Package 'Rfast'

Description

Check if any column or row is fill with values.

Usage

colrow.value(x,value=0)

Arguments

Details

Check all the column if any has all its elements equal to argument value. If found, return "TRUE". Otherwise continues with rows. If columns and rows hasn't any value vector then return "FALSE". Even if it returns "FALSE" that doesn't mean the determinant can't be value. It might be but if check before and found any value vector then for sure the determinant it'll be value.

Value

A boolean value, "TRUE" if any column OR row is all filled with value. "FALSE" otherwise.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

rowMins, rowFalse, nth, colrange, colMedians, colVars, colSort, rowSort, rowTrue

Examples

x <- matrix(runif(10*10),10,10)
res<-colrow.value(x) 

x<-NULL

Description

Deep copy.

Usage

env.copy(x,all.names=FALSE)

Arguments

Details

Deep copy of the environment object.

Value

A copy of the first argument.

Author(s)

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

colShuffle, colVars, colmeans, read.directory

Examples

x <- new.env()
x$imaginary <- NULL
x$real <- NULL

# you can library the package and just press x and R will understand 
# and search automatically for a function to print the environment
x

y <- env.copy(x)

x$real <- 10

x$real == y$real # FALSE

Description

Design Matrix.

Usage

design_matrix(x, ones = TRUE)

Arguments

Details

This function implements the R's "model.matrix" function and is used only when the x is a factor/charactervector or Dataframe.

Value

Returns the same matrix with model.matrix.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>

See Also

model.matrix

Examples

a <- design_matrix( iris[, 5] )
b <- model.matrix( ~ iris[,5] )  ## R's built-in function
all.equal(as.vector(a),as.vector(b)) ## true

a<-b<-NULL

Description

Insert/remove function names in/from the NAMESPACE file.

Usage

AddToNamespace(path.namespace,path.rfolder)
RemoveFromNamespace(path.namespace,files.to.remove)

Arguments

Details

AddToNameSpace: Reads the files that are exported in NAMESPACE and the functions that are inside rfolder (where R files are) and insert every function that is not exported. For that you must add the attribute "#[export]" above every function you wish to export. Also you can use the attribute "#[export s3]" for exporting S3methods. Finally, if you don't want the program to read a file just add at the top of the file the attribute "#[dont read]".

RemoveFromNamespace: Remove every function, from argument "files.to.remove", from NAMESPACE.

Value

AddToNameSpace:

RemoveFromNamespace: Return the files that could not be removed.

Author(s)

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

colShuffle, colVars, colmeans, read.directory

Examples

## Not run: 
	#for example: path.namespace="C:\some_file\NAMESPACE" where is NAMESPACE file
	#path.rfolder="C:\some_file\R\" where is R files are
	#system.time( a<-AddToNamespace(path.namespace,path.rfolder) )
	#if(length(a)==0){
	#	print("all the files are inserted")
	#}else{
	#	print("The new files that inserted are: \n")
	#	a
	#}
	#system.time( a<-RemoveFromNamespace(path.namespace,c("a","b")) )
	#if(length(a)==0){
	#	print("all the files are inserted")
	#}else{
	#	print("The files thatcould not be deleted are: \n")
	#	a
	#}

## End(Not run)

Description

Lower/upper triangular matrix.

Usage

lower_tri(x, suma = FALSE, diag = FALSE)
upper_tri(x, suma = FALSE, diag = FALSE)
lower_tri.assign(x, v, diag = FALSE)
upper_tri.assign(x, v, diag = FALSE)

Arguments

Value

Get a lower/upper triangular logical matrix with values TRUE/FALSE, a vector with the values of a lower/upper triangular, the sum of the upper/lower triangular if suma is set TRUE or assign to the lower/upper (only for large matrices) triangular. You can also include diagonal with any operation if argument diag is set to "TRUE".

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

rowMins, colFalse, nth, rowrange, rowMedians, rowVars, colTrue

Examples

x <- matrix(runif(10*10),10,10)

all.equal(lower_tri(c(10,10)),lower.tri(x))

all.equal(lower_tri(x),x[lower.tri(x)])

#all.equal(upper_tri(c(10,10)),upper.tri(x))

#all.equal(upper_tri(x),x[upper.tri(x)])



#all.equal(lower_tri(c(10,10),diag = TRUE),lower.tri(x,diag = TRUE))

#all.equal(lower_tri(x,diag = TRUE),x[lower.tri(x,diag = TRUE)])

#all.equal(upper_tri(c(10,10),diag = TRUE),upper.tri(x,diag = TRUE))

#all.equal(upper_tri(x,diag = TRUE),x[upper.tri(x,diag = TRUE)])

all.equal(lower_tri.assign(x,diag = TRUE,v=rep(1,1000)),x[lower.tri(x,diag = TRUE)]<-1)

all.equal(upper_tri.assign(x,diag = TRUE,v=rep(1,1000)),x[upper.tri(x,diag = TRUE)]<-1)

x<-NULL

Description

Return the positions of its first argument that matches in its second.

Usage

Match(x,key=NULL)

Arguments

Details

This function implements the R's \"match\" function. This version basicaly calculates the match(x,sort(unique(x))) for now. Do not use the argument key!

Value

Returns the position/positions of the given key/keys in the x vector.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>

See Also

match

Examples

y <- rnorm(100)
a <- Match(y)
b <-50
all.equal(as.vector(a),as.vector(b))

Description

Permute the given vector.

Usage

permutation(x, nperm = gamma(length(x)+1))
permutation.next(x, nperm = gamma(length(x)+1))
permutation.prev(x, nperm = gamma(length(x)+1))
bincomb(n)

Arguments

Details

This function implements "Permutation", which means all the possible combinations. In the permutation.next and permutation.prev if there aren't possible combinations it returns the same vector. "Binary Combinations" for "bincomb", means all the possible combinations for the binary number with length "n".

Value

Returns a matrix with all possible combinations of the given vector or a matrix row with one possible combinations.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>

See Also

combn,comb_n

Examples

y <- rnorm(3)
b <- permutation(y)
b <- permutation.next(y)
b <- permutation.prev(y)
g <- bincomb(3)

Description

Replicate columns/rows.

Usage

rep_col(x,n)
rep_row(x,n)

Arguments

Value

A matrix where each column/row is equal to "x".

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

rowMins, rowFalse, nth, colrange, colMedians, colVars, colSort, rowSort, rowTrue

Examples

x <- runif(10)
all.equal(rep_col(x,10),matrix(x,nrow=length(x),ncol=10))
all.equal(rep_row(x,10),matrix(x,ncol=length(x),nrow=10,byrow=TRUE))

Description

Represantation of Stack.

Usage

Stack(x,type=NULL)

Arguments

Details

Stack is an abstract data type - data structure based on the principle of last in first out. To access the 3 fields, use operator "$".

Value

An object of class "Stack". This object holds 3 fields:

pop: remove the first element (from the top). top: access the first element (from the top). push: add an element to the top of the Stack.

Author(s)

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

colShuffle, colVars, colmeans, read.directory

Examples

x<-Stack(10,type=integer)

x$push(5)
x$push(10)
x$top()  == 10
x$pop()
x$top() == 5

y<-rnorm(10)
x<-Stack(x)

x$push(5) # length increased to 11
x$top() # access the last element that pushed, 5
x$pop() # pop the last element that pushed

Description

Sort and unique numbers.

Usage

sort_unique(x)
sort_unique.length(x)

Arguments

Details

The "sort_unique" function implements R's "unique" function using C++'s function but also sort the result. The "sort_unique.length" returns the length of the unique numbers only for itegers.

Value

Returns the discrete values but sorted or their length (depending on the function you do).

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>

See Also

colSort, rowSort, sort_cor_vectors

Examples

y <- rnorm(100)
a <- sort_unique(y)
b <- sort.int(unique(y))
all.equal(as.vector(a),as.vector(b))
x <- rpois(1000,10)
sort_unique.length(x)
length(sort_unique(x))

x<-a<-b<-NULL

Description

A collection of fast (utility) functions for data analysis. Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771–780. <doi:10.6339/JDS.201810_16(4).00006>. c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. <doi:10.48550/arXiv.1806.10947>.

