Type:	Package
Title:	High-Dimensional Analysis of Variance
Version:	0.8.3
Date:	2025-04-01
Description:	Functions and datasets to support Smilde, Marini, Westerhuis and Liland (2025, ISBN: 978-1-394-21121-0) "Analysis of Variance for High-Dimensional Data - Applications in Life, Food and Chemical Sciences". This implements and imports a collection of methods for HD-ANOVA data analysis with common interfaces, result- and plotting functions, multiple real data sets and four vignettes covering a range different applications.
Depends:	R (≥ 3.5.0)
Imports:	car, lme4, mixlm (≥ 1.4.2), pls, pracma, progress, RSpectra
Suggests:	knitr, vegan
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://khliland.github.io/HDANOVA/, https://github.com/khliland/HDANOVA/
BugReports:	https://github.com/khliland/HDANOVA/issues/
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-04-03 08:55:03 UTC; kristian
Author:	Kristian Hovde Liland [aut, cre]
Maintainer:	Kristian Hovde Liland <kristian.liland@nmbu.no>
Repository:	CRAN
Date/Publication:	2025-04-03 09:20:05 UTC

ANOVA Principal Component Analysis - APCA

Description

APCA function for fitting ANOVA Principal Component Analysis models.

Usage

apca(
  formula,
  data,
  add_error = TRUE,
  contrasts = "contr.sum",
  permute = FALSE,
  perm.type = c("approximate", "exact"),
  ...
)

Arguments

Value

An object of class apca, inheriting from the general asca class. Further arguments and plots can be found in the asca documentation.

References

Harrington, P.d.B., Vieira, N.E., Espinoza, J., Nien, J.K., Romero, R., and Yergey, A.L. (2005) Analysis of variance–principal component analysis: A soft tool for proteomic discovery. Analytica chimica acta, 544 (1-2), 118–127.

See Also

Main methods: asca, apca, limmpca, msca, pcanova, prc and permanova. Workhorse function underpinning most methods: hdanova. Extraction of results and plotting: asca_results, asca_plots, pcanova_results and pcanova_plots

Examples

data(candies)
ap <- apca(assessment ~ candy, data=candies)
scoreplot(ap)

# Numeric effects
candies$num <- eff <- 1:165
mod <- apca(assessment ~ candy + assessor + num, data=candies)
summary(mod)
scoreplot(mod, factor=3, gr.col=rgb(eff/max(eff), 1-eff/max(eff),0), pch.scores="x")

Analysis of Variance Simultaneous Component Analysis - ASCA

Description

This is a quite general and flexible implementation of ASCA.

Usage

asca(
  formula,
  data,
  contrasts = "contr.sum",
  permute = FALSE,
  perm.type = c("approximate", "exact"),
  ...
)

Arguments

Details

ASCA is a method which decomposes a multivariate response according to one or more design variables. ANOVA is used to split variation into contributions from factors, and PCA is performed on the corresponding least squares estimates, i.e., Y = X1 B1 + X2 B2 + ... + E = T1 P1' + T2 P2' + ... + E. This version of ASCA encompasses variants of LiMM-PCA, generalized ASCA and covariates ASCA. It includes confidence ellipsoids for the balanced crossed-effect ASCA.

The formula interface is extended with the function r() to indicate random effects and comb() to indicate effects that should be combined. See Examples for use cases.

Value

An asca object containing loadings, scores, explained variances, etc. The object has associated plotting (asca_plots) and result (asca_results) functions.

References

• Smilde, A., Jansen, J., Hoefsloot, H., Lamers,R., Van Der Greef, J., and Timmerman, M.(2005). ANOVA-Simultaneous Component Analysis (ASCA): A new tool for analyzing designed metabolomics data. Bioinformatics, 21(13), 3043–3048.

• Liland, K.H., Smilde, A., Marini, F., and Næs,T. (2018). Confidence ellipsoids for ASCA models based on multivariate regression theory. Journal of Chemometrics, 32(e2990), 1–13.

