rysgran (version 2.0)

gran.stats: Statistical Analysis of Grain Size for Unconsolidated Sediments

Description

Calculates mean, median, sorting, skewness, kurtosis, fifth and sixth moments, and creates the verbal classification of the results. Uses the statistical methods of Trask (1930), Otto (1939), Folk & Ward (1957), McCammon(a) (1962), McCammon(b) (1962) and Method of Moments (TANNER, 1995) Data input can be in logarithmic (phi) or geometric (micrometers) scale. Regardless the input data, the user can choose the output result scale through output argument

Usage

gran.stats(data, output = "phi", method = "folk", verbal = FALSE, lang = "en-US")

Arguments

data

a data matrix with grain size samples

output

output result scale. Could be output="phi" for logarithmic scale or output="metric" for geometric scale. The default is "phi"

method

statistical analysis method. Could be method="folk" , method="moment" , method="otto" , method="trask" , method="mcA" and method="mcB". Default is method="folk"

verbal

logical. If TRUE, columns will be added with verbal classification of statistical paramenters. Default is TRUE

lang

language . Could be english ("en-US", "en-GR", "eng", "e"), or portuguese ("pt-BR", "pt-PT", "port", "p"). The default is "en-US"

Value

• An array of variable number of dimensions, depending on the chosen arguments, with the statistical parameters for each sample. The values of this matrix should be used in rysgran.plot function, available in this package

Details

The particle size matrix used in data entry must contain the first line of grain size classes (logarithmic or geometric scale), each following line should contain the weights of a sample. No header should be used Example of particle size matrix with classes in logarithmic scale lllllllllllllllllll{ row names V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 Samples -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 A 0.0 0.0 0.0 0.02 0.07 0.10 0.18 0.27 0.58 5.08 11.18 1.29 B 0.0 0.0 0.0 0.00 0.00 0.00 0.00 0.05 0.59 12.98 26.60 2.90 } Example of particle size matrix with classes in geometric scale (micrometers) lllllllllllllllllll{ row names V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 Samples 2828 2000 1414 1000 707 500 354 250 177 125 88 63 A 0.0 0.0 0.0 0.02 0.07 0.10 0.18 0.27 0.58 5.08 11.18 1.29 B 0.0 0.0 0.0 0.00 0.00 0.00 0.00 0.05 0.59 12.98 26.60 2.90 } gran.stats automatically detects which scale of grain size is being used and converts the results according to the output argument For further details on the structure of the input table see data examples camargo2001, sed.phi and sed.metric included in this package

References

- Folk, R. L. and Ward W. C. (1957) Brazos river bar: A study in the significance of grain size parameters. Journal of Sed. Petrol., 27: 3--27. - McCammon, R. B. (1962) Efficiencies of percentile measurements for describing the mean size and sorting of sedimentary particles. Journal of Geology, 70: 453--465. - Otto, G. H. (1939) A modified logarithmic probability paper for the interpretation of mechanical analysis of sediments. Journal os Sed. Petrol., 9: 62--76. - Tanner, W.F. (1995) Environmental clastic granulometry. Florida Geological Survey, Special Publication 40. 142 pp. - Trask, P. D. (1930) Mechanical analysis of sediments by centrifuge. Economic Geology, 25: 581--599.

See Also

rysgran.plot , rysgran.ternary , rysgran.hist , class.percent

Examples

library (rysgran)
data (camargo2001)
data (sed.metric)

#Folk & Ward

gran.stats(camargo2001, output="phi", method = "folk" , verbal = FALSE)


#Folk & Ward with verbal classification

gran.stats (camargo2001, output="phi", method = "folk" , verbal = TRUE)


#Folk & Ward with geometric data and verbal classification

gran.stats (sed.metric, output="phi", method = "folk" , verbal = TRUE)


#Method of Moments with geometric data and verbal classification

gran.stats (sed.metric, output="phi", method = "moment" , verbal = TRUE)