| Type: | Package |
| Title: | Set-Based Inference for Multiple Interval-Censored Outcomes |
| Version: | 0.2.0 |
| Description: | Contains tests for association between a set of genetic variants and multiple correlated outcomes that are interval censored. Interval-censored data arises when the exact time of the onset of an outcome of interest is unknown but known to fall between two time points. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.1 |
| Imports: | bindata, fastGHQuad, CompQuadForm, stats, ICSKAT |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2022-11-25 22:49:29 UTC; jaihe2 |
| Author: | Jaihee Choi [aut, cre], Ryan Sun [aut] |
| Maintainer: | Jaihee Choi <jaiheechoi01@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2022-11-25 23:10:02 UTC |
Get_CausalSNPs_bynum()
Description
Matrix of subsetted genetic information.
Usage
Get_CausalSNPs_bynum(gMat, num, Causal.MAF.Cutoff)
Arguments
gMat |
Matrix of SNPs. |
num |
Number of causal variants. |
Causal.MAF.Cutoff |
Minor allele frequency value cutoff for causal SNPs. |
Value
Output is a vector of indices to subset the full genetic matrix.
d/d_theta_l
Description
Calculate the first derivative of the theta terms for outcome l.
Usage
fd_term(l, temp_beta, phen,d, apply_diffs,
A_i, no_l_all,HL_array, HR_array)
Arguments
l |
Outcome of interest. |
temp_beta |
Vector of fitted coefficients. |
phen |
list containing the covariate design matrices. |
d |
Number of quadrature nodes. |
apply_diffs |
Matrix containing the differences between survival functions of the left and right time intervals. |
A_i |
Product of apply_diffs across all outcomes k summed over all quadrature nodes d. |
no_l_all |
n x (k - 1) matrix containing the product of apply_diffs across all outcomes k excluding the current outcome l. |
HL_array |
n x k matrix containing all the hazard values for the left times. |
HR_array |
n x k matrix containing all the hazard values for the right times. |
Value
The output is a 1 x (p + 2) vector of the first derivative terms for outcome l.
d/d_gamma_l
Description
Calculates the gradient term for U_g for the score statistic.
Usage
gamma_fd(l, HL_array, HR_array, tpos_all, obs_all,
temp_beta, A_i, no_l_all, gMat, a1, a2, d)
Arguments
l |
Index of first outcome of interest. |
HL_array |
n x k matrix containing all the hazard values for the left times. |
HR_array |
n x k matrix containing all the hazard values for the right times. |
tpos_all |
n x k matrix containing a indictor for whether that time is left-censored or not. |
obs_all |
n x k matrix containing a indictor for whether that time is right-censored or not. |
temp_beta |
Vector of fitted coefficients. |
A_i |
Product of apply_diffs across all outcomes k summed over all quadrature nodes d. |
no_l_all |
n x (K - 1) matrix containing the product of apply_diffs across all outcomes K excluding the current outcome l. |
gMat |
n x q matrix of genetic information. |
a1 |
First shape parameter of beta parameter. |
a2 |
Second shape parameter of beta parameter. |
d |
Number of quadrature nodes. |
Value
The output is a vector containing the first derivative with respect to gamma.
d^2/d_gamma_ldgamma_m
Description
Calculates the [off-diagonal] Information matrix term for I_gamma gamma with respect to outcome l and outcome m.
Usage
gamma_off(l, m, HL_array, HR_array,
tpos_all, obs_all, temp_beta, A_i,
no_l_all, no_two_all, gMat, a1, a2, k, d)
Arguments
l |
Index of first outcome of interest. |
m |
Index of second outcome of interest. |
HL_array |
n x k matrix containing all the hazard values for the left times. |
HR_array |
n x k matrix containing all the hazard values for the right times. |
tpos_all |
n x k matrix containing a indictor for whether that time is left-censored or not. |
obs_all |
n x k matrix containing a indictor for whether that time is right-censored or not. |
temp_beta |
Vector of fitted coefficients. |
A_i |
Product of apply_diffs across all outcomes k summed over all quadrature nodes d. |
no_l_all |
n x (k - 1) matrix containing the product of apply_diffs across all outcomes k excluding the current outcome l. |
no_two_all |
n x (k - 2) matrix containing the product of apply_diffs across all outcomes k excluding the outcomes l and m. |
gMat |
n x q matrix of genetic information. |
a1 |
First shape parameter of beta parameter. |
a2 |
Second shape parameter of beta parameter. |
k |
Total number of outcomes. |
d |
Number of quadrature nodes. |
Value
The output is a matrix containing the component of the information matrix of the gamma parameter for outcomes l and m.