• Martin, M. and Govaerts, B. (2020). LiMM-PCA: Combining ASCA+ and linear mixed models to analyse high-dimensional designed data. Journal of Chemometrics, 34(6), e3232.

See Also

Main methods: asca, apca, limmpca, msca, pcanova, prc and permanova. Workhorse function underpinning most methods: hdanova. Extraction of results and plotting: asca_results, asca_plots, pcanova_results and pcanova_plots

Examples

# Load candies data
data(candies)

# Basic ASCA model with two factors
mod <- asca(assessment ~ candy + assessor, data=candies)
print(mod)

# ASCA model with interaction
mod <- asca(assessment ~ candy * assessor, data=candies)
print(mod)

# Result plotting for first factor
loadingplot(mod, scatter=TRUE, labels="names")
scoreplot(mod)
# No backprojection
scoreplot(mod, projections=FALSE)
# Spider plot
scoreplot(mod, spider=TRUE, projections=FALSE)

# ASCA model with compressed response using 5 principal components
mod.pca <- asca(assessment ~ candy + assessor, data=candies, pca.in=5)

# Mixed Model ASCA, random assessor
mod.mix <- asca(assessment ~ candy + r(assessor), data=candies)
scoreplot(mod.mix)

# Mixed Model ASCA, REML estimation
mod.mix <- asca(assessment ~ candy + r(assessor), data=candies, REML=TRUE)
scoreplot(mod.mix)

# Load Caldana data
data(caldana)

# Combining effects in ASCA
mod.comb <- asca(compounds ~ time + comb(light + time:light), data=caldana)
summary(mod.comb)
timeplot(mod.comb, factor="light", time="time", comb=2)

# Permutation testing
mod.perm <- asca(assessment ~ candy * assessor, data=candies, permute=TRUE)
summary(mod.perm)

ASCA Plot Methods

Description

Various plotting procedures for asca objects.

Usage

## S3 method for class 'asca'
loadingplot(object, factor = 1, comps = 1:2, ...)

## S3 method for class 'asca'
scoreplot(
  object,
  factor = 1,
  comps = 1:2,
  within_level = "all",
  pch.scores = 19,
  pch.projections = 1,
  gr.col = NULL,
  projections = TRUE,
  spider = FALSE,
  ellipsoids,
  confidence,
  xlim,
  ylim,
  xlab,
  ylab,
  legendpos,
  ...
)

permutationplot(object, factor = 1, xlim, xlab = "SSQ", main, ...)

Arguments

Details

Usage of the functions are shown using generics in the examples in asca. Plot routines are available as scoreplot.asca and loadingplot.asca.

Value

The plotting routines have no return.

References

• Smilde, A., Jansen, J., Hoefsloot, H., Lamers,R., Van Der Greef, J., and Timmerman, M.(2005). ANOVA-Simultaneous Component Analysis (ASCA): A new tool for analyzing designed metabolomics data. Bioinformatics, 21(13), 3043–3048.

• Liland, K.H., Smilde, A., Marini, F., and Næs,T. (2018). Confidence ellipsoids for ASCA models based on multivariate regression theory. Journal of Chemometrics, 32(e2990), 1–13.

• Martin, M. and Govaerts, B. (2020). LiMM-PCA: Combining ASCA+ and linear mixed models to analyse high-dimensional designed data. Journal of Chemometrics, 34(6), e3232.

See Also

Main methods: asca, apca, limmpca, msca, pcanova, prc and permanova. Workhorse function underpinning most methods: hdanova. Extraction of results and plotting: asca_results, asca_plots, pcanova_results and pcanova_plots

ASCA Result Methods

Description

Standard result computation and extraction functions for ASCA (asca).

Usage

## S3 method for class 'hdanova'
print(x, ...)

## S3 method for class 'hdanova'
summary(object, extended = TRUE, df = FALSE, ...)

## S3 method for class 'summary.hdanova'
print(x, digits = 2, ...)

## S3 method for class 'asca'
loadings(object, factor = 1, ...)

## S3 method for class 'asca'
scores(object, factor = 1, ...)

projections(object, ...)

