# The HHG Package - Multivariate and Univariate non-parametric Independence and K-Sample tests" In HHG: Heller-Heller-Gorfine Tests of Independence and Equality of Distributions

title: "The HHG Package - Multivariate and Univariate non-parametric Independence and K-Sample tests" author: "Barak Brill, [email protected]" date: "Sunday, October 26, 2016" output: html_document: keep_md: yes

set.seed(2)


# Quick start

This package contains tests for independence, i.e., for discovering whether two random variables (univariate or multivariate) are dependent, as well as for equality of distributions (also known as the K-sample problem), i.e., for discovering whether the distribution is not the same across $K$ ($K\geq2$) groups.

The hhg.univariate tests can be used for both the K-sample problem and independence testing in a univariate setting.

The functions hhg.test, hhg.test.k.sample, hhg.test.2.sample can be used in both a univariate and multivariate setting.

The functions Fast.ADP.test and Fast.ADP.nulltable are variants of the \code{hhg.univariate.ind.combined.test} independence test function, which are optimized for independnece testing with a large sample size (from N>100 and up to millions.) More information about performing independence and large sample tests on large data samples, can be found in the section 4.7

The following examples use the default parameters:

## Quick start: univariate independence example

Data generation and using two univariate tests: the combined independence test on the ADP statistic, and the HHG test:

#For (N<100):
N = 30
data = hhg.example.datagen(N, 'Parabola')
X = data[1,]
Y = data[2,]
plot(X,Y)

#Option 1: Perform the ADP combined test
#using partitions sizes up to 4. see documentation for other parameters of the combined test
#(it is recommended to use mmax >= 4, or the default parameter for large data sets)
combined = hhg.univariate.ind.combined.test(X,Y,nr.perm = 200,mmax=4)
combined

#Option 2: Perform the hhg test:

## Compute distance matrices, on which the HHG test will be based
Dx = as.matrix(dist((X), diag = TRUE, upper = TRUE))
Dy = as.matrix(dist((Y), diag = TRUE, upper = TRUE))

hhg = hhg.test(Dx, Dy, nr.perm = 1000)

hhg

#For N>100, Fast.ADP.test is the reccomended option:

N_Large = 1000
data_Large = hhg.example.datagen(N_Large, 'W')
X_Large = data_Large[1,]
Y_Large = data_Large[2,]
plot(X_Large,Y_Large)

nr.atoms = 30,nr.perm=200)
nr.atoms = 30,nr.perm=200)



## Quick start: univariate K-sample example

Data generation and using two univariate tests: the combined K-sample test on the Sm statistic, and the hhg test for K samples:

N0=50
N1=50
X = c(c(rnorm(N0/2,-2,0.7),rnorm(N0/2,2,0.7)),c(rnorm(N1/2,-1.5,0.5),rnorm(N1/2,1.5,0.5)))
Y = (c(rep(0,N0),rep(1,N1)))
#plot the two distributions by group index (0 or 1)
plot(Y,X)

#Option 1: Perform the Sm combined test

combined.test = hhg.univariate.ks.combined.test(X,Y)
combined.test

#Option 2: Perform the hhg K-sample test:

Dx = as.matrix(dist(X, diag = TRUE, upper = TRUE))

hhg = hhg.test.k.sample(Dx, Y, nr.perm = 1000)

hhg


## Quick start: multivariate independence example

Data generation and using the hhg multivariate independence test.

The following code generates a random sample of size $n=30$. The $X$ vectors are of dimension 5, and are sampled from a multivariate normal distribution. The $Y$ vectors, also of dimension 5, are defined by $Y_j = X_j + 4 X_j^2 +\epsilon_j$, for $j=1,2$, where $\epsilon_j$ is a random noise normally distributed with mean zero and variance 9. Each of the other three components of $Y$ is standard normally distributed.

n=30 #number of samples
dimensions_x=5 #dimension of X matrix
dimensions_y=5 #dimension of Y matrix
X=matrix(rnorm(n*dimensions_x,mean = 0, sd = 1),nrow = n,ncol = dimensions_x) #generate noise
Y=matrix(rnorm(n*dimensions_y,mean =0, sd = 3),nrow = n,ncol = dimensions_y)

Y[,1] = Y[,1] + X[,1] + 4*(X[,1])^2 #add in the relations
Y[,2] = Y[,2] + X[,2] + 4*(X[,2])^2

#compute the distance matrix between observations.
#User may use other distance metrics.
Dx = as.matrix(dist((X)), diag = TRUE, upper = TRUE)
Dy = as.matrix(dist((Y)), diag = TRUE, upper = TRUE)

#run test
hhg = hhg.test(Dx, Dy, nr.perm = 1000)

hhg


## Quick start: multivariate K-sample example

Data generation and using the hhg multivariate k-sample test.

