taxa.compare: Compare taxa relative abundance
In metamicrobiomeR: Microbiome Data Analysis & Meta-Analysis with GAMLSS-BEZI & Random Effects
Description
Usage
Arguments
Value
Examples
Description
This function compares taxa relative abundance summary tables at all levels between groups using GAMLSS with BEZI or Linear/Linear Mixed Effect models (LM/LMEM) after filtering (using prevalence and relative abundance thresholds).
Usage
1
2
3
4
5
6
7
8
9
10
11
12
13
taxa.compare(
taxtab,
propmed.rel = "gamlss",
transform = "none",
zeroreplace.method = "none",
comvar,
adjustvar,
personid = "personid",
longitudinal = "yes",
percent.filter = 0.05,
relabund.filter = 5e-05,
p.adjust.method = "fdr"
)
Arguments
taxtab
taxa relative abundance table (already merged to mapping file) from phylum to species or any preferred highest taxa level.
propmed.rel
statistical method for comparing relative abundance. Options are "lm" for LM/LMEM or "gamlss" for GAMLSS with BEZI family.
transform
transformation of relative abundance data. Options are "none" for no transformation, "gmpr" for Geometric Mean of Pairwise Ratios (GMPR) normalization, "asin.sqrt" for arcsine transformation, "logit" for logit transformation, "clr" for centered log ratio transformation. Default is "none".
zeroreplace.method
Method for zero replacement implemented in R package *zCompositions*. Options are "none" for no replacement, "multKM" for Multiplicative Kaplan-Meier smoothing spline (KMSS) replacement, "multLN" for Multiplicative lognormal replacement, "multRepl" for Multiplicative simple replacement, "lrEM" for Log-ratio EM algorithm, "lrDA" for Log-ratio DA algorithm. Default is "none".
comvar
main variable for comparison
adjustvar
variables to be adjusted.
personid
name of variable for person id (applicable for longitudinal data)
longitudinal
whether data is longitudinal? Options are "yes" or "no". Default is "yes".
percent.filter
prevalence threshold (the percentage of number of samples the taxa/pathway available). Default is 0.05.
relabund.filter
relative abundance threshold (the minimum of the average relative abundance for a taxa/pathway to be retained). Default is 0.00005.
p.adjust.method
method for multiple testing adjustment. Options are those of the p.adjust.methods of stats:: p.adjust function. Default for this function is "fdr".
Value
matrice of coefficients, standard errors, p-values and multiple testing adjusted p-values of all variables in the models.
Examples
1
2
3
4
5
6
7
#Load summary tables of bacterial taxa relative abundance from Bangladesh data
data(taxtab6)
tab6<-as.data.frame(taxtab6)
tl<-colnames(taxtab6)[grep("k__bacteria.p__fusobacteria",colnames(taxtab6))]
taxacom.ex<-taxa.compare(taxtab=tab6[,c("personid","x.sampleid","bf","age.sample",tl)],
propmed.rel="gamlss",comvar="bf",adjustvar="age.sample",
longitudinal="yes",p.adjust.method="fdr")
Example output
Loading required package: gamlss
Loading required package: splines
Loading required package: gamlss.data
Attaching package: ‘gamlss.data’
The following object is masked from ‘package:datasets’:
sleep
Loading required package: gamlss.dist
Loading required package: MASS
Loading required package: nlme
Loading required package: parallel
********** GAMLSS Version 5.2-0 **********
For more on GAMLSS look at https://www.gamlss.com/
Type gamlssNews() to see new features/changes/bug fixes.
******************************************************************
Family: c("BEZI", "Zero Inflated Beta")
Call: gamlss::gamlss(formula = stats::as.formula(paste(taxname[i],
paste(c(comvar, adjustvar, "random(personid)"), collapse = "+"),
sep = "~")), family = BEZI, data = testdat, trace = FALSE)
Fitting method: RS()
------------------------------------------------------------------
Mu link function: logit
Mu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.77401 0.30668 -18.827 <2e-16 ***
bfNon_exclusiveBF -0.05981 0.36399 -0.164 0.870
bfNo_BF -0.43882 1.06824 -0.411 0.682
age.sample 0.02862 0.08537 0.335 0.738
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Sigma link function: log
Sigma Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.6285 0.1604 28.85 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Nu link function: logit
Nu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7177 0.1551 11.07 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
NOTE: Additive smoothing terms exist in the formulas:
i) Std. Error for smoothers are for the linear effect only.
ii) Std. Error for the linear terms may not be reliable.