## S3 method for class 'asca'
projections(object, factor = 1, ...)

Arguments

Details

Usage of the functions are shown using generics in the examples in asca. Explained variances are available (block-wise and global) through blockexpl and print.rosaexpl. Object printing and summary are available through: print.asca and summary.asca. Scores and loadings have their own extensions of scores() and loadings() through scores.asca and loadings.asca. Special to ASCA is that scores are on a factor level basis, while back-projected samples have their own function in projections.asca.

Value

Returns depend on method used, e.g. projections.asca returns projected samples, scores.asca return scores, while print and summary methods return the object invisibly.

References

• Smilde, A., Jansen, J., Hoefsloot, H., Lamers,R., Van Der Greef, J., and Timmerman, M.(2005). ANOVA-Simultaneous Component Analysis (ASCA): A new tool for analyzing designed metabolomics data. Bioinformatics, 21(13), 3043–3048.

• Liland, K.H., Smilde, A., Marini, F., and Næs,T. (2018). Confidence ellipsoids for ASCA models based on multivariate regression theory. Journal of Chemometrics, 32(e2990), 1–13.

• Martin, M. and Govaerts, B. (2020). LiMM-PCA: Combining ASCA+ and linear mixed models to analyse high-dimensional designed data. Journal of Chemometrics, 34(6), e3232.

See Also

Main methods: asca, apca, limmpca, msca, pcanova, prc and permanova. Workhorse function underpinning most methods: hdanova. Extraction of results and plotting: asca_results, asca_plots, pcanova_results and pcanova_plots

Biplot for ASCA models

Description

Biplot for ASCA models

Usage

## S3 method for class 'asca'
biplot(
  x,
  factor = 1,
  comps = 1:2,
  xlim = NULL,
  ylim = NULL,
  col = "darkgray",
  expand = 1,
  labels,
  legendpos,
  ...
)

Arguments

Value

No return, only a plot.

Examples

# Load candies data
data(candies)

# Basic ASCA model with two factors and interaction
mod <- asca(assessment ~ candy * assessor, data=candies)

# Biplot
biplot(mod)

# Biplot with named loadings
biplot(mod, labels="names")

Block-wise indexable data.frame

Description

This is a convenience function for making data.frames that are easily indexed on a block-wise basis.

Usage

block.data.frame(X, block_inds = NULL, to.matrix = TRUE)

Arguments

Value

A data.frame which can be indexed block-wise.

See Also

Main methods: asca, apca, limmpca, msca, pcanova, prc and permanova. Workhorse function underpinning most methods: hdanova. Extraction of results and plotting: asca_results, asca_plots, pcanova_results and pcanova_plots

Examples

# Random data
M <- matrix(rnorm(200), nrow = 10)
# .. with dimnames
dimnames(M) <- list(LETTERS[1:10], as.character(1:20))

# A named list for indexing
inds <- list(B1 = 1:10, B2 = 11:20)

X <- block.data.frame(M, inds)
str(X)

Arabidopsis thaliana growth experiment

Description

A dataset containing 67 metabolites from plants grown under different light and temperature conditions. This subset of the data contains only the light effect and time effect for limited conditions, while the full data also contains gene expressions.

Usage

data(caldana)

Format

A data.frame having 140 rows and 3 variables:

light

Light levels

time

Time of measurement

compound

Metabolic compounds

References

Caldana C, Degenkolbe T, Cuadros-Inostroza A, Klie S, Sulpice R, Leisse A, et al. High-density kinetic analysis of the metabolomic and transcriptomic response of Arabidopsis to eight environmental conditions. Plant J. 2011;67(5):869-884.

Sensory assessment of candies.

Description

A dataset containing 9 sensory attributes for 5 candies assessed by 11 trained assessors.

