downward: performs the downward step of the peeling algorithm and...
In LCAextend: Latent Class Analysis (LCA) with Familial Dependence in Extended Pedigrees
Description
Usage
Arguments
Details
Value
References
See Also
Examples
Description
computes the probability of measurements above connectors and their
classes given the model parameters, and returns the unnormalized
triplet and individual weights. This is an internal
function not meant to be called by the user.
Usage
1
Arguments
id
individual ID of the pedigree,
dad
dad ID,
mom
mom ID,
status
symptom status: (2: symptomatic, 1: without symptoms, 0: missing),
probs
a list of probability parameters of the model,
fyc
a matrix of n times K+1 given the density of observations of each individual if allocated to class k, where n is the
number of individuals and K is the total number of latent classes in the model,
peel
a list of pedigree peeling containing connectors by peeling order and couples of parents,
res.upward
result of the upward step of the peeling algorithm, see upward.
Details
This function computes the probability of observations above connectors and their classes using the function downward.connect, for each connector,
if Y_above(i) is the observations above connector i and S_i and C_i are his status and his class respectively, the functions computes
P(Y_above(i),S_i,C_i) by computing a downward step for the parent of connector i who is also a connector. These quantities are used by the function weight.nuc to compute the unnormalized triplet weights ww and the unnormalized
individual weights w.
Value
The function returns a list of 2 elements:
ww
unnormalized triplet weights, an array of n times 2 times K+1 times K+1 times K+1, where n is the
number of individulas and K is the total number of latent classes in the model, see e.step for more details,
w
unnormalized individual weights, an array of n times 2 times K+1, see e.step.
References
TAYEB et al.: Solving Genetic Heterogeneity in Extended Families by Identifying Sub-types of Complex Diseases. Computational Statistics, 2011, DOI: 10.1007/s00180-010-0224-2.
See Also
See also downward.connect.
Examples
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#data
data(ped.cont)
data(peel)
fam <- ped.cont[,1]
id <- ped.cont[fam==1,2]
dad <- ped.cont[fam==1,3]
mom <- ped.cont[fam==1,4]
status <- ped.cont[fam==1,6]
y <- ped.cont[fam==1,7:ncol(ped.cont)]
peel <- peel[[1]]
#standardize id to be 1, 2, 3, ...
id.origin <- id
standard <- function(vec) ifelse(vec%in%id.origin,which(id.origin==vec),0)
id <- apply(t(id),2,standard)
dad <- apply(t(dad),2,standard)
mom <- apply(t(mom),2,standard)
peel$couple <- cbind(apply(t(peel$couple[,1]),2,standard),
apply(t(peel$couple[,2]),2,standard))
for(generat in 1:peel$generation)
peel$peel.connect[generat,] <- apply(t(peel$peel.connect[generat,]),2,standard)
#probs and param
data(probs)
data(param.cont)
#densities of the observations
fyc <- matrix(1,nrow=length(id),ncol=length(probs$p)+1)
fyc[status==2,1:length(probs$p)] <- t(apply(y[status==2,],1,dens.norm,param.cont,NULL))
#the upward step
res.upward <- upward(id,dad,mom,status,probs,fyc,peel)
#the function
downward(id,dad,mom,status,probs,fyc,peel,res.upward)
Example output
Loading required package: boot
Loading required package: mvtnorm
Loading required package: rms
Loading required package: Hmisc
Loading required package: lattice
Attaching package: 'lattice'
The following object is masked from 'package:boot':
melanoma
Loading required package: survival
Attaching package: 'survival'
The following object is masked from 'package:boot':
aml
Loading required package: Formula
Loading required package: ggplot2
Attaching package: 'Hmisc'
The following objects are masked from 'package:base':
format.pval, units
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
Loading required package: kinship2
Loading required package: Matrix
Loading required package: quadprog
$ww
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$w
, , 1
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[6,] 3.404964e-18 0.000000e+00
[7,] 5.309654e-19 2.654827e-19
[8,] 1.942319e-18 0.000000e+00
[9,] 2.814457e-18 0.000000e+00
[10,] 0.000000e+00 9.672626e-19
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[12,] 5.762416e-19 4.527612e-19
[13,] 6.035101e-19 3.017551e-19
[14,] 0.000000e+00 6.968921e-19
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[1,] 0.000000e+00 3.951371e-19
[2,] 1.582147e-35 0.000000e+00
[3,] 1.965899e-50 0.000000e+00
[4,] 4.120711e-32 0.000000e+00
[5,] 0.000000e+00 3.951371e-19
[6,] 1.951465e-20 0.000000e+00
[7,] 3.539770e-19 1.769885e-19
[8,] 6.307279e-33 0.000000e+00
[9,] 2.022576e-28 0.000000e+00
[10,] 0.000000e+00 9.672626e-19
[11,] 0.000000e+00 9.672626e-19
[12,] 5.762416e-19 4.527612e-19
[13,] 4.023401e-19 2.011700e-19
[14,] 0.000000e+00 4.645948e-19
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[1,] 0.000000e+00 9.878427e-19
[2,] 1.009403e-19 0.000000e+00
[3,] 4.417500e-23 0.000000e+00
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LCAextend documentation built on May 2, 2019, 2:02 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value References See Also Examples
Description
computes the probability of measurements above connectors and their classes given the model parameters, and returns the unnormalized triplet and individual weights. This is an internal function not meant to be called by the user.
