Package 'RCM' reference manual

Title:	Fit row-column association models with the negative binomial distribution for the microbiome
Description:	Combine ideas of log-linear analysis of contingency table, flexible response function estimation and empirical Bayes dispersion estimation for explorative visualization of microbiome datasets. The package includes unconstrained as well as constrained analysis. In addition, diagnostic plot to detect lack of fit are available.
Authors:	Stijn Hawinkel [cre, aut]
Maintainer:	Stijn Hawinkel <[email protected]>
License:	GPL-2
Version:	1.23.0
Built:	2024-12-18 04:11:02 UTC
Source:	https://github.com/bioc/RCM

This function adds orthogonal projections to a given plot

Description

This function adds orthogonal projections to a given plot

Usage

addOrthProjection(
  RCMplot,
  sample = NULL,
  species = NULL,
  variable = NULL,
  Dims = c(1, 2),
  addLabel = FALSE,
  labPos = NULL
)
addOrthProjection(
  RCMplot,
  sample = NULL,
  species = NULL,
  variable = NULL,
  Dims = c(1, 2),
  addLabel = FALSE,
  labPos = NULL
)

Arguments

`RCMplot`	the RCMplot object
`sample`, `species`, `variable`	names or approximate coordinates of sample, species or variable
`Dims`	The dimensions of the solutions that have been plotted
`addLabel`	a boolean, should the r-s-psi label be added?
`labPos`	the position of the label. Will be calculated if not provided

Value

a modified ggplot object that contains the geom_segment object that draws the projection

Examples

data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[seq_len(100)],
prune_samples(sample_names(Zeller)[seq_len(50)], Zeller))
zellerRCM = RCM(tmpPhy, k = 2, round = TRUE)
zellerPlot = plot(zellerRCM, returnCoords = TRUE)
addOrthProjection(zellerPlot, species = c(-0.35,1.1), sample = c(1,1.2))
data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[seq_len(100)],
prune_samples(sample_names(Zeller)[seq_len(50)], Zeller))
zellerRCM = RCM(tmpPhy, k = 2, round = TRUE)
zellerPlot = plot(zellerRCM, returnCoords = TRUE)
addOrthProjection(zellerPlot, species = c(-0.35,1.1), sample = c(1,1.2))

An auxiliary R function to 'array' multiply an array with a vector, kindly provided by Joris Meys

Description

An auxiliary R function to 'array' multiply an array with a vector, kindly provided by Joris Meys

Usage

arrayprod(x, y)
arrayprod(x, y)

Arguments

`x`	a axbxc array
`y`	a vector of length c

Value

a axb matrix. The ij-th element equals sum(x[i,j,]*y)

A function to build a centering matrix based on a dataframe

Description

A function to build a centering matrix based on a dataframe

Usage

buildCentMat(object)
buildCentMat(object)

Arguments

object

an rcm object or dataframe

Value

a centering matrix consisting of ones and zeroes, or a list with components

`centMat`	a centering matrix consisting of ones and zeroes
`datFrame`	The dataframe with factors with one level removed

A function to build the confounder matrices

Description

A function to build the confounder matrices

Usage

buildConfMat(x, ...)
buildConfMat(x, ...)

Arguments

x

a matrix, data frame or character string

...

further arguments passed on to other methods

For the preliminary trimming, we do not include an intercept, but we do include all the levels of the factors using contrasts=FALSE: we want to do the trimming in every subgroup, so no hidden reference levels For the filtering we just use a model with an intercept and treatment coding, here the interest is only in adjusting the offset

Value

a list with components

`confModelMatTrim`	A confounder matrix without intercept, with all levels of factors present. This will be used to trim out taxa that have zero abundances in any subgroup defined by confounders
`confModelMat`	A confounder matrix with intercept, and with reference levels for factors absent. This will be used to fit the model to modify the independence model, and may include continuous variables

buildConfMat.character

Description

buildConfMat.character

Usage

## S3 method for class 'character'
buildConfMat(confounders, physeq)
## S3 method for class 'character'
buildConfMat(confounders, physeq)

Arguments

`confounders`	a numeric matrix of confounders
`physeq`	a physeq object with a sample_data slot

Value

see buidConfMat.numeric

buildConfMat.data.frame

Description

buildConfMat.data.frame

Usage

## S3 method for class 'data.frame'
buildConfMat(confounders, n)
## S3 method for class 'data.frame'
buildConfMat(confounders, n)

Arguments

`confounders`	a data frame of confounders
`n`	the number of rows of the count matrix

Value

see buidConfMat

A function to build the covariate matrix of the constraints

Description

A function to build the covariate matrix of the constraints

Usage

buildCovMat(covariates, dat)
buildCovMat(covariates, dat)

Arguments

covariates

the covariates, either as dataframe or as character string

dat

the phyloseq object

In this case we will 1) Include dummy's for every level of the categorical variable, and force them to sum to zero. This is needed for plotting and required for reference level independent normalization. 2) Exclude an intercept. The density function f() will provide this already.

Value

a list with components

`covModelMat`	The model matrix
`datFrame`	The dataframe used to construct the model matrix

A function to build the design matrix

Description

A function to build the design matrix

Usage

buildDesign(sampleScore, responseFun)
buildDesign(sampleScore, responseFun)

Arguments

sampleScore

a vector of environmental scores

responseFun

A character string, indicating the shape of the response function

For dynamic response function estimation, the same desing matrix as for the quadratic one is returned. Will throw an error when an unknown repsonse function is provided

Value

A design matrix of dimension n-by-f

Check for alias structures in a dataframe, and throw an error when one is found

Description

Check for alias structures in a dataframe, and throw an error when one is found

Usage

checkAlias(datFrame, covariatesNames)
checkAlias(datFrame, covariatesNames)

Arguments

`datFrame`	the data frame to be checked for alias structure
`covariatesNames`	The names of the variables to be considered

Value

Throws an error when an alias structure is detected, returns invisible otherwise

Examples

#Make a dataframe with aliased variables
df = data.frame(foo = rnorm(10), baa = rep(c(TRUE, FALSE), each = 5),
foo2 = factor(rep(c("male", "female"), each = 5)))
checkAlias(df, c("foo", "baa"))
#Check test files for the error being thrown
#Make a dataframe with aliased variables
df = data.frame(foo = rnorm(10), baa = rep(c(TRUE, FALSE), each = 5),
foo2 = factor(rep(c("male", "female"), each = 5)))
checkAlias(df, c("foo", "baa"))
#Check test files for the error being thrown

Constrained correspondence analysis with adapted powers

Description

Constrained correspondence analysis with adapted powers

Usage

constrCorresp(
  X,
  Y,
  rowExp,
  colExp,
  muMarg = outer(rowSums(X), colSums(X))/sum(X)
)
constrCorresp(
  X,
  Y,
  rowExp,
  colExp,
  muMarg = outer(rowSums(X), colSums(X))/sum(X)
)

Arguments

`X`	outcome matrix
`Y`	constraining matrix
`rowExp`, `colExp`	see ?RCM_NB
`muMarg`	mean matrix under independence model

Details

the vegan version, adapted for flexible powers rowExp and colExp

Value

a list with eigenvalues, aliased variables and environmentam gradients

Replace missing entries in X by their expectation to set their contribution to the estimating equations to zero

Description

Replace missing entries in X by their expectation to set their contribution to the estimating equations to zero

Usage

correctXMissingness(X, mu, allowMissingness, naId)
correctXMissingness(X, mu, allowMissingness, naId)

Arguments

`X`	the matrix of counts
`mu`	the matrix of expectations
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X

Value

The matrix X with the NA entries replaced by the corresponding entries in mu

Note

This may seem like a hacky approach, but it avoids having to deal with NAs in functions like crossprod().

A function to extract deviances for all dimension, including after filtering on confounders

Description

A function to extract deviances for all dimension, including after filtering on confounders

Usage

deviances(rcm, squaredSum = FALSE)
deviances(rcm, squaredSum = FALSE)

Arguments

rcm

an object of the RCM class

squaredSum

a boolean, should total deviance be returned?

Total deviances can be deceptive and not correspond to the differences in log-likelihood. As the dispersion is different for each model. To compare models it is better to compare likelihoods.

Value

If Sum is FALSE, a named array of deviance residuals of the independence model and all models with dimension 1 to k, including after filtering on confounders. Otherwise a table with total deviances (the sum of squared deviance residuals), deviance explained and cumulative deviance explained.