Usage

data(candies)

Format

A data.frame having 165 rows and 3 variables:

assessment

Matrix of sensory attributes

assessor

Factor of assessors

candy

Factor of candies

References

Luciano G, Næs T. Interpreting sensory data by combining principal component analysis and analysis of variance. Food Qual Prefer. 2009;20(3):167-175.

Dummy-coding of a single vector

Description

Flexible dummy-coding allowing for all R's built-in types of contrasts and optional dropping of a factor level to reduce rank defficiency probability.

Usage

dummycode(Y, contrast = "contr.sum", drop = TRUE)

Arguments

Value

matrix made by dummy-coding the input vector.

Examples

vec <- c("a","a","b","b","c","c")
dummycode(vec)

Extracting the Extended Model Frame from a Formula or Fit

Description

This function attempts to apply model.frame and extend the result with columns of interactions.

Usage

extended.model.frame(formula, data, ..., sep = ".")

Arguments

Value

A data.frame that includes everything a model.frame does plus interaction terms.

See Also

Main methods: asca, apca, limmpca, msca, pcanova, prc and permanova. Workhorse function underpinning most methods: hdanova. Extraction of results and plotting: asca_results, asca_plots, pcanova_results and pcanova_plots

Examples

dat <- data.frame(Y = c(1,2,3,4,5,6),
                  X = factor(LETTERS[c(1,1,2,2,3,3)]),
                  W = factor(letters[c(1,2,1,2,1,2)]))
extended.model.frame(Y ~ X*W, dat)

Extract estimates for a given factor combination

Description

Extracts and sums the LS estimates for a given factor combination from an object of class hdanova. If add_residuals is TRUE, the residuals are added to the LS estimates. If substract is TRUE, the returned matrix is the data with chosen estimates subtracted.

Usage

extract_estimates(object, factors, subtract = FALSE, add_residuals = FALSE)

Arguments

Value

A matrix of the extracted estimates.

Examples

# Load candies data
data(candies)

# Basic HDANOVA model with two factors and interaction
mod <- hdanova(assessment ~ candy * assessor, data=candies)

# Extract estimates for the interaction
inter <- extract_estimates(mod, c("assessor:candy"))

# Visualize the interaction effect
image(t(inter), main="Interaction effect", xlab="Attribute", ylab="Sample")

High-Dimensional Analysis of Variance

Description

This function provides a high-dimensional analysis of variance (HDANOVA) method which can be used alone or as part of a larger analysis, e.g., ASCA, APCA, LiMM-PCA, MSCA or PC-ANOVA. It can be called directly or through the convenince functions asca, apca, limmpca, msca and pcanova.

Usage

hdanova(
  formula,
  data,
  subset,
  weights,
  na.action,
  family,
  unrestricted = FALSE,
  add_error = FALSE,
  aug_error = "denominator",
  use_ED = FALSE,
  pca.in = FALSE,
  contrasts = "contr.sum",
  coding,
  equal_baseline = FALSE,
  SStype = "II",
  REML = NULL
)

Arguments

Value

An hdanova object containing loadings, scores, explained variances, etc. The object has associated plotting (asca_plots) and result (asca_results) functions.

Examples

# Load candies data
data(candies)

# Basic HDANOVA model with two factors
mod <- hdanova(assessment ~ candy + assessor, data=candies)
summary(mod)

Linear Mixed Model PCA

Description

This function mimics parts of the LiMM-PCA framework, combining ASCA+ and linear mixed models to analyse high-dimensional designed data. The default is to use REML estimation and scaling of the backprojected errors. See examples for alternatives.

Usage

limmpca(
  formula,
  data,
  pca.in = 5,
  aug_error = 0.05,
  use_ED = FALSE,
  REML = TRUE,
  contrasts = "contr.sum",
  permute = FALSE,
  perm.type = c("approximate", "exact"),
  ...
)

Arguments

Value

An object of class limmpca, inheriting from the general asca class.

References

• Martin, M. and Govaerts, B. (2020). LiMM-PCA: Combining ASCA+ and linear mixed models to analyse high-dimensional designed data. Journal of Chemometrics, 34(6), e3232.

