make_fabi_data_pos {fabia} | R Documentation |
make_fabi_data_pos
: R implementation of make_fabi_data_pos
.
make_fabi_data_pos(n,l,p,f1,f2,of1,of2,sd_noise,sd_z_noise, mean_z,sd_z,sd_l_noise,mean_l,sd_l)
n |
number of observations. |
l |
number of samples. |
p |
number of biclusters. |
f1 |
nn/f1 max. additional samples are active in a bicluster. |
f2 |
n/f2 max. additional observations that form a pattern in a bicluster. |
of1 |
minimal active samples in a bicluster. |
of2 |
minimal observations that form a pattern in a bicluster. |
sd_noise |
Gaussian zero mean noise std on data matrix. |
sd_z_noise |
Gaussian zero mean noise std for deactivated hidden factors. |
mean_z |
Gaussian mean for activated factors. |
sd_z |
Gaussian std for activated factors. |
sd_l_noise |
Gaussian zero mean noise std if no observation patterns are present. |
mean_l |
Gaussian mean for observation patterns. |
sd_l |
Gaussian std for observation patterns. |
Essentially the data generation model is the sum of outer products of sparse vectors. The number of summands p is the number of biclusters.
X = L Z + U
Y = L Z
X = sum_{i=1}^{p} L_i (Z_i )^T + U
Here L_i are from R^n, Z_i from R^l, and X,Y from R^{n times l}.
Sequentially L_i are generated using
n
, f2
, of2
, sd_l_noise
, mean_l
,
sd_l
.
of2
gives the minimal observations participating in a
bicluster to which between 0 and n/f2 observations are added,
where the number is uniformly chosen. sd_l_noise
gives the
noise of observations not participating in the
bicluster. mean_l
and sd_l
determines the Gaussian from
which the values are drawn for the observations that participate in
the bicluster. "POS": The sign of the mean is fixed.
Sequentially Z_i are generated using
l
, f1
, of1
, sd_z_noise
, mean_z
,
sd_z
.
of1
gives the minimal samples participating in a
bicluster to which between 0 and l/f1 samples are added,
where the number is uniformly chosen. sd_z_noise
gives the
noise of samples not participating in the
bicluster. mean_z
and sd_z
determines the Gaussian from
which the values are drawn for the samples that participate in
the bicluster.
U is the overall Gaussian zero mean
noise generated by sd_noise
.
Implementation in R.
X |
the noise data from R^{n times l}. |
Y |
the noise free data from R^{n times l}. |
ZC |
list where ith element gives samples beloning to ith bicluster. |
LC |
list where ith element gives observations beloning to ith bicluster. |
Sepp Hochreiter
fabi
,
fabia
,
fabiap
,
fabias
,
fabiasp
,
mfsc
,
nmfdiv
,
nmfeu
,
nmfsc
,
nprojfunc
,
projfunc
,
make_fabi_data
,
make_fabi_data_blocks
,
make_fabi_data_blocks_pos
,
extract_plot
,
extract_bic
,
myImagePlot
,
PlotBicluster
,
Breast_A
,
DLBCL_B
,
Multi_A
,
fabiaDemo
,
fabiaVersion
#--------------- # TEST #--------------- dat <- make_fabi_data_pos(n = 100,l= 50,p = 3,f1 = 5,f2 = 5, of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0, sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0) X <- dat[[1]] Y <- dat[[2]] myImagePlot(Y) x11() myImagePlot(X) ## Not run: #--------------- # DEMO #--------------- dat <- make_fabi_data_pos(n = 1000,l= 100,p = 10,f1 = 5,f2 = 5, of1 = 5,of2 = 10,sd_noise = 3.0,sd_z_noise = 0.2,mean_z = 2.0, sd_z = 1.0,sd_l_noise = 0.2,mean_l = 3.0,sd_l = 1.0) X <- dat[[1]] Y <- dat[[2]] myImagePlot(Y) x11() myImagePlot(X) ## End(Not run)