Usage
1 |
Arguments
id |
individual ID of the pedigree, |
dad |
dad ID, |
mom |
mom ID, |
status |
symptom status: (2: symptomatic, 1: without symptoms, 0: missing), |
probs |
a list of probability parameters of the model, |
fyc |
a matrix of |
peel |
a list of pedigree peeling containing connectors by peeling order and couples of parents, |
res.upward |
result of the upward step of the peeling algorithm, see |
Details
This function computes the probability of observations above connectors and their classes using the function downward.connect, for each connector,
if Y_above(i) is the observations above connector i and S_i and C_i are his status and his class respectively, the functions computes
P(Y_above(i),S_i,C_i) by computing a downward step for the parent of connector i who is also a connector. These quantities are used by the function weight.nuc to compute the unnormalized triplet weights ww and the unnormalized
individual weights w.
Value
The function returns a list of 2 elements:
ww |
unnormalized triplet weights, an array of |
w |
unnormalized individual weights, an array of |
References
TAYEB et al.: Solving Genetic Heterogeneity in Extended Families by Identifying Sub-types of Complex Diseases. Computational Statistics, 2011, DOI: 10.1007/s00180-010-0224-2.
See Also
See also downward.connect.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | #data
data(ped.cont)
data(peel)
fam <- ped.cont[,1]
id <- ped.cont[fam==1,2]
dad <- ped.cont[fam==1,3]
mom <- ped.cont[fam==1,4]
status <- ped.cont[fam==1,6]
y <- ped.cont[fam==1,7:ncol(ped.cont)]
peel <- peel[[1]]
#standardize id to be 1, 2, 3, ...
id.origin <- id
standard <- function(vec) ifelse(vec%in%id.origin,which(id.origin==vec),0)
id <- apply(t(id),2,standard)
dad <- apply(t(dad),2,standard)
mom <- apply(t(mom),2,standard)
peel$couple <- cbind(apply(t(peel$couple[,1]),2,standard),
apply(t(peel$couple[,2]),2,standard))
for(generat in 1:peel$generation)
peel$peel.connect[generat,] <- apply(t(peel$peel.connect[generat,]),2,standard)
#probs and param
data(probs)
data(param.cont)
#densities of the observations
fyc <- matrix(1,nrow=length(id),ncol=length(probs$p)+1)
fyc[status==2,1:length(probs$p)] <- t(apply(y[status==2,],1,dens.norm,param.cont,NULL))
#the upward step
res.upward <- upward(id,dad,mom,status,probs,fyc,peel)
#the function
downward(id,dad,mom,status,probs,fyc,peel,res.upward)
|
Example output
Loading required package: boot
Loading required package: mvtnorm
Loading required package: rms
Loading required package: Hmisc
Loading required package: lattice
Attaching package: 'lattice'
The following object is masked from 'package:boot':
melanoma
Loading required package: survival
Attaching package: 'survival'
The following object is masked from 'package:boot':
aml
Loading required package: Formula
Loading required package: ggplot2
Attaching package: 'Hmisc'
The following objects are masked from 'package:base':
format.pval, units
Loading required package: SparseM
Attaching package: 'SparseM'
The following object is masked from 'package:base':
backsolve
Loading required package: kinship2
Loading required package: Matrix
Loading required package: quadprog
$ww
, , 1, 1, 1, 1, 1
[,1] [,2]
[1,] 0.000000e+00 0.000000e+00
[2,] 0.000000e+00 0.000000e+00
[3,] 0.000000e+00 0.000000e+00
[4,] 0.000000e+00 0.000000e+00
[5,] 0.000000e+00 0.000000e+00
[6,] 0.000000e+00 0.000000e+00
[7,] 0.000000e+00 0.000000e+00
[8,] 4.899542e-20 3.849640e-20
[9,] 7.099530e-20 5.578202e-20
[10,] 0.000000e+00 0.000000e+00
[11,] 0.000000e+00 0.000000e+00
[12,] 0.000000e+00 0.000000e+00
[13,] 0.000000e+00 0.000000e+00
[14,] 0.000000e+00 0.000000e+00
, , 2, 1, 1, 1, 1
[,1] [,2]
[1,] 0.000000e+00 0.000000e+00
[2,] 0.000000e+00 1.408153e-19
[3,] 0.000000e+00 1.450875e-19
[4,] 0.000000e+00 3.413619e-20