A function that returns the value of the partial derivative of the log-likelihood ratio to alpha, keeping the response functions fixed

Description

A function that returns the value of the partial derivative of the log-likelihood ratio to alpha, keeping the response functions fixed

Usage

dLR_nb(
  Alpha,
  X,
  CC,
  responseFun = c("linear", "quadratic", "nonparametric", "dynamic"),
  psi,
  NB_params,
  NB_params_noLab,
  d,
  alphaK,
  k,
  centMat,
  nLambda,
  nLambda1s,
  thetaMat,
  muMarg,
  ncols,
  envGradEst,
  allowMissingness,
  naId,
  ...
)
dLR_nb(
  Alpha,
  X,
  CC,
  responseFun = c("linear", "quadratic", "nonparametric", "dynamic"),
  psi,
  NB_params,
  NB_params_noLab,
  d,
  alphaK,
  k,
  centMat,
  nLambda,
  nLambda1s,
  thetaMat,
  muMarg,
  ncols,
  envGradEst,
  allowMissingness,
  naId,
  ...
)

Arguments

`Alpha`	a vector of length d + k*(2+(k-1)/2), the environmental gradient plus the lagrangian multipliers
`X`	the n-by-p count matrix
`CC`	a n-by-d covariate vector
`responseFun`	a character string indicating the type of response function
`psi`	a scalar, an importance parameter
`NB_params`	Starting values for the NB_params
`NB_params_noLab`	Starting values for the NB_params without label
`d`	an integer, the number of covariate parameters
`alphaK`	a matrix of environmental gradients of lower dimensions
`k`	an integer, the current dimension
`centMat`	a nLambda1s-by-d centering matrix
`nLambda`	an integer, number of lagrangian multipliers
`nLambda1s`	an integer, number of centering restrictions
`thetaMat`	a matrix of size n-by-p with estimated dispersion parameters
`muMarg`	an n-by-p offset matrix
`ncols`	a scalar, the number of columns of X
`envGradEst`	a character string, indicating how the environmental gradient should be fitted. 'LR' using the likelihood-ratio criterion, or 'ML' a full maximum likelihood solution
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X
`...`	further arguments passed on to other methods

Value

: The value of the lagrangian and the constraining equations

A score function for the column components of the independence model (mean relative abundances)

Description

A score function for the column components of the independence model (mean relative abundances)

Usage

dNBabundsOld(beta, X, reg, thetas, allowMissingness, naId)
dNBabundsOld(beta, X, reg, thetas, allowMissingness, naId)

Arguments

`beta`	a vector of length p with current abundance estimates
`X`	a n-by-p count matrix
`reg`	a vector of length n with library sizes estimates
`thetas`	a n-by-p matrix with overdispersion estimates in the rows
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X

Value

a vector of length p with evaluations of the score function

A score function for the row components of the independence model (library sizes)

Description

A score function for the row components of the independence model (library sizes)

Usage

dNBlibSizes(beta, X, reg, thetas, allowMissingness, naId)
dNBlibSizes(beta, X, reg, thetas, allowMissingness, naId)

Arguments

`beta`	a vector of length n with current library size estimates
`X`	a n-by-p count matrix
`reg`	a vector of length p with relative abundance estimates
`thetas`	a n-by-p matrix with overdispersion estimates in the rows
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X

Value

a vector of length n with evaluations of the score function

The score function of the response function for 1 taxon at the time

Description

The score function of the response function for 1 taxon at the time

Usage

dNBllcol_constr(betas, X, reg, theta, muMarg, psi, allowMissingness, naId)
dNBllcol_constr(betas, X, reg, theta, muMarg, psi, allowMissingness, naId)

Arguments

`betas`	a vector of v parameters of the response function of a single taxon
`X`	the count vector of length n
`reg`	a n-by-v matrix of regressors
`theta`	The dispersion parameter of this taxon
`muMarg`	offset of length n
`psi`	a scalar, the importance parameter
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X Even though this approach does not imply normalization over the parameters of all taxa, it is very fast and they can be normalized afterwards

Value

A vector of length v with the evaluation of the score functions

The score function of the general response function

Description

The score function of the general response function

Usage

dNBllcol_constr_noLab(
  betas,
  X,
  reg,
  thetasMat,
  muMarg,
  psi,
  allowMissingness,
  naId,
  ...
)
dNBllcol_constr_noLab(
  betas,
  X,
  reg,
  thetasMat,
  muMarg,
  psi,
  allowMissingness,
  naId,
  ...
)

Arguments

`betas`	a vector of regression parameters with length v
`X`	the nxp data matrix
`reg`	a matrix of regressors of dimension nxv
`thetasMat`	A matrix of dispersion parameters
`muMarg`	offset matrix of dimension nxp
`psi`	a scalar, the importance parameter
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X
`...`	further arguments passed on to the jacobian

Value

The evaluation of the score functions (a vector length v)

Estimation of the parameters of a third degree GLM

Description

Estimation of the parameters of a third degree GLM

Usage

dNBllcolNP(beta, X, reg, theta, muMarg, allowMissingness, naId, ...)
dNBllcolNP(beta, X, reg, theta, muMarg, allowMissingness, naId, ...)

Arguments

`beta`	A vector of any length
`X`	the data vector of length n
`reg`	a nxlength(beta) regressor matrix
`theta`	a scalar, the overdispersion
`muMarg`	the offset of length n
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X
`...`	further arguments passed on to the jacobian

Value

A vector of the same length as beta with evaluations of the score function

A score function for the estimation of the column scores in an unconstrained RC(M) model

Description

A score function for the estimation of the column scores in an unconstrained RC(M) model

Usage

dNBllcolOld(
  beta,
  X,
  reg,
  thetas,
  muMarg,
  k,
  p,
  n,
  colWeights,
  nLambda,
  cMatK,
  allowMissingness,
  naId,
  ...
)
dNBllcolOld(
  beta,
  X,
  reg,
  thetas,
  muMarg,
  k,
  p,
  n,
  colWeights,
  nLambda,
  cMatK,
  allowMissingness,
  naId,
  ...
)

Arguments

`beta`	vector of length p+1+1+(k-1): p row scores, 1 centering, one normalization and (k-1) orhtogonality lagrangian multipliers
`X`	the nxp data matrix
`reg`	a nx1 regressor matrix: outer product of rowScores and psis
`thetas`	nxp matrix with the dispersion parameters (converted to matrix for numeric reasons)
`muMarg`	the nxp offset
`k`	an integer, the dimension of the RC solution
`p`	an integer, the number of taxa
`n`	an integer, the number of samples
`colWeights`	the weights used for the restrictions
`nLambda`	an integer, the number of restrictions
`cMatK`	the lower dimensions of the colScores
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X
`...`	further arguments passed on to the jacobian

Value

A vector of length p+1+1+(k-1) with evaluations of the derivative of lagrangian

A score function of the NB for the row scores

Description

A score function of the NB for the row scores

Usage

dNBllrow(
  beta,
  X,
  reg,
  thetas,
  muMarg,
  k,
  n,
  p,
  rowWeights,
  nLambda,
  rMatK,
  allowMissingness,
  naId,
  ...
)
dNBllrow(
  beta,
  X,
  reg,
  thetas,
  muMarg,
  k,
  n,
  p,
  rowWeights,
  nLambda,
  rMatK,
  allowMissingness,
  naId,
  ...
)

Arguments

`beta`	a vector of of length n + k +1 regression parameters to optimize
`X`	the data matrix of dimensions nxp
`reg`	a 1xp regressor matrix: outer product of column scores and psis
`thetas`	nxp matrix with the dispersion parameters (converted to matrix for numeric reasons)
`muMarg`	an nxp offset matrix
`k`	a scalar, the dimension of the RC solution
`n`	a scalar, the number of samples
`p`	a scalar, the number of taxa
`rowWeights`	a vector of length n, the weights used for the restrictions
`nLambda`	an integer, the number of lagrangian multipliers
`rMatK`	the lower dimension row scores
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X
`...`	Other arguments passed on to the jacobian

Value

A vector of length n + k +1 with evaluations of the derivative of the lagrangian

A score function for the psi of a given dimension

Description

A score function for the psi of a given dimension

Usage

dNBpsis(beta, X, reg, theta, muMarg, allowMissingness, naId, ...)
dNBpsis(beta, X, reg, theta, muMarg, allowMissingness, naId, ...)

Arguments

`beta`	a scalar, the initial estimate
`X`	the n-by-p count matrix
`reg`	the regressor matrix, the outer product of current row and column scores
`theta`	a n-by-p matrix with the dispersion parameters
`muMarg`	the nxp offset matrix
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X
`...`	other arguments passed on to the jacobian

Value

The evaluation of the score function at beta, a scalar

A function that returns the coordinates of an ellipse

Description

A function that returns the coordinates of an ellipse

Usage

ellipseCoord(a, b, c, quadDrop = 0.95, nPoints = 100)
ellipseCoord(a, b, c, quadDrop = 0.95, nPoints = 100)

Arguments

`a`, `b`, `c`	parameters of the quadratic function a^2x+bx+c
`quadDrop`	A scalar, fraction of peak height at which to draw the ellipse
`nPoints`	an integer, number of points to use to draw the ellipse

Value

a matrix with x and y coordinates of the ellipse

Estimate the overdispersion

Description

Estimate the overdispersion

Usage

estDisp(
  X,
  cMat = NULL,
  rMat = NULL,
  muMarg,
  psis,
  trended.dispersion = NULL,
  prior.df = 10,
  dispWeights = NULL,
  rowMat = NULL,
  allowMissingness = FALSE,
  naId
)
estDisp(
  X,
  cMat = NULL,
  rMat = NULL,
  muMarg,
  psis,
  trended.dispersion = NULL,
  prior.df = 10,
  dispWeights = NULL,
  rowMat = NULL,
  allowMissingness = FALSE,
  naId
)

Arguments

`X`	the data matrix of dimensions nxp
`cMat`	a 1xp colum scores matrix
`rMat`	a nx1 rowscores matrix, if unconstrained
`muMarg`	an nxp offset matrix
`psis`	a scalar, the current psi estimate
`trended.dispersion`	a vector of length p with pre-calculated trended.dispersion estimates. They do not vary in function of the offset anyway
`prior.df`	an integer, number of degrees of freedom of the prior for the Bayesian shrinkage
`dispWeights`	Weights for estimating the dispersion in a zero-inflated model
`rowMat`	matrix of row scores in case of constrained ordination
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X

Details

Information between taxa is shared with empirical Bayes using the edgeR pacakage, where the time-limiting steps are programmed in C.

