prctilemlx | R Documentation |
Compute and display percentiles of the empiricial distribution of longitudinal data.
prctilemlx( r = NULL, col = NULL, project = NULL, outputVariableName = NULL, number = 8, level = 80, plot = TRUE, color = NULL, group = NULL, facet = TRUE, labels = NULL, band = NULL )
r |
a data frame with a column id, a column time and a column with values. The times should be the same for each individual. |
col |
a vector with the three column indexes for id, time/x and y. Default = c(1, 2,3). |
project |
simulx project filename (with extension ".smlx") |
outputVariableName |
name of the output to consider. By default the first output will be consider. You must define either a 'r' dataframe and the associated 'col' argument or a simulx project and the name of the output 'outputVariableName" |
number |
the number of intervals (i.e. the number of percentiles minus 1). |
level |
the largest interval (i.e. the difference between the lowest and the highest percentile). |
plot |
if |
color |
colors to be used for the plots In case of one group or facet = TRUE, only the first color will be used |
group |
variable to be used for defining groups (by default, group is used when it exists) |
facet |
makes subplots for different groups if |
labels |
vector of strings |
band |
is deprecated (use number and level instead) ; a list with two fields
|
See http://simulx.webpopix.org/mlxr/prctilemlx/ for more details.
a ggplot object if plot=TRUE
; otherwise, a list with fields:
proba a vector of probabilities of length band$number+1
color a vector of colors used for the plot of length band$number
y a data frame with the values of the empirical percentiles computed at each time point
## Not run: myModel <- inlineModel(" [LONGITUDINAL] input = {ka, V, Cl} EQUATION: C = pkmodel(ka,V,Cl) [INDIVIDUAL] input = {ka_pop, V_pop, Cl_pop, omega_ka, omega_V, omega_Cl} DEFINITION: ka = {distribution=lognormal, reference=ka_pop, sd=omega_ka} V = {distribution=lognormal, reference=V_pop, sd=omega_V } Cl = {distribution=lognormal, reference=Cl_pop, sd=omega_Cl} ") N=2000 pop.param <- c( ka_pop = 1, omega_ka = 0.5, V_pop = 10, omega_V = 0.4, Cl_pop = 1, omega_Cl = 0.3) res <- simulx(model = myModel, parameter = pop.param, treatment = list(time=0, amount=100), group = list(size=N, level='individual'), output = list(name='C', time=seq(0,24,by=0.1))) # res$C is a data.frame with 2000x241=482000 rows and 3 columns head(res$C) # we can display the empirical percentiles of C using the default # settings (i.e. percentiles of order 10%, 20%, ... 90%) prctilemlx(res$C) # The 3 quartiles (i.e. percentiles of order 25%, 50% and 75%) are displayed by # selecting a 50% interval splitted into 2 subintervals prctilemlx(res$C, number=2, level=50) # A one 90% interval can be displayed using only one interval prctilemlx(res$C, number=1, level=90) # or 75 subintervals in order to better represent the continuous distribution # of the data within this interval prctilemlx(res$C, number=75, level=90) # prctilemlx produces a ggplot object that can be modified pl <- prctilemlx(res$C, number=4, level=80) pl + ylab("concentration") + ggtitle("predictive distribution") # The percentiles are not plotted by setting plot=FALSE pr.out <- prctilemlx(res$C, number=4, level=80, plot=FALSE) print(pr.out$proba) print(pr.out$color) print(pr.out$y[1:5,]) ## End(Not run)
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