MC_var_yibar_mis {SMARTp} | R Documentation |
The estimated Monte Carlo mean and variance of the average change in clinical attachment level (CAL) for each subject
MC_var_yibar_mis(mu, Sigma, sigma1, lambda, nu, sigma0, Num, a0, b0, cutoff)
mu |
Mean matrix, where row represents each treatment path, and column represents each cluster unit |
Sigma |
Within-mouth teeth covariance matrix |
sigma1 |
Standard deviation of the residual for the continuous outcome |
lambda |
The skewness parameter of the residual for the continuous outcome |
nu |
The degree freedom, or kurtosis parameter of the residual for the continuous outcome |
sigma0 |
Standard deviation of the residual for the binary outcome |
Num |
Number of samples to estimate mean or variance of |
a0 |
Intercept parameter in the probit model for the binary outcome |
b0 |
Slope parameter corresponding to the spatial random effect in the probit model for the binary outcome |
cutoff |
Cut-off value in the binary outcome regression |
MC_var_yibar_mis computes the Monte-Carlo estimates of expectation and variance of the sample mean among the teeth within each mouth, i.e
\bar{Y}_i = \sum Y_{it}(1 - M_{it})/\sum(1 - M_{it})
, where Y_{it}
is the change in CAL (measured in mm) for patient i
and tooth t
, and M_{it}
is the misingness indicator, i.e., M_{it} = 1
implies tooth t
in subject i
is mising. The joint regression models for Y_{it}
and M_{it}
are available in Reich & Bandyopadhyay (2010, Annals of Applied Statistics).
The simulated dataset of CAL change "Y_{it}
", missingness "M_{it}
" and function inside the indicator of "M_{it} I_{it}
" for
each tooth of each patient, with the corresponding estimated mean "mY_i
", variance "VarY_i
" and missing proportion "PM" for each patient
Jing Xu, Dipankar Bandyopadhyay, Douglas Azevedo, Bibhas Chakraborty
Besag, J., York, J. & Mollie, A. (1991), "Bayesian image restoration, with two applications in spatial statistics (With Discussion)", Annals of the Institute of Statistical Mathematics 43, 159.
Reich, B. & Bandyopadhyay, D. (2010), "A latent factor model for spatial data with informative missingness", The Annals of Applied Statistics 4, 439–459.
CAR_cov_teeth, SampleSize_SMARTp
m <- 28
Num <- 1000
cutoff <- 0
sigma1 <- 0.95
sigma0 <- 1
lambda <- 0
nu <- Inf
b0 <- 0.5
a0 <- -1.0
rho <- 0.975
tau <- 0.85
del1 <- 0.5
del2 <- 2
Sigma <- CAR_cov_teeth(m, rho, tau)
Sigma_comp <- array(Sigma, c(m, m, 4))
Sigma_sim <- array(Sigma, c(m, m, 10))
mu_comp <- array(0, c(2, m, 2))
mu_comp[, , 1] <- rbind(rep(0, m), rep(del1, m))
mu_comp[, , 2] <- rbind(rep(0, m), rep(del2, m))
VarYitd1R = MC_var_yibar_mis(mu = mu_comp[1, , 1], Sigma = Sigma,
sigma1 = sigma1,
lambda = lambda, nu = nu,
sigma0 = sigma0, Num = Num, a0 = a0, b0 = b0,
cutoff = cutoff)
PM <- VarYitd1R$PM
VarYid1R <- VarYitd1R$VarYi
mYid1R <- VarYitd1R$mYi
VarYitd1NR <- MC_var_yibar_mis(mu = mu_comp[2, , 1], Sigma = Sigma,
sigma1 = sigma1,
lambda = lambda, nu = nu,
sigma0 = sigma0, Num = Num, a0 = a0, b0 = b0, cutoff = cutoff)
PM <- VarYitd1NR$PM
VarYid1NR <- VarYitd1NR$VarYi
mYid1NR <- VarYitd1NR$mYi
VarYitd3R <- MC_var_yibar_mis(mu = mu_comp[1, , 2], Sigma = Sigma,
sigma1 = sigma1,
lambda = lambda, nu = nu,
sigma0 = sigma0, Num = Num, a0 = a0, b0 = b0,
cutoff = cutoff)
PM <- VarYitd3R$PM
VarYid3R <- VarYitd3R$VarYi
mYid3R <- VarYitd3R$mYi
VarYitd3NR <- MC_var_yibar_mis(mu = mu_comp[2,,2], Sigma = Sigma,
sigma1 = sigma1,
lambda = lambda, nu = nu,
sigma0 = sigma0, Num = Num, a0 = a0, b0 = b0, cutoff = cutoff)
PM <- VarYitd3NR$PM
VarYid3NR <- VarYitd3NR$VarYi
mYid3NR <- VarYitd3NR$mYi