Statistic for non-numeric factor tests

Description

Statistic for non-numeric factor tests (same statistic as H-test).

Usage

T_stat_discr(permu, al)

Arguments

Value

the statistic.

Examples

permu = runif(100,-10,10)
al = as.factor(sample(1:3,100,replace=TRUE))
T_stat_discr(permu, al)

Statistic for non-numeric factor multiple tests

Description

Statistic for non-numeric factor multiple tests (difference in mean ranks).

Usage

T_stat_multi(permu, al)

Arguments

Value

the means difference of two levels for a discrete factor.

Examples

permu = runif(100,-10,10)
al = as.factor(sample(1:3,100,replace=TRUE))
T_stat_multi(permu, al)

ZIprop: A package for Zero-Inflated Proportions data (ZIprop)

Description

We propose a by block-permutation-based methodology (i) to identify factors (discrete or continuous) that are potentially significant, (ii) to define a performance indicator to quantify the percentage of correlation explained by the significant factors subset for Zero-Inflated Proportions data (ZIprop).

References

Melina Ribaud, Edith Gabriel, Joseph Hughes, Samuel Soubeyrand. Identifying potential significant factors impacting zero-inflated proportions data. 2020. hal-02936779

The scalar delta

Description

Calculate the scalar delta. This parameter comes from the optimal Spearman’s correlation when the rank of two vectors X and proba are equal except on a given set of indices. In our context, this set correspond to the zero-values of the vector proba.

Usage

delta(X, proba)

Arguments

Value

Delta the scalar Delta calculated for the vector x and the vector proba.

Examples

X = rnorm(100)
proba = runif(100)
proba[sample(1:100,80)]=0
Delta = delta(X,proba)
print(Delta)

diffFactors

Description

Data for the comparison of COVID-19 mortality in European and North American geographic entities

Usage

data(diffFactors)

Format

A data frame with 483 rows and 32 variables

Details

• geographic_entity_receptor are the entity receptor

• geographic_entity_source are the entity source

• proba is the probability that the receptor follows the mortality dynamics of the source

• other columns are the difference between factors

Author(s)

Melina Ribaud, Davide Martinetti and Samuel Soubeyrand

References

doi: 10.5281/zenodo.4769671

equineDiffFactors

Description

Equine Influenza dataset

Usage

data(equineDiffFactors)

Format

A data frame with 2256 rows and 8 variables

Details

• ID.source are the ID of source hosts

• ID.recep are the ID of receiver hosts

• y are the vector of transmission probabilities source -> receiver

• other columns are the factors

Author(s)

Melina Ribaud and Joseph Hughes

References

doi: /10.5281/zenodo.4837560

Zero-inflated proportions dataset

Description

A dataset example to test the package functions. The factor X1 to X5 and F1 to F5 are correlated to the responses y.

Usage

data(example_data)

Format

A data frame with 440 rows and 23 variables

Details

• ID.source are the ID of source hosts

• ID.recep are the ID of receiver hosts

• y are the vector of transmission probabilities source -> receiver

• X1 to X10 are continuous factor

• F1 to F10 are discrete factor

Turn factor into multiple column

Description

Turns a factor with several levels into a matrix with several columns composed of zeros and ones.

Usage

fact2mat(x)

Arguments

Value

Columns with zeros and ones.

Examples

x = sample(1:3,100,replace = TRUE)
fact2mat(x)

The performance indicator

Description

Calculate the indicator for a vector X and a zero-inflated proportions response proba.

Usage

indicator(X, proba)

Arguments

Value

a scalar represents the performance indicator and the vector proba.

Examples

X = rnorm(100)
proba = runif(100)
proba[sample(1:100,80)]=0
print(indicator(X,proba))

The max performance indicator

Description

Search for the set of parameters that maximize the indicator (equivalent to Spearman correlation). For a given set of factors scaled between 0 and 1 and a zero-inflated proportions response.

Usage

indicator_max(
  DT,
  ColNameFactor,
  ColNameWeight = "weight",
  bounds = c(-10, 10),
  max_generations = 200,
  hard_limit = TRUE,
  wait_generations = 50,
  other_class = NULL
)

Arguments

Value

Return a list of two elements with the value of the indicator and the associate set of parameters (beta).

Examples

library(data.table)
data(example_data)
# For real cases increase max_generations and wait_generations
I_max = indicator_max(example_data,
names(example_data)[c(4:8, 14:18)],
ColNameWeight = "proba",
max_generations = 20,
wait_generations = 5)
print(I_max)

Construct Design Matrix

Description

Creates a design matrix by expanding factors to a set of dummy variables.

Usage

model_matrix(DT, ColNameFactor, other_class)

Arguments

Value

return the value.

Examples

library(data.table)
data(example_data)
m = model_matrix (example_data,
colnames(example_data)[-c(1:3)],
other_class = colnames(example_data)[14:23])
print(m)

Permutations tests

Description

Permutations tests to identify factor correlated to a zero-inflated proportions response. The statistic are the Spearman's correlation for numeric factor and mean by level for other factor.

Usage

permDT(
  DT,
  ColNameFactor,
  B = 1000,
  nclust = 1,
  ColNameWeight = "weight",
  ColNameRecep = "ID.recep",
  ColNameSource = "ID.source",
  seed = NULL,
  no_const = FALSE,
  num_class = ColNameFactor,
  other_class = NULL,
  multiple_test = FALSE,
  adjust_method = "none",
  alpha = 0.05
)

Arguments

Value

A data frame with two columns. One for the statistics and the other one for the p-value.

Examples

library(data.table)
data(example_data)
res = permDT (example_data,
colnames(example_data)[c(4,10,14,20)],
B = 10,
nclust = 1,
ColNameWeight = "y",
ColNameRecep = "ID.recep",
ColNameSource = "ID.source",
seed = NULL,
num_class = colnames(example_data)[c(4,10)],
other_class = colnames(example_data)[c(14,20)])
print(res)

Scale vector

Description

Scale a vector between 0 and 1.

Usage

scale_01(x)

Arguments

Value

the scaled vector of x.

Examples

x = runif(100,-10,10)
x_scale = scale_01(x)
range(x_scale)