pseudoreg {SOHPIE}

pseudoreg

Description

A function to regress pseudo-values across set of covariates.

Usage

pseudoreg(pseudoval, clindat, c)

Arguments

pseudoval

Jackknife pseudo-values calculated.

clindat

A metadata/phenotypic data consisting of the clinical and demographic variables that the user wants to include in the regression. (e.g., binary group indicator for intervention vs. control, continuous age, ...)

c

The choice of trimming proportion for the least trimmed estimator of robust regression. A value has to be between 0.5 and 1 as specified in ltsReg() function in robustbase package.

Value

A pseudo-value regression is fitted. Please use pseudoreg.summary() to output p-values, q-values, and coefficient estimates.

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]

#Clinical/demographic covariates (phenotypic data):
# Note: All of these covariates will be included in the regression, so
# please make sure that phenodat includes the variables that will be analyzed only.
phenodat = combinedamgut[, 1:7] # first column is ID, so not using it.

# 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)

thetatilde = rbind(thetatilde_grpA, thetatilde_grpB)

# Map the column names (taxa names)
colnames(thetatilde) = colnames(OTUtab)

# Fit a pseudo-value regression using jackknife pseudovalues
# and phenotypic data. A reminder that the phenotypic data should
# contain a set of predictor variables to be fitted.
fitmod = pseudoreg(pseudoval=thetatilde, clindat=phenodat, c=0.5)