Details

Maintainers

Manos Papadakis <[email protected]>

Note

Acknowledgments: We would like to acknowledge:

• Professor Kurt Hornik, Doctor Uwe Ligges (and the rest of R core team) for their invaluable help with this R package.

• Erich Studerus for his invaluable comments.

• Neal Fultz for his suggestions.

• Vassilis Vasdekis for his invaluable help with the random effects models.

• Marios Dimitriadis' work was funded by the Special Account for Research Funds of the University of Crete, Department of Computer Science.

• Phillip Si is greatly acknowledged for his help with the Floyd-Warshal algorithm.

• Keefe Murphy for his invaluable help with NEWS file and for his suggestions.

• Zacharias Papadovassilakis gave us the inspiration for the memory efficient version of the k-NN algorithm.

• Yannis Pantazis explained us how the orhtogonal matching pursuit works.

• Achim Zeileis for his help with the quasi Poisson regression models.

• Pedro J. Aphalo for finding a bug.

• Dimitris Kyriakis for finding a bug.

• Cyrille Conord for finding a bug.

• Aaron Robotham for finding a bug.

• Calvin Pan from the Department of Human Genetics at UCLA found a bug in the function glm_logistic and he is greatly acknowledged for that.

• Adam Muschielok from Rodenstock GmbH found a bug in the function vmf.mle and he is greatly acknowledged for that.

• Bret Presnell for detecting and correcting a bug in the function rvmf.

• Gabriel Hoffman for spotting a mistake in the fuction dirimultinom.mle.

• Lutz von der Burchard for spotting a bug in the function bic.corfsreg.

Author(s)

• Manos Papadakis <[email protected]>

• Michail Tsagris <[email protected]>

• Marios Dimitriadis <[email protected]>

• Stefanos Fafalios <[email protected]>

• Ioannis Tsamardinos <[email protected]>

• Matteo Fasiolo <[email protected]>

• Giorgos Borboudakis <[email protected]>

• John Burkardt <[email protected]>

• Kleanthi Lakiotaki <[email protected]>

• Changliang Zou <[email protected]>

• Christina Chatzipantsiou <[email protected]>

Description

All k possible combinations from n elements.

Usage

comb_n(n, k,simplify=TRUE)

Arguments

Value

A matrix with k columns and rows equal to the number of possible unique combinations of n with k elements. If simplify is set to TRUE then a list with k values where each value has length equal to the number of possible unique combinations of n with k elements.

Author(s)

Manos Papadakis and Marios Dimitriadis

R implementation and documentation: Manos Papadakis <[email protected]> and Marios Dimitriadis <[email protected]>.

References

Nijenhuis A. and Wilf H.S. (1978). Combinatorial Algorithms for Computers and Calculators. Academic Press, NY.

See Also

nth, colMaxs, colMins, colrange

Examples

comb_n(20, 4)
combn(20, 4)
x <- rnorm(5)
res<-comb_n(x, 3)

Description

Analysis of covariance

Usage

ancova1(y, ina, x, logged = FALSE)

Arguments

Details

Analysis of covariance is performed. No interaction between the factor and the covariate is tested. Only the main effects. The design need not be balanced. The values of ina need not have the same frequency. The sums of squares have been adjusted to accept balanced and unbalanced designs.

Value

A matrix with the test statistic and the p-value for the factor variable and the covariate.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

References

D.C. Montgomery (2001). Design and analysis of experiments (5th Edition). New York: John Wiley & Sons

See Also

ancovas, ftests, ttests, anova1

Examples

y <- rnorm(90)
ina <- rbinom(90, 2, 0.5) + 1
x <- rnorm(90)
a <- ancova1(y, ina, x)

Description

Analysis of variance with a count variable.

Usage

poisson.anova(y, ina, logged = FALSE)
geom.anova(y, ina, type = 1, logged = FALSE)
quasipoisson.anova(y, ina, logged = FALSE)

Arguments

Details

This is the analysis of variance with Poisson or geometric distributed data. What we do is a log-likelihood ratio test. However, this is exactly the same as Poisson regression with a single predictor variable who happens to be categorical. Needless to say that this is faster function than the glm command in R. For the same purpose with a Bernoulli variable use g2Test. The quasinpoisson.anova is when in the glm function you specify family = quasipoisson. This is suitable for the case of over or under-dispersed data.

Value

A vector with two values, the difference in the deviances (or the scale difference in the case of quasi poisson) and the relevant p-value. The quasipoisson.anova also returns the estimate of the $\phi$ parameter.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

See Also

logistic.cat1, g2Test, poisson.anovas, anova, poisson_only, poisson.mle

Examples

y <- rpois(300, 10)
ina <- rbinom(300, 3, 0.5) + 1 
a1 <- poisson.anova(y, ina) 
a2 <- glm(y ~ ina, poisson) 


res<-anova(a2, test = "Chisq")

y <- rgeom(300, 0.7)
res<-geom.anova(y, ina)

Description

Angular central Gaussian random values simulation.

Usage

racg(n, sigma, seed = NULL)

Arguments

Details

The algorithm uses univariate normal random values and transforms them to multivariate via a spectral decomposition. The vectors are then scaled to have unit length.

Value

A matrix with the simulated data.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]>

References

Tyler D. E. (1987). Statistical analysis for the angular central Gaussian distribution on the sphere. Biometrika 74(3): 579-589.

See Also

acg.mle, rmvnorm, rmvlaplace, rmvt

Examples

s <- cov( iris[, 1:4] )
x <- racg(100, s)
res<-acg.mle(x)  
res<-vmf.mle(x)  ## the concentration parameter, kappa, is very low, close to zero, as expected.

Description

ANOVA for two quasi Poisson regression models.

Usage

anova_quasipois.reg(mod0, mod1, n)

Arguments

Details

This is an ANOVA type significance testing for two quasi Poisson models.

Value

A vector with 4 elements, the test statistic value, its associated p-value and the relevant degrees of freedom of the numerator and the denominator.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

References

Papke L. E. & Wooldridge J. (1996). Econometric methods for fractional response variables with an application to 401(K) plan participation rates. Journal of Applied Econometrics, 11(6): 619–632.

McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.

See Also

anova_qpois.reg, qpois.reg, univglms, quasipoisson.anova

Examples

y <- rnbinom(200, 10, 0.5)
x <- matrix(rnorm(200 * 3), ncol = 3)
a1 <- qpois.reg(x, y)
a0 <- qpois.reg(x[, 1], y)
res<-anova_quasipois.reg(a0, a1, 200)
b1 <- glm(y ~ x, family = quasipoisson)
b0 <- glm(y ~ x[, 1], family = quasipoisson)
res<-anova(b0, b1, test = "F")
c1 <- glm(y ~ x, family = poisson)
c0 <- glm(y ~ x[, 1], family = poisson)
res<-anova(c0, c1, test = "Chisq")

Description

Apply method to Positive and Negative number.

Usage

negative(x,method = "min")
positive(x,method = "min")
positive.negative(x,method = "min")

Arguments

Details

These functions apply the chosen method to the chosen subset (negative, positive, or both) from the vector and return the result.

Value

negative: apply the chosen method to every negative number of the input vector. positive: apply the chosen method to every positive number of the input vector. positive.negative: apply the chosen method to every negative and positive number of the input vector.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

nth, colnth, rownth,sort_unique, Round

Examples

x <- rnorm(1000)

identical(negative(x,"min"), min(x<0))
identical(positive(x,"min"), min(x>0))
identical(positive.negative(x,"min"), c(min(x<0),min(x>0)))

x<-NULL

Description

Apply to each column a method under condition.

Usage

apply.condition(x,method = "+",oper = ">",cond.val = 0)

Arguments

Details

Apply to each col the specified method using the condition.

Value

An integer vector with the coresponding values.