d^2/d_gamma_ldgamma_l
Description
Calculates the [on-diagonal] Information matrix term for I_gamma gamma with respect to outcome l.
Usage
gamma_on(l, HL_array, HR_array, tpos_all, obs_all,
temp_beta, A_i, no_l_all, gMat, a1, a2, d)
Arguments
l |
Index of first outcome of interest. |
HL_array |
n x k matrix containing all the hazard values for the left times. |
HR_array |
n x k matrix containing all the hazard values for the right times. |
tpos_all |
n x k matrix containing a indictor for whether that time is left-censored or not. |
obs_all |
n x k matrix containing a indictor for whether that time is right-censored or not. |
temp_beta |
Vector of fitted coefficients. |
A_i |
Product of apply_diffs across all outcomes k summed over all quadrature nodes d. |
no_l_all |
n x (k - 1) matrix containing the product of apply_diffs across all outcomes k excluding the current outcome l. |
gMat |
n x q matrix of genetic information. |
a1 |
First shape parameter of beta parameter. |
a2 |
Second shape parameter of beta parameter. |
d |
Number of quadrature nodes. |
Value
The output is a matrix containing the component of the information matrix of the gamma parameter for outcome l.
d^2/d_gamma_ldsigma^2
Description
Calculates the Information matrix term of I_eta gamma for one outcome of interest l.
Usage
gammasigma(
l, HL_array, HR_array, tpos_all, obs_all,
apply_diffs, temp_beta, A_i, xDats, no_l_all,
no_two_all, gMat, a1, a2, k, d)
Arguments
l |
Index of first outcome of interest. |
HL_array |
n x K matrix containing all the hazard values for the left times. |
HR_array |
n x K matrix containing all the hazard values for the right times. |
tpos_all |
n x k matrix containing a indictor for whether that time is left-censored or not. |
obs_all |
n x k matrix containing a indictor for whether that time is right-censored or not. |
apply_diffs |
Matrix containing the differences between survival functions of the left and right time intervals. |
temp_beta |
vector of fitted coefficients. |
A_i |
Product of apply_diffs across all outcomes K summed over all quadrature nodes D. |
xDats |
List of design matrices. |
no_l_all |
n x (k - 1) matrix containing the product of apply_diffs across all outcomes K excluding the current outcome l. |
no_two_all |
n x (k - 2) matrix containing the product of apply_diffs across all outcomes k excluding the outcomes l and m. |
gMat |
n x q matrix of genetic information. |
a1 |
First shape parameter of beta parameter. |
a2 |
Second shape parameter of beta parameter. |
k |
Total number of outcomes. |
d |
Number of quadrature nodes. |
Value
The output is a matrix containing the component of the information matrix of the gamma and sigma^2 parameters for outcome l.
d^2/d_gamma_kdtheta_k
Description
Calculates the Information matrix term of I_eta gamma for outcome k.
Usage
gammatheta(l, HL_array, HR_array, tpos_all, obs_all, apply_diffs,
temp_beta, A_i, xDats, no_l_all, gMat, a1, a2, d)
Arguments
l |
Index of first outcome of interest. |
HL_array |
n x k matrix containing all the hazard values for the left times. |
HR_array |
n x k matrix containing all the hazard values for the right times. |
tpos_all |
n x k matrix containing a indictor for whether that time is left-censored or not. |
obs_all |
n x k matrix containing a indictor for whether that time is right-censored or not. |
apply_diffs |
Matrix containing the differences between survival functions of the left and right time intervals. |
temp_beta |
vector of fitted coefficients. |
A_i |
Product of apply_diffs across all outcomes k summed over all quadrature nodes d. |
xDats |
List of design matrices for all outcomes. |
no_l_all |
n x (k - 1) matrix containing the product of apply_diffs across all outcomes k excluding the current outcome l. |
gMat |
n x q matrix of genetic information. |
a1 |
First shape parameter of beta parameter. |
a2 |
Second shape parameter of beta parameter. |
d |
Number of quadrature nodes. |
Value
The output is a matrix containing the component of the information matrix of the gamma and theta parameters for outcome l.
