will automatically detect them.

method

A character string specifying the method applied to calculate and generate the pollen missing data. The implemented methods that can be used are: "lineal", "movingmean", "spline", "tseries" or "neighbour". A more detailed information about the different methods may be consulted in Details. The method argument will be "lineal" by default.

maxdays

A numeric (interger) value specifying the maximum number of consecutive days with missing data that the algorithm is going to interpolate. If the gap is bigger than the argument value, the gap will not be interpolated. Not valid with "tseries" method. The maxdays argument will be 30 by default.

plot

A logical argument. If TRUE, graphical previews of the input database will be plot at the end of the interpolation process. All the interpolated gaps will be marked in red. The plot argument will be TRUE by default.

factor

A numeric (interger) value bigger than 1. Only valid if the "movingmean" method is chosen. The argument specifies the factor which will multiply the gap size to stablish the range of the moving mean that will fulfill the gap. A more detailed information about the selection of the factor may be consulted in Details. The argument factor will be 1 by default.

ndays

A numeric (interger) value bigger than 1. Only valid if the "spline" method is chosen. Specifies the number of days beyond each side of the gap which are used to perform the spline regression. The argument ndays will be 3 by default.

spar

A numeric (double) value ranging 0_1 specifying the degree of smoothness of the spline regression adjustment. As smooth as the adjustment is, more data are considered as outliers for the spline regression. Only valid if the "spline" method is chosen. The argument "spar" will be 0.5 by default.

data2, data3, data4, data5

A data.frame object (each one) including database of a neighbour pollen station which will be used to interpolate missing data in the target station. Only valid if the "neighbour" method is chosen. This data.frame must include a first column in Date format and the rest of columns in numeric format belonging to each pollen type by column. It is not necessary to insert the missing gaps; the function will automatically detect them. The arguments will be NULL by default.

mincorr

A numeric (double) value ranging 0_1. It specifies the minimal correlation coefficient (Spearman correlations) that neighbour stations must have with the target station to be taken into account for the interpolation. Only valid if the "neighbour" method is chosen. The argument "mincorr" will be 0.6 by default.

result

A character string specifying the format of the resulting data.frame. Only "wide" or "long". The result argument will be "wide" by default.

Details

This function allows to interpolate missing data in a pollen database using 4 different methods which are described below. Interpolation for each pollen type will be automatically done for gaps smaller than the "maxdays" argument.

Value

This function returns different results:

References

Cleveland RB, Cleveland WS, McRae JE, Terpenning I (1990) STL: a seasonal_trend decomposition procedure based on loess. J Off Stat 6(1):3_33.

See Also

ma

Examples

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data("munich_pollen")
interpollen(munich_pollen, method = "lineal", plot = FALSE)

AeRobiology documentation built on June 3, 2019, 9:03 a.m.