Value

A vector of length p with dispersion estimates

A function to estimate the taxon-wise NB-params

Description

A function to estimate the taxon-wise NB-params

Usage

estNBparams(
  design,
  thetas,
  muMarg,
  psi,
  X,
  nleqslv.control,
  ncols,
  initParam,
  v,
  dynamic = FALSE,
  envRange,
  allowMissingness,
  naId
)
estNBparams(
  design,
  thetas,
  muMarg,
  psi,
  X,
  nleqslv.control,
  ncols,
  initParam,
  v,
  dynamic = FALSE,
  envRange,
  allowMissingness,
  naId
)

Arguments

`design`	an n-by-v design matrix
`thetas`	a vector of dispersion parameters of length p
`muMarg`	an offset matrix
`psi`	a scalar, the importance parameter
`X`	the data matrix
`nleqslv.control`	a list of control elements, passed on to nleqslv()
`ncols`	an integer, the number of columns of X
`initParam`	a v-by-p matrix of initial parameter estimates
`v`	an integer, the number of parameters per taxon
`dynamic`	a boolean, should response function be determined dynamically? See details
`envRange`	a vector of length 2, giving the range of observed environmental scores
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X If dynamic is TRUE, quadratic response functions are fitted for every taxon. If the optimum falls outside of the observed range of environmental scores, a linear response function is fitted instead

Value

a v-by-p matrix of parameters of the response function

A function to estimate the NB-params ignoring the taxon labels

Description

A function to estimate the NB-params ignoring the taxon labels

Usage

estNBparamsNoLab(
  design,
  thetasMat,
  muMarg,
  psi,
  X,
  nleqslv.control,
  initParam,
  n,
  v,
  dynamic,
  envRange,
  preFabMat,
  allowMissingness,
  naId
)
estNBparamsNoLab(
  design,
  thetasMat,
  muMarg,
  psi,
  X,
  nleqslv.control,
  initParam,
  n,
  v,
  dynamic,
  envRange,
  preFabMat,
  allowMissingness,
  naId
)

Arguments

`design`	an n-by-v design matrix
`thetasMat`	A matrix of dispersion parameters
`muMarg`	an offset matrix
`psi`	a scalar, the importance parameter
`X`	the data matrix
`nleqslv.control`	a list of control elements, passed on to nleqslv()
`initParam`	a vector of length v of initial parameter estimates
`n`	an integer, the number of samples
`v`	an integer, the number of parameters per taxon
`dynamic`	a boolean, should response function be determined dynamically? See details
`envRange`	a vector of length 2, giving the range of observed environmental scores
`preFabMat`	a pre-fabricated auxiliary matrix
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X If dynamic is TRUE, quadratic response functions are fitted for every taxon. If the optimum falls outside of the observed range of environmental scores, a linear response function is fitted instead

Value

a v-by-p matrix of parameters of the response function

Estimate the taxon-wise response functions non-parametrically

Description

Estimate the taxon-wise response functions non-parametrically

Usage

estNPresp(
  sampleScore,
  muMarg,
  X,
  ncols,
  thetas,
  n,
  coefInit,
  coefInitOverall,
  dfSpline,
  vgamMaxit,
  degree,
  verbose,
  allowMissingness,
  naId,
  ...
)
estNPresp(
  sampleScore,
  muMarg,
  X,
  ncols,
  thetas,
  n,
  coefInit,
  coefInitOverall,
  dfSpline,
  vgamMaxit,
  degree,
  verbose,
  allowMissingness,
  naId,
  ...
)

Arguments

`sampleScore`	a vector of length n with environmental scores
`muMarg`	the offset matrix
`X`	the n-by-p data matrix
`ncols`	an integer, the number of columns of X
`thetas`	a vector of length p with dispersion parameters
`n`	an integer, the number of samples
`coefInit`	a 2-by-p matrix with current taxon-wise parameter estimates
`coefInitOverall`	a vector of length 2 with current overall parameters
`dfSpline`	a scalar, the degrees of freedom for the smoothing spline.
`vgamMaxit`	Maximal number of iterations in the fitting of the GAM model
`degree`	The degree if the parametric fit if the VGAM fit fails
`verbose`	a boolean, should number of failed fits be reported
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X
`...`	further arguments, passed on to the VGAM:::vgam() function The negative binomial likelihood is still maximized, but now the response function is a non-parametric one. To avoid a perfect fit and overly flexible functions, we enforce smoothness restrictions. In practice we use a generalized additive model (GAM), i.e. with splines. The same fitting procedure is carried out ignoring species labels. We do not normalize the parameters related to the splines: the psis can be calculated afterwards.

Value

A list with components

`taxonCoef`	The fitted coefficients of the sample-wise response curves
`splinesList`	A list of all the B-spline objects
`rowMar`	The row matrix
`overall`	The overall fit ignoring taxon labels, as a list of coefficients and a spline
`rowVecOverall`	The overall row vector, ignoring taxon labels

A function to extract plotting coordinates, either for plot.RCM or to export to other plotting software

Description

A function to extract plotting coordinates, either for plot.RCM or to export to other plotting software

Usage

extractCoord(RCM, Dim = c(1, 2))
extractCoord(RCM, Dim = c(1, 2))

Arguments

RCM

an RCm object

Dim

an integer vector of required dimensions

The parameters for the ellipses of the quadratic response function come from the parametrization f(x) = a*x^2 + b*x + c For an unconstrained object the row and column coordinates are returned in separate matrices. The row names will correspond to the labels. For a constrained analysis also the variable points are returned. All variables still need to be scaled to optimally fill the available space

Value

A list with components

`samples`	A dataframe of sample scores
`species`	A dataframe of column scores, with origin, slope, end and ellipse coordinates as needed
`variables`	A dataframe of variable scores, loadings of the environmental gradient

Examples

data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:100],
prune_samples(sample_names(Zeller)[1:50], Zeller))
zellerRCM = RCM(tmpPhy, k = 2, round = TRUE)
coordsZeller = extractCoord(zellerRCM)
data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:100],
prune_samples(sample_names(Zeller)[1:50], Zeller))
zellerRCM = RCM(tmpPhy, k = 2, round = TRUE)
coordsZeller = extractCoord(zellerRCM)

A function to extract a matrix of expected values for any dimension of the fit

Description

A function to extract a matrix of expected values for any dimension of the fit

Usage

extractE(rcm, Dim = rcm$k)
extractE(rcm, Dim = rcm$k)

Arguments

`rcm`	an object of class RCM
`Dim`	the desired dimension. Defaults to the maximum of the fit. Choose 0 for the independence model, 0.5 for the confounders filter model.

Value

The matrix of expected values

Filters out the effect of known confounders. This is done by fitting interactions of every taxon with the levels of the confounders. It returns a modified offset matrix for the remainder of the fitting procedure.

Description

Filters out the effect of known confounders. This is done by fitting interactions of every taxon with the levels of the confounders. It returns a modified offset matrix for the remainder of the fitting procedure.

Usage

filterConfounders(
  muMarg,
  confMat,
  X,
  thetas,
  p,
  n,
  nleqslv.control,
  trended.dispersion,
  tol = 0.001,
  maxIt = 20,
  allowMissingness,
  naId
)
filterConfounders(
  muMarg,
  confMat,
  X,
  thetas,
  p,
  n,
  nleqslv.control,
  trended.dispersion,
  tol = 0.001,
  maxIt = 20,
  allowMissingness,
  naId
)

Arguments

`muMarg`	a nxp matrix, the current offset
`confMat`	a nxt confounder matrix
`X`	the nxp data matrix
`thetas`	a vector of length p with the current dispersion estimates
`p`	an integer, the number of columns of X
`n`	an integer, the number of rows of X
`nleqslv.control`	see nleqslv()
`trended.dispersion`	a vector of length p with trended dispersion estimates
`tol`	a scalar, the convergence tolerance
`maxIt`	maximum number of iterations
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X Fits the negative binomial mean parameters and overdispersion parameters iteratively. Convergence is determined based on the L2-norm of the absolute change of mean parameters

Value

a list with components:

`thetas`	new theta estimates
`NB_params`	The estimated parameters of the interaction terms

A function to calculate the matrix of deviance residuals.

Description

A function to calculate the matrix of deviance residuals.

Usage

getDevianceRes(RCM, Dim = RCM$k)
getDevianceRes(RCM, Dim = RCM$k)

Arguments

RCM

an RCM object

Dim

The dimensions to use

For the deviance residuals we use the overdispersions from the reduced model. Standard dimensions used are only first and second, since these are also plotted

Value

A matrix with deviance residuals of the same size as the original data matrix

Examples

data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:120],
prune_samples(sample_names(Zeller)[1:75], Zeller))
#Subset for a quick fit
zellerRCM = RCM(tmpPhy, k = 2, round = TRUE, prevCutOff = 0.03)
devRes = getDevianceRes(zellerRCM)
data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:120],
prune_samples(sample_names(Zeller)[1:75], Zeller))
#Subset for a quick fit
zellerRCM = RCM(tmpPhy, k = 2, round = TRUE, prevCutOff = 0.03)
devRes = getDevianceRes(zellerRCM)

ACalculate the matrix of deviance residuals

Description

ACalculate the matrix of deviance residuals

Usage

getDevMat(X, thetaMat, mu)
getDevMat(X, thetaMat, mu)

Arguments

`X`	the data matrix
`thetaMat`	the matrix of dispersions
`mu`	the matrix of means

Value

The matrix of deviance residuals

A function to extract the influence for a given parameter index

Description

A function to extract the influence for a given parameter index

Usage

getInflCol(score, InvJac, taxon)
getInflCol(score, InvJac, taxon)

Arguments

`score`	a score matrix
`InvJac`	The inverted jacobian
`taxon`	The taxon name or index

Value

A matrix with all observations' influence on the given taxon

Extract the influence of all observations on a given row score

Description

Extract the influence of all observations on a given row score

Usage

getInflRow(score, InvJac, sample)
getInflRow(score, InvJac, sample)

Arguments

`score`	the score function evaluated for every observation
`InvJac`	The inverse jacobian
`sample`	the row score or sample index

Value

A matrix with all observations' influence on the row score

Integrate the spline of an vgam object

Description

Integrate the spline of an vgam object

Usage

getInt(coef, spline, sampleScore, stop.on.error = FALSE, ...)
getInt(coef, spline, sampleScore, stop.on.error = FALSE, ...)