See Also

Main methods: asca, apca, limmpca, msca, pcanova, prc and permanova. Workhorse function underpinning most methods: hdanova. Extraction of results and plotting: asca_results, asca_plots, pcanova_results and pcanova_plots

Examples

# Load candies data
data(candies)

# Default LiMM-PCA model with two factors and interaction, 5 PCA components
mod <- limmpca(assessment ~ candy*r(assessor), data=candies)
summary(mod)
scoreplot(mod, factor = "candy")

# LiMM-PCA with least squares estimation and 8 PCA components
modLS <- limmpca(assessment ~ candy*r(assessor), data=candies, REML=NULL, pca.in=8)
summary(modLS)
scoreplot(modLS, factor = "candy")

# Load Caldana data
data(caldana)

# Combining effects in LiMM-PCA (assuming light is a random factor)
mod.comb <- limmpca(compounds ~ time + comb(r(light) + r(time:light)), data=caldana, pca.in=8)
summary(mod.comb)

Model Frame and Model Matrix for ASCA-like Models

Description

Extraction functions to retrieve the model.frame and model.matrix of an asca object.

Usage

## S3 method for class 'asca'
model.frame(formula, ...)

## S3 method for class 'asca'
model.matrix(object, ...)

Arguments

Value

A data.frame or matrix object.

Examples

# Load candies data
data(candies)

# Basic ASCA model with two factors
mod <- asca(assessment ~ candy + assessor, data=candies)

# Extract model frame and model matrix
mf <- model.frame(mod)
head(mf)
mm <- model.matrix(mod)
par.old <- par(mar=c(3,3,3,1), mgp=c(1,0.7,0))
image(t(mm[seq(165,1,-1),]), main="Model Matrix", xlab="dummy values", ylab="samples",
     axes=FALSE)
par(par.old)

Multilevel Simultaneous Component Analysis - MSCA

Description

This MSCA implementation assumes a single factor to be used as between-individuals factor.

Usage

msca(
  formula,
  data,
  contrasts = "contr.sum",
  permute = FALSE,
  perm.type = c("approximate", "exact"),
  ...
)

Arguments

Value

An asca object containing loadings, scores, explained variances, etc. The object has associated plotting (asca_plots) and result (asca_results) functions.

References

• Smilde, A., Jansen, J., Hoefsloot, H., Lamers,R., Van Der Greef, J., and Timmerman, M.(2005). ANOVA-Simultaneous Component Analysis (ASCA): A new tool for analyzing designed metabolomics data. Bioinformatics, 21(13), 3043–3048.

• Liland, K.H., Smilde, A., Marini, F., and Næs,T. (2018). Confidence ellipsoids for ASCA models based on multivariate regression theory. Journal of Chemometrics, 32(e2990), 1–13.

See Also

Main methods: asca, apca, limmpca, msca, pcanova, prc and permanova. Workhorse function underpinning most methods: hdanova. Extraction of results and plotting: asca_results, asca_plots, pcanova_results and pcanova_plots

Examples

# Load candies data
data(candies)

# Basic MSCA model with a single factor
mod <- msca(assessment ~ candy, data=candies)
print(mod)
summary(mod)

# Result plotting for first factor
loadingplot(mod, scatter=TRUE, labels="names")
scoreplot(mod)

# Within scores
scoreplot(mod, factor="within")

# Within scores per factor level
par.old <- par(mfrow=c(3,2), mar=c(4,4,2,1), mgp=c(2,0.7,0))
for(i in 1:length(mod$scores.within))
  scoreplot(mod, factor="within", within_level=i,
            main=paste0("Level: ", names(mod$scores.within)[i]),
            panel.first=abline(v=0,h=0,col="gray",lty=2))
par(par.old)

# Permutation testing
mod.perm <- asca(assessment ~ candy * assessor, data=candies, permute=TRUE)
summary(mod.perm)

Principal Components Analysis of Variance Simultaneous Component Analysis - PC-ANOVA

Description

This is a quite general and flexible implementation of PC-ANOVA.