#multivariate k-sample, with k=3 groups
n=100 #number of samples in each group
x1 = matrix(rnorm(2*n),ncol = 2) #group 1
x2 = matrix(rnorm(2*n),ncol = 2) #group 2
x2[,2] = 1*x2[,1] + x2[,2]
x3 = matrix(rnorm(2*n),ncol = 2) #group 3
x3[,2] = -1*x3[,1] + x3[,2]
x= rbind(x1,x2,x3)
y=c(rep(0,n),rep(1,n),rep(2,n)) #group numbers, starting from 0 to k-1

plot(x[,1],x[,2],col = y+1,xlab = 'first component of X',ylab = 'second component of X',
main = 'Multivariate K-Sample Example with K=3 \n Groups Marked by Different Colors')

Dx = as.matrix(dist(x, diag = TRUE, upper = TRUE)) #distance matrix

hhg = hhg.test.k.sample(Dx, y, nr.perm = 1000)

hhg


# Introduction

The HHG Package contains non-parametric tests for independence and the K-Sample problem.

nonparametric tests are statistical tests that do not assume a statistical model or distribution on the data.

A test of independence aims to discover if there is a relationship between the variables. In the K-Sample problem, several groups are compared, and the aim is to identify whether the distribution of the random variable is not the same across all groups (hence, it is essentially an independence test between group labels and that random variable).

Tests in the package are Omnibus Consistent, meaning, given enough data, they will be able to reject the null hypothesis of independence (or equality of distribution in the K-Sample case) for any type of relationship.

Some of the tests come with a variety of tuning parameters, but the default parameters will work just fine for most problems.

In this vignette, we will go over the different tests in the package, describe (very generally) how they work, how to operate them, and some key switches that can be used to tune them to your problem.

The computation of the hhg.univariate statistics can be quite extensive. Thus, existing null tables (lookup tables for the null distributions, from which pvalues can be computed) can be found at our website: http://www.math.tau.ac.il/~ruheller/Software.html. Section 4.6 of this document also explains how to compute a null tables on the same machine, using multiple cores (both for the independence problem, and for the K-sample problem).

If you have generated a high precision null table, we would like to use it on our site, so that it is also available for other research groups, so please share it with us!

Please do not hesitate to contact us with any question regarding the package or statistical procedures, we will be happy to help.

# hhg.test: multivariate independence and K-sample tests

We are given pairs of independent observations $(x_1, y_1),\ldots,(x_n,y_n)$, where $x_i$ and $y_i$ can be of any dimension (1 or above) and the dimensionality of $x_i$ and $y_i$ need not be the same. Let $X = (x_1,\ldots,x_n)$ be the $X$ vector, and $Y=(y_1,\ldots,y_n)$ the $Y$ vector.

If, for example, we want to test for independence between dates and house prices, we might be given two vectors of dimensiona 1 ($X$ being a vector of dates, $Y$ being a vector of prices, each row index corresponding to the same observation, over the two data structures).

Another example: we want to test for association between the drug dose and hours of psychological treatment (given together, as an $X$ vector of dimension two), to various symptoms (given together, as a $Y$ vector). Since the effect of drug dose on symptoms may be complex (possibly U-shaped), the relationship between $X$ and $Y$ may be complex as well, and therefore it is useful to use an omnibus test such as HHG.

## What does the test do?

For every pair of observations P0 and P1 the test goes over the remaining observations and counts how many observations are:

1.Closer to P0 in X than P1 AND closer to P0 in Y than P1

2.Closer to P0 in X than P1 AND not closer to P0 in Y than P1

3.Not closer to P0 in X than P1 AND closer to P0 in Y than P1

4.Not closer to P0 in X than P1 AND not closer to P0 in Y than P1

The diagram below corresponds to the above description with the green, red, blue and purple points, corresponding to conditions 1-4, respectively.

