Description Usage Arguments Details Value See Also Examples
Function to define the input variables (imprecise, random or fixed). Five types are accounted for:
Probability distribution (either pre-defined (Normal, log-Normal, triangular, Beta, Gumbel) or user-defined).
Possibility distribution (either trapezoidal or triangular).
Intervals.
Fixed scalar value.
Imprecise probability distributions (Normal, log-Normal, triangular, Beta, Gumbel) with parameters represented by by intervals, possibility distributions, fixed scalar values, or probability distributions.
1 2 |
name |
String of the name of the input variable. |
type |
String to specify the type of input variable:
|
distr |
String to specify the type of distribution:
|
param |
Vector of parameter values. This depends on the choice of distr and type.
|
monoton |
String to specify the monotony of the model function regarding the input variable.
|
quser |
If distr="user", this string specifies the quantile function of a probability distribution non listed in the pre-defined ones. The vector of param should be updated according to this law. |
ruser |
If distr="user", string to specify the random sampling function of a probability distribution non listed in the pre-defined ones. The vector of param should be updated according to this law. |
Details of the theory and the example in Dubois & Guyonnet (2011) Available at: https://hal-brgm.archives-ouvertes.fr/file/index/docid/578821/filename/Uncertainties_RA_09_l_dg.pdf
list with the afore-described arguments.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | #################################################
#### EXAMPLE 1 of Dubois & Guyonnet (2011)
#### Probability and Possibility distributions
#################################################
ninput<-5 #Number of input parameters
input<-vector(mode="list", length=ninput) # Initialisation
input[[1]]=CREATE_INPUT(
name="UER",
type="possi",
distr="triangle",
param=c(2.e-2, 5.7e-2, 1.e-1),
monoton="incr"
)
input[[2]]=CREATE_INPUT(
name="EF",
type="possi",
distr="triangle",
param=c(200,250,350),
monoton="incr"
)
input[[3]]=CREATE_INPUT(
name="I",
type="possi",
distr="triangle",
param=c(1,1.5,2.5),
monoton="incr"
)
input[[4]]=CREATE_INPUT(
name="C",
type="proba",
distr="triangle",
param=c(5e-3,20e-3,10e-3)
)
input[[5]]=CREATE_INPUT(
name="ED",
type="proba",
distr="triangle",
param=c(10,50,30)
)
#################################################
#### EXAMPLE 2 of Sch\"obi & Sudret (2016)
#### Imprecise Probability distributions
#################################################
ninput<-6 #Number of input parameters
input<-vector(mode="list", length=ninput) # Initialisation
# Imprecise normal probability
# whose parameters are described by the 3rd and 5th parameters
input[[1]]=CREATE_INPUT(
name="A",
type="impr proba",
distr="normal",
param=c(3,5),
monoton="dunno"
)
# Imprecise normal probability
# whose parameters are described by the 4th and 6th parameters
input[[2]]=CREATE_INPUT(
name="B",
type="impr proba",
distr="normal",
param=c(4,6),
monoton="dunno"
)
# imprecise paramters of afore-described probability distribution
# mean of input number 1 as an interval
input[[3]]=CREATE_INPUT(
name="mu_A",
type="possi",
distr="interval",
param=c(-0.5,0.5)
)
# mean of input number 2 as an interval
input[[4]]=CREATE_INPUT(
name="mu_B",
type="possi",
distr="interval",
param=c(-0.5,0.5)
)
# standard deviation of input number 1 as an interval
input[[5]]=CREATE_INPUT(
name="s_A",
type="possi",
distr="interval",
param=c(0.7,1)
)
# standard deviation of input number 2 as an interval
input[[6]]=CREATE_INPUT(
name="s_B",
type="possi",
distr="interval",
param=c(0.7,1)
)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.