Usage

pcanova(formula, data, ncomp = 0.9, contrasts = "contr.sum", ...)

Arguments

Details

PC-ANOVA works in the opposite order of ASCA. First the response matrix is decomposed using ANOVA. Then the components are analysed using ANOVA with respect to a design or grouping in the data. The latter can be ordinary fixed effects modelling or mixed models.

Value

A pcanova object containing loadings, scores, explained variances, etc. The object has associated plotting (pcanova_plots) and result (pcanova_results) functions.

References

Luciano G, Næs T. Interpreting sensory data by combining principal component analysis and analysis of variance. Food Qual Prefer. 2009;20(3):167-175.

See Also

Main methods: asca, apca, limmpca, msca, pcanova, prc and permanova. Workhorse function underpinning most methods: hdanova. Extraction of results and plotting: asca_results, asca_plots, pcanova_results and pcanova_plots

Examples

# Load candies data
data(candies)

# Basic PC-ANOVA model with two factors, cross-validated opt. of #components
mod <- pcanova(assessment ~ candy + assessor, data = candies)
print(mod)

# PC-ANOVA model with interaction, minimum 90% explained variance
mod <- pcanova(assessment ~ candy * assessor, data = candies, ncomp = 0.9)
print(mod)
summary(mod)

# Tukey group letters for 'candy' per component
lapply(mod$models, function(x)
       mixlm::cld(mixlm::simple.glht(x,
                                     effect = "candy")))

# Result plotting
loadingplot(mod, scatter=TRUE, labels="names")
scoreplot(mod)

# Mixed Model PC-ANOVA, random assessor
mod.mix <- pcanova(assessment ~ candy + r(assessor), data=candies, ncomp = 0.9)
scoreplot(mod.mix)
# Fixed effects
summary(mod.mix)

PC-ANOVA Result Methods

Description

Various plotting procedures for pcanova objects.

Usage

## S3 method for class 'pcanova'
scoreplot(object, factor = 1, comps = 1:2, col = "factor", ...)

Arguments

Details

Usage of the functions are shown using generics in the examples in pcanova. Plot routines are available as scoreplot.pcanova and loadingplot.pcanova.

Value

The plotting routines have no return.

References

Luciano G, Næs T. Interpreting sensory data by combining principal component analysis and analysis of variance. Food Qual Prefer. 2009;20(3):167-175.

See Also

Main methods: asca, apca, limmpca, msca, pcanova, prc and permanova. Workhorse function underpinning most methods: hdanova. Extraction of results and plotting: asca_results, asca_plots, pcanova_results and pcanova_plots

PC-ANOVA Result Methods

Description

Standard result computation and extraction functions for ASCA (pcanova).

Usage

## S3 method for class 'pcanova'
summary(object, ...)

## S3 method for class 'summary.pcanova'
print(x, digits = 2, ...)

## S3 method for class 'pcanova'
print(x, ...)

## S3 method for class 'pcanova'
summary(object, ...)

Arguments

Details

Usage of the functions are shown using generics in the examples in pcanova. Explained variances are available (block-wise and global) through blockexpl and print.rosaexpl. Object printing and summary are available through: print.pcanova and summary.pcanova. Scores and loadings have their own extensions of scores() and loadings() through scores.pcanova and loadings.pcanova. Special to ASCA is that scores are on a factor level basis, while back-projected samples have their own function in projections.pcanova.

Value

Returns depend on method used, e.g. projections.pcanova returns projected samples, scores.pcanova return scores, while print and summary methods return the object invisibly.

References

Luciano G, Næs T. Interpreting sensory data by combining principal component analysis and analysis of variance. Food Qual Prefer. 2009;20(3):167-175.

See Also

Main methods: asca, apca, limmpca, msca, pcanova, prc and permanova. Workhorse function underpinning most methods: hdanova. Extraction of results and plotting: asca_results, asca_plots, pcanova_results and pcanova_plots

Permutation Based MANOVA - PERMANOVA

Description

Wrapper for the adonis2 function to allow ordinary formula input.

