lmdme-lmdme: High level constructor of lmdme class object

In lmdme: Linear Model decomposition for Designed Multivariate Experiments

Description

Linear model ANOVA decomposition of Designed Multivariate Experiments based on limma lmFit implementation. For example in a two factor experimental design with interaction, the linear model of the i-th observation (gene) can be written:
X=μ+A+B+AB+ε
where

• X stands for the observed value

• the intercept μ

• A, B and AB are the first, second and interaction terms respectively

• The error term ε ~ N(0,σ^2).

The model is iteratively decomposed in a step by step fashion decomposing one term each time:

1. The intercept is estimated using X=μ+E_1

2. The first factor (A) using E_1=A+E_2

3. The second factor (B) using E_2=B+E_3

4. The interaction (AB) using E_3=AB+E_4.

For each decomposed step the model, residuals, coefficients, p-values and F-value are stored in a list container, so their corresponding length is equal to the number of model terms + 1 (the intercept).

Arguments

model	formula object to carry out the decomposition.
data	matrix or data.frame with individuals/genes (per rows) and samples/conditions (per columns).
design	data.frame with the design of the experiment, (rows) samples/conditions as in data columns and as many columns to indicate the factors present in each sample.
Bayes	Should limma estimate empirical Bayes statistics, i. e., moderated t-statistics? Default value is FALSE.
verbose	Should the process progress be printed? Default value is FALSE.
...	Additional parameters for lmFit function.

Value

lmdme

lmdme class object with the corresponding completed slots according to the given model

Note

use lmdme high level constructor for the creation of the class instead of directly calling its constructor by means of new.

Author(s)

Cristobal Fresno and Elmer A Fernandez

References

1. Smyth, G. K. (2005). Limma: linear models for microarray data. In: Bioinformatics and Computational Biology Solutions using R and Bioconductor. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds), Springer, New York, pages 397–420.

2. Cristobal Fresno, Monica G. Balzarini, Elmer A. Fernandez (2014) lmdme: Linear Models on Designed Multivariate Experiments in R, Journal of Statistical Software, 56(7), 1-16, http://www.jstatsoft.org/v56/i07/.

See Also

decomposition, lmFit

Examples

{
data(stemHypoxia)

##Just to make a balanced dataset in the Fisher sense (2 samples per
## time*oxygen levels)
design<-design[design$time %in% c(0.5,1,5) & design$oxygen %in% c(1,5,21), ]
design$time<-as.factor(design$time)
design$oxygen<-as.factor(design$oxygen)
rownames(M)<-M[, 1]

#Keeping appropriate samples only
M<-M[, colnames(M) %in% design$samplename]

##ANOVA decomposition
fit<-lmdme(model=~time+oxygen+time:oxygen, data=M, design=design)
}

lmdme documentation built on Nov. 8, 2020, 7:46 p.m.