is part of
has part
denotes
is_specified_input_of
data item
information content entity
study design independent variable
data transformation
study design
statistical model
model fitting
design matrix
Added following a term request by Nolan Nichols: https://github.com/ISA-tools/stato/issues/9
Alejandra Gonzalez-Beltran
Orlaith Burke
Philippe Rocca-Serra
a design matrix is an information content entity which denotes a study design. The design matrix is a n by m matrix where n the number of rows, corresponds to the number of observations (4 rows if quadruplicates) and where m, the number of columns corresponds to the number of independent variables. Each element in the matrix correspond to a discretized value representing one of the factor levels for a given factor.
A design matrix can be used as input to statistical modeling or statistical analysis.
The design matrix contains data on the independent variables (also called explanatory variables) in statistical models which attempt to explain observed data on a response variable (often called a dependent variable) in terms of the explanatory variables. The theory relating to such models makes substantial use of matrix manipulations involving the design matrix: see for example linear regression. A notable feature of the concept of a design matrix is that it is able to represent a number of different experimental designs and statistical models, e.g., ANOVA, ANCOVA, and linear regression
adapted from:
Design of Experiments: Principles and Applications
edited by Lennart Eriksson, 1999-2008 Umetrics. ISBN-13:978-91-973730-4-3
and
http://en.wikipedia.org/wiki/Design_matrix
[last accessed: 22-05-2014]
let's consider an experiment evaluating 2 compounds (aspirin & ibuprofen) at 3 distinct dose levels (low, medium, high) and 4 time points post exposure (0h, 6h, 12h, 24h). Assuming the treatments are applied only once (no replication), the number of observation in a full factorial design is 2 x 3 x 4 = 24 so the design matrix would have 24 rows and 3 columns (1 per factor (independent variable).
model matrix
model.matrix(object, data = environment(object),
contrasts.arg = NULL, xlev = NULL, ...)
http://stat.ethz.ch/R-manual/R-patched/library/stats/html/model.matrix.html
ready for release