Arguments

`coef`	A vector of coefficients
`spline`	The cubic smoothing spline
`sampleScore`	the observed environmental scores
`stop.on.error`	see ?integrate
`...`	additional arguments passed on to integrate()

Value

a scalar, the value of the integral

Extract the logged likelihood of every count

Description

Extract the logged likelihood of every count

Usage

getLogLik(rcm, Dim)
getLogLik(rcm, Dim)

Arguments

`rcm`	an RCM object
`Dim`	A vector of integers indicating which dimensions to take along, or Inf for the saturated model, or 0 for the independence model

Value

A matrix with logged likelihood of the size of the data matrix

A function to construct a model matrix of a certain degree

Description

A function to construct a model matrix of a certain degree

Usage

getModelMat(y, degree)
getModelMat(y, degree)

Arguments

`y`	the variable
`degree`	the degree

Value

A model matrix with degree+1 columns and as many rows as lenght(y)

Return a matrix of row scores

Description

Return a matrix of row scores

Usage

getRowMat(sampleScore, responseFun, NB_params, taxonCoef, spline)
getRowMat(sampleScore, responseFun, NB_params, taxonCoef, spline)

Arguments

`sampleScore`	a vector of length n with sample scores
`responseFun`	a character string, the type of response function, either 'linear' or 'quadratic'
`NB_params`	a v-by-p matrix of parameters of theresponse function
`taxonCoef`	A vector of coefficients
`spline`	The cubic smoothing spline Multiplying the old offset with the exponent matrix times the importance parameter obtains the new one based on lower dimension

Value

a n-by-p matrix of scores

Gram-Schmidt orthogonalization of vectors

Description

Gram-Schmidt orthogonalization of vectors

Usage

GramSchmidt(x, otherVecs, weights = rep(1, length(x)))
GramSchmidt(x, otherVecs, weights = rep(1, length(x)))

Arguments

`x`	The vector that is to be orthogonalized
`otherVecs`	a matrix; x is orthogonalized with respect to its rows
`weights`	The weights used in the orthogonalization

Value

The orthogonalized vector

Define linear equality constraints for env. gradient

Description

Define linear equality constraints for env. gradient

Usage

heq_nb(Alpha, alphaK, d, k, centMat, ...)
heq_nb(Alpha, alphaK, d, k, centMat, ...)

Arguments

`Alpha`	the current estimate of the environmental gradient
`alphaK`	a matrix with the environmental gradients of the lower dimensions
`d`	an integer, the number of environmental variables, including dummies
`k`	an integer, the current dimension
`centMat`	a centering matrix
`...`	further arguments for other methods, not needed in this one The centering matrix centMat ensures that the parameters of the dummies of the same categorical variable sum to zero

Value

a vector of with current values of the constraints, should evolve to zeroes only

The jacobian of the linear equality constraints

Description

The jacobian of the linear equality constraints

Usage

heq_nb_jac(Alpha, alphaK, d, k, centMat, ...)
heq_nb_jac(Alpha, alphaK, d, k, centMat, ...)

Arguments

`Alpha`	the current estimate of the environmental gradient
`alphaK`	a matrix with the environmental gradients of the lower dimensions
`d`	an integer, the number of environmental variables, including dummies
`k`	an integer, the current dimension
`centMat`	a centering matrix
`...`	further arguments for other methods, not needed in this one

Value

The jacobian matrix

Functions to indent the plot to include the entire labels

Description

Functions to indent the plot to include the entire labels

Usage

indentPlot(plt, xInd = 0, yInd = 0)
indentPlot(plt, xInd = 0, yInd = 0)

Arguments

`plt`	a ggplot object
`xInd`	a scalar or a vector of length 2, specifying the indentation left and right of the plot to allow for the labels to be printed entirely
`yInd`	a a scalar or a vector of length 2, specifying the indentation top and bottom of the plot to allow for the labels to be printed entirely

Value

a ggplot object, squared

Calculate the log-likelihoods of all possible models

Description

Calculate the log-likelihoods of all possible models

Usage

inertia(rcm)
inertia(rcm)

Arguments

rcm

an object of the RCM class

Value

A table with inertias, proportion inertia explained and cumulative proportion of inertia explained.

Examples

data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:100],
prune_samples(sample_names(Zeller)[1:50], Zeller))
zellerRCM = RCM(tmpPhy, round = TRUE)
inertia(zellerRCM)
data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:100],
prune_samples(sample_names(Zeller)[1:50], Zeller))
zellerRCM = RCM(tmpPhy, round = TRUE)
inertia(zellerRCM)

Jacobian of the constrained analysis with linear response function.

Description

Jacobian of the constrained analysis with linear response function.

Usage

JacCol_constr(betas, X, reg, theta, muMarg, psi, allowMissingness, naId)
JacCol_constr(betas, X, reg, theta, muMarg, psi, allowMissingness, naId)

Arguments

`betas`	a vector of v parameters of the response function of a single taxon
`X`	the count vector of length n
`reg`	a n-by-v matrix of regressors
`theta`	The dispersion parameter of this taxon
`muMarg`	offset of length n
`psi`	a scalar, the importance parameter
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X Even though this approach does not imply normalization over the parameters of all taxa, it is very fast and they can be normalized afterwards

Value

The jacobian, a square symmetric matrix of dimension v

The jacobian of the response function without taxon labels

Description

The jacobian of the response function without taxon labels

Usage

JacCol_constr_noLab(
  betas,
  X,
  reg,
  thetasMat,
  muMarg,
  psi,
  n,
  v,
  preFabMat,
  allowMissingness,
  naId
)
JacCol_constr_noLab(
  betas,
  X,
  reg,
  thetasMat,
  muMarg,
  psi,
  n,
  v,
  preFabMat,
  allowMissingness,
  naId
)

Arguments

`betas`	a vector of regression parameters with length v
`X`	the nxp data matrix
`reg`	a matrix of regressors of dimension nxv
`thetasMat`	A matrix of dispersion parameters
`muMarg`	offset matrix of dimension nxp
`psi`	a scalar, the importance parameter
`n`	an integer, number of rows of X
`v`	an integer, the number of parameters of the response function
`preFabMat`	a prefabricated matrix
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X

Value

The jacobian (a v-by-v matrix)

Calculate the log-likelihoods of all possible models

Description

Calculate the log-likelihoods of all possible models

Usage

liks(rcm, Sum = TRUE)
liks(rcm, Sum = TRUE)

Arguments

`rcm`	an object of the RCM class
`Sum`	a boolean, should log-likelihoods be summed?

Value

If Sum is FALSE, a named array log-likelihoods of the independence model and all models with dimension 1 to k, including after filtering on confounders. Otherwise a table with log-likelihoods, deviance explained and cumulative deviance explained.

Examples

data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:100],
prune_samples(sample_names(Zeller)[1:50], Zeller))
zellerRCM = RCM(tmpPhy, round = TRUE)
liks(zellerRCM)
data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:100],
prune_samples(sample_names(Zeller)[1:50], Zeller))
zellerRCM = RCM(tmpPhy, round = TRUE)
liks(zellerRCM)

Get the value of the log-likelihood ratio of alpha

Description

Get the value of the log-likelihood ratio of alpha

Usage

LR_nb(
  Alpha,
  X,
  CC,
  responseFun = c("linear", "quadratic", "nonparametric", "dynamic"),
  muMarg,
  psi,
  nleqslv.control = list(trace = FALSE),
  n,
  NB_params,
  NB_params_noLab,
  thetaMat,
  ncols,
  nonParamRespFun,
  envGradEst,
  ...
)
LR_nb(
  Alpha,
  X,
  CC,
  responseFun = c("linear", "quadratic", "nonparametric", "dynamic"),
  muMarg,
  psi,
  nleqslv.control = list(trace = FALSE),
  n,
  NB_params,
  NB_params_noLab,
  thetaMat,
  ncols,
  nonParamRespFun,
  envGradEst,
  ...
)

Arguments

`Alpha`	a vector of length d, the environmental gradient
`X`	the n-by-p count matrix
`CC`	the n-by-d covariate matrix
`responseFun`	a character string indicating the type of response function
`muMarg`	an n-by-p offset matrix
`psi`	a scalar, an importance parameter
`nleqslv.control`	the control list for the nleqslv() function
`n`	number of samples
`NB_params`	Starting values for the NB_params
`NB_params_noLab`	Starting values for the NB_params without label
`thetaMat`	a matrix of size n-by-p with estimated dispersion parameters
`ncols`	a scalar, the number of columns of X
`nonParamRespFun`	A list, the result of the estNPresp() function
`envGradEst`	a character string, indicating how the environmental gradient should be fitted. 'LR' using the likelihood-ratio criterion, or 'ML' a full maximum likelihood solution
`...`	Further arguments passed on to other functions DON'T USE 'p' as variable name, partial matching in the grad-function in the numDeriv package

Value

: a scalar, the evaluation of the log-likelihood ratio at the given alpha

A function that returns the Jacobian of the likelihood ratio

Description

A function that returns the Jacobian of the likelihood ratio

Usage

LR_nb_Jac(
  Alpha,
  X,
  CC,
  responseFun = c("linear", "quadratic", "nonparametric", "dynamic"),
  psi,
  NB_params,
  NB_params_noLab,
  d,
  alphaK,
  k,
  centMat,
  nLambda,
  nLambda1s,
  thetaMat,
  muMarg,
  n,
  ncols,
  preFabMat,
  envGradEst,
  allowMissingness,
  naId,
  ...
)
LR_nb_Jac(
  Alpha,
  X,
  CC,
  responseFun = c("linear", "quadratic", "nonparametric", "dynamic"),
  psi,
  NB_params,
  NB_params_noLab,
  d,
  alphaK,
  k,
  centMat,
  nLambda,
  nLambda1s,
  thetaMat,
  muMarg,
  n,
  ncols,
  preFabMat,
  envGradEst,
  allowMissingness,
  naId,
  ...
)