[5,] 0.000000e+00 0.000000e+00
[6,] 0.000000e+00 1.441742e-19
[7,] 0.000000e+00 0.000000e+00
[8,] 2.449771e-20 1.924820e-20
[9,] 3.549765e-20 2.789101e-20
[10,] 0.000000e+00 0.000000e+00
[11,] 0.000000e+00 0.000000e+00
[12,] 1.975685e-20 9.878427e-21
[13,] 0.000000e+00 0.000000e+00
[14,] 0.000000e+00 0.000000e+00
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[3,] 0.000000e+00 1.450875e-19
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[,1] [,2]
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[,1] [,2]
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[7,] 0.000000e+00 0.000000e+00
[8,] 0.000000e+00 0.000000e+00
[9,] 0.000000e+00 0.000000e+00
[10,] 4.630512e-20 2.315256e-20
[11,] 4.630512e-20 2.315256e-20
[12,] 2.778307e-20 1.389154e-20
[13,] 0.000000e+00 0.000000e+00
[14,] 0.000000e+00 0.000000e+00
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[,1] [,2]
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[,1] [,2]
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[,1] [,2]
[1,] 0.000000e+00 0.000000e+00
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[3,] 0.000000e+00 0.000000e+00
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[6,] 0.000000e+00 0.000000e+00
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[10,] 1.157628e-19 5.788141e-20
[11,] 1.157628e-19 5.788141e-20
[12,] 6.945769e-20 3.472884e-20
[13,] 0.000000e+00 0.000000e+00
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, , 1, 1, 4, 4, 4
[,1] [,2]
[1,] 0 0
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[5,] 0 0
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[,1] [,2]
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[,1] [,2]
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[,1] [,2]
[1,] 0 0.000000e+00
[2,] 0 0.000000e+00
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[4,] 0 0.000000e+00
[5,] 0 0.000000e+00
[6,] 0 0.000000e+00
[7,] 0 0.000000e+00
[8,] 0 0.000000e+00
[9,] 0 0.000000e+00
[10,] 0 6.174017e-20
[11,] 0 6.174017e-20
[12,] 0 6.174017e-20
[13,] 0 0.000000e+00
[14,] 0 0.000000e+00
$w
, , 1
[,1] [,2]
[1,] 0.000000e+00 5.927056e-19
[2,] 3.325639e-18 0.000000e+00
[3,] 3.426535e-18 0.000000e+00
[4,] 8.061951e-19 0.000000e+00
[5,] 0.000000e+00 5.927056e-19
[6,] 3.404964e-18 0.000000e+00
[7,] 5.309654e-19 2.654827e-19
[8,] 1.942319e-18 0.000000e+00
[9,] 2.814457e-18 0.000000e+00
[10,] 0.000000e+00 9.672626e-19
[11,] 0.000000e+00 9.672626e-19
[12,] 5.762416e-19 4.527612e-19
[13,] 6.035101e-19 3.017551e-19
[14,] 0.000000e+00 6.968921e-19
, , 2
[,1] [,2]
[1,] 0.000000e+00 3.951371e-19
[2,] 1.582147e-35 0.000000e+00
[3,] 1.965899e-50 0.000000e+00
[4,] 4.120711e-32 0.000000e+00
[5,] 0.000000e+00 3.951371e-19
[6,] 1.951465e-20 0.000000e+00
[7,] 3.539770e-19 1.769885e-19
[8,] 6.307279e-33 0.000000e+00
[9,] 2.022576e-28 0.000000e+00
[10,] 0.000000e+00 9.672626e-19
[11,] 0.000000e+00 9.672626e-19
[12,] 5.762416e-19 4.527612e-19
[13,] 4.023401e-19 2.011700e-19
[14,] 0.000000e+00 4.645948e-19
, , 3
[,1] [,2]
[1,] 0.000000e+00 9.878427e-19
[2,] 1.009403e-19 0.000000e+00
[3,] 4.417500e-23 0.000000e+00
[4,] 2.620384e-18 0.000000e+00
[5,] 0.000000e+00 9.878427e-19
[6,] 2.100272e-21 0.000000e+00
[7,] 8.849424e-19 4.424712e-19
[8,] 1.484261e-18 0.000000e+00
[9,] 6.121226e-19 0.000000e+00
[10,] 0.000000e+00 9.672626e-19
[11,] 0.000000e+00 9.672626e-19
[12,] 5.762416e-19 4.527612e-19
[13,] 1.005850e-18 5.029251e-19
[14,] 0.000000e+00 1.161487e-18
, , 4
[,1] [,2]
[1,] 0 1.450894e-18
[2,] 0 0.000000e+00
[3,] 0 0.000000e+00
[4,] 0 0.000000e+00
[5,] 0 1.450894e-18
[6,] 0 0.000000e+00
[7,] 0 7.717521e-19
[8,] 0 0.000000e+00
[9,] 0 0.000000e+00
[10,] 0 5.247914e-19
[11,] 0 5.247914e-19
[12,] 0 3.395709e-19
[13,] 0 4.090286e-19
[14,] 0 1.103605e-18
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