Author(s)

Manos Papadakis and Michail Tsagris

R implementation and documentation: Manos Papadakis <[email protected]> and Michail Tsagris <[email protected]>.

See Also

colsums, colMedians, colVars

Examples

x <- matrix(rpois(100,6),10, 10)
identical(apply(x,2,function(x){ sum(x[x>0]) }), apply.condition(x,"+",">",0))
x<-NULL

Description

Backward selection regression.

Usage

bs.reg(y, x, alpha = 0.05, type = "logistic")

Arguments

Details

This function currently implements only the binary Logistic and Poisson regressions. If the sample size is less than the number of variables a notification message will appear and no backward regression will be performed.

Value

The output of the algorithm is an S3 object including:

Author(s)

Marios Dimitriadis

R implementation and documentation: Marios Dimitriadis <[email protected]>

See Also

fs.reg, univglms, cor.fsreg

Examples

y <- rbinom(50, 1, 0.5)
x <- matrnorm(50, 10)
res<-bs.reg(y, x)

Description

BIC (using partial correlation) forward regression.

Usage

bic.corfsreg(y, x, tol = 2)

Arguments

Details

The forward regression tries one by one the variables using the F-test, basically partial F-test every time for the latest variable. This is the same as testing the significance of the coefficient of this latest enetered variable. Alternatively the correlation can be used and this case the partial correlation coefficient. There is a direct relationship between the t-test statistic and the partial correlation coefficient. Now, instead of having to calculate the test statistic, we calculate the partial correlation coefficient. The largest partial correlation indicates the candidate variable to enter the model. If the BIC of the regression model with that variable included, reduces, less than "tol" from the previous model without this variable, the variable enters.

Value

A matrix with two columns, the index of the selected variable(s) and the BIC of each model. The first line is always 0 and the BIC of the model with no predictor variables.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

References

Draper, N.R. and Smith H. (1988). Applied regression analysis. New York, Wiley, 3rd edition.

See Also

cor.fsreg, score.glms, univglms, logistic_only, poisson_only, regression

Examples

## 200 variables, hence 200 univariate regressions are to be fitted
x <- matrix( rnorm(200 * 200), ncol = 200 )
y <- rnorm(200)
a1 <- bic.corfsreg(y, x)
a2 <- cor.fsreg(y, x)
x <- NULL

Description

BIC forward regression with generalised linear models.

Usage

bic.fs.reg(y, x, tol = 2, type = "logistic")

Arguments

Details

The forward regression tries one by one the variables using the BIC at each step for the latest variable. If the BIC of the regression model with that variable included, is less than "tol" from the previous model without this variable, the variable enters.

Value

A matrix with two columns, the index of the selected variable(s) and the BIC of each model.

Author(s)

Marios Dimitriadis

R implementation and documentation: Marios Dimitriadis <[email protected]>.

References

Draper, N.R. and Smith H. (1988). Applied regression analysis. New York, Wiley, 3rd edition.

See Also

fs.reg, bic.corfsreg, cor.fsreg, score.glms, univglms, logistic_only, poisson_only, regression

Examples

x <- matrix(rnorm(200 * 50), ncol = 50)
## 200 variables, hence 200 univariate regressions are to be fitted
y <- rbinom(200, 1, 0.5)
a <- bic.fs.reg(y, x)
x <- NULL

Description

Search a value in an ordered vector.

Usage

binary_search(x, v, index=FALSE)

Arguments

Details

The functions is written in C++ in order to be as fast as possible.

Value

Search if the v exists in x. Then returns TRUE/FALSE if the value is been found.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

is_element

Examples

x <- sort(rnorm(1000))
v <- x[50]
b <- binary_search(x,v) 
b1 <- binary_search(x,v,TRUE)

Description

Binomial coefficient and its logarithm.

Usage

Lchoose(x, k)
Choose(x, k)

Arguments

Details

The binomial coefficient or its logarithm are evaluated.

Value

A vector with the answers.

Author(s)

Manos Papadakis

R implementation and documentation: Michail Tsagris <[email protected]>

See Also

comb_n, Lbeta, Lgamma

Examples

x <- sample(20:30, 100, replace = TRUE)
res<-Choose(x, 4)
res<-Lchoose(x, 4)

x<-NULL

Description

Bootstrap t-test for 2 independent samples.

Usage

boot.ttest2(x, y, B = 999)

Arguments

Details

Instead of sampling B times from each sample, we sample $\sqrt{B}$ from each of them and then take all pairs. Each bootstrap sample is independent of each other, hence there is no violation of the theory.

Value

A vector with the test statistic and the bootstrap p-value.

Author(s)

Michail Tsagris and Christina Chatzipantsiou

R implementation and documentation: Michail Tsagris <[email protected]> and Christina Chatzipantsiou <[email protected]>.

References

B.L. Welch (1951). On the comparison of several mean values: an alternative approach. Biometrika, 38(3/4), 330-336.

Efron Bradley and Robert J. Tibshirani (1993). An introduction to the bootstrap. New York: Chapman & Hall/CRC.

Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2019). Extremely efficient permutation and bootstrap hypothesis tests using R. To appear in the Journal of Modern Applied Statistical Methods.

https://arxiv.org/ftp/arxiv/papers/1806/1806.10947.pdf

See Also

ttest2, exact.ttest2, ftest

Examples

tic <- proc.time()
x <- rexp(40, 4)
y <- rbeta(50, 2.5, 7.5)
a <- boot.ttest2(x, y, 9999)
a

Description

Check if values are integers and convert to integer.

Usage

is_integer(x)
as_integer(x,result.sort = TRUE,init = 1)

Arguments

Details

The behavior of these functions are different than R's built in.

is_integer: check if all the values are integers in memory. If typeof is double, and the values are integers in range -2^31 : 2^31 then it is better to convert to integer vector for using less memory. Also you can decrease the time complexity.

as_integer: converts the discrete values to integers.

Value

is_integer: A logical value, TRUE if all values are integers and in range -2^31 : 2^31. Otherwise FALSE.

as_integer: By default the function will return the same result with "as.numeric" but the user can change the "init" value not start from 1 like R's. Also the result can be unsorted using "result.sort".

Author(s)

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

as_integer, colVars, colmeans, read.directory

Examples

x<-runif(10)
y1<-is_integer(x) # y1 is FALSE
x<-as.numeric(rpois(10,10)) # integers but typeof is double
y1<-is_integer(x) # y1 is TRUE so you can convert to integer vector.

as_integer(letters) ## as.numeric(letters) produce errors
x<-y1<-NULL

Description

Check Namespace/Rd and examples files.

Usage

checkNamespace(path.namespace,path.rfolder)
checkAliases(path.man,path.rfolder)
checkTF(path.man)
checkExamples(path.man,package,each = 1,print.errors = stderr(),
	print.names = FALSE)
checkUsage(path.man,path.rfolder)

Arguments

Details

checkNamespace: reads from the NAMESPACE folder all the export R functions, reads from folder R all the R functions and check if all the functions are export.

checkAliases: reads from the man directory all the Rd files, then reads from each file the aliases and check if:

• All the R files has man file or an alias.

• All aliases belongs to functions.

• If there are dublicated aliases.

checkExamples: reads from the man directory all the Rd files, then read from each file the examples and then run each of them. If you want to print the errors in any file then set "print.errors=file_name" or in the standard error "print.errors=stderr()" and then you will see all the errors for every file. Set to argument "package" the name of your package. The argument "print.names" it is very helpful because if any of you function crashes R during running you will never know which one was. So setting it "TRUE", it will print the name of each file before running it's example.It might crash, but you will know which file. Remember that there is always an error timeout so it might didn't crash the current file but one from the previous.

checkTF: reads from the man directory all the Rd files, then read from each file the examples and checks if any examples has the values "T" and "F" instead "TRUE" and "FALSE". The "T","F" is wrong.

checkUsage: reads from the man directory all the Rd files and for each man check if the usage section has the right signature for the functions from the R directory.

checkTF, checkUsage, checkAliases: you can choose which files not to read for both R and Rd. You must add in the first line of the file in comment the "attribute" "[dont read]". Then each function will know which file to read or not. For Rd you add "%[dont read]" and for R "#[dont read]". Finally, these functions will return in the result a list of which files had this attribute.

