moments-methods: Calculate Moments of Poisson Distribution
In dpcR: Digital PCR Analysis
Description
Usage
Arguments
Value
Note
Author(s)
Examples
Description
Computes moments of a Poisson distribution. The calculations are based on
values of positive and total partitions or the theoretical lambda value.
Usage
1
moments(input)
Arguments
input
a vector with two elements (the first element is treated as
a number of positive partitions and the second as a number of total partitions)
or a matrix with two columns (first columns contains numbers of
positive partitions and the second total numbers of total partitions)
or an object of class dpcr .
Value
A data frame with four columns: name of the experiment,
name of the replicate, method of computation (theoretical or empirical),
name of the moment and the value of the moment. The theoretical moments are
computed using the lambda value and the empirical using the sample values.
Note
Four first moments of a Poisson distribution.
Mean : lambda.
Variance: lambda.
Skewness: lambda^(-0.5).
Kurtosis: lambda^(-1).
Author(s)
Michal Burdukiewicz.
Examples
1
2
3
4
5
# moments for 100 positive partitions of 765 total partitions
moments(c(100, 765))
# calculate moments for an array digital PCR
moments(six_panels)
dpcR documentation built on May 2, 2019, 7:04 a.m.
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Description Usage Arguments Value Note Author(s) Examples
Description
Computes moments of a Poisson distribution. The calculations are based on values of positive and total partitions or the theoretical lambda value.
Usage
1 | moments(input)
|
Arguments
input |
a vector with two elements (the first element is treated as
a number of positive partitions and the second as a number of total partitions)
or a matrix with two columns (first columns contains numbers of
positive partitions and the second total numbers of total partitions)
or an object of class |
Value
A data frame with four columns: name of the experiment, name of the replicate, method of computation (theoretical or empirical), name of the moment and the value of the moment. The theoretical moments are computed using the lambda value and the empirical using the sample values.
Note
Four first moments of a Poisson distribution.
Mean : lambda.
Variance: lambda.
Skewness: lambda^(-0.5).
Kurtosis: lambda^(-1).
Author(s)
Michal Burdukiewicz.
Examples
1 2 3 4 5 | # moments for 100 positive partitions of 765 total partitions
moments(c(100, 765))
# calculate moments for an array digital PCR
moments(six_panels)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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