d^2/d_gamma_ldtheta_m
Description
Calculates the Information matrix term of I_eta gamma for outcomes l and m
Usage
gammatheta_off(l,m, HL_array, HR_array, xAll, apply_diffs, temp_beta,
A_i, no_l_all, no_two_all, gMat, a1, a2, k, d)
Arguments
l |
Index of first outcome of interest. |
m |
Index of second outcome of interest. |
HL_array |
n x k matrix containing all the hazard values for the left times. |
HR_array |
n x k matrix containing all the hazard values for the right times. |
xAll |
List of design matrices and censoring terms. |
apply_diffs |
Matrix containing the differences between survival functions of the left and right time intervals. |
temp_beta |
vector of fitted coefficients. |
A_i |
Product of apply_diffs across all outcomes K summed over all quadrature nodes d. |
no_l_all |
n x (k - 1) matrix containing the product of apply_diffs across all outcomes k excluding the current outcome l. |
no_two_all |
n x (k - 2) matrix containing the product of apply_diffs across all outcomes k excluding the outcomes l and m. |
gMat |
n x q matrix of genetic information. |
a1 |
First shape parameter of beta parameter. |
a2 |
Second shape parameter of beta parameter. |
k |
Total number of outcomes. |
d |
Number of quadrature nodes. |
Value
The output is a matrix containing the component of the information matrix of the gamma and theta parameters for outcomes l and m.
Get A vector
Description
Product of difference of survival terms of the left and right interval times, across all outcomes k, summed over all quadrature nodes d.
Usage
get_A(store, weights, d, n)
Arguments
store |
Matrix of difference of survival values of the left and right time intervals. |
weights |
Gaussian quadrature weights. |
d |
Total number of Gaussian quadrature nodes. |
n |
Total number of observations. |
Value
The output is a vector used to compute the derivative terms.
H_ik(L_ik)
Description
Calculates the hazard function of the left time interval for outcome l.
Usage
haz_left(l, d, temp_beta, phen, r1, k)
Arguments
l |
Outcome of interest. |
d |
Total number of Gaussian quadrature nodes. |
temp_beta |
vector of fitted coefficients. |
phen |
list of data matrices containing both left and right information. |
r1 |
Gaussian quadrature nodes. |
k |
Total number of outcomes. |
Value
The output is a vector of the hazard values of the left times.
H_ik(R_ik)
Description
Calculates the hazard function of the right time interval for outcome l.
Usage
haz_right(l, d, temp_beta, phen, r1, k)
Arguments
l |
Outcome of interest. |
d |
Total number of Gaussian quadrature nodes. |
temp_beta |
vector of fitted coefficients. |
phen |
list of data matrices containing both left and right information. |
r1 |
Gaussian quadrature nodes. |
k |
Total number of outcomes. |
Value
The output is a vector of the hazard values of the right times.
d^2/d_theta_kdsigma^2
Description
Calculates the Information matrix term of I_theta sigma^2 for outcomes l and m.
