mms: Fitting Mixed Models Separately for Many Responses Using...

In nyiuab/NBZIMM: Negative Binomial and Zero-Inflated Mixed Models

Fitting Mixed Models Separately for Many Responses Using glmm.nb, lme, glmm.zinb, or lme.zig

Description

This function fits mixed models separately for many responses using glmm.nb, lme, glmm.zinb, or lme.zig.

Usage

  
mms(y, fixed, random, data, method = c("nb", "lme", "zinb", "zig"),
    correlation, zi_fixed = ~1, zi_random = NULL,
    niter = 30, epsilon = 1e-05, min.p = 0, verbose = TRUE)

Arguments

y	a matrix of responses; each column is a response and rows are samples.
fixed	a one-sided formula of the form ~ x (i.e., the respose is omitted); the right side of ~ is the same as in glmm.nb, lme, glmm.zinb, or lme.zig.
data, random, correlation, zi_fixed, zi_random, niter, epsilon	These arguments are the same as in glmm.nb, lme, glmm.zinb, or lme.zig.
method	character specification of the method: "nb": glmm.nb; "lme": lme; "zinb": glmm.zinb; "zig": lme.zig.
min.p	a value in [0, 1). The responses with the proportion of non-zero values > min.p are analyzed.
verbose	logical. If TRUE, print out computational time.

Details

This function analyzes the responses in y by repeated calls to glmm.nb, lme, glmm.zinb, or lme.zig.

Value

A list including fit, responses, variables, and call:

fit	fitted models for all the responses;
responses	names of all the responses;
variables	names of all covariates;

Author(s)

Nengjun Yi, [e-mail]

See Also

lme, glmm.nb, glmm.zinb, lme.zig

Examples

library(NBZIMM)

data(Romero)
names(Romero)

otu = Romero$OTU; dim(otu)
sam = Romero$SampleData; dim(sam)
colnames(sam)

N = sam[, "Total.Read.Counts"]        
Days = sam$GA_Days; Days = scale(Days)
Age = sam$Age; Age = scale(Age)
Race = sam$Race
preg = sam$pregnant; table(preg)
subject = sam[, "Subect_ID"]; table(subject)

# analyze all taxa with a given nonzero proportion

data = data.frame(Days=Days, Age=Age, Race=Race, preg=preg, N=N, subject=subject)

# negative binomial mixed model
f1 = mms(y = otu, fixed = ~ Days + Age + Race + preg + offset(log(N)), 
        random = ~ 1 | subject, data = data,
        min.p = 0.2, method = "nb")

# zero-inflated negative binomial mixed model        
f2 = mms(y = otu, fixed = ~ Days + Age + Race + preg + offset(log(N)), 
        random = ~ 1 | subject, zi_fixed = ~1, data = data,
        min.p = 0.2, method = "zinb")
        
# zero-inflated gaussian mixed model        
f3 = mms(y = log2(otu+1), fixed = ~ Days + Age + Race + preg + offset(log(N)), 
        random = ~ 1 | subject, zi_fixed = ~1, data = data,
        min.p = 0.2, method = "zig")

# display the results
f = f1
out = fixed(f)
out = out$dist
out = out[out[,2]!="(Intercept)", ]

par(mfrow = c(1, 1), cex.axis = 1, mar = c(2, 12, 4, 4))
plot.fixed(res=out[,c("Estimate","Std.Error","padj")], 
           threshold=0.001, gap=500, col.pts=c("black", "grey"),
           cex.axis=0.6, cex.var=0.7)

preg.coefs = out[out$variables=="preg",]
par(mfrow = c(1, 1), cex.axis = 1, mar = c(2, 10, 4, 4))
plot.fixed(res=preg.coefs[,c("Estimate","Std.Error","padj")], 
           threshold=0.05, gap=300, main="Covariate: pregnant",
           cex.axis=0.6, cex.var=0.7)

g = heat.p(df=out, p.breaks = c(0.001, 0.01, 0.05), 
       colors = c("black", "darkgrey", "grey", "lightgrey"),
       zigzag=c(T,F), abbrv=c(T,F), margin=c(1,6), y.size=8,
       legend=T)

nyiuab/NBZIMM documentation built on April 21, 2022, 7 a.m.