Description Usage Arguments Details Author(s) References See Also Examples
Various types of plots could be produced for simplexreg Objects, including plots of correlation structure, plots of different types of residuals and plots of partial deviance.
1 2 |
x |
fitted model object of class "simplexreg" |
type |
character specifying types of plots: the correlation ( |
res |
character specifying types of residuals:approximate Pearson residual ( |
lag |
when |
... |
other parameters to be passed through to the plot function |
This function provides graphical presentations for simplexreg objects. The plot of correlation aims
examine the correlation structure of the longitudinal data set. Let r_{ij} be the standardised
score residuals of the i
th observation at time t_{ij}, and lag = k
, then r_{ij}
are plotted against r_{ik} for all i and j < k, if |t_{ij} - t_{ik}| = k.
Residuals can be plotted when specifying type = "residuals"
, The upper and lower 95
(1.96) are also lined.
Plots of partial deviance are for the goodness-of-fit test in the presence of within-subject dependence for longitudinal data. The partial deviances are defined as
D_j^P=sum d(y_{ij}-mu_{ij}) / σ_{ij}^2, j in T
where T denotes a collection of all distinct times on which observation are made. Cross-sectionally, y_{ij}'s are independent and hence D_j^P follows approximately χ^2, with m_j being the total number of y_{ij}'s observed cross-sectionally at time t_j. Both observed partial deviance D_j^P statistics and the corresponding critical values are depicted and compared at each time point.
Chengchun Shi
Song, P. and Qiu, Z. and Tan, M. (2004) Modelling Heterogeneous Dispersion in Marginal Models for Longitudinal Proportional Data. Biometrical Journal, 46: 540–553
Qiu Z. (2001) Simplex Mixed Models for Longitudinal Proportional Data. Ph.D. Dissertation, York University
Zhang, P. and Qiu, Z. and Shi, C. (2016) simplexreg: An R Package for Regression Analysis of Proportional Data Using the Simplex Distribution. Journal of Statistical Software, 71: 1–21
summary.simplexreg
, residuals.simplexreg
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## fit the model
data("sdac", package="simplexreg")
sim.glm2 <- simplexreg(rcd~ageadj+chemo|age,
link = "logit", data = sdac)
data("retinal", package = "simplexreg")
sim.gee2 <- simplexreg(Gas~LogT+LogT2+Level|LogT+Level|Time,
link = "logit", corr = "AR1", id = ID, data = retinal)
## produce the plots
plot(sim.glm2, type = "residuals", res = "stdPerr", ylim = c(-3, 3))
plot(sim.gee2, type = "corr", xlab = "", ylab = "")
plot(sim.gee2, type = "GOF", xlab = "", ylab = "")
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