| Type: | Package |
| Title: | GLM-Based Ordination Method |
| Version: | 1.0 |
| Date: | 2017-05-13 |
| Author: | Michael B. Sohn |
| Maintainer: | Michael B. Sohn <msohn@mail.med.upenn.edu> |
| Description: | A zero-inflated quasi-Poisson factor model to display similarity between samples visually in a low (2 or 3) dimensional space. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Packaged: | 2017-05-14 00:43:31 UTC; mbsohn |
| Repository: | CRAN |
| Date/Publication: | 2017-05-14 17:30:27 UTC |
GLM-Based Ordination Method
Description
preliminary analysis of similarity between samples in a low (2 or 3) dimensional display.
Author(s)
Michael B. Sohn
Maintainer: Michael B. Sohn <msohn@mail.med.upenn.edu>
References
Sohn, M.B. and Li, H. (2017). A GLM-Based Latent Variable Ordination Method for Microbiome Samples (Submitted).
Examples
## Not run:
# load test data
data(gomms_test_data);
# estimate factor scores
cdat <- as.matrix(gomms_test_data[,-ncol(gomms_test_data)]);
rslt <- gomms(cdat);
# plot estimated factor scores
y <- as.matrix(gomms_test_data$group);
gomms.plot(rslt, y);
## End(Not run)
Probability of a Zero from a Zero State
Description
estimate the probability of a zero from a zero state.
Usage
Qqpois(cdat, eta.hat, mu.hat, dispersion)
Arguments
cdat |
count Data. |
eta.hat |
estimated proportion of zeros from a zero state. |
mu.hat |
estimated mean count. |
dispersion |
estimated values for dispersion. |
Value
estimated probability of a zero from a zero state.
GLM-Based Ordination Method for Microbiome Samples
Description
estimate factor loadings and scores.
Usage
gomms(X, n.factors = 2, min.prop.nonzeros = 0.05, show.max.delta = FALSE)
Arguments
X |
raw count data. |
n.factors |
number of factors. Default value is 2. |
min.prop.nonzeros |
minimum proportion of nonzeros allowed in analysis. |
show.max.delta |
display the maximum different between jth and (j+1)th iterations. |
Value
estimated factor scores.
Author(s)
Michael B. Sohn
Maintainer: Michael B. Sohn <msohn@mail.med.upenn.edu>
References
Sohn, M.B. and Li, H. (2017). A GLM-Based Latent Variable Ordination Method for Microbiome Samples (Submitted).
Plot Factor Loadings
Description
plot estimated factor loadings for each sample.
Usage
gomms.plot(X, Y, col.markers = NULL, pch.markers = NULL, ...)
Arguments
X |
two dimnsional matrix of factor scores. |
Y |
one or two dimensional matrix of classification. |
col.markers |
user specified colors for classification. |
pch.markers |
user specified plot symbols for classification. |
... |
optional graphical parameters to be passed. |
Test Data
Description
70 samples and 83 features. The last column contains the population identification for each sample.
Usage
data(gomms_test_data)