thetatildefun {SOHPIE}

thetatildefun

Description

A function to calculate jackknife pseudo-values

Usage

thetatildefun(thetahatinput, thetahatdropinput, sizegroup)

Arguments

thetahatinput

A network centrality calculated from association matrix for whole sample.

thetahatdropinput

Network centralities calculated from re-estimated association matrices for leave-one-out samples.

sizegroup

Sample size for group.

Value

A jackknife pseudo-value will be returned.

Examples

# In this example, the subset of the American Gut Project data will be used.
data(combinedamgut) # A complete data containing columns with taxa and clinical covariates.

# Note: The line below will use a toy example with the first 30 out of 138 taxa.
OTUtab = combinedamgut[ , 8:37]

# Obtain indices of each grouping factor
# In this example, a variable indicating the status of living
# with a dog was chosen (i.e. bin_dog).
# Accordingly, Groups A and B imply living without and with a dog, respectively.
newindex_grpA = which(combinedamgut$bin_dog == 0)
newindex_grpB = which(combinedamgut$bin_dog == 1)

# Now, we estimate (and re-estimate) association matrices 
# for each group separately.
asso_matA = asso_mat(OTUdat=OTUtab, group=newindex_grpA) 
asso_matB = asso_mat(OTUdat=OTUtab, group=newindex_grpB) 

# Calculate the network centrality.
thetahat_grpA = thetahats(asso_matA$assomat) 
thetahat_grpB = thetahats(asso_matB$assomat)

# Obtain network centrality for the re-estimated association matrices.
thetahat_drop_grpA = sapply(asso_matA$reest.assomat, thetahats)
thetahat_drop_grpB = sapply(asso_matB$reest.assomat, thetahats)

# Sample sizes for each group.
n_A <- length(newindex_grpA) 
n_B <- length(newindex_grpB)

# Now calculate jackknife pseudo-values for each group.
thetatilde_grpA = thetatildefun(thetahat_grpA, thetahat_drop_grpA, n_A)
thetatilde_grpB = thetatildefun(thetahat_grpB, thetahat_drop_grpB, n_B)