Description
Performs diagnostic plots for the results of the EVPPI
Usage
diag.evppi(x,y,diag=c("residuals","qqplot"),int=1)
Arguments
x
A evppi object obtained by running the function evppi on a bcea
model.
y
A bcea object containing the results of the Bayesian modelling and the economic
evaluation.
diag
The type of diagnostics to be performed. It can be the 'residual plot' or the 'qqplot
plot'.
int
Specifies the interventions for which diagnostic tests should be performed (if there are many
options being compared)
Value
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.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.
References
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