Description
Usage
Arguments
Value
See Also
Examples
View source: R/fusedanova.R
Adjust a penalized ANOVA model with FusedLASSO (or Total
Variation) penality, ie. a sum of weighted
l1norm on the difference of each
coefficient. See details below.
x 
matrix (or column vector) which rows represent
individuals and columns independant variables.

class 
vector or factor giving the class of each
individual. If missing, 1:nrow(x) is used
(clustering mode with one individual per class).

... 
list of additional parameters to overwrite the
defaults of the fitting procedure. Include :
weights : character; which type of weights
is supposed to be used. The supported weights are :
"default" , "laplace" , "gaussian" ,
"adaptive" , "naivettest" , "ttest" ,
"welch" and "personal" . Se details below.
By default, its value is "default" .
gamma : numeric; the gamma
parameter needed for "laplace" , "gaussian"
and "adaptive" weights. By default, 0.
W : numeric matrix; the matrix of weights
needed if the "personal" weights were selected. By
default, a matrix with zero row and zero column.
standardize : logical; should each
variable be standardized before the calculus ? By
default, TRUE .
splits : integer; coding for forcing split
or nosplit algorithms :
0 : Default value, let the programm decide
which algorithm to use depending on the choosen
weights .
1 : Forces the algorithm not to take the
splits into account.
2 : Forces the algorithm to take the
splits into account even if the solution paths contains
no split.
Note : For the moment, only the no split
algorithm has been coded. Please ensure that your weights
choice leads to the no split algorithm or set
split to 1 .
epsilon : numeric; tolerance parameter for
numeric calculations. By default, eps. Note
: this is currently not used.
checkargs : logical; should arguments be
checked to (hopefully) avoid internal crashes? Default is
TRUE . Automatically set to FALSE when a
call is made from crossvalidation
lambdalist : numeric vector; a set of
lambda value for which a prediction is
asked. By default, a null vector. If the length of
lambdalist is not 0 , the fusedanova
class returnded by the fusedanova function will
have a not null attribute prediction .
mc.cores : integer; the number of cores to
use. The default uses all the cores available.
verbose : boolean; should the code print
out its progress. By default, FALSE.
mxSplitSize : integer; the maximum size
for a group for being checked the cores available.

an object with class fusedanova
, see the
documentation page fusedanova
for
details.
The optimization problem solved by fusedANOVA is
β^{hat}
_{λ1} = argmin_{β}
sum_{k} sum_i (Y_{ik} 
&beta_{k})^{2} + λ sum_{k,l}
w_{k,l}  β_{k} 
β_{l} ,
where Y_ik is the intensity of a continuous
random variable for sample i in condition
k and beta_k is the mean parameter
of condition k. We denote by K the total
number of conditions and n_k the number of
sample in each condition.
More details related to the weights are coming...
See also fusedanova
,
plot,fusedanovamethod
and
cv.fa
.
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12  ## Not run:
()
fa.laplace < (x=$weight, =$, ="laplace", =5)
(fa.laplace, =$)
fa.ttest < (x=$weight, =$, ="naivettest")
(fa.ttest, =$)
fa.ada < (x=$weight, =$, ="adaptive", =2)
(fa.ada, =$)
## End(Not run)

fusedanova documentation built on May 31, 2017, 1:38 a.m.