Hence, the data can be aggregated in the following table:

Since we have n choices for P0 and n-1 choices for P1, a chi-squared statistic is produced from each table, and these are aggregated to form the sum of Pearson (chi-squared) statistics, T:

$$S(i,j) = \frac{(N-2)(A_{12}A_{21}-A_{11}A_{22})^2 }{A_{1\cdot}A_{2\cdot}A_{\cdot1}A_{\cdot2}} ;T = \sum\limits_{i=1}^N \sum\limits_{j=1, j\ne i}^N S(i,j)$$

Similarly, $S$ can be defined using the likelihood-ratio statistic, and $T$ can be defined based on the maximum instead of the sum.

The test statistic is computed in $O(n^2 log(n))$ time, as described in Heller et al. (2013).

The null hypothesis in question is of independence: $H_0: F_{xy} = F_x * F_y$ against the alternative: $H_0: F_{xy} \ne F_x * F_y$. So a permutation test can be performed, by permuting the Y vectors of the observations and thus reassigning the pairs: for each permutation $(y_{\pi(1)}, \ldots, y_{\pi(n)})$, the test statistic is computed for the reassigned pairs $(x_1,y_{\pi(1)}), \ldots, (x_n, y_{\pi(n)})$. If the test statistic $T$ of the original sample is greater than or equal to the $1-\alpha$ quantile of the population of permutation statistics, one may reject the null hypothesis with significance level $\alpha$. The $p$-value is the fraction of reassignments for which the computed test statistics are at least as large as the one observed, where the fraction is computed out of the $B+1$ assignments that include the $B$ permuted samples and the observed sample.

Examples for using hhg.test, hhg.test.2.sample and hhg.test.k.sample, are available in Section 1 above, or type '?hhg.test' in the R console.

# hhg.univariate : univariate distribution-free independence and K-sample tests

In the hhg.test the null distribution is calculated by performing permutations of the $Y$ vectors and then recomputing the test statistic. When many data sets are considered, calculating the null distribution for each one could be computationaly intensive. When $X$ and $Y$ are univariate hhg.univariate serves as a computationally more efficient and potentially more powerful alternative.

## How is this done for numerical $X$ and $Y$ ?

Let us divide the ranked observations, i.e., $rank(X)$ and $rank(Y)$, according to the cartesian product of partitions along each of the axes (a partition of size $m$ means a division of the axis into $m$ cells). Then we count Observed - the number of observations in each cell.

If there is no association between $X$ and $Y$, the points should be equally spread across the cells, depending only on the cell size. Expected is the expected number of obervations per cell under the null hypothesis of independence between X and Y, i.e., the cell width times the cell height divided by the sample size, where cell dimensions are measured in observations' ranks (e.g., a cell from obervation 1 to 4 on the Y axis, is of height 3).

A measure of dependence for the $m\times m$ partition based on the likelihood ratio sums up the values of $Observed * log(Observed/Expected)$ across the cells, while a measure based on the Pearson scores sums up $(Observed-Expected)^2/Expected$ across the cells.

The above diagram of partitions, is of a DDP type (standing for Data Derived Partitions), for DDP. If we partition along the x-axis at sample rank(x_i) then we must partition along the y-axis at sample point rank(y_i) too. Another type of partitioning is ADP (standing for All Derived Partitions). Using ADP, we allow partitions along the x-axis independelty of the y-axis. The DDP and ADP statistics sum over all $m \times m$ partitions efficiently, as described in Heller et al. (2014).

For the ADP partitioning type, the likelihood ratio and Pearson scores can be computed simultaneously for all $m\times m$ tables (i.e. $2\times 2,3\times 3,\ldots$ up to $m.max \times m.max$) in time complexity $O(n^4)$, even though the number of partitons is (choose(n-1,m-1))^2. Note that for m<5 there are specific much more efficient algorithms (see Heller et al. (2014) for details).

Since the test statistics only make use of the relative ordering of observations in X and Y, and not the actual distances (the above plot is on the ranks of X and Y), one can speed the process of hypothesis testing, by creating a null table only once in advance.

Essentially, a null table can be computed by permuting Y ranks, and calculating the test statistic for each permutation. One only needs to specify the sample size in order to access or generate the correct null table (for example, a sum of Pearson scores with ADP partitions and sample size n=25).

One may use existing null tables from the site, as explained under the 'Using Null Tables from the Website' section, or create null tables for the relevant sample size.

