Create a multivariate estimate object.
Description
estimate
creates an object of class estimate
. The concept of an estimate is
extended from the 1dimensional (cf. estimate1d
) to the multivariate case. This
includes the description of correlations between the different variables. An estimate of an
ndimensional variable is at minimum defined by each component being a 1dimensional estimate.
This means, that for each component, at minimum, the type of its univariate parametric
distribution, its 5%  and 95% quantiles must be provided. In probability theoretic terms,
these are the marginal distributions of the components. Optionally, the individual median
and the correlations between the components can be supplied.
as.estimate
tries to coerce a set of objects and transform them to class estimate
.
Usage
1 2 3  estimate(distribution, lower, upper, ..., correlation_matrix = NULL)
as.estimate(..., correlation_matrix = NULL)

Arguments
distribution 

lower 

upper 

... 
in 
correlation_matrix 

Details
The input arguments inform the estimate about its marginal distributions and joint distribution, i.e. the correlation matrix.
The structure of the estimates marginal input information
 in
estimate

The marginal distributions are defined by the arguments
distribution
,lower
andupper
and, optionally, by further columns supplied in...
that can be coerced to adata.frame
with the same length as the mandatory arguments.  in
as.estimate

The marginal distributions are completely defined in
...
. These arguments must be coercible to a data.frame, all having the same length. Mandatory columns aredistribution
,lower
andupper
.
Mandatory input columns
Column  Rtype  Explanation 
distribution  character vector  Marginal distribution types 
lower  numeric vector  Marginal 5%quantiles 
upper  numeric vector  Marginal 95%quantiles 
It must hold that lower <= upper
for every component of the estimate.
Optional input columns
The optional parameters in ...
provide additional characteristics of the marginal
distributions of the estimate. Frequent optional columns are:
Column  Rtype  Explanation 
variable  character vector  Variable names 
median  cf. below  Marginal 50%quantiles 
method  character vector  Methods for calculation of marginal distribution parameters 
The median
column
If supplied as input, any component of median
can be either NA
, numeric
(and not NA
) or the character string "mean"
. If it equals "mean"
it is
set to rowMeans(cbind(lower, upper))
of this component; if it is numeric
it must
hold that lower <= median <= upper
for this component. In case that no element
median
is provided, the default is median=rep(NA, length(distribution))
.
The median
is important for the different methods possible in generating the random
numbers (cf. random.estimate
).
The structure of the estimates correlation input information
The argument correlation_matrix
is the sub matrix of the full correlation matrix of
the estimate containing all correlated elements. Thus, its row and column names must be a
subset of the variable names of the marginal distributions. This means, that the information
which variables are uncorrelated does not need to be provided explicitly.
correlation_matrix
must have all the properties of a correlation matrix, viz. symmetry,
all diagonal elements equal 1 and all of diagonal elements are between 1 and 1.
Value
An object of class estimate
which is a list with components $marginal
and
$correlation_matrix
:
$marginal

is a
data.frame
with mandatory columns:Mandatory column Rtype Explanation distribution
character vector
Distribution types lower
numeric vector
5%quantiles median
numeric vector
50%quantiles or NA
upper
numeric vector
95%quantiles The
row.names
are the names of the variables. Each row has the properties of anestimate1d
.Note that the
median
is a mandatory element of anestimate
, although it is not necessary as input. If a component ofmedian
is numeric and notNA
it holds that:lower <= median <= upper
. In any case anestimate
object has the propertyany(lower <= upper)
. $correlation_matrix

is a symmetric matrix with row and column names being the subset of the variables supplied in
$marginal
which are correlated. Its elements are the corresponding correlations.
See Also
estimate1d
, random.estimate
,
row.names.estimate
, names.estimate
, corMat
,
estimate_read_csv
and estimate_write_csv
.
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  # Create a minimum estimate (only mandatory marginal information supplied):
estimateMin<estimate(c("posnorm", "lnorm"),
c( 4, 4),
c( 50, 10))
print(estimateMin)
# Create an estimate with optional columns (only marginal information supplied):
estimateMarg<estimate( c("posnorm", "lnorm"),
c( 4, 4),
c( 50, 10),
variable=c("revenue", "costs"),
median = c( "mean", NA),
method = c( "fit", ""))
print(estimateMarg)
print(corMat(estimateMarg))
# Create a minimum estimate from text (only mandatory marginal information supplied):
estimateTextMin<"distribution, lower, upper
posnorm, 100, 1000
posnorm, 50, 2000
posnorm, 50, 2000
posnorm, 100, 1000"
estimateMin<as.estimate(read.csv(header=TRUE, text=estimateTextMin,
strip.white=TRUE, stringsAsFactors=FALSE))
print(estimateMin)
# Create an estimate from text (only marginal information supplied):
estimateText<"variable, distribution, lower, upper, median, method
revenue1, posnorm, 100, 1000, NA,
revenue2, posnorm, 50, 2000, , fit
costs1, posnorm, 50, 2000, 70, calculate
costs2, posnorm, 100, 1000, mean, "
estimateMarg<as.estimate(read.csv(header=TRUE, text=estimateText,
strip.white=TRUE, stringsAsFactors=FALSE))
print(estimateMarg)
print(corMat(estimateMarg))
# Create an estimate from text (with correlated components):
estimateTextMarg<"variable, distribution, lower, upper
revenue1, posnorm, 100, 1000
revenue2, posnorm, 50, 2000
costs1, posnorm, 50, 2000
costs2, posnorm, 100, 1000"
estimateTextCor<", revenue1, costs2
revenue1, 1, 0.3
costs2, 0.3, 1"
estimateCor<as.estimate(read.csv(header=TRUE, text=estimateTextMarg,
strip.white=TRUE, stringsAsFactors=FALSE),
correlation_matrix=data.matrix(read.csv(text=estimateTextCor,
row.names=1,
strip.white=TRUE)))
print(estimateCor)
print(corMat(estimateCor))

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