The R package modes was designed with a dual purpose of accurately estimating the mode (or modes) as well as characterizing the modality of data. The specific application area includes complex or mixture distibutions particularly in a big data environment. The heterogenous nature of (big) data may require deep introspective statistical and machine learning techniques, but these statistical tools often fail when applied without first understanding the data. In small datasets, this often isn't a big issue, but when dealing with large scale data analysis or big data thoroughly inspecting each dimension typically yields an O(n^n-1) problem. As such, dealing with big data require an alternative toolkit. This package not only identifies the mode or modes for various data types, it also provides a programmatic way of understanding the modality (i.e. unimodal, bimodal, etc.) of a dataset (whether it's big data or not). See <http://www.sdeevi.com/modes_package> for examples and discussion.

This package was designed to find the modes and aide in assessing the modality of a dataset. It was optimized programmatically to be as efficient on big data as possible. The enclosed techniques span various fields of statistics and machine learning from exploratory data analysis, to distribution theory, to univariate & multivariate statistics as well as data munging and multi-stage machine learning.

The key functions that are included in this package include:

Nonparametric

Mode: This function calculates the mode for an integer valued data vector by default. It also calculates "nmore" more modes than the most frequently occurring value and can take in data that should be treated as integers, real numbers (which can optionally be rounded to the "digits" number of significant digits), or factors. This mode function finds the value(s) that occur most frequently so, crucially, if there is a tie in the frequency count for the mode it will yield two modes instead of the lower valued mode. Yielding all modes instead of just the lowest mode is particularly important when more advanced statistics and machine learning techniques are employed.

Bimodality Amplitude: This function calculates the Bimodality Ampltiude of a data vector. This is a measure of the proportion of bimodality and the existence of bimodality. The value lies between zero and one (that is: [0,1]) where the value of zero implies that the data is unimodal and the value of one implies the data is two point masses. The proportion of bimodality here is referring to the mixture proportions of two, say, Gaussian (normal) components that can have different frequencies.

Bimodality Coefficient: This function calculates the Bimodality Coefficient of a data vector with the option for a finite sample (bias) correction. This bias correction is important to correct for the (well-documented) finite sample bias. The bimodality coefficient has a range of zero to one (that is: [0,1]) where a value greater than "5/9" suggests bimodality. The maximum value of one ("1") can only be reached when the distribution is composed of two point masses.

Bimodality Ratio: This function calculates the Bimodality Ratio which is a measure of the proportion of bimodality. The proportion of bimodality here is referring to the mixture proportions of two, say, Gaussian (normal) components that can have different frequencies. For instance, a 50 separation will be different from a 25

Parametric

Ashman, Bird, and Zepf's D Statistic (Ashman's D): This function calculates a measure of how well differentiated two distributions (distribution components) are. For instance, if the two distributions are identical, this statistic is zero. A good rule of thumb is that if the statistic is above ~2, there is good separation. If you suspect that your data is bimodal this can be used by replicating the suspected mixture components and checking the statistic. Alternatively, if the components are known outright this is straightforward to implement.

Bimodality Separation: the Bimodality Separation statistic measures how differentiated two distributions (distribution components) are. However, this statistic uses the added assumption that both are Gaussian (normal) distributions (or that the distribution is a mixture of two Gaussian (normal) components).

Sathish Deevi & 4D Strategies

Ashman, K., Bird, C., & Zepf, S. (1994). Detecting bimodality in astronomical datasets. The Astronomical Journal, 2348-2361.

Ellison, A. (1987). Effect of Seed Dimorphism on the Density-Dependent Dynamics of Experimental Populations of Atriplex triangularis (Chenopodiaceae). American Journal of Botany, 74(8), 1280-1288.

Zhang, C., Mapes, B., & Soden, B. (2003). Bimodality in tropical water vapour. Quarterly Journal of the Royal Meteorological Society, 129(594), 2847-2866.

`http://www.sdeevi.com/modes_package`

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#12 Examples of the most useful and common features of this package
#are included below.
##### Nonparametric Examples #####
### 1) Mode examples
##Example 1.1
#data<-c(rep(6,9),rep(3,3))
#mode(data,type=1,"NULL","NULL")
##Example 1.2
#data<-c(rep(6,9),rep(3,9))
#mode(data,type=1,"NULL","NULL")
##Example 1.3
#data<-c(rep(6,9),rep(3,8),rep(7,7),rep(2,6))
#mode(data,type=1,"NULL",2)
##Example 1.4
#data<-c(rnorm(15,0,1),rnorm(21,5,1),rep(3,3))
#mode(data)
##Example 1.5
#data<-c(rep(6,3),rep(3,3),rnorm(15,0,1))
#mode(data,3,NULL,4)
#mode(data,type=2,digits=1,3)
### 2) Other General Parametric Examples
##Example 2.1
#data<-c(rnorm(15,0,1),rnorm(21,5,1))
#hist(data)
#bimodality_amplitude(data,TRUE)
#bimodality_coefficient(data,TRUE)
#bimodality_ratio(data,FALSE)
##Example 2.2
#data<-c(rnorm(21,0,1),rnorm(21,5,1))
#hist(data)
#bimodality_amplitude(data,TRUE)
#bimodality_coefficient(data,TRUE)
#bimodality_ratio(data,FALSE)
### 3) Mixture Proportions Examples
##Example 3.1
#dist1<-rnorm(21,5,2)
#dist2<-dist1+11
#data<-c(dist1,dist2)
#hist(data)
#bimodality_amplitude(data,TRUE)
#bimodality_ratio(data,FALSE)
##Example 3.2
#dist1<-rnorm(21,-15,1)
#dist2<-rep(dist1,3)+30
#data<-c(dist1,dist2)
#hist(data)
#bimodality_amplitude(data,TRUE)
#bimodality_ratio(data,FALSE)
##Example 3.4
#dist1<-rep(7,70)
#dist2<-rep(-7,70)
#data<-c(dist1,dist2)
#hist(data)
#bimodality_ratio(data,FALSE)
##### Parametric Examples #####
### 4) Replicating a two component Gaussian (normal) mixture
### Example 4.1
##Draw data & plot the distribution
#dist1<-rnorm(14,-5,1)
#dist2<-rnorm(21,5,1)
#plot(density(c(dist1,dist2)), main="Bimodal Gaussian mixture distribution")
##Calculate the means and standard deviations
#mu1<-mean(dist1)
#mu2<-mean(dist2)
#sd1<-sd(dist1)
#sd2<-sd(dist2)
##Apply measures
#Ashmans_D(mu1,mu2,sd1,sd2)
#bimodality_separation(mu1,mu2,sd1,sd2)
### 5) Applying to know mixture components
### Example 5.1
##Draw data & plot the distribution
#data<-c(rnorm(15,0,1),rnorm(21,15,3))
#plot(density(c(dist1,dist2)), main="Bimodal Gaussian mixture distribution")
##Apply measures
#Ashmans_D(mu1,mu2,sd1,sd2)
#bimodality_separation(mu1,mu2,sd1,sd2)
``` |

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