Value

checkNamespace: a vector with the names of missing R files. (Don't use it for now)

checkAliases: a list with 4 fields.

• Missing Man files: A vector with the names of the missing Rd files or nothing.

• Missing R files: A vector with the names of the missing R files or nothing.

• Duplicate alias: A vector with the names of the dublicate aliases or nothing.

• dont read: A list with 2 fields

  • R: A character vector whith the names of the files that had attribute "#[dont read]" or nothing.

  • Rd: A character vector whith the names of the files that had attribute "%[dont read]" or nothing.

checkExamples: a list with 3 fields

• Errors: A character vector with the names of the Rd files that produced an error.

• Big Examples: A character vector with the names of the Rd files that has big examples per line.

• dont read: A list with 2 fields

  • R: A character vector whith the names of the files that had attribute "#[dont read]" or nothing.

  • Rd: A character vector whith the names of the files that had attribute "%[dont read]" or nothing.

checkTF: a list with 3 fields

• TRUE: A character vector with the names of the Rd files that has "T" or nothing.

• FALSE: A character vector with the names of the Rd files that has "F" or nothing.

• dont read: A list with 2 fields

  • R: A character vector whith the names of the files that had attribute "#[dont read]" or nothing.

  • Rd: A character vector whith the names of the files that had attribute "%[dont read]" or nothing.

checkUsage: a list with 3 fields

• missing functions: A character vector with the name of the file that is missing and the Rd file that is found or nothing.

• missmatch functions: A character vector with the name of the file that has missmatch function and the Rd file that is found or nothing.

• dont read: A list with 2 fields

  • R: A character vector whith the names of the files that had attribute "#[dont read]" or nothing.

  • Rd: A character vector whith the names of the files that had attribute "%[dont read]" or nothing.

• hidden functions: A character vector with the name of the functions thath have been declared as hidden.

• usage lines wider than 90 characters: A list with the Rd's that have usages that are wider than 90 characters.

Author(s)

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

read.directory, AddToNamespace, sourceR, sourceRd, read.examples

Examples

#for example: path.namespace="C:\some_file\NAMESPACE"
	#for example: path.rfolder="C:\some_file\R\"
	#for example: path.man="C:\some_file\man\"
	#system.time( a<-checkNamespace(path.namespace,path.rfolder) )
	#system.time( b<-checkAliases(path.man,path.rfolder) )
	#system.time( b<-checkExamples(path.man) )
	#system.time( b<-checkExamples(path.man,2) )
	#system.time( b<-checkTF(path.man) )
	#system.time( b<-checkTF(path.man,path.rfolder) )

Description

Check whether a square matrix is symmetric.

Usage

is.symmetric(x)

Arguments

Details

Instead of going through the whole matrix, the function will stop if the first disagreement is met.

Value

A boolean value, TRUE of FALSE.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

cholesky, cora, cova

Examples

x <-matrix( rnorm( 100 * 400), ncol = 400 )
s1 <- cor(x)
is.symmetric(s1)
x <- x[1:100, ]
is.symmetric(x)

x<-s1<-NULL

Description

Chi-square and G-square tests of (unconditional) indepdence.

Usage

gchi2Test(x, y, logged = FALSE)

Arguments

Details

The function calculates the test statistic of the $\chi^2$ and the $G^2$ tests of unconditional independence between x and y. x and y need not be numerical vectors like in g2Test. This function is more close to the spirit of MASS' loglm function which calculates both statistics using Poisson log-linear models (Tsagris, 2017).

Value

A matrix with two rows. In each row the X2 or G2 test statistic, its p-value and the degrees of freedom are returned.

Author(s)

Manos Papadakis and Michail Tsagris

R implementation and documentation: Manos Papadakis <[email protected]> and Michail Tsagris <[email protected]>.

References

Tsagris M. (2017). Conditional independence test for categorical data using Poisson log-linear model. Journal of Data Science, 15(2):347-356.

See Also

g2Test_univariate, g2Test_univariate_perm, g2Test

Examples

nvalues <- 3
nvars <- 2
nsamples <- 5000
data <- matrix( sample( 0:(nvalues - 1), nvars * nsamples, replace = TRUE ), nsamples, nvars )

res<-gchi2Test(data[, 1], data[, 2])
res<-g2Test_univariate( data, rep(3, 2) )  ## G^2 test
res<-chisq.test(data[, 1], data[, 2])  ## X^2 test from R
  
data<-NULL

Description

Cholesky decomposition of a square matrix.

Usage

cholesky(x,parallel = FALSE)

Arguments

Details

The Cholesky decomposition of a square positive definite matrix is computed. The use of parallel is suggested for matrices with dimensions of 1000 or more.

Value

An upper triangular matrix.

Author(s)

Manos Papadakis

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>

See Also

is.symmetric

Examples

x = matrix(rnorm(1000 * 50), ncol = 50)
s = cov(x)
a1 <- cholesky(s)
#a2 <- chol(s)
#all.equal(a1[upper.tri(a1)], a2[upper.tri(a2)])
x <- NULL
s <- NULL
a1 <- NULL
a2 <- NULL

Description

Regression with circular dependent variable and Euclidean or categorical independent variables.

Usage

spml.reg(y, x, tol = 1e-07, seb = FALSE, maxiters = 100)

Arguments

Details

The Newton-Raphson algorithm is fitted in this regression as described in Presnell et al. (1998).

Value

A list including:

Author(s)

Michail Tsagris and Manos Papadakis

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>

References

Presnell Brett, Morrison Scott P. and Littell Ramon C. (1998). Projected multivariate linear models for directional data. Journal of the American Statistical Association, 93(443): 1068-1077.

See Also

spml.mle, iag.mle, acg.mle

Examples

x <- rnorm(100)
z <- cbind(3 + 2 * x, 1 -3 * x)
y <- cbind( rnorm(100,z[ ,1], 1), rnorm(100, z[ ,2], 1) )
y <- y / sqrt( rowsums(y^2) )
a1 <- spml.reg(y, x)
y <- atan( y[, 2] / y[, 1] ) + pi * I(y[, 1] < 0) 
a2 <- spml.reg(y, x)

Description

It calculates the squared correlation between a circular and one or more linear variables.

Usage

circlin.cor(theta, x)

Arguments

Details

The squared correlation between a circular and one or more linear variables is calculated.

Value

A matrix with as many rows as linear variables including:

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]>

References

Mardia, K. V. and Jupp, P. E. (2000). Directional statistics. Chicester: John Wiley & Sons.

See Also

spml.reg

Examples

phi <- rvonmises(50, 2, 20, rads = TRUE)
x <- 2 * phi + rnorm(50)
y <- matrix(rnorm(50 * 5), ncol = 5)
res<-circlin.cor(phi, x)
res<-circlin.cor(phi, y)
y <- NULL

Description

Coefficient matrices.

Usage

coeff(x, method,vector = FALSE)

Arguments

Details

• bhattacharyya : $\sum \sqrt(P_i * Q_i)$

Value

A square matrix with the pairwise coefficients.

Author(s)

Manos Papadakis.

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

dista, Dist

Examples

x <- matrix(rnorm(50 * 10), ncol = 10)
a1 <- coeff(x,"bhattacharyya")
x<-a1<-NULL

Description

Colum-wise cumulative operations (sum, prod, min, max).

Usage

colCumSums(x)
colCumProds(x)
colCumMins(x)
colCumMaxs(x)

Arguments

Details

Cumulative mins, maxs, sums and prods are returned.

Value

A matrix with the results. It has one row less than the initial matrix.

Author(s)

Manos Papadakis and Michail Tsagris

R implementation and documentation: Manos Papadakis <[email protected]> and Michail Tsagris <[email protected]>.