This small example, explains how to generate a null table, compute the test statistic and Pvalue:

N = 35
data = hhg.example.datagen(N, 'Parabola')
X = data[1,]
Y = data[2,]
plot(X,Y)

#I) Computing test statistics , with default parameters:

#statistic:

#null table:
#pvalue:


Adjusting variant (ADP or DDP) and score type (likelihood ratio and Pearson):

#II) Computing test statistics , with summation over Data Derived Partitions (DDP), using Pearson scores, and partition sizes up to 5:

#statistic:
hhg.univariate.DDP.Pearson.result = hhg.univariate.ind.stat(X,Y,variant = 'DDP',score.type = 'Pearson', mmax = 5)
hhg.univariate.DDP.Pearson.result

#null table:
DDP.null = hhg.univariate.ind.nulltable(N,mmax = 5,variant = 'DDP',score.type = 'Pearson', nr.replicates = 1000)
#pvalue , for different partition sizes:
hhg.univariate.ind.pvalue(hhg.univariate.DDP.Pearson.result, DDP.null, m =2)
hhg.univariate.ind.pvalue(hhg.univariate.DDP.Pearson.result, DDP.null, m =5)


## Our recommendation: the combined test over all partition sizes

Rather than choose the partition size (number of cells) heuristically, we suggest the following method:

Compute the $p$-values, $p.val_{m}$, for independence tests for all $m$s, from $m=2$ through $m.max$. Then, combine their pvalues by taking as the test statistic either the minimum $p$-value, $\min_{m\in{2,\ldots,m_{\max}}} p.val_{m}$, or the Fisher combined $p$-value, $-\Sigma_{m=1}^{m.max}log(p.val_m)$. Note that the combined $p$-value is NOT a valid $p$-value, but it is a test statistic for which a null distribution can be created from which the $p$-value can be extracted. By holding a null table of MinP and Fisher statistics for the null distribution, we can calculate the pvalue of these statistics based on their null tables.

N = 35
data = hhg.example.datagen(N, 'Parabola')
X = data[1,]
Y = data[2,]
plot(X,Y)

#Perform MinP & Fisher Tests - without existing null tables. Null tables are generated by the test function.
#using partitions sizes up to 4. see documentation for other parameters of the combined test (using existing null tables when performing many tests)
results = hhg.univariate.ind.combined.test(X,Y,variant='DDP' ,nr.perm = 200,mmax=4)
results


## Performing the test for the K-sample problem

Similar to the independence problem, a K-sample version of the test is available. However in this case the computational complexity is much lower. The aggregation by sum scores can be calculated in $O(n^2)$, while the aggregation by maximum can be calculated in $O(n^3)$. Function interface is similar, but no ADP or DDP variants are needed. Test statistics may be either the sum of likelihood-ratio/ Pearson statistics over sample partitions, or the maximum statistic acheived over the different partitions.

Code for computing the maximum has been adapted from Jiang et al.(2014).

#KS example - two groups of size 50
N0=50
N1=50
X = c(c(rnorm(N0/2,-2,0.7),rnorm(N0/2,2,0.7)),c(rnorm(N1/2,-1.5,0.5),rnorm(N1/2,1.5,0.5)))
Y = (c(rep(0,N0),rep(1,N1)))
#plot distributions of result by group
plot(Y,X)

statistic = hhg.univariate.ks.stat(X,Y)
statistic

nulltable = hhg.univariate.ks.nulltable(c(N0,N1))
hhg.univariate.ks.pvalue(statistic , nulltable) #pvalue of the default number of partitions

hhg.univariate.ks.pvalue(statistic , nulltable,m=5) #pvalue of partition size 5


The combined, multiple partition size test is also available, under hhg.univariate.ks.combined.