Arguments

`Alpha`	a vector of length d + k*(2+(k-1)/2), the environmental gradient plus the lagrangian multipliers
`X`	the n-by-p count matrix
`CC`	a n-by-d covariate vector
`responseFun`	a character string indicating the type of response function
`psi`	a scalar, an importance parameter
`NB_params`	Starting values for the NB_params
`NB_params_noLab`	Starting values for the NB_params without label
`d`	an integer, the number of covariate parameters
`alphaK`	a matrix of environmental gradients of lower dimensions
`k`	an integer, the current dimension
`centMat`	a nLambda1s-by-d centering matrix
`nLambda`	an integer, number of lagrangian multipliers
`nLambda1s`	an integer, number of centering restrictions
`thetaMat`	a matrix of size n-by-p with estimated dispersion parameters
`muMarg`	an n-by-p offset matrix
`n`	an integer, the number of rows of X
`ncols`	a scalar, the number of columns of X
`preFabMat`	a prefabricated matrix
`envGradEst`	a character string, indicating how the environmental gradient should be fitted. 'LR' using the likelihood-ratio criterion, or 'ML' a full maximum likelihood solution
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X
`...`	Further arguments passed on to other functions

Value

A symmetric matrix, the evaluated Jacobian

Calculate the components of the influence functions

Description

Calculate the components of the influence functions

Usage

NBalphaInfl(rcm, Dim)
NBalphaInfl(rcm, Dim)

Arguments

`rcm`	an rcm object
`Dim`	the required dimension

Value

An n-by-p-by-d array with the influence of every observation on every alpha parameter

The influence function for the column scores

Description

The influence function for the column scores

Usage

NBcolInfl(rcm, Dim = 1)
NBcolInfl(rcm, Dim = 1)

Arguments

`rcm`	an rcm object
`Dim`	the required dimension

Value

A list with components

`score`	a matrix with components of the score function
`InvJac`	A square matrix of dimension p with the components of the Jacobian related to the column scores

Jacobian for the column components of the independence model

Description

Jacobian for the column components of the independence model

Usage

NBjacobianAbundsOld(beta, X, reg, thetas, allowMissingness, naId)
NBjacobianAbundsOld(beta, X, reg, thetas, allowMissingness, naId)

Arguments

`beta`	a vector of length p with current abundance estimates
`X`	a n-by-p count matrix
`reg`	a vector of length n with library sizes estimates
`thetas`	a n-by-p matrix with overdispersion estimates in the rows
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X

Value

a diagonal matrix of dimension p with evaluations of the jacobian function

Jacobian function for the estimation of a third degree GLM

Description

Jacobian function for the estimation of a third degree GLM

Usage

NBjacobianColNP(beta, X, reg, theta, muMarg)
NBjacobianColNP(beta, X, reg, theta, muMarg)

Arguments

`beta`	vector of any length
`X`	the data vector of length n
`reg`	a nxlength(beta) regressor matrix
`theta`	a scalar, the overdispersion
`muMarg`	the offset of length n

Value

A matrix of dimension 8-by-8

Jacobian for the estimation of the column scores

Description

Jacobian for the estimation of the column scores

Usage

NBjacobianColOld(
  beta,
  X,
  reg,
  thetas,
  muMarg,
  k,
  n,
  p,
  colWeights,
  nLambda,
  cMatK,
  preFabMat,
  Jac,
  allowMissingness,
  naId
)
NBjacobianColOld(
  beta,
  X,
  reg,
  thetas,
  muMarg,
  k,
  n,
  p,
  colWeights,
  nLambda,
  cMatK,
  preFabMat,
  Jac,
  allowMissingness,
  naId
)

Arguments

`beta`	vector of length p+1+1+(k-1): p row scores, 1 centering, one normalization and (k-1) orhtogonality lagrangian multipliers
`X`	the nxp data matrix
`reg`	a nx1 regressor matrix: outer product of rowScores and psis
`thetas`	nxp matrix with the dispersion parameters (converted to matrix for numeric reasons)
`muMarg`	the nxp offset
`k`	an integer, the dimension of the RC solution
`n`	an integer, the number of samples
`p`	an integer, the number of taxa
`colWeights`	the weights used for the restrictions
`nLambda`	an integer, the number of restrictions
`cMatK`	the lower dimensions of the colScores
`preFabMat`	a prefab matrix, (1+X/thetas)
`Jac`	an empty Jacobian matrix
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X

Value

A matrix of dimension p+1+1+(k-1) with evaluations of the Jacobian

Jacobian for the raw components of the independence model

Description

Jacobian for the raw components of the independence model

Usage

NBjacobianLibSizes(beta, X, reg, thetas, allowMissingness, naId)
NBjacobianLibSizes(beta, X, reg, thetas, allowMissingness, naId)

Arguments

`beta`	a vector of length n with current library size estimates
`X`	a n-by-p count matrix
`reg`	a vector of length p with relative abundance estimates
`thetas`	a n-by-p matrix with overdispersion estimates in the rows
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X

Value

a diagonal matrix of dimension n: the Fisher information matrix

Jacobian for the psi of a given dimension

Description

Jacobian for the psi of a given dimension

Usage

NBjacobianPsi(beta, X, reg, muMarg, theta, preFabMat, allowMissingness, naId)
NBjacobianPsi(beta, X, reg, muMarg, theta, preFabMat, allowMissingness, naId)

Arguments

`beta`	a scalar, the current estimate
`X`	the n-by-p count matrix
`reg`	the regressor matrix, the outer product of current row and column scores
`muMarg`	the nxp offset matrix
`theta`	a n-by-p matrix with the dispersion parameters
`preFabMat`	a prefab matrix, (1+X/thetas)
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X

Value

The evaluation of the jacobian function at beta, a 1-by-1 matrix

A jacobian function of the NB for the row scores

Description

A jacobian function of the NB for the row scores

Usage

NBjacobianRow(
  beta,
  X,
  reg,
  thetas,
  muMarg,
  k,
  n,
  p,
  rowWeights,
  nLambda,
  rMatK,
  preFabMat,
  Jac,
  allowMissingness,
  naId
)
NBjacobianRow(
  beta,
  X,
  reg,
  thetas,
  muMarg,
  k,
  n,
  p,
  rowWeights,
  nLambda,
  rMatK,
  preFabMat,
  Jac,
  allowMissingness,
  naId
)

Arguments

`beta`	a vector of of length n + k +1 regression parameters to optimize
`X`	the data matrix of dimensions nxp
`reg`	a 1xp regressor matrix: outer product of column scores and psis
`thetas`	nxp matrix with the dispersion parameters (converted to matrix for numeric reasons)
`muMarg`	an nxp offset matrix
`k`	a scalar, the dimension of the RC solution
`n`	a scalar, the number of samples
`p`	a scalar, the number of taxa
`rowWeights`	a vector of length n, the weights used for the restrictions
`nLambda`	an integer, the number of lagrangian multipliers
`rMatK`	the lower dimension row scores
`preFabMat`	a prefab matrix, (1+X/thetas)
`Jac`	an empty Jacobian matrix
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X

Value

a symmetric jacobian matrix of size n+k + 1

The influence function for the psis

Description

The influence function for the psis

Usage

NBpsiInfl(rcm, Dim = 1)
NBpsiInfl(rcm, Dim = 1)

Arguments

`rcm`	an rcm object
`Dim`	the required dimensions

Value

The influence of every single observation on the psi value of this dimension

The influence function for the row scores

Description

The influence function for the row scores

Usage

NBrowInfl(rcm, Dim = 1)
NBrowInfl(rcm, Dim = 1)

Arguments

`rcm`	an rcm object
`Dim`	the required dimension

Value

A list with components

`score`	a matrix with components of the score function
`InvJac`	A square matrix of dimension n with the components of the Jacobian related to the row scores

Plot RC(M) ordination result with the help of ggplot2

Description

Plot RC(M) ordination result with the help of ggplot2

Usage

## S3 method for class 'RCM'
plot(
  x,
  ...,
  Dim = c(1, 2),
  plotType = c("samples", "species", "variables"),
  samColour = if (is.null(inflVar)) NULL else "Influence",
  taxNum = if (all(plotType == "species") || !is.null(taxRegExp)) {     ncol(x$X) }
    else {     10 },
  taxRegExp = NULL,
  varNum = 15,
  arrowSize = 0.25,
  inflDim = 1,
  inflVar = NULL,
  returnCoords = FALSE,
  alpha = TRUE,
  varPlot = NULL,
  colLegend = if (!is.null(inflVar)) paste0("Influence on\n", inflVar,
    "\nparameter \nin dimension", inflDim) else samColour,
  samShape = NULL,
  shapeLegend = samShape,
  samSize = 2,
  scalingFactor = NULL,
  quadDrop = 0.995,
  plotEllipse = TRUE,
  taxaScale = 0.5,
  Palette = if (!all(plotType == "species")) "Set1" else "Paired",
  taxLabels = !all(plotType == "species"),
  taxDots = FALSE,
  taxCol = "blue",
  taxColSingle = "blue",
  nudge_y = 0.08,
  axesFixed = TRUE,
  aspRatio = 1,
  xInd = if (all(plotType == "samples")) c(0, 0) else c(-0.75, 0.75),
  yInd = c(0, 0),
  taxLabSize = 4,
  varLabSize = 3.5,
  alphaRange = c(0.2, 1),
  varExpFactor = 10,
  manExpFactorTaxa = 0.975,
  nPhyl = 10,
  phylOther = c(""),
  legendSize = samSize,
  noLegend = is.null(samColour),
  crossSize = 4,
  contCol = c("orange", "darkgreen"),
  legendLabSize = 15,
  legendTitleSize = 16,
  axisLabSize = 14,
  axisTitleSize = 16,
  plotPsi = "psi",
  breakChar = "\n"
)
## S3 method for class 'RCM'
plot(
  x,
  ...,
  Dim = c(1, 2),
  plotType = c("samples", "species", "variables"),
  samColour = if (is.null(inflVar)) NULL else "Influence",
  taxNum = if (all(plotType == "species") || !is.null(taxRegExp)) {     ncol(x$X) }
    else {     10 },
  taxRegExp = NULL,
  varNum = 15,
  arrowSize = 0.25,
  inflDim = 1,
  inflVar = NULL,
  returnCoords = FALSE,
  alpha = TRUE,
  varPlot = NULL,
  colLegend = if (!is.null(inflVar)) paste0("Influence on\n", inflVar,
    "\nparameter \nin dimension", inflDim) else samColour,
  samShape = NULL,
  shapeLegend = samShape,
  samSize = 2,
  scalingFactor = NULL,
  quadDrop = 0.995,
  plotEllipse = TRUE,
  taxaScale = 0.5,
  Palette = if (!all(plotType == "species")) "Set1" else "Paired",
  taxLabels = !all(plotType == "species"),
  taxDots = FALSE,
  taxCol = "blue",
  taxColSingle = "blue",
  nudge_y = 0.08,
  axesFixed = TRUE,
  aspRatio = 1,
  xInd = if (all(plotType == "samples")) c(0, 0) else c(-0.75, 0.75),
  yInd = c(0, 0),
  taxLabSize = 4,
  varLabSize = 3.5,
  alphaRange = c(0.2, 1),
  varExpFactor = 10,
  manExpFactorTaxa = 0.975,
  nPhyl = 10,
  phylOther = c(""),
  legendSize = samSize,
  noLegend = is.null(samColour),
  crossSize = 4,
  contCol = c("orange", "darkgreen"),
  legendLabSize = 15,
  legendTitleSize = 16,
  axisLabSize = 14,
  axisTitleSize = 16,
  plotPsi = "psi",
  breakChar = "\n"
)

