ppc {morse} | R Documentation |
Posterior predictive check plot
Description
Plots posterior predictive check for reproFitTT
, survFitTT
,
survFitTKTD
, survFitCstExp
and survFitVarExp
objects.
This is the generic ppc
S3 method for the reproFitTT
class.
It plots the predicted values with 95% credible intervals versus the observed
values.
This is the generic ppc
S3 method for the survFitCstExp
class. It
plots the predicted values along with 95% credible intervals
versus the observed values for survFit
objects.
This is the generic ppc
S3 method for the survFitPredict_Nsurv
class. It
plots the predicted values along with 95% credible intervals
versus the observed values for survFitPredict_Nsurv
objects.
This is the generic ppc
S3 method for the survFitTKTD
class. It
plots the predicted values along with 95% credible intervals
versus the observed values for survFitTKTD
objects.
This is the generic ppc
S3 method for the survFitTT
class. It
plots the predicted values with 95 % credible intervals versus the observed
values for survFitTT
objects.
This is the generic ppc
S3 method for the survFitVarExp
class. It
plots the predicted values along with 95% credible intervals
versus the observed values for survFit
objects.
Usage
ppc(x, ...)
## S3 method for class 'reproFitTT'
ppc(
x,
style = "ggplot",
xlab = "Observed Cumul. Nbr. of offspring",
ylab = "Predicted Cumul. Nbr. of offspring",
main = NULL,
...
)
## S3 method for class 'survFitCstExp'
ppc(x, style = "ggplot", main = NULL, ...)
## S3 method for class 'survFitPredict_Nsurv'
ppc(
x,
xlab = "Observed nb of survivors",
ylab = "Predicted nb of survivors",
main = NULL,
...
)
## S3 method for class 'survFitTKTD'
ppc(x, style = "ggplot", main = NULL, ...)
## S3 method for class 'survFitTT'
ppc(x, style = "ggplot", main = NULL, ...)
## S3 method for class 'survFitVarExp'
ppc(
x,
xlab = "Observed nb of survivors",
ylab = "Predicted nb of survivors",
main = NULL,
...
)
Arguments
x |
An object of class |
... |
Further arguments to be passed to generic methods |
style |
graphical backend, can be |
xlab |
A label for the |
ylab |
A label for the |
main |
A main title for the plot. |
Details
Depending on the class of the object x
see their links.
for class reproFitTT
: ppc.reproFitTT ;
for class survFitTT
: ppc.survFitTT ;
for class survFitTKTD
: ppc.survFitTKTD ;
for class survFitCstExp
: ppc.survFitCstExp and
for class survFitVarExp
: ppc.survFitVarExp.
The coordinates of black points are the observed values of the cumulated number
of reproduction outputs for a given concentration (X
-scale) and the corresponding
predicted values (Y
-scale). 95% prediction intervals are added to each predicted
value, colored in green if this interval contains the observed value and in red
in the other case. As replicates are not pooled in this plot, overlapped points
are shifted on the X-
axis to help the visualization of replicates. The bisecting
line (y = x) is added to the plot in order to see if each prediction interval
contains each observed value. As replicates are shifted on the X
-axis, this
line may be represented by steps.
The black points show the observed number of survivors (pooled
replicates, on X
-axis) against the corresponding predicted
number (Y
-axis). Predictions come along with 95% prediction
intervals, which are depicted in green when they contain the
observed value and in red otherwise. Samples with equal observed
value are shifted on the X
-axis. For that reason, the
bisecting line (y = x), is represented by steps when observed
values are low. That way we ensure green intervals do intersect the
bisecting line.
For survFitPredict_Nsurv
object, PPC is based on times series simulated
for each replicate. In addition, the black points show the observed
number of survivors (on X
-axis)
against the corresponding predicted
number (Y
-axis). Predictions come along with 95% prediction
intervals, which are depicted in green when they contain the
observed value and in red otherwise.
The black points show the observed number of survivors (pooled
replicates, on X
-axis) against the corresponding predicted
number (Y
-axis). Predictions come along with 95% prediction
intervals, which are depicted in green when they contain the
observed value and in red otherwise. Samples with equal observed
value are shifted on the X
-axis. For that reason, the
bisecting line (y = x), is represented by steps when observed
values are low. That way we ensure green intervals do intersect the
bisecting line.
The coordinates of black points are the observed values of the number of survivors
(pooled replicates) for a given concentration (X
-axis) and the corresponding
predicted values (Y
-axis). 95% prediction intervals are added to each predicted
value, colored in green if this interval contains the observed value and in red
otherwise.
The bisecting line (y = x) is added to the plot in order to see if each
prediction interval contains each observed value. As replicates are shifted
on the x-axis, this line is represented by steps.
The black points show the observed number of survivors (on X
-axis)
against the corresponding predicted
number (Y
-axis). Predictions come along with 95% prediction
intervals, which are depicted in green when they contain the
observed value and in red otherwise.
Value
a plot of class ggplot
a plot of class ggplot
a plot of class ggplot
a plot of class ggplot
a plot of class ggplot
a plot of class ggplot
a plot of class ggplot
Examples
# (1) Load the data
data(cadmium1)
# (2) Create an object of class "reproData"
dataset <- reproData(cadmium1)
# (3) Run the reproFitTT function with the log-logistic gamma-Poisson model
out <- reproFitTT(dataset, stoc.part = "gammapoisson",
ecx = c(5, 10, 15, 20, 30, 50, 80), quiet = TRUE)
# (4) Plot observed versus predicted values
ppc(out)
# (1) Load the data
data(propiconazole)
# (2) Create an object of class "survData"
dataset <- survData(propiconazole)
# (3) Run the survFitTKTD function with the TKTD model ('SD' or 'IT')
out <- survFit(dataset, model_type = "SD")
# (4) Plot observed versus predicted values
ppc(out)
# (1) Load the data
data(propiconazole)
# (2) Create an object of class "survData"
dat <- survData(propiconazole)
# (3) Run the survFitTKTD function with the TKTD model ('SD' only)
out <- survFitTKTD(dat)
# (4) Plot observed versus predicted values
ppc(out)
# (1) Load the data
data(cadmium1)
# (2) Create an object of class "survData"
dat <- survData(cadmium1)
# (3) Run the survFitTT function with the log-logistic binomial model
out <- survFitTT(dat, lcx = c(5, 10, 15, 20, 30, 50, 80),
quiet = TRUE)
# (4) Plot observed versus predicted values
ppc(out)
# (1) Load the data
data(propiconazole_pulse_exposure)
# (2) Create an object of class "survData"
dat <- survData(propiconazole_pulse_exposure)
# (3) Run the survFitTKTD function with the TKTD model ('SD' or 'IT')
out <- survFit(dat, model_type = "SD")
# (4) Plot observed versus predicted values
ppc(out)