## Our recommendation for the K-sample problem: the combined test over all partition sizes

The combined test (by MinP and Fisher combination types) is also available for the K Sample problem. It can be used either with an exisiting null table, or by supplying the parameters for generating a new null table.

combined.test = hhg.univariate.ks.combined.test(X,Y,nulltable)
combined.test


## Using null tables from the website

This section shows how to use one of the null.tables on the site, and convert it to a null table object. Note that all tables are also already availalble in object format, using an mmax parameter defined for the maximum partition size taken into account (relevant for MinP and Fisher, typically with a value of 75, 100 or 150 for large sample sizes)

How to convert an existing null table to a null table test object:

# download from site http://www.math.tau.ac.il/~ruheller/Software.html

#using an already ready null table as object (for use in test functions)
#for example, ADP likelihood ratio statistics, for the independence problem, for sample size n=300

#or using a matrix of statistics generated for the null distribution, to create your own table.
null.table = hhg.univariate.nulltable.from.mstats(m.stats = mat,minm = 2,maxm = 5,type = 'Independence',
variant = 'ADP',size = 300,score.type = 'LikelihoodRatio',aggregation.type = 'sum')


## Generating null distribution tables, on multiple cores

The following examples illustrate how to generate null table objects for the hhg.univariate tests, using multiple cores. This is done by computing tables of statistics over different cores. Statistics are sampled from the null distribution by permuting the ranks of the observations (tests are distribution free, and depend only on the ranks of the data).

Then, statistics are collected to a null table object and used in a simple example test.

### Generating null table for independence on multiple cores

The following code demonstrates how to generate a null table of size 100 (for example uses only) over multiple cores (the number of available cores minus one). The test statistic is a MinP combined statistic, combining the ADP statistics of partition sizes m=2,3,4,5,6. ADP statistics are computed using the likelihood ratio scores. Statistics generated from the null distribution are then combined to form a null lookup table object, to be used in a test.

Additional switches are shown below, demonstrating how to change the summation of tables from all $m\times m$ partitions of the data to $m\times l$ partitions, make use of DDP partitions and perform summation over Pearson Chi Square scores rather than Likelihood Ratio Test scores

library(parallel)
library(doParallel)
library(foreach)
library(doRNG)

#function for generating independence null table

Create.IND.Null.Parallelized = function(N,NR.CORES,MMAX,variant.p = 'ADP-EQP-ML',NR.ATOMS = 60,NR.REPS = 1000,agg.type = 'sum',score.type.p = 'LikelihoodRatio',keep=F){
nr.cores = NR.CORES
n = N
nr.reps.per.core = ceiling(NR.REPS/nr.cores)
mmax =MMAX
score.type = score.type.p
aggregation.type = agg.type
variant = variant.p
nr.atoms = NR.ATOMS

