asca {HDANOVA} | R Documentation |
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
formula |
Model formula accepting a single response (block) and predictors. See Details for more information. |
data |
The data set to analyse. |
contrasts |
Effect coding: "sum" (default = sum-coding), "weighted", "reference", "treatment". |
permute |
Number of permutations to perform (default = 1000). |
perm.type |
Type of permutation to perform, either "approximate" or "exact" (default = "approximate"). |
... |
Additional arguments to |
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)