Size of gravels collected from a sandbar in the Mamquam River,
British Columbia, Canada. Summary data, giving the frequency of
observations in 16 different size classes.
Usage
data(mamquam)
Format
The mamquam data frame has 16 rows and 2 columns.
[, 1]
midpoints
midpoints of intervals (psi units)
[, 2]
counts
number of observations in interval
Details
Gravel sizes are determined by passing clasts through templates of
particular sizes. This gives a range in which the size of each clast
lies. Sizes (in mm) are then converted into psi units by taking the
base 2 logarithm of the size. The midpoints specified are the midpoints
of the psi unit ranges, and counts gives the number of observations
in each size range. The classes are of length 0.5 psi units.
There are 3574 observations.
Source
Rice, Stephen and Church, Michael (1996)
Sampling surficial gravels: the precision of size distribution
percentile estimates.
J. of Sedimentary Research,
66, 654–665.
Examples
data(mamquam)
str(mamquam)
### Construct data from frequency summary, taking all observations
### at midpoints of intervals
psi <- rep(mamquam$midpoints, mamquam$counts)
barplot(table(psi))
### Fit the hyperbolic distribution
hyperbFit(psi)
### Actually hyperbFit can deal with frequency data
hyperbFit(mamquam$midpoints, freq = mamquam$counts)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(GeneralizedHyperbolic)
Loading required package: DistributionUtils
Loading required package: RUnit
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GeneralizedHyperbolic/mamquam.Rd_%03d_medium.png", width=480, height=480)
> ### Name: mamquam
> ### Title: Size of Gravels from Mamquam River
> ### Aliases: mamquam
> ### Keywords: datasets
>
> ### ** Examples
>
> data(mamquam)
> str(mamquam)
'data.frame': 16 obs. of 2 variables:
$ midpoints: num 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 5.25 ...
$ counts : num 1 6 34 58 94 161 262 340 497 628 ...
> ### Construct data from frequency summary, taking all observations
> ### at midpoints of intervals
> psi <- rep(mamquam$midpoints, mamquam$counts)
> barplot(table(psi))
> ### Fit the hyperbolic distribution
> hyperbFit(psi)
Data: psi
Parameter estimates:
mu delta alpha beta
7.754 2.340 5.618 -3.907
Likelihood: -5542.066
criterion : MLE
Method: Nelder-Mead
Convergence code: 0
Iterations: 575
>
> ### Actually hyperbFit can deal with frequency data
> hyperbFit(mamquam$midpoints, freq = mamquam$counts)
Data: mamquam$midpoints
Parameter estimates:
mu delta alpha beta
7.754 2.340 5.618 -3.907
Likelihood: -5542.066
criterion : MLE
Method: Nelder-Mead
Convergence code: 0
Iterations: 575
>
>
>
>
>
> dev.off()
null device
1
>