See Also

colsums, colMedians, colVars

Examples

x <- matrnorm(10, 10)
res<-colCumSums(x)
res<-colCumMins(x)
res<-colCumMaxs(x)
res<-colCumProds(x)

Description

Column and row wise coefficients of variation.

Usage

colcvs(x, ln = FALSE, unbiased = FALSE) 
rowcvs(x, ln = FALSE, unbiased = FALSE)

Arguments

Details

The colum-wise coefficients of variation are calculated.

Value

A vector with the coefficient of variation for each column or row.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

See Also

colsums, colVars

Examples

m <- rnorm(100, 10)
x <- matrix(rnorm(100 * 100, m, 1), ncol = 100)
a1 <- colcvs(x)
a2 <- colcvs(x[1:25, ], unbiased = TRUE)
a3 <- colcvs( exp(x), ln = TRUE)
x <- NULL

Description

Column and row-wise Any/All of a matrix.

Usage

colAny(x)
rowAny(x)
colAll(x, parallel = FALSE, cores = 0)
rowAll(x, parallel = FALSE, cores = 0)

Arguments

Details

The functions is written in C++ in order to be as fast as possible.

Value

A vector where item "i" is true if found Any/All true in column/row "i". Otherwise false.

Author(s)

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

Median, colMedians, colMeans (buit-in R function)

Examples

x <- matrix(as.logical(rbinom(100*100,1,0.5)),100,100)
a<-colAny(x)
#b<-apply(x,2,any)
#all.equal(a,b)

a<-rowAny(x)
#b<-apply(x,1,any)
#all.equal(a,b)

a<-colAll(x)
#b<-apply(x,2,all)
#all.equal(a,b)

a<-b<-x<-NULL

Description

Column and row-wise means of a matrix.

Usage

colmeans(x, parallel = FALSE, cores = 0)
## S3 method for class 'matrix'
colmeans(x, parallel = FALSE, cores = 0)
## S3 method for class 'data.frame'
colmeans(x, parallel = FALSE, cores = 0)
rowmeans(x)
colhameans(x, parallel = FALSE)
rowhameans(x)

Arguments

Value

A vector with the column or row arithmetic or harmonic means.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

colsums, rowsums, colMins, colMedians, colMads

Examples

x <- matrix(rpois(100 * 100, 10),ncol = 100)
x1 <- colmeans(x)
#x2 <- colMeans(x)
#all.equal(x1,x2)

x1 <- rowmeans(x)
#x2 <- rowMeans(x)
#all.equal(x1,x2)

colhameans(x)
rowhameans(x) 

x<-x1<-x2<-NULL

Description

Column and row-wise medians of a matrix or median of a vector.

Usage

colMedians(x,na.rm = FALSE, parallel = FALSE, cores = 0)
rowMedians(x,na.rm = FALSE, parallel = FALSE, cores = 0)
Median(x,na.rm=FALSE)
med(x,na.rm=FALSE)

Arguments

Details

The functions is written in C++ in order to be as fast as possible.

Value

A vector with the column medians.

Author(s)

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

Median, colVars, colMeans (buit-in R function)

Examples

x <- matrix( rnorm(100 * 100), ncol = 100 )
a <- apply(x, 2, median) 
b1 <- colMedians(x) 
all.equal(as.vector(a), b1)

x<-a<-b1<-NULL

Description

Column and row-wise nth smallest value of a matrix/vector.

Usage

colnth(x,elems, num.of.nths = 1,descending = FALSE,na.rm = FALSE,
	index.return = FALSE, parallel = FALSE, cores = 0)
rownth(x,elems, num.of.nths = 1,descending = FALSE,na.rm = FALSE,
	index.return = FALSE, parallel = FALSE, cores = 0)
nth(x, k, num.of.nths = 1,descending = FALSE,index.return = FALSE,na.rm = FALSE)

Arguments

Details

The functions is written in C++ in order to be as fast as possible.

Value

For "colnth" , "rownth": A vector with the column/row nth

For "nth": The nth value.

Author(s)

Manos Papadakis <[email protected]>

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

See Also

Median, colMedians, colMeans (buit-in R function)

Examples

x <- matrix( rnorm(100 * 100), ncol = 100 )
elems <- sample(1:100,100,TRUE)
colnth(x,elems)
rownth(x,elems)
x <- rnorm(1000)

nth(x, 500)
#sort(x)[500]

x<-elems<-NULL

Description

Column and row-wise Order - Sort Indices.

Usage

colOrder(x,stable=FALSE,descending=FALSE, parallel = FALSE, cores = 0)
rowOrder(x,stable=FALSE,descending=FALSE, parallel = FALSE, cores = 0)
Order(x,stable=FALSE,descending=FALSE,partial = NULL,parallel = FALSE)

Arguments

Details

The function applies "order" in a column or row-wise fashion or Order a vector. If you want the same results as R's, then set "stable=TRUE" because "stable=FALSE" uses a sorting algorithm that it is not stable like R's sort. But it is faster to use the default. This verion is faster for large data, more than 300.

Value

For "colOrder" and "rowOrder" a matrix with integer numbers. The result is the same as apply(x, 2, order) or apply(x, 1, order).

For "Order" sort the vector and returns the indices of each element that it has before the sorting. The result is the same as order(x) but for the same exactly results set argument "stable" to "TRUE".

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

colsums, coldiffs, colMedians, colprods

Examples

x <- matrix( runif(10 * 10), ncol = 10 )
res<-colOrder(x)
res<-apply(x, 2, order)
res<-rowOrder(x)
t(apply(x, 1, order))

y <- rnorm(100)
b <- Order(y)
a <- order(y)
all.equal(a,b) ## false because it is not stable
b <- Order(y,stable=TRUE)
all.equal(a,b) ## true because it is stable

x<-y<-b<-a<-NULL

Description

Column and row-wise products.

Usage

colprods(x, method = "direct")
rowprods(x)

Arguments

Details

The product of the numbers in a matrix is returned either column-wise or row-wise.

Value

A vector with the column or the row products.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

colsums, coldiffs, colMedians

Examples

x <- matrix( runif(100 * 10), ncol = 10 )
res<-colprods(x)
res<-rowprods(x)

x<-NULL

Description

Column and row-wise range of values of a matrix.

Usage

colrange(x, cont = TRUE, parallel = FALSE,cores = 0)
rowrange(x, cont = TRUE)

Arguments

Value

A vector with the relevant values.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

colMins, colMaxs, rowMins, rowMaxs, nth, colMedians, colVars, colSort, rowSort

Examples

x <- matrix( rnorm(100 * 100), ncol = 100 )

a1 <- colrange(x) 
a2 <- apply(x, 2, function(x) diff( range(x)) ) 
all.equal(a1, a2)

a1 <- rowrange(x) 
a2 <- apply(x, 1, function(x) diff( range(x)) ) 
all.equal(a1, a2)

x<-a1<-a2<-NULL

Description

Column and row-wise ranks.

Usage

colRanks(x,method = "average",descending = FALSE,
     stable = FALSE, parallel = FALSE, cores = 0)
rowRanks(x,method = "average",descending = FALSE,
     stable = FALSE, parallel = FALSE, cores = 0)
Rank(x,method = "average",descending = FALSE,stable = FALSE, parallel = FALSE)

Arguments

Details

For each column or row of a matrix the ranks are calculated and they are returned.

Value

A matrix with the column or row-wise ranks.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

Rank, correls

Examples

x <- matrnorm(100, 10)
a1 <- colRanks(x)
a2 <- apply(x, 2, rank)
b1 <- rowRanks(x)
b2 <- apply(x, 1, rank)

a1 <- Rank(x[,1])
a1 <- rank(x[,1])

x<-a1<-a2<-b1<-b2<-NULL

Description

Column and row-wise shuffle of a matrix.

Usage

colShuffle(x)
rowShuffle(x)

Arguments

Details

The functions is written in C++ in order to be as fast as possible.

Value

A vector with the column/row Shuffle.

