CREATE_INPUT: Function to define the input variables (imprecise, random or...
In HYRISK: Hybrid Methods for Addressing Uncertainty in RISK Assessments
Description
Usage
Arguments
Details
Value
See Also
Examples
Description
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.
Usage
1
2
Arguments
name
String of the name of the input variable.
type
String to specify the type of input variable:
"proba": probability distribution.
"possi": possibility distribution.
"impr proba": imprecise probability distribution
"fixed": fixed scalar value.
distr
String to specify the type of distribution:
If type="proba" or type="impr proba", distr should be: "normal", "lognormal", "triangle", "beta", "gumbel","user".
If type="possi", distr should be: "trapeze", "triangle", "interval".
param
Vector of parameter values. This depends on the choice of distr and type.
If type="proba" and distr="normal", param should be a vector of two values. For example c(0,1) corresponds to a normal distribution with mean=0 and standard deviation of 1.
If type="proba" and distr="lognormal", param should be a vector of two values. For example c(0,1) corresponds to a lognormal distribution with meanlog=0 and standard deviation of sdlog=1.
If type="proba" and distr="triangle", param should be a vector of three values. For example c(0,2,1) corresponds to a traingle distribution with apex=1 and support[0,2]. See help from package triangle.
If type="proba" and distr="beta", param should be a vector of two values. For example c(1,1) corresponds to a beta distribution with parameters shape1 and shape2 equal to 1 (see help of qbeta in stats package).
If type="proba" and distr="gumbel", param should be a vector of two values. For example c(0,1) corresponds to a gumbel distribution with location=0 and scale parameter=1.
If type="possi" and distr="trapeze", param should be a vector of four values. For example c(0,1,2,3) corresponds to a possibility distribution with core=[1,2] and support=[0,3].
If type="possi" and distr="triangle", param should be a vector of three values. For example c(0,1,2) corresponds to a possibility distribution with core=1 and support=[0,2].
If type="possi" and distr="interval", param should be a vector of two values. For example c(0,1) corresponds to the interval [0,1].
If type="impr proba",
param should be a vector of integers; each integer points to the rank of the input, which represents the uncertainty on the corresponding parameter of chosen distribution
For example, if type="impr proba" and distr="normal", c(2,3) corresponds to an imprecise normal distribution, whose imprecise mean is input[[2]] and standard deviation is input[[3]].
monoton
String to specify the monotony of the model function regarding the input variable.
"decr" for decreasing.
"incr" for increasing.
"dunno" for unknown monotony or known no-monotony.
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
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
Value
list with the afore-described arguments.
See Also
Examples
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#################################################
#### 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)
)
HYRISK documentation built on May 2, 2019, 12:54 p.m.
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Description Usage Arguments Details Value See Also Examples
Description
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.
Usage
1 2 |
Arguments
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
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
Value
list with the afore-described arguments.
See Also
Examples
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)
)
|
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