------------------------------------------------------------------
No. of observations in the fit: 322
Degrees of Freedom for the fit: 41.46391
Residual Deg. of Freedom: 280.5361
at cycle: 20
Global Deviance: -377.117
AIC: -294.1892
SBC: -137.6816
******************************************************************
******************************************************************
Family: c("BEZI", "Zero Inflated Beta")
Call: gamlss::gamlss(formula = stats::as.formula(paste(taxname[i],
paste(c(comvar, adjustvar, "random(personid)"), collapse = "+"),
sep = "~")), family = BEZI, data = testdat, trace = FALSE)
Fitting method: RS()
------------------------------------------------------------------
Mu link function: logit
Mu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.77401 0.30668 -18.827 <2e-16 ***
bfNon_exclusiveBF -0.05981 0.36399 -0.164 0.870
bfNo_BF -0.43882 1.06824 -0.411 0.682
age.sample 0.02862 0.08537 0.335 0.738
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Sigma link function: log
Sigma Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.6285 0.1604 28.85 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Nu link function: logit
Nu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7177 0.1551 11.07 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
NOTE: Additive smoothing terms exist in the formulas:
i) Std. Error for smoothers are for the linear effect only.
ii) Std. Error for the linear terms may not be reliable.
------------------------------------------------------------------
No. of observations in the fit: 322
Degrees of Freedom for the fit: 41.46391
Residual Deg. of Freedom: 280.5361
at cycle: 20
Global Deviance: -377.117
AIC: -294.1892
SBC: -137.6816
******************************************************************
******************************************************************
Family: c("BEZI", "Zero Inflated Beta")
Call: gamlss::gamlss(formula = stats::as.formula(paste(taxname[i],
paste(c(comvar, adjustvar, "random(personid)"), collapse = "+"),
sep = "~")), family = BEZI, data = testdat, trace = FALSE)
Fitting method: RS()
------------------------------------------------------------------
Mu link function: logit
Mu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.77401 0.30668 -18.827 <2e-16 ***
bfNon_exclusiveBF -0.05981 0.36399 -0.164 0.870
bfNo_BF -0.43882 1.06824 -0.411 0.682
age.sample 0.02862 0.08537 0.335 0.738
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Sigma link function: log
Sigma Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.6285 0.1604 28.85 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Nu link function: logit
Nu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7177 0.1551 11.07 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
NOTE: Additive smoothing terms exist in the formulas:
i) Std. Error for smoothers are for the linear effect only.
ii) Std. Error for the linear terms may not be reliable.
------------------------------------------------------------------
No. of observations in the fit: 322
Degrees of Freedom for the fit: 41.46391
Residual Deg. of Freedom: 280.5361
at cycle: 20
Global Deviance: -377.117
AIC: -294.1892
SBC: -137.6816
******************************************************************
******************************************************************
Family: c("BEZI", "Zero Inflated Beta")
Call: gamlss::gamlss(formula = stats::as.formula(paste(taxname[i],
paste(c(comvar, adjustvar, "random(personid)"), collapse = "+"),
sep = "~")), family = BEZI, data = testdat, trace = FALSE)
Fitting method: RS()
------------------------------------------------------------------
Mu link function: logit
Mu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.78117 0.32065 -18.029 <2e-16 ***
bfNon_exclusiveBF -0.06259 0.36574 -0.171 0.864
bfNo_BF -0.44786 1.06816 -0.419 0.675
age.sample 0.02855 0.08629 0.331 0.741
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Sigma link function: log
Sigma Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.6510 0.1622 28.67 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Nu link function: logit
Nu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7419 0.1565 11.13 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
NOTE: Additive smoothing terms exist in the formulas:
i) Std. Error for smoothers are for the linear effect only.
ii) Std. Error for the linear terms may not be reliable.
