SSJ
3.3.1
Stochastic Simulation in Java
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This class helps managing the partitions of possible outcomes into categories for applying chi-square tests. More...
Public Member Functions | |
OutcomeCategoriesChi2 (double[] nbExp) | |
Constructs an OutcomeCategoriesChi2 object using the array nbExp for the number of expected observations in each category. More... | |
OutcomeCategoriesChi2 (double[] nbExp, int smin, int smax) | |
Constructs an OutcomeCategoriesChi2 object using the given nbExp expected observations array. More... | |
OutcomeCategoriesChi2 (double[] nbExp, int[] loc, int smin, int smax, int nbCat) | |
Constructs an OutcomeCategoriesChi2 object. More... | |
void | regroupCategories (double minExp) |
Regroup categories as explained earlier, so that the expected number of observations in each category is at least minExp . More... | |
String | toString () |
Provides a report on the categories. More... | |
Public Attributes | |
int | nbCategories |
Total number of categories. | |
int | smin |
Minimum index for valid expected numbers in the array nbExp . | |
int | smax |
Maximum index for valid expected numbers in the array nbExp . | |
double [] | nbExp |
Expected number of observations for each category. | |
int [] | loc |
loc[i] gives the relocation of the category i in the nbExp array. | |
This class helps managing the partitions of possible outcomes into categories for applying chi-square tests.
It permits one to automatically regroup categories to make sure that the expected number of observations in each category is large enough. To use this facility, one must first construct an OutcomeCategoriesChi2
object by passing to the constructor the expected number of observations for each original category. Then, calling the method regroupCategories will regroup categories in a way that the expected number of observations in each category reaches a given threshold minExp
. Experts in statistics recommend that minExp
be always larger than or equal to 5 for the chi-square test to be valid. Thus, minExp
= 10 is a safe value to use. After the call, nbExp
gives the expected numbers in the new categories and loc[i]
gives the relocation of category \(i\), for each \(i\). That is, loc[i] = j
means that category \(i\) has been merged with category \(j\) because its original expected number was too small, and nbExp[i]
has been added to nbExp[j]
and then set to zero. In this case, all observations that previously belonged to category \(i\) are redirected to category \(j\). The variable nbCategories
gives the final number of categories, smin
contains the new index of the lowest category, and smax
the new index of the highest category.
OutcomeCategoriesChi2 | ( | double [] | nbExp | ) |
Constructs an OutcomeCategoriesChi2
object using the array nbExp
for the number of expected observations in each category.
The smin
and smax
fields are set to 0 and \((n-1)\) respectively, where \(n\) is the length of array nbExp
. The loc
field is set such that loc[i]=i
for each i
. The field nbCategories
is set to \(n\).
nbExp | array of expected observations for each category |
OutcomeCategoriesChi2 | ( | double [] | nbExp, |
int | smin, | ||
int | smax | ||
) |
Constructs an OutcomeCategoriesChi2
object using the given nbExp
expected observations array.
Only the expected numbers from the smin
to smax
(inclusive) indices will be considered valid. The loc
field is set such that loc[i]=i
for each i
in the interval [smin, smax]
. All loc[i]
for i
\(\le\) smin
are set to smin
, and all loc[i]
for i
\(\ge\) smax
are set to smax
. The field nbCategories
is set to (smax - smin + 1
).
nbExp | array of expected observations for each category |
smin | Minimum index for valid expected number of observations |
smax | Maximum index for valid expected number of observations |
OutcomeCategoriesChi2 | ( | double [] | nbExp, |
int [] | loc, | ||
int | smin, | ||
int | smax, | ||
int | nbCat | ||
) |
Constructs an OutcomeCategoriesChi2
object.
The field nbCategories
is set to nbCat
.
nbExp | array of expected observations for each category |
smin | Minimum index for valid expected number of observations |
smax | Maximum index for valid expected number of observations |
loc | array for which loc[i] gives the relocation of the category i |
void regroupCategories | ( | double | minExp | ) |
Regroup categories as explained earlier, so that the expected number of observations in each category is at least minExp
.
We usually choose minExp
= 10.
minExp | mininum number of expected observations in each category |
String toString | ( | ) |
Provides a report on the categories.