Usage

permanova(formula, data, ...)

Arguments

Value

An ANOVA table with permutation-based p-values.

Examples

data(caldana)
(pr <- permanova(compounds ~ light * time, caldana))

Permutation for HDANOVA

Description

Permutation testing for HDANOVA. This function performes permutation testing for the effects in the HDANOVA model and adds them to the hdanova object.

Usage

permutation(object, permute = 1000, perm.type = c("approximate", "exact"))

Arguments

Value

An updated hdanova object with permutation results.

Examples

# Load candies data
data(candies)

# Basic HDANOVA model with two factors
mod <- hdanova(assessment ~ candy + assessor, data=candies)
mod <- permutation(mod)
summary(mod)

Principal Response Curves

Description

Wrapper for the prc function to allow for formula input.

Usage

prc(formula, data, ...)

Arguments

Value

An object of class prc.

See Also

Main methods: asca, apca, limmpca, msca, pcanova, prc and permanova. Workhorse function underpinning most methods: hdanova. Extraction of results and plotting: asca_results, asca_plots, pcanova_results and pcanova_plots

Examples

data(caldana)
(pr <- prc(compounds ~ light * time, caldana))
summary(pr)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

pls

MSEP, R2, RMSEP, coefplot, cvsegments, loading.weights, loadingplot, loadings, mvrValstats, pcr, plsr, predplot, scoreplot, scores, validationplot

Simultaenous Component Analysis

Description

This function performs Simultaneous Component Analysis (SCA) on a hdanova object.

Usage

sca(object)

Arguments

Value

An updated hdanova object with SCA results.

Examples

# Load candies data
data(candies)

# Basic HDANOVA model with two factors
mod <- hdanova(assessment ~ candy + assessor, data=candies)
mod <- sca(mod)
scoreplot(mod)

Flip signs of a component/factor combination in a SCA/PCA object

Description

This function flips the sign of a selected component in a selected factor of an asca object. This affects both scores, loadings and projected data.

Usage

signflip(object, factor, comp)

Arguments

Value

An asca object with the sign of the selected component flipped.

Examples

# Load candies data
data(candies)

# Basic HDANOVA model with two factors
mod <- hdanova(assessment ~ candy + assessor, data=candies)
mod <- sca(mod)
old.par <- par(mfrow=c(1,2), mar=c(4,4,1,1))
scoreplot(mod, factor="candy")
loadingplot(mod, factor="candy")
par(old.par)

# Flip the sign of the first component of the candy factor
mod <- signflip(mod, factor="candy", comp=1)
old.par <- par(mfrow=c(1,2), mar=c(4,4,1,1))
scoreplot(mod, factor="candy")
loadingplot(mod, factor="candy")
par(old.par)

Timeplot for Combined Effects

Description

Timeplot for Combined Effects

Usage

timeplot(
  object,
  factor,
  time,
  comb,
  comp = 1,
  ylim,
  x_time = FALSE,
  xlab = time,
  ylab = paste0("Score ", comp),
  lwd = 2,
  ...
)

Arguments

Value

Nothing

Examples

data("caldana")
mod.comb <- asca(compounds ~ time + comb(light + light:time), data=caldana)

# Default time axis
timeplot(mod.comb, factor="light", time="time", comb=2)

# Non-equispaced time axis (using time levels)
timeplot(mod.comb, factor="light", time="time", comb=2, x_time=TRUE)

# Second component
timeplot(mod.comb, factor="light", time="time", comb=2, comp=2, x_time=TRUE)

Update a Model without Factor

Description

Perform a model update while removing a chosen factor. Hierarchical corresponds to type "II" sum-of-squares, i.e., obeying marginality, while non-hierarchical corresponds to type "III" sum-of-squares.

Usage

update_without_factor(model, fac, hierarchical = TRUE)

Arguments

Value

An updated model object is returned. If the supplied model is of type lmerMod and no random effects are left, the model is automatically converted to a linear model before updating.