Usage
sd_off(l, m, phen_l, phen_m, temp_beta, d, apply_diffs, A_i,
HL_array, HR_array, no_l_all, no_two_all, tpos_all, obs_all, k)
Arguments
l |
Index of first outcome of interest. |
m |
Index of second outcome of interest. |
phen_l |
List containing the left and right design matrices and interval times for outcome l. |
phen_m |
List containing the left and right design matrices and interval times for outcome m. |
temp_beta |
vector of fitted coefficients. |
d |
Total number of quadrature nodes. |
apply_diffs |
Matrix containing the differences between survival functions of the left and right time intervals. |
A_i |
Product of apply_diffs across all outcomes k summed over all quadrature nodes d. |
HL_array |
n x k matrix containing all the hazard values for the left times. |
HR_array |
n x k matrix containing all the hazard values for the right times. |
no_l_all |
n x (k - 1) matrix containing the product of apply_diffs across all outcomes k excluding the current outcome l. |
no_two_all |
n x (k - 2) matrix containing the product of apply_diffs across all outcomes K excluding outcomes l and m. |
tpos_all |
n x k matrix containing a indictor for whether that time is left-censored or not. |
obs_all |
n x k matrix containing a indictor for whether that time is right-censored or not. |
k |
Total number of outcomes. |
Value
The output is a matrix containing the component of the information matrix of the sigma and theta parameters.
d^2/dsigma^2^2
Description
Calculates the Information matrix term of I_sigma^2 sigma^2 for outcome l.
Usage
sd_on(l, k, temp_beta, phen, d, apply_diffs, A_i,
no_l_all, HL_array, HR_array)
Arguments
l |
Index of first outcome of interest. |
k |
Total number of outcomes. |
temp_beta |
vector of fitted coefficients. |
phen |
List containing the left and right design matrices and interval times for outcome l. |
d |
Total number of quadrature nodes. |
apply_diffs |
Matrix containing the differences between survival functions of the left and right time intervals. |
A_i |
Product of apply_diffs across all outcomes K summed over all quadrature nodes D. |
no_l_all |
n x (k - 1) matrix containing the product of apply_diffs across all outcomes k excluding the current outcome l. |
HL_array |
n x k matrix containing all the hazard values for the left times. |
HR_array |
n x k matrix containing all the hazard values for the right times. |
Value
The output is a single value for the second derivative with respect to sigma.
Simulate genetic matrix.
Description
Simulates a n x q genetic matrix with the option to specify the common pairwise correlation.
Usage
sim_gmat(n,q,rho)
Arguments
n |
Total number of observations. |
q |
Total number of SNPs. |
rho |
Common pairwise correlation parameter. |
Value
The result of a n x q genetic matrix of q SNPs.
Examples
# Set sample size
n = 100
# Set number of SNPs
q = 5
# Set common pairwise correlation
rho = 0.1
# Simulate genetic matrix
gMat <- sim_gmat(n, q, rho)
simico_fit_null()
Description
Fit the null model via newton raphson for multiple outcomes interval-censored skat.
Usage
simico_fit_null(init_beta, epsilon, xDats, lt_all, rt_all, k, d)
Arguments
init_beta |
Starting values for NR. |
epsilon |
Stopping criterion for NR. |
xDats |
List of left and right design matrices. |
lt_all |
n x k matrix of left times. |
rt_all |
n x k matrix of right times. |
k |
Total number of outcomes. |
d |
Total number of quadrature nodes. |
Value
beta_fit |
Vector of fitted coefficients. |
iter |
Number of iterations needed for the Newton-Raphson to converge. |
diff |
Difference between the current values of temp_beta and the previous iteration of temp_beta. |
jmat |
Information matrix of the theta parameters. |
grad |
Vector of the first derivaive of the theta parameters. |
Examples
# Set number of outcomes
k = 2
# Set number of observations
n = 100
# Set number of covariates
p = 2
# Set number of SNPs
q = 50
# Set number of causal SNPs
num = 5
# Set number of quadrature nodes
d = 100
# Variance of subject-specific random effect
tauSq = 1
# Define the effect sizes
effectSizes <- c(.03, .15)
# Set MAF cutoff for causal SNPs
Causal.MAF.Cutoff = 0.1
# the baseline cumulative hazard function
bhFunInv <- function(x) {x}
set.seed(1)
# Generate covariate matrix
xMat <- cbind(rnorm(n), rbinom(n=n, size=1, prob=0.5))
# Generate genetic matrix
gMat <- matrix(data=rbinom(n=n*q, size=2, prob=0.1), nrow=n)
# Get indices to specific select causal variants
idx <- Get_CausalSNPs_bynum(gMat, num, Causal.MAF.Cutoff)
# Subset the gMat
gMatCausal <- gMat[,idx]
# Generate the multiple outcomes
exampleDat <- simico_gen_dat(bhFunInv = bhFunInv, obsTimes = 1:3,
windowHalf = 0.1, n, p, k, tauSq, gMatCausal,
xMat, effectSizes)
# Set the initial estimate values
init_beta <-c (rep(c(0, 0, 0, 1, 0), k), 1)
# Run the Newton-Raphson
nullFit <- simico_fit_null(init_beta = init_beta,
epsilon = 10^-5, xDats = exampleDat$fullDat$xDats,
lt_all = exampleDat$leftTimesMat,
rt_all = exampleDat$rightTimesMat,
k = k, d = d)
simico_gen_dat()
Description
Generate multiple interval-censored data under proportional hazards model.
