lmdme {lmdme} | R Documentation |
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:
The intercept is estimated using X=μ+E_1
The first factor (A) using E_1=A+E_2
The second factor (B) using E_2=B+E_3
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).
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 |
lmdme |
lmdme class object with the corresponding completed slots according to the given model |
use lmdme
high level constructor for
the creation of the class instead of directly calling its
constructor by means of new.
Cristobal Fresno and Elmer A Fernandez
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.
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/.
{ 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) }