| Type: | Package |
| Title: | Zero-Inflated Poisson-Gamma Regression |
| Version: | 1.1 |
| Author: | Roulan Jiang [aut, cre], Tianying Wang [aut] |
| Maintainer: | Roulan Jiang <roulan2000@gmail.com> |
| Description: | We provide a flexible Zero-inflated Poisson-Gamma Model (ZIPG) by connecting both the mean abundance and the variability to different covariates, and build valid statistical inference procedures for both parameter estimation and hypothesis testing. These functions can be used to analyze microbiome count data with zero-inflation and overdispersion. The model is discussed in Jiang et al (2023) <doi:10.1080/01621459.2022.2151447>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/roulan2000/ZIPG |
| BugReports: | https://github.com/roulan2000/ZIPG/issues |
| Encoding: | UTF-8 |
| LazyData: | true |
| Depends: | R (≥ 2.10) |
| RoxygenNote: | 7.2.1 |
| Imports: | pscl, MASS |
| NeedsCompilation: | no |
| Packaged: | 2024-02-05 18:44:12 UTC; roulan2000 |
| Repository: | CRAN |
| Date/Publication: | 2024-02-05 22:40:18 UTC |
A diet-microbiome data
Description
A diet-microbiome data with shotgun metagenomic sequencing results of fecal samples and daily dietary records of 34 subjects in 17 consecutive days. See original data from article "Daily Sampling Reveals Personalized Diet-Microbiome Associations in Humans" (Johnson et al. 2019).
Usage
Dietary
Format
A list with OTU table and other covariates
- OTU
OTU table
- COV
Major covariates extracted from original data
- M
Sequencing depth
Source
https://github.com/knights-lab/dietstudy_analyses
Get confidence interval from ZIPG model
Description
Get confidence interval from ZIPG model
Usage
ZIPG_CI(ZIPG_res, type = "Wald", CI_type = "normal", alpha = 0.05)
Arguments
ZIPG_res |
Result from ZIPG_main() |
type |
Type of hypothesis testing method, 'Wald' or 'bWald'. |
CI_type |
Type of confidence interval, 'Wald','bWald' or 'pbWald'. |
alpha |
We construct (1- alpha)% confidence interval by alpha/2 and (1-alpha/2). |
Value
Table of confidence interval
Examples
data(Dietary)
dat = Dietary
ZIPG_res <- ZIPG_main(data = dat$COV,
X = ~ALC01+nutrPC1+nutrPC2, X_star = ~ ALC01,
W = dat$OTU[,100], M = dat$M )
ZIPG_CI(ZIPG_res)
Fit zero-inflated poisson-gamma model via EM Algorithm
Description
Fit zero-inflated poisson-gamma model via EM Algorithm
Usage
ZIPG_main(
data,
W,
M,
X,
X_star,
return_model = TRUE,
pbWald_list = NULL,
bWald_list = NULL
)
Arguments
data |
Data.frame for covariates of interest |
W |
Count data |
M |
Sequencing depth, ZIPG use log(M) as offset by default |
X |
Formula of covariates of differential abundance |
X_star |
Formula of covariates of differential varibility |
return_model |
whether return full complete imfomation for fitted model |
pbWald_list |
A list of arguments for parameteric bootstrap Wald test, B for bootstrap sample size, X0 and X_star0 for formula of covariates included in H0 |
bWald_list |
A list of arguments for non-parameteric bootstrap Wald test, B for bootstrap sample size, |
Value
A list of ZIPG fitted model. Use ZIPG_summary() for a quick look at the results.
Examples
data(Dietary)
dat = Dietary
ZIPG_res <- ZIPG_main(data = dat$COV,
X = ~ALC01+nutrPC1+nutrPC2, X_star = ~ ALC01,
W = dat$OTU[,100], M = dat$M )
ZIPG_summary(ZIPG_res)
Simulate W from ZIPG model
Description
Simulate W from ZIPG model
Usage
ZIPG_simulate(
M,
X,
X_star,
A = 1,
d,
d_star,
parms,
N,
zi = TRUE,
returnU = FALSE
)
Arguments
M |
Sequencing depth |
X |
Covariates matrix with intercept, n * (d+1) |
X_star |
Covariates matrix with intercept, n * (d_star+1) |
A |
no use, reserved for multi-taxa |
d |
number of covariates in X |
d_star |
number of covariates in X_star |
parms |
model paraneters, input c(beta,beta*,gamma) |
N |
repetition times |
zi |
whether generate zero-inflated distribution |
returnU |
whether return fluctuation factor U |
Value
A list of W generated from ZIPG model with input parameter
Examples
data(Dietary)
dat = Dietary
sim_M = sample(dat$M,100,replace = TRUE)
sim_pre = rep(sample(rep(c(0,1),each = 10)),each = 5)
sim_PC1_mean = rep(rnorm(20,mean = 0,sd = 1),each = 5)
sim_PC1_error = rnorm(100,0,0.1)
sim_PC1 = sim_PC1_mean + sim_PC1_error
X = as.matrix(cbind(1,data.frame(X1 = sim_pre,X2 = sim_PC1)))
parms = c(-4.23,1,0.45,0.6,1,0,0) #p = 0.5
W_sim <- ZIPG_simulate(M = sim_M,X=X,X_star=X,d=2,d_star=2,parms = parms,N=100)
hist(W_sim$W_list[[1]])
ZIPG_res <- ZIPG_main(data = data.frame(X1 = sim_pre,X2 = sim_PC1),
X = ~X1+X2, X_star = ~ X1,W = W_sim$W_list[[2]], M = sim_M )
ZIPG_summary(ZIPG_res)
Summary for ZIPG_main() result.
Description
Summary for ZIPG_main() result.
Usage
ZIPG_summary(ZIPG_res, type = "Wald")
Arguments
ZIPG_res |
Result from ZIPG_main() |
type |
Type of hypothesis testing method, 'Wald','bWald' or 'pbWald'. |
Value
pvalue
Examples
data(Dietary)
dat = Dietary
ZIPG_res <- ZIPG_main(data = dat$COV,
X = ~ALC01+nutrPC1+nutrPC2, X_star = ~ ALC01,
W = dat$OTU[,100], M = dat$M )
ZIPG_summary(ZIPG_res)