Author(s)

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

Median, colVars, colMeans (buit-in R function)

Examples

x <- matrix( rnorm(100 * 100), ncol = 100 )
colShuffle(x)
rowShuffle(x)

x<-NULL

Description

Column and row-wise sums of a matrix.

Usage

colsums(x,indices = NULL, parallel = FALSE, na.rm = FALSE, cores = 0)
rowsums(x,indices = NULL, parallel = FALSE, na.rm = FALSE, cores = 0)

Arguments

Value

A vector with sums.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

colMedians, colmeans, colVars

Examples

x <- matrix(rpois(500 * 100, 10),ncol = 100)
x1 <- colsums(x)
x2 <- colSums(x)
all.equal(x1,x2)

x1 <- rowsums(x)
x2 <- rowSums(x)
all.equal(x1,x2)

x<-x1<-x2<-NULL

Description

Column and row-wise tabulate of a matrix.

Usage

colTabulate(x, max_number = max(x))
rowTabulate(x, max_number = max(x))

Arguments

Details

The functions is written in C++ in order to be as fast as possible.

Value

A matrix where in each column the command "tabulate" has been performed. The number of rows of the returned matrix will be equal to the max_number if given. Otherwise, the functions will find this number.

Author(s)

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

colShuffle, colVars, colmeans

Examples

x <- matrix( rbinom(100 * 100, 4, 0.5), ncol = 100 )
colTabulate(x)
rowTabulate(x)

x<-NULL

Description

Column and row-wise variances and standard deviations of a matrix

Usage

## S3 method for class 'matrix'
colVars(x, std = FALSE, na.rm = FALSE, parallel = FALSE, cores = 0)
## S3 method for class 'data.frame'
colVars(x, std = FALSE, na.rm = FALSE, parallel = FALSE, cores = 0)
colVars(x, std = FALSE, na.rm = FALSE, parallel = FALSE, cores = 0)
rowVars(x, std = FALSE, na.rm = FALSE, parallel = FALSE, cores = 0)

Arguments

Details

We found this on stackoverflow which was created by David Arenburg. We then modified the function to match the sums type formula of the variance, which is faster.

Value

A vector with the column variances or standard deviations.

Author(s)

Michail Tsagris and Manos Papadakis.

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

See Also

colmeans, colMedians, colrange

Examples

x <- matrix( rnorm(100 * 100), ncol = 100 )
a2 <- colVars(x)
x<-a2<-NULL

Description

Column and row-wise mean absolute deviations.

Usage

colMads(x,method = "median",na.rm=FALSE,parallel = FALSE, cores = 0)
rowMads(x,method = "median",na.rm=FALSE,parallel = FALSE, cores = 0)
Mad(x,method = "median",na.rm=FALSE)

Arguments

Details

The functions is written in C++ in order to be as fast as possible.

Value

A vector with the column-wise mean absolute deviations.

Author(s)

Manos Papadakis

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

See Also

colMedians, rowMedians, colVars, colmeans, colMeans (buit-in R function)

Examples

x <- matrix( rnorm(100 * 100), ncol = 100 )
a <- colMads(x)

x<-NULL

Description

Column-wise differences.

Usage

coldiffs(x)

Arguments

Details

This function simply does this function x[, -1] - x[, -k], where k is the last column of the matrix x. But it does it a lot faster. That is, 2nd column - 1st column, 3rd column - 2nd column, and so on.

Value

A matrix with one column less containing the differences between the successive columns.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

Dist, dista, colmeans

Examples

x <- matrix( rnorm(50 * 10), ncol = 10 )
res<-coldiffs(x)

x<-NULL

Description

Column-wise kurtosis and skewness coefficients.

Usage

colkurtosis(x, pvalue = FALSE)

colskewness(x, pvalue = FALSE)

Arguments

Details

The skewness and kurtosis coefficients are calculated. For the skewness coefficient we use the sample unbiased version of the standard deviation. For the kurtosis, we do not subtract 3.

Value

If "pvalue" is FALSE, a vector with the relevant coefficient. Otherwise a matrix with two columns. The kurtosis or skewness coefficient and the p-value from the hypothesis test that they are significantly different from 3 or 0 respectively.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

See Also

skew, skew.test2, colMedians, colmeans, colVars, sftests

Examples

## 200 variables, hence 200 F-tests will be performed
x = matrix( rnorm(200 * 50), ncol = 50 )
## 200 observations in total
colkurtosis(x)
colskewness(x)
x <- NULL

Description

Column-wise matching coefficients.

Usage

match.coefs(x, y = NULL, ina, type = "jacc")

Arguments

Details

Two matrices are given as imput and for each column matching coefficients are calculated, either the Jaccard or the simple matching coefficient or both.

Value

A matrix with one or two columns, depending on the type you have specified. If you specify "both", there will be two columns, if you specify "jacc" or "smc" then just one column.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

See Also

odds, colTabulate

Examples

x <- matrix(rbinom(400 * 10, 1, 0.5), ncol = 10)
y <- matrix(rbinom(400 * 10, 1, 0.5), ncol = 10)
a <- match.coefs(x, y, type = "both")
x <- NULL
y <- NULL

Description

Column-wise minimum and maximum of a matrix.

Usage

colMins(x, value = FALSE, parallel = FALSE, cores = 0)
colMaxs(x, value = FALSE, parallel = FALSE, cores = 0)
colMinsMaxs(x, parallel = FALSE, cores = 0)

Arguments

Value

A vector with the relevant values.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

rowMins, rowMaxs, nth, colrange, colMedians, colVars, colSort, rowSort

Examples

x <- matrix( rnorm(100 * 200), ncol = 200 )

s1 <- colMins(x) 
s2 <- apply(x, 2, min) 

s1 <- colMaxs(x) 
s2 <- apply(x, 2, max) 

s1 <- colMinsMaxs(x)
s2 <- c(apply(x, 2, min), apply(x, 2, max)) 

x<-s1<-s2<-NULL

Description

Column-wise MLE of some univariate distributions.

Usage

colexpmle(x)
colexp2.mle(x)
colgammamle(x, tol = 1e-07)
colinvgauss.mle(x)
collaplace.mle(x)
collindley.mle(x)
colmaxboltz.mle(x)
colnormal.mle(x)
colpareto.mle(x)
colrayleigh.mle(x)
colvm.mle(x, tol = 1e-07)
colweibull.mle(x, tol = 1e-09, maxiters = 100, parallel = FALSE)
colnormlog.mle(x)

Arguments

Details

For each column, the same distribution is fitted and its parameter and log-likelihood are computed.

Value

A matrix with two, three or five (for the colnormlog.mle) columns. The first one or the first two contain the parameter(s) of the distribution and the other columns contain the log-likelihood values.

Author(s)

Michail Tsagris and Stefanos Fafalios

R implementation and documentation: Michail Tsagris <[email protected]> and Stefanos Fafalios <[email protected]>

References

Kalimuthu Krishnamoorthy, Meesook Lee and Wang Xiao (2015). Likelihood ratio tests for comparing several gamma distributions. Environmetrics, 26(8):571-583.

N.L. Johnson, S. Kotz and N. Balakrishnan (1994). Continuous Univariate Distributions, Volume 1 (2nd Edition).

N.L. Johnson, S. Kotz and N. Balakrishnan (1970). Distributions in statistics: continuous univariate distributions, Volume 2.

Sharma V. K., Singh S. K., Singh U. and Agiwal V. (2015). The inverse Lindley distribution: a stress-strength reliability model with application to head and neck cancer data. Journal of Industrial and Production Engineering, 32(3): 162-173.

See Also

vm.mle, poisson.mle, normal.mle, gammamle

Examples

x <- matrix(rnorm(1000 * 50), ncol = 50)
a <- colnormal.mle(x)
b <- collaplace.mle(x)
x <- NULL

Description

Column-wise true/false value of a matrix.

