LCx {morse} | R Documentation |
Predict X
% Lethal Concentration at the maximum time point (default).
Description
Predict median and 95% credible interval of the x% Lethal Concentration.
The function LCx
, x
% Lethal Concentration (LC_x
), is use to compute
the dose required to kill x
% of the members of a tested population
after a specified test duration (time_LCx
) (default is the maximum
time point of the experiment).
Mathematical definition of x
% Lethal Concentration at time t
,
denoted LC(x,t)
, is:
S(LC(x,t), t) = S(0, t)*(1- x/100)
,
where S(LC(x,t), t)
is the survival probability at concentration
LC(x,t)
at time t
, and S(0,t)
is the survival probability at
no concentration (i.e. concentration is 0
) at time t
which
reflect the background mortality h_b
:
S(0, t) = exp(-hb* t)
.
In the function LCx
, we use the median of S(0,t)
to rescale the
x
% Lethal Concentration at time t
.
Usage
LCx(object, ...)
## S3 method for class 'survFit'
LCx(object, X, time_LCx = NULL, conc_range = NULL, npoints = 100, ...)
Arguments
object |
An object of class |
... |
Further arguments to be passed to generic methods |
X |
Percentage of individuals dying (e.g., |
time_LCx |
A number giving the time at which |
conc_range |
A vector of length 2 with minimal and maximal value of the range of concentration. If NULL, the range is define between 0 and the highest tested concentration of the experiment. |
npoints |
Number of time point in |
Details
When class of object
is survFit
, see LCx.survFit.
Value
returns an object of class LCx
.
The function returns an object of class LCx
, which is a list
with the following information:
X_prop |
Survival probability of individuals surviving considering the median
of the background mortality (i.e. |
X_prop_provided |
Survival probability of individuals surviving as provided in arguments (i.e. |
time_LCx |
A number giving the time at which |
df_LCx |
A |
df_dose |
A |
Examples
# (1) Load the data
data("propiconazole")
# (2) Create an object of class 'survData'
dataset <- survData(propiconazole)
# (3) Run the survFit function with model_type SD (or IT)
out_SD <- survFit(dataset, model_type = "SD")
# (4) estimate LC50 at time 4
LCx(out_SD, X = 50, time_LCx = 4)