Description Usage Arguments Value References See Also Examples
Joint modeling of longitudinal ordinal data and competing risks
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p |
The dimension of proportional odds covariates (not including intercept) in yfile. |
s |
The dimension of non-proportional odds covariates in yfile. |
yfile |
Y matrix for longitudinal measurements in long format. For example, for a subject with n measurements, there are n rows for this subject. The # of rows in y matrix is the total number of measurements for all subjects. The columns in Y are ordered this way: the longitudinal outcome (column 1), then the covariates for random effects, and lastly, the covariates for fixed effects (no intercept). |
cfile |
C matrix for competing risks failure time data. Each subject has one data entry, so the number of rows equals to the number of subjects. The survival / censoring time is included in the first column, and the failure type coded as 0 (censored events), 1 (risk 1), or 2 (risk 2) is given in the second column. Two competing risks are assumed. The covariates are included in the third column and on. |
mfile |
M vector to indicate the number of longitudinal measurements per subject. The number of rows equals to the number of subjects. |
point |
Quadrature points used in the EM procedure. Default is 20. |
maxiterations |
Maximum values of iterations. Default is 100000. |
do.trace |
Print detailed information of each iteration. Default is false, not to print the iteration details. |
type_file |
Types of inputs. Default is true, i.e. data files with headers. If set to "F", inputs are changed to data matrixes or data.frames (with headers) |
Object of class JMcmprsk
with elements
vcmatrix | The variance-covariance matrix for all the parameters. The parameters are in the order: β, α, θ, γ, ν, and Σ. The elements in Σ are output in the order along the main diagonal line, then the second main diagonal line, and so on. |
betas | The point estimates of β. |
se_betas | The standard error estimate of β. |
alphamatrix | The point estimates of α. |
se_alphas | The standard error estimate of α. |
theta | The point estimates of θ. |
se_theta | The standard error estimate of θ. |
gamma_matrix | The point estimate of γ. |
se_gamma_matrix | The standard error estimate of γ. |
v_estimate | The point estimate of ν. |
se_v_estimate | The standard error estimate of ν. |
sigma_matrix | The point estimate of Σ (only the upper triangle portion of the matrix is output). |
se_sigma | The standard error estimate of Σ.The standard errors are given in this order: main diagonal, the second main diagonal, and so on. |
loglike | Log Likelihood. |
Ning Li,Robert M. Elashoff,Gang Li and Jeffrey Saver. "Joint modeling of longitudinal ordinal data and competing risks survival times and analysis of the NINDS rt-PA stroke trial." Statistics in medicine 29.5 (2010): 546-557.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | require(JMcmprsk)
set.seed(123)
# A toy example on a dataset called from file paths
yfn=system.file("extdata", "jmosimy.txt", package = "JMcmprsk")
cfn=system.file("extdata", "jmosimc.txt", package = "JMcmprsk")
mfn=system.file("extdata", "jmosimm.txt", package = "JMcmprsk")
fit <- jmo_0(p=3,s=1, yfn,cfn,mfn,point=6,do.trace = FALSE)
fit
## Not run:
# A toy example on a dataset called from data frame
data(ninds)
yread <- ninds[, c(2:14)]
mread <- as.data.frame(table(ninds$ID))
mread <- as.data.frame(mread[, 2])
cread <- ninds[, c(1, 15, 16, 6, 10:14)]
cread <- unique(cread)
cread <- cread[, -1]
jmofit=jmo_0(p=9,s=2, yread,cread,mread,point=6,do.trace = FALSE, type_file = FALSE)
jmofit
## End(Not run)
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