Usage

colTrue(x)
colFalse(x)
colTrueFalse(x)

Arguments

Value

An integer vector where item "i" is the number of the true/false values of "i" column.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

rowMins, rowFalse, nth, colrange, colMedians, colVars, colSort, rowSort, rowTrue

Examples

x <- matrix(as.logical(rbinom(100*100,1,0.5)),100,100)

s1 <- colTrue(x) 

s1 <- colFalse(x)  

s1 <- colTrueFalse(x)

x<-s1<-NULL

Description

Column-wise uniformity tests for circular data.

Usage

colwatsons(u)

Arguments

Details

These tests are used to test the hypothesis that the data come from a circular uniform distribution. The Kuiper test is much more time consuming and this is why it not implemented yet. Once we figure out a way to make it fast, we will incldue it.

Value

A matrix with two columns, the value of the test statistic and its associated p-value.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]>

References

Jammalamadaka, S. Rao and SenGupta, A. (2001). Topics in Circular Statistics, pg. 153-55 (Kuiper's test) & 156-157 (Watson's test).

See Also

watson, vmf.mle, rvonmises

Examples

x <- matrix( rvonmises(n = 50 * 10, m = 2, k = 0), ncol = 10 )
res<-colwatsons(x)
x <- NULL

Description

Column-wise Yule's Y (coefficient of colligation).

Usage

col.yule(x, y = NULL, ina)

Arguments

Details

Yule's coefficient of colligation is calculated for every column.

Value

A vector with Yule's Y, one for every column of x is returned.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]>

References

Yule G. Udny (1912). On the Methods of Measuring Association Between Two Attributes. Journal of the Royal Statistical Society, 75(6):579-652.

See Also

yule, odds

Examples

x <- matrix(rbinom(300 * 10, 1, 0.5), ncol = 10)
ina <- rep(1:2, each = 150)
res<-col.yule( x, ina = ina )

Description

Convert a dataframe to matrix.

Usage

data.frame.to_matrix(x,col.names = NULL,row.names = NULL)

Arguments

Details

This functions converts a dataframe to matrix. Even if there are factors, the function converts them into numerical values. Attributes are not allowed for now.

Value

A matrix wich has the numrical values from the dataframe.

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>

See Also

Match, is.symmetric, permutation

Examples

res<-data.frame.to_matrix(iris)

Description

Convert R function to the Rfast's coresponding.

Usage

as.Rfast.function(Rfunction.name,margin=NULL)

Arguments

Details

Given the name of R function, it returns the coresponding function's name from Rfast.

Value

The coresponding Rfast function.

Author(s)

Manos Papadakis and Michail Tsagris

R implementation and documentation: Manos Papadakis <[email protected]> and Michail Tsagris <[email protected]>.

See Also

colsums, colMedians, colVars

Examples

res<-as.Rfast.function("var")

Description

Correlation based forward regression.

Usage

cor.fsreg(y, x, ystand = TRUE, xstand = TRUE, threshold = 0.05, 
tolb = 2, tolr = 0.02, stopping = "BIC")

Arguments

Details

The forward regression tries one by one the variables using the F-test, basically partial F-test every time for the latest variable. This is the same as testing the significance of the coefficient of this latest enetered variable. Alternatively the correlation can be used and this case the partial correlation coefficient. There is a direct relationship between the t-test statistic and the partial correlation coefficient. Now, instead of having to calculate the test statistic, we calculate the partial correlation coefficient. Using Fisher's z-transform we get the variance imediately. The partial correlation coefficient, using Fisher's z-transform, and the partial F-test (or the coefficient's t-test statistic) are not identical. They will be identical for large sample sizes though.

Value

A matrix with three columns, the index of the selected variables, the logged p-value and the the test statistic value and the BIC or adjusted $R^2$ of each model. In the case of stopping="BICR2" both of these criteria will be returned.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

References

Draper, N.R. and Smith H. (1988). Applied regression analysis. New York, Wiley, 3rd edition.

See Also

score.glms, univglms, logistic_only, poisson_only, regression

Examples

## 200 variables, hence 200 univariate regressions are to be fitted
x <- matrnorm(200,  100)
y <- rnorm(200)
cor.fsreg(y, x)
x <- NULL

Description

Correlations between pairs of variables.

Usage

corpairs(x, y, rho = NULL, logged = FALSE, parallel = FALSE)

Arguments

Details

The paired correlations are calculated. For each column of the matrices x and y the correlation between them is calculated.

Value

A vector of correlations in the case of "rho" being NULL, or a matrix with two extra columns, the test statistic and the (logged) p-value.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

References

Lambert Diane (1992). Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics. 34(1):1-14.

Johnson Norman L., Kotz Samuel and Kemp Adrienne W. (1992). Univariate Discrete Distributions (2nd ed.). Wiley

Cohen, A. Clifford (1960). Estimating parameters in a conditional Poisson distribution. Biometrics. 16:203-211.

Johnson, Norman L. Kemp, Adrianne W. Kotz, Samuel (2005). Univariate Discrete Distributions (third edition). Hoboken, NJ: Wiley-Interscience.

See Also

correls, allbetas, mvbetas

Examples

x <- matrnorm(100, 100)
y <- matrnorm(100, 100)
corpairs(x, y)
a <- corpairs(x, y)
x <- NULL
y <- NULL

Description

Correlation between a vector and a set of variables.

Usage

correls(y, x, type = "pearson", a = 0.05, rho = 0)
groupcorrels(y, x, type = "pearson", ina)

Arguments

Details

The functions uses the built-in function "cor" which is very fast and then includes confidence intervals and produces a p-value for the hypothesis test.

Value

For the "correls" a matrix with 5 column; the correlation, the p-value for the hypothesis test that each of them is eaqual to "rho", the test statistic and the $a/2%$ lower and upper confidence limits.

For the "groupcorrels" a matrix with rows equal to the number of groups and columns equal to the number of columns of x. The matrix contains the correlations only, no statistical hypothesis test is performed.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

See Also

allbetas, univglms

Examples

x <- matrnorm(60, 100 )
y <- rnorm(60)
r <- cor(y, x)  ## correlation of y with each of the xs
a <- allbetas(y, x)  ## the coefficients of each simple linear regression of y with x
b <- correls(y, x)
ina <- rep(1:2, each = 30)
b2 <- groupcorrels(y, x, ina = ina) 
x <- NULL

Description

Fast covariance and correlation matrix calculation.

Usage

cova(x, center = FALSE, large = FALSE)

cora(x, large = FALSE)

Arguments

Details

The calculations take place faster than the built-in functions cor as the number of variables increases. This is true if the number of variables is high, say from 500 and above. The "cova" on the other hand is always faster. For the "cova" in specific, we have an option to center the data prior to the cross product. This can be more stable if you have many tens of thousands of rows due to numerical issues that can arise.

For the correlation matrix we took the code from here

https://stackoverflow.com/questions/18964837/fast-correlation-in-r-using-c-and-parallelization/18965892#18965892

Value

The covariance or the correlation matrix.

Author(s)

Michail Tsagris and Manos Papadakis <[email protected]>.

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

See Also

colVars, cor, cov

Examples

x <- matrnorm(100, 40)
s1 <- cov(x) 
s2 <- cova(x)
all.equal(s1, s2)
x <- NULL

Description

Cox confidence interval for the ratio of two Poisson variables.

Usage

cox.poisrat(x, y, alpha = 0.05)
col.coxpoisrat(x, y, alpha = 0.05)

Arguments

Details

Cox confidence interval for the ratio of two Poisson means is calculated.

Value

For the cox.poisrat a vector with three elements, the ratio and the lower and upper confidence interval limits. For the col.coxpoisrat a matrix with three columns, the ratio and the lower and upper confidence interval limits.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]>

References

Krishnamoorthy K., Peng J. and Zhang D. (2016). Modified large sample confidence intervals for Poisson distributions: Ratio, weighted average, and product of means. Communications in Statistics-Theory and Methods, 45(1): 83-97.

See Also

correls, Table

Examples

x <- rpois(100, 10)
y <- rpois(100, 10)
res<-cox.poisrat(x, y)

Description

Cross-Validation for the k-NN algorithm.