Usage
simico_gen_dat(bhFunInv, obsTimes = 1:3, windowHalf = 0.1,
n, p, k, tauSq, gMatCausal, xMat, effectSizes)
Arguments
bhFunInv |
The inverse of the baseline hazard function. |
obsTimes |
Vector of the intended observation times. |
windowHalf |
The amount of time before or after the intended obsTimes that a visit might take place. |
n |
Total number of observations. |
p |
Total number of covariates. |
k |
Total number of outcomes. |
tauSq |
Variance of the subject specific random effect. |
gMatCausal |
Matrix of subsetted genetic information for only a select causal SNPs. |
xMat |
Matrix of covariates. |
effectSizes |
Vector of genetic effect sizes. Should be entered as a vector the same length as the number of outcomes. |
Value
exactTimesMat |
n x k matrix containing the simulated exact times that the event occurred. |
leftTimesMat |
n x k matrix containing the left time interval that is observed. |
rightTimesMat |
n x k matrix containing the right time interval that is observed. |
obsInd |
n x k matrix containing a indictor for whether that time is right-censored or not. |
tposInd |
n x k matrix containing a indictor for whether that time is left-censored or not. |
fullDat |
Data in complete form to enter into SIMICO functions. |
Examples
# Set number of outcomes
k = 2
# Set number of observations
n = 100
# Set number of covariates
p = 2
# Set number of SNPs
q = 50
# Set number of causal SNPs
num = 5
# Set number of quadrature nodes
d = 100
# Variance of subject-specific random effect
tauSq = 1
# Define the effect sizes
effectSizes <- c(.03, .15)
# Set MAF cutoff for causal SNPs
Causal.MAF.Cutoff = 0.1
# the baseline cumulative hazard function
bhFunInv <- function(x) {x}
set.seed(1)
# Generate covariate matrix
xMat <- cbind(rnorm(n), rbinom(n=n, size=1, prob=0.5))
# Generate genetic matrix
gMat <- matrix(data=rbinom(n=n*q, size=2, prob=0.1), nrow=n)
# Get indices to specific select causal variants
idx <- Get_CausalSNPs_bynum(gMat, num, Causal.MAF.Cutoff)
# Subset the gMat
gMatCausal <- gMat[,idx]
# Generate the multiple outcomes
exampleDat <- simico_gen_dat(bhFunInv = bhFunInv, obsTimes = 1:3,
windowHalf = 0.1, n, p, k, tauSq, gMatCausal,
xMat, effectSizes)
Get P-Values
Description
Calculate test statistic and p-values for multiple outcome test and multiple burden test.
