diag.evppi {BCEA} | R Documentation |
The function produces either a residual plot comparing the fitted values from the INLA-SPDE Gaussian Process regression to the residuals. This is a scatter plot of residuals on the y axis and fitted values (estimated responses) on the x axis. The plot is used to detect non-linearity, unequal error variances, and outliers. A well-behaved residual plot supporting the appropriateness of the simple linear regression model has the following characteristics: 1) The residuals bounce randomly around the 0 line. This suggests that the assumption that the relationship is linear is reasonable. 2) The residuals roughly form a horizontal band around the 0 line. This suggests that the variances of the error terms are equal. 3) None of the residual stands out from the basic random pattern of residuals. This suggests that there are no outliers.
diag.evppi(evppi, he, plot_type = c("residuals", "qqplot"), interv = 1)
evppi |
A |
he |
A |
plot_type |
The type of diagnostics to be performed. It can be the 'residual plot' or the 'qqplot plot'. |
interv |
Specifies the interventions for which diagnostic tests should be performed (if there are many options being compared) |
The second possible diagnostic is the qqplot for the fitted value. This is a graphical method for comparing the fitted values distributions with the assumed underlying normal distribution by plotting their quantiles against each other. First, the set of intervals for the quantiles is chosen. A point (x,y) on the plot corresponds to one of the quantiles of the second distribution (y-coordinate) plotted against the same quantile of the first distribution (x-coordinate). If the two distributions being compared are identical, the Q-Q plot follows the 45 degrees line.
Plot
Gianluca Baio, Anna Heath
Baio, G., Dawid, A. P. (2011). Probabilistic Sensitivity Analysis in Health Economics. Statistical Methods in Medical Research doi:10.1177/0962280211419832.
Baio G. (2012). Bayesian Methods in Health Economics. CRC/Chapman Hall, London.