generate.null.distribution.statistic =function(){
library(HHG)
null.table = NULL
for(i in 1:nr.reps.per.core){
statistic = hhg.univariate.ind.stat(1:n,sample(1:n),
variant = variant,
aggregation.type = aggregation.type,
score.type = score.type,
mmax = mmax,nr.atoms = nr.atoms)$statistic null.table=rbind(null.table, statistic) } rownames(null.table)=NULL return(null.table) } cl = makeCluster(NR.CORES) registerDoParallel(cl) res = foreach(core = 1:nr.cores, .combine = rbind, .packages = 'HHG', .export=c('variant', 'aggregation.type', 'score.type','mmax','nr.reps.per.core','n','NR.ATOMS'),.options.RNG = 1) %dorng% { generate.null.distribution.statistic() } stopCluster(cl) temp = HHG::hhg.univariate.nulltable.from.mstats(res,minm=2, maxm = mmax, type = 'Independence', variant = variant, size = n,score.type = score.type, keep.simulation.data=keep, aggregation.type = aggregation.type, nr.atoms = nr.atoms, compress = T) return(temp) } #end of function for generating null tables #null table (look up table) : sum all 2X2,3X3,..., mmax X mmax tables, using LRT scores nt_ADP_LR = Create.IND.Null.Parallelized(N = 40,NR.CORES = detectCores()-1 ,MMAX = 6,variant.p = 'ADP', NR.REPS = 1000,agg.type = 'sum') #null table (look up table) : sum all 2X2,2X3,3X2,2X4,3X3,4X2..., mmax -1 X mmax , # mmax X mmax -1 , mmax X mmax tables, using LRT scores nt_ADP_MXL_LR = Create.IND.Null.Parallelized(N = 40, NR.CORES = detectCores()-1, MMAX = 6, variant.p = 'ADP-ML', NR.REPS = 1000,agg.type = 'sum') #null table (look up table) : sum all 2X2,3X3,..., mmax X mmax tables, using Pearson scores nt_ADP_Pearson = Create.IND.Null.Parallelized(N = 40,NR.CORES = detectCores()-1 ,MMAX = 6, variant.p = 'ADP', NR.REPS = 1000, agg.type = 'sum', score.type.p = 'Pearson') #null table (look up table) : sum all DDP partitions of size 2X2, ..., mmax X mmax nt_DDP_LR = Create.IND.Null.Parallelized(N = 40, NR.CORES = detectCores()-1 , MMAX = 6, variant.p = 'DDP', NR.REPS = 1000, agg.type = 'sum', score.type.p = 'Pearson') set.seed(1) x=rnorm(40) y=0.25 * x + rnorm(40) # different look up tables enforce different tests res = HHG::hhg.univariate.ind.combined.test(x,y,nt_ADP_LR); res res = HHG::hhg.univariate.ind.combined.test(x,y,nt_ADP_MXL_LR); res res = HHG::hhg.univariate.ind.combined.test(x,y,nt_ADP_Pearson); res res = HHG::hhg.univariate.ind.combined.test(x,y,nt_DDP_LR); res  ### Generating null table for K-sample testing on multiple cores The following example generates a null table of size 100 (for example use only) over multiple cores (number of available cores minus one). The test statistic is a MinP combined statistic, combining the Sm statistics over m=2,...,10. Statistics generated from the null distribution are then combined to form a null lookup table object, to be used in a test. Additional switches are shown below, demonstrating how to change the summation of tables from all$m\times m$partitions of the data to$m\times l$partitions, make use of DDP partitions and perform summation over Pearson Chi Square scores rather than Likelihood Ratio Test scores library(parallel) library(doParallel) library(foreach) library(doRNG) #generate a K-Sample null table Create.KSAMPLE.Null.Parallelized = function(Group_Sizes,NR.CORES,MMAX,variant.p = 'KSample-Variant',NR.ATOMS = 60,NR.REPS = 1000,agg.type = 'sum',score.type.p = 'LikelihoodRatio',keep=F){ nr.cores = NR.CORES n = sum(Group_Sizes) nr.reps.per.core = ceiling(NR.REPS/nr.cores) mmax =MMAX score.type = score.type.p aggregation.type = agg.type variant = variant.p nr.atoms = NR.ATOMS sample_y=NULL for(i in 1:length(Group_Sizes)){ sample_y = c(sample_y,rep(i-1,Group_Sizes[i])) } generate.null.distribution.statistic =function(){ library(HHG) null.table = NULL for(i in 1:nr.reps.per.core){ statistic = hhg.univariate.ks.stat(1:n,sample(sample_y), variant = variant, aggregation.type = aggregation.type, score.type = score.type, mmax = mmax,nr.atoms = nr.atoms)$statistic
null.table=rbind(null.table, statistic)
}
rownames(null.table)=NULL
return(null.table)
}

cl = makeCluster(NR.CORES)
registerDoParallel(cl)
res = foreach(core = 1:nr.cores, .combine = rbind, .packages = 'HHG',
.export=c('variant','aggregation.type','score.type','mmax','nr.reps.per.core','n','NR.ATOMS'),.options.RNG = 1) %dorng% {
generate.null.distribution.statistic()
}
stopCluster(cl)

temp =  HHG::hhg.univariate.nulltable.from.mstats(res, minm=2, maxm = mmax, type = 'KSample', variant = variant, size = Group_Sizes, score.type = score.type,keep.simulation.data=keep, aggregation.type = aggregation.type, nr.atoms = nr.atoms, compress = T)
return(temp)
}
# end of function for generating null tables

#null table (look up table) : sum of all partitions of the data with 2,3,...,m cells, using LRT scores
nt_Sm_LR = Create.KSAMPLE.Null.Parallelized(Group_Sizes = c(50,50) , NR.CORES = detectCores()-1 ,MMAX = 10,NR.REPS = 1000)

#null table (look up table) : sum of all partitions of the data
#with 2,3,...,m cells, using Pearson Chi-Square scores
nt_Sm_Pearson = Create.KSAMPLE.Null.Parallelized(Group_Sizes = c(50,50) , NR.CORES = detectCores()-1 ,MMAX = 10,NR.REPS = 1000,score.type.p = 'Pearson')