Usage
simico_out(nullFit, xDats, lt_all, rt_all, Itt, a1, a2, G, k, d)
Arguments
nullFit |
Results of the Newton-Raphson: estimates of the beta coefficients. |
xDats |
List of design matrices. |
lt_all |
Matrix containing the generated left interval times. |
rt_all |
Matrix containing the generated right interval times. |
Itt |
I_theta theta - Information matrix of theta. |
G |
n x q matrix of genetic information. |
a1 |
First shape parameter of beta parameter. |
a2 |
Second shape parameter of beta parameter. |
k |
Total number of outcomes. |
d |
Number of quadrature nodes. |
Value
multQ |
Score statistic for multiple outcome test. |
multP |
P-value for multiple outcome test. |
burdQ |
Score statistic for multiple burden test. |
burdP |
P-value for multiple burden test. |
Examples
# Set number of outcomes
k = 2
# Set number of observations
n = 100
# Set number of covariates
p = 2
# Set number of SNPs
q = 50
# Set number of causal SNPs
num = 5
# Set number of quadrature nodes
d = 100
# Variance of subject-specific random effect
tauSq = 1
# Define the effect sizes
effectSizes <- c(.03, .15)
# Set MAF cutoff for causal SNPs
Causal.MAF.Cutoff = 0.1
# the baseline cumulative hazard function
bhFunInv <- function(x) {x}
set.seed(1)
# Generate covariate matrix
xMat <- cbind(rnorm(n), rbinom(n=n, size=1, prob=0.5))
# Generate genetic matrix
gMat <- matrix(data=rbinom(n=n*q, size=2, prob=0.1), nrow=n)
# Get indices to specific select causal variants
idx <- Get_CausalSNPs_bynum(gMat, num, Causal.MAF.Cutoff)
# Subset the gMat
gMatCausal <- gMat[,idx]
# Generate the multiple outcomes
exampleDat <- simico_gen_dat(bhFunInv = bhFunInv, obsTimes = 1:3,
windowHalf = 0.1, n, p, k, tauSq, gMatCausal,
xMat, effectSizes)
# Set the initial estimate values
init_beta <-c (rep(c(0, 0, 0, 1, 0), k), 1)
# Run the newton-raphson
nullFit <- simico_fit_null(init_beta = init_beta,
epsilon = 10^-5, xDats = exampleDat$fullDat$xDats,
lt_all = exampleDat$leftTimesMat,
rt_all = exampleDat$rightTimesMat,
k = k, d = d)
# Get the test statistics p-values
out <- simico_out(nullFit = nullFit$beta_fit,
xDats = exampleDat$fullDat$xDats,
lt_all = exampleDat$leftTimesMat,
rt_all = exampleDat$rightTimesMat,
Itt = nullFit$jmat, a1 = 1, a2 = 25,
G = gMat, k = k, d = d)
# Print results
# Score statistic
out$multQ
# P-values
out$multP
d/d_sigma^2
Description
Calculates the first derivative term with respect to sigma^2.
Usage
ss_fd(l, phen, HL_array, HR_array, tpos_all, obs_all,
apply_diffs, temp_beta, A_i, no_l_all, k, d)
Arguments
l |
Index of first outcome of interest. |
phen |
List containing all the left and right design matrices. |
HL_array |
n x k matrix containing all the hazard values for the left times. |
HR_array |
n x k matrix containing all the hazard values for the right times. |
tpos_all |
n x k matrix containing a indictor for whether that time is left-censored or not. |
obs_all |
n x k matrix containing a indictor for whether that time is right-censored or not. |
apply_diffs |
Matrix containing the differences between survival functions of the left and right time intervals. |
temp_beta |
vector of fitted coefficients. |
A_i |
Product of apply_diffs across all outcomes k summed over all quadrature nodes d. |
no_l_all |
n x (k - 1) matrix containing the product of apply_diffs across all outcomes k excluding the current outcome l. |
k |
Total number of outcomes. |
d |
Number of quadrature nodes. |
Value
The output is a single value for the first derivative with respect to sigma.
d^2/d_sigma^2^2
Description
Calculates the second derivative term with respect to sigma^2.
Usage
ss_sd(HL_array, HR_array, xAll, apply_diffs, temp_beta,
A_i, no_l_all, no_two_all, k, d)
Arguments
HL_array |
n x k matrix containing all the hazard values for the left times. |
HR_array |
n x k matrix containing all the hazard values for the right times. |
xAll |
List containing the left and right matrices and event times. |
apply_diffs |
Matrix containing the differences between survival functions of the left and right time intervals. |
temp_beta |
vector of fitted coefficients. |
A_i |
Product of apply_diffs across all outcomes K summed over all quadrature nodes D. |
no_l_all |
n x (k - 1) matrix containing the product of apply_diffs across all outcomes k excluding the current outcome l. |
no_two_all |
n x (k - 2) matrix containing the product of apply_diffs across all outcomes k excluding outcomes l and m. |
k |
Total number of outcomes. |
d |
Number of quadrature nodes. |
Value
The output is a single value for the second derivative with respect to sigma^2.
d^2/d_theta_ldsigma^2
Description
Calculates the Information matrix term of I_eta theta for one outcome of interest l.