Usage

knn.cv(folds = NULL, nfolds = 10, stratified = FALSE, seed = NULL, y, x, k, 
dist.type = "euclidean", type = "C", method = "average", freq.option = 0, 
pred.ret = FALSE, mem.eff = FALSE)

Arguments

Details

The concept behind k-NN is simple. Suppose we have a matrix with predictor variables and a vector with the response variable (numerical or categorical). When a new vector with observations (predictor variables) is available, its corresponding response value, numerical or categorical, is to be predicted. Instead of using a model, parametric or not, one can use this ad hoc algorithm.

The k smallest distances between the new predictor variables and the existing ones are calculated. In the case of regression, the average, median, or harmonic mean of the corresponding response values of these closest predictor values are calculated. In the case of classification, i.e. categorical response value, a voting rule is applied. The most frequent group (response value) is where the new observation is to be allocated.

This function does the cross-validation procedure to select the optimal k, the optimal number of nearest neighbours. The optimal in terms of some accuracy metric. For the classification it is the percentage of correct classification and for the regression the mean squared error.

Value

A list including:

Author(s)

Marios Dimitriadis

R implementation and documentation: Marios Dimitriadis <[email protected]>

References

Friedman J., Hastie T. and Tibshirani R. (2017). The elements of statistical learning. New York: Springer.

Cover TM and Hart PE (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory. 13(1):21-27.

Tsagris Michail, Simon Preston and Andrew T.A. Wood (2016). Improved classification for compositional data using the $\alpha$-transformation. Journal of classification 33(2): 243-261.

See Also

knn, Dist, dista, dirknn.cv

Examples

x <- as.matrix(iris[, 1:4])
y <- iris[, 5]
mod <- knn.cv(folds = NULL, nfolds = 10, stratified = FALSE, seed = NULL, y = y, x = x, 
k = c(3, 4), dist.type = "euclidean", type = "C", method = "average", 
freq.option = 0, pred.ret = FALSE, mem.eff = FALSE)

Description

Cross-Validation for the k-NN algorithm using the arc cosinus distance.

Usage

dirknn.cv(y, x, k = 5:10, type = "C", folds = NULL, nfolds = 10,
stratified = TRUE, seed = NULL, parallel = FALSE, pred.ret = FALSE)

Arguments

Details

The concept behind k-NN is simple. Suppose we have a matrix with predictor variables and a vector with the response variable (numerical or categorical). When a new vector with observations (predictor variables) is available, its corresponding response value, numerical or categorical, is to be predicted. Instead of using a model, parametric or not, one can use this ad hoc algorithm.

The k smallest distances between the new predictor variables and the existing ones are calculated. In the case of regression, the average, median, or harmonic mean of the corresponding response values of these closest predictor values are calculated. In the case of classification, i.e. categorical response value, a voting rule is applied. The most frequent group (response value) is where the new observation is to be allocated.

This function does the cross-validation procedure to select the optimal k, the optimal number of nearest neighbours. The optimal in terms of some accuracy metric. For the classification it is the percentage of correct classification and for the regression the mean squared error.

Value

A list including:

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris [email protected].

References

Friedman J., Hastie T. and Tibshirani R. (2017). The elements of statistical learning. New York: Springer.

Cover TM and Hart PE (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory. 13(1):21-27.

See Also

dirknn, knn.cv, knn

Examples

x <- as.matrix(iris[, 1:4])
x <- x / sqrt( Rfast::rowsums(x^2) )
y <- iris[, 5]
mod <- dirknn.cv(y = y, x = x, k = c(3, 4) )

Description

Density of the multivariate normal and t distributions.

Usage

dmvnorm(x, mu, sigma, logged = FALSE) 
dmvt(x, mu, sigma, nu, logged = FALSE)

Arguments

Details

The (log) density of the multivariate normal distribution is calculated for given mean vector and covariance matrix.

Value

A numerical vector with the density values calculated at each vector (row of the matrix x).

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[email protected]> and Manos Papadakis <[email protected]>.

References

Kanti V. Mardia, John T. Kent and John M. Bibby (1979). Multivariate analysis. Academic Press, London.

See Also

rmvnorm, rmvt, mvnorm.mle, iag.mle

Examples

x <- matrnorm(100, 20)
mu <- colmeans(x)
s <- cova(x)
a1 <- dmvnorm(x, mu, s) 
a2 <- dmvt(x, mu, s, 1)
x <- NULL

Description

Fill the diagonal of a matrix or create a diagonal and initialize it with a specific value.

Usage

Diag.fill(x,v=0)
Diag.matrix(len,v=0)

Arguments

Value

Diag.fill returns a diagonal matrix where all the elements in the diagonal are equal to "v".

Diag.matrix returns a diagonal matrix where has dimension "len,len" and all the elements in the diagonal are equal to "v". It is fast for huge matrices with dimensions more than [row,col] = [500,500]

Author(s)

Manos Papadakis

R implementation and documentation: Manos Papadakis <[email protected]>.

See Also

rowMins, colFalse, nth, rowrange, rowMedians, rowVars, colSort, rowSort, colTrue

Examples

x <- matrix(rbinom(100*100,1,0.5),100,100)

f <- Diag.fill(x,1)
f <- Diag.fill(x,1:100) ##equals to diag(x)<-1:100
f <- Diag.matrix(100,1) ##equals to diag(1,100,100)
f <- Diag.matrix(100,1:100) ##equals to diag(1:100,100,100)

f<-x<-NULL

Description

Distance between vectors and a matrix - Sum of all pairwise distances in a distance matrix..

Usage

dista(xnew, x, type = "euclidean", k = 0, index = FALSE, 
 trans = TRUE, square = FALSE, p = 0, parallel = FALSE)
total.dista(xnew, x, type = "euclidean", k = 0,
 square = FALSE, p = 0, parallel = FALSE)

Arguments

Details

The target of this function is to calculate the distances between xnew and x without having to calculate the whole distance matrix of xnew and x. The latter does extra calculations, which can be avoided.

• euclidean : $\sum \sqrt( \sum | P_i - Q_i |^2)$

• manhattan : $\sum \sum | P_i - Q_i |$

• minimum : $\sum \min | P_i - Q_i |$

• maximum : $\sum \max | P_i - Q_i |$

• minkowski : $\sum ( \sum | P_i - Q_i |^p)^(1/p)$

• bhattacharyya : $\sum - ln \sum \sqrt(P_i * Q_i)$

• hellinger : $\sum 2 * \sqrt( 1 - \sum \sqrt(P_i * Q_i))$

• kullback_leibler : $\sum \sum P_i * log(P_i / Q_i)$

• jensen_shannon : $\sum 0.5 * ( \sum P_i * log(2 * P_i / P_i + Q_i) + \sum Q_i * log(2 * Q_i / P_i + Q_i))$

• canberra : $\sum \sum | P_i - Q_i | / (P_i + Q_i)$

• chi_square $X$^2 : $\sum \sum ( (P_i - Q_i )^2 / (P_i + Q_i) )$

• soergel : $\sum \sum | P_i - Q_i | / \sum \max(P_i , Q_i)$

• sorensen : $\sum \sum | P_i - Q_i | / \sum (P_i + Q_i)$

• cosine : $\sum (P_i * Q_i) / \sqrt(\sum P_i^2) * \sqrt(\sum Q_i^2)$

• wave_hedges : $\sum \sum | P_i - Q_i | / \max(P_i , Q_i)$

• motyka : $\sum \sum \min(P_i , Q_i) / (P_i + Q_i)$

• harmonic_mean : $2 * \sum (P_i * Q_i) / (P_i + Q_i)$

• jeffries_matusita : $\sum \sqrt( 2 - 2 * \sum \sqrt(P_i * Q_i))$

• gower : $\sum 1/d * \sum | P_i - Q_i |$

• kulczynski : $\sum 1 / \sum | P_i - Q_i | / \sum \min(P_i , Q_i)$