Arguments

`x`	an RCM object
`...`	further arguments, passed on to aes in the the ggplot() function
`Dim`	An integer vector of length two, which dimensions to plot
`plotType`	a character string: which components to plot. Can be any combination of 'samples','species' and 'variables'
`samColour`	a character string, the variable to use for the colour of the sample dots. Can also be a richness measure, or "influence". Alternatively, a vector equal to the number of samples in the RCM object can be supplied. See details.
`taxNum`	an integer, the number of taxa to be plotted
`taxRegExp`	a character vector indicating which taxa to plot. Any taxa matcing this regular expression will be plotted
`varNum`	an integehr, number of variable arrows to draw
`arrowSize`	a scalar, the size of the arrows
`inflDim`	an integer, the dimension for which the influence should be calculated
`inflVar`	the variable on which the influence should be plotted. See details.
`returnCoords`	a boolean, should final coordinates be returned?
`alpha`	a boolean, should small arrows be made transparent?
`varPlot`	the names of the variable arrows to plot. Overrides the varNum argument
`colLegend`	a character string, the legend text for the sample colour. Defaults to the name of the colour variable
`samShape`	a character string, the variable to use for the shape of the sample dots
`shapeLegend`	a character string, the text to use for the shapeLegend. Defaults to the name of the shape variable
`samSize`	a scalar, the size of the sample dots
`scalingFactor`	a scalar, a user supplied scaling factor for the taxon arrows. If not supplied it will be calculated to make sample and taxon plots on the same scale
`quadDrop`	a number between 0 and 1. At this fraction of the peak height are the ellipses of the quadratic response functions drawn
`plotEllipse`	a boolean, whether to add the ellipses
`taxaScale`	a scalar, by which to scale the rectangles of the quadratic taxon plot
`Palette`	the colour palette
`taxLabels`	a boolean, should taxon labels be plotted?
`taxDots`	a boolean, should taxa be plotted as dots?
`taxCol`	the taxon colour
`taxColSingle`	the taxon colour if there is only one
`nudge_y`	a scalar, the offet for the taxon labels
`axesFixed`	A boolean, should the aspect ratio of the plot (the scale between the x and y-axis) be fixed. It is highly recommended to keep this argument at TRUE for honest representation of the ordination. If set to FALSE, the plotting space will be optimally used but the plot may be deformed in the process.
`aspRatio`	The aspect ratio of the plot when 'axesfixed' is TRUE (otherwise this argument is ignored), passde on to ggplot2::coord_fixed(). It is highly recommended to keep this argument at 1 for honest representation of the ordination.
`xInd`	a scalar or a vector of length 2, specifying the indentation left and right of the plot to allow for the labels to be printed entirely. Defaults to 0.75 at every side
`yInd`	a scalar or a vector of length 2, specifying the indentation top and bottom of the plot to allow for the labels to be printed entirely. Defaults to 0 at every side
`taxLabSize`	the size of taxon labels
`varLabSize`	the size of the variable label
`alphaRange`	The range of transparency
`varExpFactor`	a scalar, the factor by which to expand the variable coordinates
`manExpFactorTaxa`	a manual expansion factor for the taxa. Setting it to a high value allows you to plot the taxa around the samples
`nPhyl`	an integer, number of phylogenetic levels to show
`phylOther`	a character vector of phylogenetic levels to be included in the 'other' group
`legendSize`	a size for the coloured dots in the legend
`noLegend`	a boolean indicating you do not want a legend
`crossSize`	the size of the central cross
`contCol`	a character vector of length two, giving the low and high values of the continuous colour scale
`legendLabSize`	size of the legend labels
`legendTitleSize`	size of the legend title
`axisLabSize`	size of the axis labels
`axisTitleSize`	size of the axis title
`plotPsi`	a character vector, describing what to plot on the axis. Can be either 'psi', 'none' or 'loglik'. The latter plots the log-likelihood explained
`breakChar`	a character string indicating how the taxon names should be broken

Details

This function relies on the ggplot2 machinery to produce the plots, and the result can be modified accordingly. Monoplots, biplots and for constrained analysis even triplots can be produced, depending on the 'plotType' argument.

When one of either 'Observed', 'Chao1', 'ACE', 'Shannon', 'Simpson', 'InvSimpson' or 'Fisher' are supplied to the 'samColour' argument, the according richness measure (as calculated by phyloseq::estimate_richness) is mapped to the sample colour. When "influence" is supplied, the influence on the variable supplied is plotted. This 'inflVar' variable should be either "psi", or a variable name.

Value

plots a ggplot2-object to output

Note

Supplying only few categorical variables as constraining variables may cause the samples to be plotted on top of each other, since the number of unique sample scores is limited. The plot is still valid, but consider adding more sample variables to spread out the samples

Examples

data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:100],
prune_samples(sample_names(Zeller)[1:50], Zeller))
# Subset for a quick fit
zellerRCM = RCM(tmpPhy)
plot(zellerRCM)
data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:100],
prune_samples(sample_names(Zeller)[1:50], Zeller))
# Subset for a quick fit
zellerRCM = RCM(tmpPhy)
plot(zellerRCM)

Plot the non-parametric response functions

Description

Plots a number of response functions over the observed range of the environmental score. If no taxa are provided those who react most strongly to the environmental score are chosen.

Usage

plotRespFun(
  RCM,
  taxa = NULL,
  type = "link",
  logTransformYAxis = FALSE,
  addSamples = TRUE,
  samSize = NULL,
  Dim = 1L,
  nPoints = 100L,
  labSize = 2.5,
  yLocVar = NULL,
  yLocSam = NULL,
  Palette = "Set3",
  addJitter = FALSE,
  nTaxa = 9L,
  angle = 90,
  legendLabSize = 15,
  legendTitleSize = 16,
  axisLabSize = 14,
  axisTitleSize = 16,
  lineSize = 0.75,
  ...
)
plotRespFun(
  RCM,
  taxa = NULL,
  type = "link",
  logTransformYAxis = FALSE,
  addSamples = TRUE,
  samSize = NULL,
  Dim = 1L,
  nPoints = 100L,
  labSize = 2.5,
  yLocVar = NULL,
  yLocSam = NULL,
  Palette = "Set3",
  addJitter = FALSE,
  nTaxa = 9L,
  angle = 90,
  legendLabSize = 15,
  legendTitleSize = 16,
  axisLabSize = 14,
  axisTitleSize = 16,
  lineSize = 0.75,
  ...
)

Arguments

`RCM`	an RCM object
`taxa`	a character vector of taxa to be plotted
`type`	a character string, plot the response function on the log-scale ('link') or the abundance scale 'response', similar to predict.glm().
`logTransformYAxis`	a boolean, should y-axis be log transformed?
`addSamples`	a boolean, should sample points be shown?
`samSize`	a sample variable name or a vector of length equal to the number of samples, for the sample sizes
`Dim`	An integer, the dimension to be plotted
`nPoints`	the number of points to be used to plot the lines
`labSize`	the label size for the variables
`yLocVar`	the y-location of the variables, recycled if necessary
`yLocSam`	the y-location of the samples, recycled if necessary
`Palette`	which color palette to use
`addJitter`	A boolean, should variable names be jittered to make them more readable
`nTaxa`	an integer, number of taxa to plot
`angle`	angle at which variable labels should be turned
`legendLabSize`	size of the legend labels
`legendTitleSize`	size of the legend title
`axisLabSize`	size of the axis labels
`axisTitleSize`	size of the axis title
`lineSize`	size of the response function lines
`...`	Other argumens passed on to the ggplot() function

Value

Plots a ggplot2-object to output

Examples

data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:100],
prune_samples(sample_names(Zeller)[1:50], Zeller))
#Subset for a quick fit
zellerRCMnp = RCM(tmpPhy, k = 2,
covariates = c('BMI','Age','Country','Diagnosis','Gender'),
round = TRUE, responseFun = 'nonparametric')
plotRespFun(zellerRCMnp)
data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:100],
prune_samples(sample_names(Zeller)[1:50], Zeller))
#Subset for a quick fit
zellerRCMnp = RCM(tmpPhy, k = 2,
covariates = c('BMI','Age','Country','Diagnosis','Gender'),
round = TRUE, responseFun = 'nonparametric')
plotRespFun(zellerRCMnp)

Wrapper function for the RCM() function

Description

This is a wrapper function, which currently only fits the negative binomial distribution, but which could easily be extended to other ones.