Usage
st_off(l, HL_array, HR_array, xAll, apply_diffs,
temp_beta, A_i, no_l_all, no_two_all, k, d)
Arguments
l |
Index of first outcome of interest. |
HL_array |
n x k matrix containing all the hazard values for the left times. |
HR_array |
n x k matrix containing all the hazard values for the right times. |
xAll |
List containing the left and right matrices and event times. |
apply_diffs |
Matrix containing the differences between survival functions of the left and right time intervals. |
temp_beta |
vector of fitted coefficients. |
A_i |
Product of apply_diffs across all outcomes K summed over all quadrature nodes D. |
no_l_all |
n x (k - 1) matrix containing the product of apply_diffs across all outcomes k excluding the current outcome l. |
no_two_all |
n x (k - 2) matrix containing the product of apply_diffs across all outcomes k excluding outcomes l and m. |
k |
Total number of outcomes. |
d |
Number of quadrature nodes. |
Value
The output is a matrix containing the component of the information matrix of the theta eta parameters for outcome l.
S_ik(L_ik) - S_ik(R_ik)
Description
Calculates the difference between the survival functions of the left and right time intervals for outcome k for quadrature node d.
Usage
surv_diff(l, d, temp_beta, phen, r1, k)
Arguments
l |
Outcome of interest. |
d |
Total number of Gaussian quadrature nodes. |
temp_beta |
Vector of fitted coefficients. |
phen |
List of data matrices containing both left and right information. |
r1 |
Gaussian quadrature nodes. |
k |
Total number of outcomes. |
Value
The output is a vector of the difference of the survival values of the left times and right times.
S_ik(L_ik)
Description
Calculates the survival function of the left time interval for outcome k for quadrature node d.
Usage
surv_left(l, d, temp_beta, phen, r1, k)
Arguments
l |
Outcome of interest. |
d |
Total number of Gaussian quadrature nodes. |
temp_beta |
Vector of fitted coefficients. |
phen |
List of data matrices containing both left and right information. |
r1 |
Gaussian quadrature nodes. |
k |
Total number of outcomes. |
Value
The output is a vector of the survival values of the left times.
S_ik(R_ik)
Description
Calculates the survival function of the right time interval for outcome k for quadrature node d.
Usage
surv_right(l, d, temp_beta, phen, r1, k)
Arguments
l |
Outcome of interest. |
d |
Total number of Gaussian quadrature nodes. |
temp_beta |
Vector of fitted coefficients. |
phen |
List of data matrices containing both left and right information. |
r1 |
Gaussian quadrature nodes. |
k |
Total number of outcomes. |
Value
The output is a vector of the survival values of the left times.
Survival Difference Product without Outcome l
Description
Calculate the product of the difference between survival terms excluding that of the outcome of interest.
Usage
without_one_phen(l, k, store)
Arguments
l |
Outcome of interest. |
k |
Total number of outcomes. |
store |
Array of difference between left and right survival values. |
Value
A n x (k-1) matrix where each column is the product of all the differences of left and right survival values across all outcomes excluding the column index outcome.
Survival Difference Product without Outcomes l and m
Description
Differnence of survival functions multiplied across all outcomes excluding outcomes l and m.
Usage
without_two_phen(l, m, k, store, n, d)
Arguments
l |
The first outcome of interest. |
m |
The second outcome of interest. |
k |
Total number of outcomes. |
store |
Array of difference between left and right survival values. |
n |
Total number of observation. |
d |
Total number of quadrature nodes. |
Value
A n x (k-2) matrix containing the product of all the differences of left and right survival values across all outcomes excluding outcomes l and m.