#null table (look up table) : maxmimum over all partitions of the data with 2,3,...,m cells
nt_Mm_LR = Create.KSAMPLE.Null.Parallelized(Group_Sizes = c(50,50) , NR.CORES = detectCores()-1 ,MMAX = 10,NR.REPS = 1000,agg.type = 'max')

set.seed(1)
x = rnorm(100)
x[1:50] = x[1:50] + 1 #create some difference between groups
y = c(rep(0,50),rep(1,50)) #group identity

res = HHG::hhg.univariate.ks.combined.test(x,y,nt_Sm_LR);res

res = HHG::hhg.univariate.ks.combined.test(x,y,nt_Sm_Pearson);res

res = HHG::hhg.univariate.ks.combined.test(x,y,nt_Mm_LR);res


## Hypotheses testing for large samples

Hypotheses testing (Independence and K-Sample equality of distributions) for large samples can become computationally expensive when performing the above tests for large samples. This section explains about the different variants of \code{hhg.univariate.ks.combined.test}, and \code{hhg.univariate.ind.combined test} which are of a lower computational complexity.

### Independence testing for large samples

For the 'sum' aggregation type of \code{hhg.univariate.ind.stat} and \code{hhg.univariate.ind.combined test}, The test statistic is the sum of log likelihood (or Pearson Chi-square) scores, of all partitions of size \eqn{m X m} of the data, normalized by the number of partitions and the data size (thus, being an estimator of the Mutual Information). For variant type \code{"ADP-ML"}, the statistics calculated include not only the sum over \eqn{m X m} tables (symmetric tables, same number of cells on each axis), but also assymetric tables (i.e. \eqn{m X l} tables).

Variant types \code{"ADP-EQP"} and \code{"ADP-EQP-ML"}, are the computationally efficient versions of the \code{"ADP"} and \code{"ADP-ML"}. EQP type variants reduce calculation time by summing over a subset of partitions, where a split between cells may be performed only every \eqn{n/nr.atoms} observations (an equipartition over atoms is performed first). This allows for a complexity of O(nr.atoms^4). These variants are only available for \code{aggregation.type=='sum'} type aggregation.

#Option 1:
# It is better to use the Fast.ADP.test, which has nr.atoms and paritition size m automatically set for large data:

N_Large = 1000
data_Large = hhg.example.datagen(N_Large, 'W')
X_Large = data_Large[1,]
Y_Large = data_Large[2,]
plot(X_Large,Y_Large)

NullTable_for_N_Large_MXM_tables = Fast.independence.test.nulltable(N_Large, variant = 'ADP-EQP',
nr.atoms = 30,nr.perm=200)
NullTable_for_N_Large_MXL_tables = Fast.independence.test.nulltable(N_Large, variant = 'ADP-EQP-ML',
nr.atoms = 30,nr.perm=200)

#null distribution depends only on data size (length(X)), so same null table can be used many times.
#For example, another data set:
data_Large = hhg.example.datagen(N_Large, 'Circle')
X_Large = data_Large[1,]
Y_Large = data_Large[2,]
plot(X_Large,Y_Large)

#you may use Fisher type scores:
combining.type='Fisher')
#or both MinP and Fisher:
combining.type='Both')

#Option 2: use the 'ADP-EQP' and 'ADP-EQP-ML' variants directly, with the combined test function (combining all partition sizes):

N_Large = 1000
data_Large = hhg.example.datagen(N_Large, 'W')
X_Large = data_Large[1,]
Y_Large = data_Large[2,]
plot(X_Large,Y_Large)

NullTable_for_N_Large_MXM_tables = hhg.univariate.ind.nulltable(N_Large, variant = 'ADP-EQP',
nr.atoms = 30,nr.replicates=200)
NullTable_for_N_Large_MXL_tables = hhg.univariate.ind.nulltable(N_Large, variant = 'ADP-EQP-ML',
nr.atoms = 30,nr.replicates=200)

#Option 3: perform ADP-EQP and ADP-EQP-ML for a specific partition size (M cells on x axis and L cells on y axis)

#P-value for the S_(5X5) statistic, the sum over all 5X5 partitions:

#P-value for the S_(5X3) statistic, the sum over all 5X3 partitions:


### K-Sample problem and testing for equality of distribution for large samples

For the 'sum' aggregation type (default), The test statistic is the sum of log likelihood (or Pearson Chi-square) scores, of all partitions of size \eqn{m} of the data, normalized by the number of partitions and the data size (thus, being an estimator of the Mutual Information). For the 'max' aggregation type, the test statistic is the maximum log likelihood (or Pearson Chi-square) score acheived by a partition of data of size \code{m}.