Usage

RCM(dat, ...)

## S4 method for signature 'phyloseq'
RCM(dat, covariates = NULL, confounders = NULL, ...)

## S4 method for signature 'matrix'
RCM(
  dat,
  k = 2,
  round = FALSE,
  prevCutOff = 0.05,
  minFraction = 0.1,
  rowWeights = "uniform",
  colWeights = "marginal",
  confModelMat = NULL,
  confTrimMat = NULL,
  covModelMat = NULL,
  centMat = NULL,
  allowMissingness = FALSE,
  ...
)
RCM(dat, ...)

## S4 method for signature 'phyloseq'
RCM(dat, covariates = NULL, confounders = NULL, ...)

## S4 method for signature 'matrix'
RCM(
  dat,
  k = 2,
  round = FALSE,
  prevCutOff = 0.05,
  minFraction = 0.1,
  rowWeights = "uniform",
  colWeights = "marginal",
  confModelMat = NULL,
  confTrimMat = NULL,
  covModelMat = NULL,
  centMat = NULL,
  allowMissingness = FALSE,
  ...
)

Arguments

`dat`	an nxp count matrix or a phyloseq object with an otu_table slot
`...`	Further arguments passed on to the RCM.NB() function
`covariates`	In case 'dat' is a phyloseq object, the names of the sample variables to be used as covariates in the constrained analysis, or 'all' to indicate all variables to be used. In case 'dat' is a matrix, a nxf matrix or dataframe of covariates. Character variables will be converted to factors, with a warning. Defaults to NULL, in which case an unconstrained analysis is carried out.
`confounders`	In case 'dat' is a phyloseq object, the names of the sample variables to be used as confounders to be filtered out. In case 'dat' is a matrix, a nxf dataframe of confounders. Character variables will be converted to factors, with a warning. Defaults to NULL, in which case no filtering occurs.
`k`	an integer, the number of dimensions of the RCM solution
`round`	a boolean, whether to round to nearest integer. Defaults to FALSE.
`prevCutOff`	a scalar, the prevalance cutoff for the trimming. Defaults to 2.5e-2
`minFraction`	a scalar, each taxon's total abundance should equal at least the number of samples n times minFraction, otherwise it is trimmed. Defaults to 10%
`rowWeights`, `colWeights`	character strings, the weighting procedures for the normalization of row and column scores. Defaults to 'uniform' and 'marginal' respectively
`confTrimMat`, `confModelMat`, `covModelMat`, `centMat`	Dedicated model matrices constructed based on phyloseq object.
`allowMissingness`	A boolean, should NA values be tolerated?

Details

This function should be called on a raw count matrix, without rarefying or normalization to proportions. This functions trims on prevalence and total abundance to avoid instability of the algorithm. Covariate and confounder matrices are constructed, so that everything is passed on to the workhorse function RCM.NB() as matrices.

Value

see RCM_NB

Examples

data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:100],
prune_samples(sample_names(Zeller)[1:50], Zeller))
zellerRCM = RCM(tmpPhy, round = TRUE)

data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:100],
prune_samples(sample_names(Zeller)[1:50], Zeller))
zellerRCM = RCM(tmpPhy, round = TRUE)

Fit the RC(M) model with the negative binomial distribution.

Description

Fit the RC(M) model with the negative binomial distribution.

Usage

RCM_NB(
  X,
  k,
  rowWeights = "uniform",
  colWeights = "marginal",
  tol = 0.001,
  maxItOut = 1000L,
  Psitol = 0.001,
  verbose = FALSE,
  global = "dbldog",
  nleqslv.control = list(maxit = 500L, cndtol = 1e-16),
  jacMethod = "Broyden",
  dispFreq = 10L,
  convNorm = 2,
  prior.df = 10,
  marginEst = "MLE",
  confModelMat = NULL,
  confTrimMat = NULL,
  prevCutOff,
  minFraction = 0.1,
  covModelMat = NULL,
  centMat = NULL,
  responseFun = c("linear", "quadratic", "dynamic", "nonparametric"),
  record = FALSE,
  control.outer = list(trace = FALSE),
  control.optim = list(),
  envGradEst = "LR",
  dfSpline = 3,
  vgamMaxit = 100L,
  degree = switch(responseFun[1], nonparametric = 3, NULL),
  rowExp = if (is.null(covModelMat)) 1 else 0.5,
  colExp = rowExp,
  allowMissingness = FALSE
)
RCM_NB(
  X,
  k,
  rowWeights = "uniform",
  colWeights = "marginal",
  tol = 0.001,
  maxItOut = 1000L,
  Psitol = 0.001,
  verbose = FALSE,
  global = "dbldog",
  nleqslv.control = list(maxit = 500L, cndtol = 1e-16),
  jacMethod = "Broyden",
  dispFreq = 10L,
  convNorm = 2,
  prior.df = 10,
  marginEst = "MLE",
  confModelMat = NULL,
  confTrimMat = NULL,
  prevCutOff,
  minFraction = 0.1,
  covModelMat = NULL,
  centMat = NULL,
  responseFun = c("linear", "quadratic", "dynamic", "nonparametric"),
  record = FALSE,
  control.outer = list(trace = FALSE),
  control.optim = list(),
  envGradEst = "LR",
  dfSpline = 3,
  vgamMaxit = 100L,
  degree = switch(responseFun[1], nonparametric = 3, NULL),
  rowExp = if (is.null(covModelMat)) 1 else 0.5,
  colExp = rowExp,
  allowMissingness = FALSE
)

Arguments

`X`	a nxp data matrix
`k`	an scalar, number of dimensions in the RC(M) model
`rowWeights`	a character string, either 'uniform' or 'marginal' row weights.
`colWeights`	a character string, either 'uniform' or 'marginal' column weights.
`tol`	a scalar, the relative convergende tolerance for the row scores and column scores parameters.
`maxItOut`	an integer, the maximum number of iterations in the outer loop.
`Psitol`	a scalar, the relative convergence tolerance for the psi parameters.
`verbose`	a boolean, should information on iterations be printed?
`global`	global strategy for solving non-linear systems, see ?nleqslv
`nleqslv.control`	a list with control options, see nleqslv
`jacMethod`	Method for solving non-linear equations, ?see nleqslv. Defaults to Broyden. The difference with the newton method is that the Jacobian is not recalculated at every iteration, thereby speeding up the algorithm
`dispFreq`	an integer, how many iterations the algorithm should wait before reestimationg the dispersions.
`convNorm`	a scalar, the norm to use to determine convergence
`prior.df`	an integer, see estDisp()
`marginEst`	a character string, either 'MLE' or 'marginSums', indicating how the independence model should be estimated
`confModelMat`	an nxg matrix with confounders, with no reference levels and with intercept
`confTrimMat`	an nxh matrix with confounders for filtering, with all levels and without intercept
`prevCutOff`	a scalar the minimum prevalence needed to retain a taxon before the the confounder filtering
`minFraction`	a scalar, total taxon abundance should equal minFraction*n if it wants to be retained before the confounder filtering
`covModelMat`	an nxd matrix with covariates. If set to null an unconstrained analysis is carried out, otherwise a constrained one. Factors must have been converted to dummy variables already
`centMat`	a fxd matrix containing the contrasts to center the categorical variables. f equals the number of continuous variables + the total number of levels of the categorical variables.
`responseFun`	a characters string indicating the shape of the response function
`record`	A boolean, should intermediate parameter estimates be stored?
`control.outer`	a list of control options for the outer loop constrOptim.nl function
`control.optim`	a list of control options for the optim() function
`envGradEst`	a character string, indicating how the environmental gradient should be fitted. 'LR' using the likelihood-ratio criterion, or 'ML' a full maximum likelihood solution
`dfSpline`	a scalar, the number of degrees of freedom for the splines of the non-parametric response function, see VGAM::s()
`vgamMaxit`	an integer, the maximum number of iteration in the vgam() function
`degree`	an integer, the degree of the polynomial fit if the spline fit fails
`rowExp`, `colExp`	exponents for the row and column weights of the singular value decomposition used to calculate starting values. Can be played around with in case of numerical troubles.
`allowMissingness`	See RCM()

Details

Includes fitting of the independence model, filtering out the effect of confounders and fitting the RC(M) components in a constrained or an unconstrained way for any dimension k. Not intended to be called directly but only through the RCM() function

Value

A list with elements

`converged`	a vector of booleans of length k indicating if the algorithm converged for every dimension
`rMat`	if not constrained a nxk matrix with estimated row scores
`cMat`	a kxp matrix with estimated column scores
`psis`	a vector of length k with estimates for the importance parameters psi
`thetas`	a vector of length p with estimates for the overdispersion
`rowRec`	(if not constrained) a n x k x maxItOut array with a record of all rMat estimates through the iterations
`colRec`	a k x p x maxItOut array with a record of all cMat estimates through the iterations
`psiRec`	a k x maxItOut array with a record of all psi estimates through the iterations
`thetaRec`	a matrix of dimension pxmaxItOut with estimates for the overdispersion along the way
`iter`	number of iterations
`Xorig`	(if confounders provided) the original fitting matrix
`X`	the trimmed matrix if confounders provided, otherwise the original one
`fit`	type of fit, either 'RCM_NB' or 'RCM_NB_constr'
`lambdaRow`	(if not constrained) vector of Lagrange multipliers for the rows
`lambdaCol`	vector of Lagrange multipliers for the columns
`rowWeights`	(if not constrained) the row weights used
`colWeights`	the column weights used
`alpha`	(if constrained) the kxd matrix of environmental gradients
`alphaRec`	(if constrained) the kxdxmaxItOut array of alpha estimates along the iterations
`covariates`	(if constrained) the matrix of covariates
`libSizes`	a vector of length n with estimated library sizes
`abunds`	a vector of length p with estimated mean relative abundances
`confounders`	(if provided) the confounder matrix
`confParams`	the parameters used to filter out the confounders
`nonParamRespFun`	A list of the non parametric response functions
`degree`	The degree of the alternative parametric fit
`NApresent`	A boolean, were NA values present?

Note

Plotting is not supported for quadratic response functions

Examples

data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[seq_len(100)],
prune_samples(sample_names(Zeller)[seq_len(50)], Zeller))
mat = as(otu_table(tmpPhy), "matrix")
mat = mat[rowSums(mat)>0, colSums(mat)>0]
zellerRCM = RCM_NB(mat, k = 2)
#Needs to be called directly onto a matrix
data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[seq_len(100)],
prune_samples(sample_names(Zeller)[seq_len(50)], Zeller))
mat = as(otu_table(tmpPhy), "matrix")
mat = mat[rowSums(mat)>0, colSums(mat)>0]
zellerRCM = RCM_NB(mat, k = 2)
#Needs to be called directly onto a matrix

Make residual plots

Description

Make residual plots

Usage

residualPlot(
  RCM,
  Dim = 1,
  whichTaxa = "response",
  resid = "Deviance",
  numTaxa = 9,
  mfrow = NULL,
  samColour = NULL,
  samShape = NULL,
  legendLabSize = 15,
  legendTitleSize = 16,
  axisLabSize = 14,
  axisTitleSize = 16,
  taxTitle = TRUE,
  h = 0
)
residualPlot(
  RCM,
  Dim = 1,
  whichTaxa = "response",
  resid = "Deviance",
  numTaxa = 9,
  mfrow = NULL,
  samColour = NULL,
  samShape = NULL,
  legendLabSize = 15,
  legendTitleSize = 16,
  axisLabSize = 14,
  axisTitleSize = 16,
  taxTitle = TRUE,
  h = 0
)

Arguments

`RCM`	an RCM object
`Dim`	an integer, which dimension?
`whichTaxa`	a character string or a character vector, for which taxa to plot the diagnostic plots
`resid`	the type of residuals to use, either 'Deviance' or 'Pearson'
`numTaxa`	an integer, the number of taxa to plot
`mfrow`	passed on to par(). If not supplied will be calculated based on numTaxa
`samColour`, `samShape`	Vectors or character strings denoting the sample colour and shape respectively. If character string is provided, the variables with this name is extracted from the phyloseq object in RCM
`legendLabSize`	size of the legend labels
`legendTitleSize`	size of the legend title
`axisLabSize`	size of the axis labels
`axisTitleSize`	size of the axis title
`taxTitle`	A boolean, should taxon title be printed
`h`	Position of reference line. Set to NA for no line

Details

If whichTaxa is 'run' or 'response' the taxa with the highest run statistics or steepest slopes of the response function are plotted, numTax indicates the number. If whichTaxa is a character vector, these are interpreted as taxon names to plot. This function is mainly meant for linear response functions, but can be used for others too. The runs test statistic from the tseries package is used.

Value

Plots a ggplot2-object to output

Examples

data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:120],
prune_samples(sample_names(Zeller)[1:75], Zeller))
#Subset for a quick fit
zellerRCMlin = RCM(tmpPhy, k = 2,
covariates = c('BMI','Age','Country','Diagnosis','Gender'),
responseFun = 'linear', round = TRUE, prevCutOff = 0.03)
residualPlot(zellerRCMlin)
data(Zeller)
require(phyloseq)
tmpPhy = prune_taxa(taxa_names(Zeller)[1:120],
prune_samples(sample_names(Zeller)[1:75], Zeller))
#Subset for a quick fit
zellerRCMlin = RCM(tmpPhy, k = 2,
covariates = c('BMI','Age','Country','Diagnosis','Gender'),
responseFun = 'linear', round = TRUE, prevCutOff = 0.03)
residualPlot(zellerRCMlin)

Calculates the Jacobian of the parametric response functions

Description

Calculates the Jacobian of the parametric response functions

Usage

respFunJacMat(
  betas,
  X,
  reg,
  thetaMat,
  muMarg,
  psi,
  v,
  p,
  IDmat,
  IndVec,
  allowMissingness,
  naId
)
respFunJacMat(
  betas,
  X,
  reg,
  thetaMat,
  muMarg,
  psi,
  v,
  p,
  IDmat,
  IndVec,
  allowMissingness,
  naId
)

Arguments

`betas`	a vector of length (deg+1)*(p+1) with regression parameters with deg the degree of the response function and the lagrangian multipliers
`X`	the nxp data matrix
`reg`	a vector of regressors with the dimension n-by-v
`thetaMat`	The n-by-p matrix with dispersion parameters
`muMarg`	offset matrix of size nxp
`psi`	a scalar, the importance parameter
`v`	an integer, one plus the degree of the response function
`p`	an integer, the number of taxa
`IDmat`	an logical matrix with indices of non-zero elements
`IndVec`	a vector with indices with non-zero elements
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X

Value

The jacobian, a square matrix of dimension (deg+1)*(p+1)

Derivative of the Lagrangian of the parametric response function

Description

Derivative of the Lagrangian of the parametric response function

Usage

respFunScoreMat(
  betas,
  X,
  reg,
  thetaMat,
  muMarg,
  psi,
  p,
  v,
  allowMissingness,
  naId,
  ...
)
respFunScoreMat(
  betas,
  X,
  reg,
  thetaMat,
  muMarg,
  psi,
  p,
  v,
  allowMissingness,
  naId,
  ...
)

Arguments

`betas`	a vector of length (deg+1)*(p+1) with regression parameters with deg the degree of the response function and the lagrangian multipliers
`X`	the nxp data matrix
`reg`	a matrix of regressors with the dimension nx(deg+1)
`thetaMat`	The n-by-p matrix with dispersion parameters
`muMarg`	offset matrix of size nxp
`psi`	a scalar, the importance parameter
`p`	an integer, the number of taxa
`v`	an integer, one plus the degree of the response function
`allowMissingness`	A boolean, are missing values present
`naId`	The numeric index of the missing values in X
`...`	further arguments passed on to the jacobian The parameters are restricted to be normalized, i.e. all squared intercepts, first order and second order parameters sum to 1

Value

The evaluation of the score functions, a vector of length (p+1)* (deg+1)

A function to efficiently row multiply a matrix and a vector

Description

A function to efficiently row multiply a matrix and a vector

Usage

rowMultiply(matrix, vector)
rowMultiply(matrix, vector)

Arguments

matrix

a numeric matrix of dimension a-by-b

vector

a numeric vector of length b

t(t(matrix)*vector) but then faster

Details

Memory intensive but that does not matter with given matrix sizes

Value

a matrix, row multplied by the vector

A small auxiliary function for the length of the lambdas

Description

A small auxiliary function for the length of the lambdas

Usage

seq_k(y, nLambda1s = 1)
seq_k(y, nLambda1s = 1)

Arguments

`y`	an integer, the current dimension
`nLambda1s`	the number of centering restrictions

Value

a vector containing the ranks of the current lagrangian multipliers

Trim based on confounders to avoid taxa with only zero counts

Description

Trim based on confounders to avoid taxa with only zero counts

Usage

trimOnConfounders(confounders, X, prevCutOff, minFraction, n)
trimOnConfounders(confounders, X, prevCutOff, minFraction, n)

Arguments

`confounders`	a nxt confounder matrix
`X`	the nxp data matrix
`prevCutOff`	a scalar between 0 and 1, the prevalence cut off
`minFraction`	a scalar between 0 and 1, each taxon's total abundance should equal at least the number of samples n times minFraction, otherwise it is trimmed
`n`	the number of samples Should be called prior to fitting the independence model

Value

A trimmed data matrix nxp'

Microbiomes of colorectal cancer patients and healthy controls

Description

Microbiome sequencing data of colorectal cancer patients, patients with small adenoma and healthy controls, together with other baseline covariates

Usage

Zeller
Zeller

Format

A phyloseq object with an OTU-table and sample data

otu_table: Count data matrix of 709 taxa in 194 samples
sample_data: Data frame of patient covariates

Source

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299606/

Package 'RCM'

Help Index

This function adds orthogonal projections to a given plot

Description

Usage

Arguments

Value

See Also

Examples

An auxiliary R function to 'array' multiply an array with a vector, kindly provided by Joris Meys

Description

Usage

Arguments

Value

A function to build a centering matrix based on a dataframe

Description

Usage

Arguments

Value

A function to build the confounder matrices

Description

Usage

Arguments

Value

buildConfMat.character

Description

Usage

Arguments

Value

buildConfMat.data.frame

Description

Usage

Arguments

Value

A function to build the covariate matrix of the constraints

Description

Usage

Arguments

Value

A function to build the design matrix

Description

Usage

Arguments

Value

Check for alias structures in a dataframe, and throw an error when one is found

Description

Usage

Arguments

Value

Examples

Constrained correspondence analysis with adapted powers

Description

Usage

Arguments

Details

Value

Replace missing entries in X by their expectation to set their contribution to the estimating equations to zero

Description

Usage

Arguments

Value

Note

A function to extract deviances for all dimension, including after filtering on confounders

Description

Usage

Arguments

Value

A function that returns the value of the partial derivative of the log-likelihood ratio to alpha, keeping the response functions fixed

Description

Usage

Arguments

Value

A score function for the column components of the independence model (mean relative abundances)

Description

Usage

Arguments

Value

A score function for the row components of the independence model (library sizes)

Description

Usage