Variant type \code{"KSample-Equipartition"} is the computationally efficient version of the K-sample test. calculation time is reducing by aggregating over a subset of partitions, where a split between cells may be performed only every \eqn{n/nr.atoms} observations. This allows for a complexity of O(nr.atoms^2) (instead of O(n^2)). Computationly efficient versions are available for \code{aggregation.type=='sum'} and \code{aggregation.type=='max'} variants.

#Two groups, each from a different normal mixture, total sample size is 10^4:
X_Large = c(c(rnorm(2500,-2,0.7),rnorm(2500,2,0.7)),c(rnorm(2500,-1.5,0.5),rnorm(2500,1.5,0.5)))
Y_Large = (c(rep(0,5000),rep(1,5000)))
plot(Y_Large,X_Large)

N0_large = 5000
N1_large = 5000

Sm.EQP.null.table = hhg.univariate.ks.nulltable(c(N0_large,N1_large), nr.replicates=200,
variant = 'KSample-Equipartition', mmax = 30)
Mm.EQP.null.table = hhg.univariate.ks.nulltable(c(N0_large,N1_large), nr.replicates=200,
aggregation.type='max', variant = 'KSample-Equipartition', mmax = 30)

MinPFisher.Sm.EQP.result = hhg.univariate.ks.combined.test(X_Large, Y_Large,
NullTable =  Sm.EQP.null.table ,
combining.type = 'Both')
MinPFisher.Sm.EQP.result

MinPFisher.Mm.EQP.result = hhg.univariate.ks.combined.test(X_Large, Y_Large,
NullTable =  Mm.EQP.null.table ,
combining.type = 'Both')
MinPFisher.Mm.EQP.result


### Constructing null tables for large samples using multiple cores

The following are two code examples, for constructing null tables using many CPUs, for the univariate test statistics tailored to fit large sample sizes.

#### Contructing null tables for large samples using multiple cores, for the independence problem

Below is a code example for constructing a null table for the independence problem, for large sample sizes, using multiple cores. Note that the dimensionality of the ADP-EQP-ML statistic is $(m.max - 1)^2$ while the dimensionality of the ADP-EQP statistic is $(m.max - 1)$.

We make use of the function Create.IND.Null.Parallelized give above.

library(parallel)
library(doParallel)
library(foreach)
library(doRNG)

nt_ADP_EQP_LR = Create.IND.Null.Parallelized(N = 10000, NR.CORES = detectCores()-1 , MMAX = 6, variant.p = 'ADP-EQP', NR.REPS = 1000, agg.type = 'sum', NR.ATOMS = 40)

nt_ADP_EQP_MXL_LR = Create.IND.Null.Parallelized(N = 10000, NR.CORES = detectCores()-1, MMAX = 6, variant.p = 'ADP-EQP-ML', NR.REPS = 1000, agg.type = 'sum', NR.ATOMS = 40)

set.seed(1)
x=rnorm(10000)
y=0.1*x+rnorm(10000)



#### Contructing null tables for large samples using multiple cores, for the K-Sample problem

Below is a code example for constructing a null table for the 2-Sample problem, for large sample sizes, using multiple cores.

We make use of the function Create.KSAMPLE.Null.Parallelized give above.

library(parallel)
library(doParallel)
library(foreach)
library(doRNG)

nt_Sm_EQP_LR = Create.KSAMPLE.Null.Parallelized(Group_Sizes = c(5000,5000) , NR.CORES = detectCores()-1, variant.p = 'KSample-Equipartition', MMAX = 20,NR.REPS = 1000,NR.ATOMS = 100)

nt_Mm_EQP_LR = Create.KSAMPLE.Null.Parallelized(Group_Sizes = c(5000,5000) ,agg.type = 'max', NR.CORES = detectCores()-1 , variant.p = 'KSample-Equipartition', MMAX = 20,NR.REPS = 1000,NR.ATOMS = 100)

set.seed(1)
x = rnorm(10000)
x[1:5000] = x[1:5000] + 1 #create some difference between groups
y = c(rep(0,5000),rep(1,5000)) #group identity

res = HHG::hhg.univariate.ks.combined.test(x,y,nt_Sm_EQP_LR);res

res = HHG::hhg.univariate.ks.combined.test(x,y,nt_Mm_EQP_LR);res


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HHG documentation built on Nov. 17, 2017, 7:07 a.m.