------------------------------------------------------------------
No. of observations in the fit: 322
Degrees of Freedom for the fit: 40.49761
Residual Deg. of Freedom: 281.5024
at cycle: 20
Global Deviance: -366.568
AIC: -285.5728
SBC: -132.7125
******************************************************************
******************************************************************
Family: c("BEZI", "Zero Inflated Beta")
Call: gamlss::gamlss(formula = stats::as.formula(paste(taxname[i],
paste(c(comvar, adjustvar, "random(personid)"), collapse = "+"),
sep = "~")), family = BEZI, data = testdat, trace = FALSE)
Fitting method: RS()
------------------------------------------------------------------
Mu link function: logit
Mu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.76889 0.37948 -15.202 <2e-16 ***
bfNon_exclusiveBF -0.11675 0.42666 -0.274 0.785
bfNo_BF -0.51402 1.09430 -0.470 0.639
age.sample 0.03977 0.09919 0.401 0.689
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Sigma link function: log
Sigma Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.6293 0.1898 24.39 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Nu link function: logit
Nu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.104 0.179 11.75 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
NOTE: Additive smoothing terms exist in the formulas:
i) Std. Error for smoothers are for the linear effect only.
ii) Std. Error for the linear terms may not be reliable.
------------------------------------------------------------------
No. of observations in the fit: 322
Degrees of Freedom for the fit: 32.7031
Residual Deg. of Freedom: 289.2969
at cycle: 20
Global Deviance: -244.1
AIC: -178.6938
SBC: -55.2543
******************************************************************
Warning messages:
1: In RS() : Algorithm RS has not yet converged
2: In summary.gamlss(gamlss::gamlss(stats::as.formula(paste(taxname[i], :
summary: vcov has failed, option qr is used instead
3: In RS() : Algorithm RS has not yet converged
4: In summary.gamlss(gamlss::gamlss(stats::as.formula(paste(taxname[i], :
summary: vcov has failed, option qr is used instead
5: In RS() : Algorithm RS has not yet converged
6: In summary.gamlss(gamlss::gamlss(stats::as.formula(paste(taxname[i], :
summary: vcov has failed, option qr is used instead
7: In RS() : Algorithm RS has not yet converged
8: In summary.gamlss(gamlss::gamlss(stats::as.formula(paste(taxname[i], :
summary: vcov has failed, option qr is used instead
9: In RS() : Algorithm RS has not yet converged
10: In summary.gamlss(gamlss::gamlss(stats::as.formula(paste(taxname[i], :
summary: vcov has failed, option qr is used instead
metamicrobiomeR documentation built on Nov. 9, 2020, 5:06 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Examples
Description
This function compares taxa relative abundance summary tables at all levels between groups using GAMLSS with BEZI or Linear/Linear Mixed Effect models (LM/LMEM) after filtering (using prevalence and relative abundance thresholds).
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 | taxa.compare(
taxtab,
propmed.rel = "gamlss",
transform = "none",
zeroreplace.method = "none",
comvar,
adjustvar,
personid = "personid",
longitudinal = "yes",
percent.filter = 0.05,
relabund.filter = 5e-05,
p.adjust.method = "fdr"
)
|
Arguments
taxtab |
taxa relative abundance table (already merged to mapping file) from phylum to species or any preferred highest taxa level. |
propmed.rel |
statistical method for comparing relative abundance. Options are "lm" for LM/LMEM or "gamlss" for GAMLSS with BEZI family. |
transform |
transformation of relative abundance data. Options are "none" for no transformation, "gmpr" for Geometric Mean of Pairwise Ratios (GMPR) normalization, "asin.sqrt" for arcsine transformation, "logit" for logit transformation, "clr" for centered log ratio transformation. Default is "none". |
zeroreplace.method |
Method for zero replacement implemented in R package *zCompositions*. Options are "none" for no replacement, "multKM" for Multiplicative Kaplan-Meier smoothing spline (KMSS) replacement, "multLN" for Multiplicative lognormal replacement, "multRepl" for Multiplicative simple replacement, "lrEM" for Log-ratio EM algorithm, "lrDA" for Log-ratio DA algorithm. Default is "none". |
comvar |
main variable for comparison |
adjustvar |
variables to be adjusted. |
personid |
name of variable for person id (applicable for longitudinal data) |
longitudinal |
whether data is longitudinal? Options are "yes" or "no". Default is "yes". |
percent.filter |
prevalence threshold (the percentage of number of samples the taxa/pathway available). Default is 0.05. |
relabund.filter |
relative abundance threshold (the minimum of the average relative abundance for a taxa/pathway to be retained). Default is 0.00005. |
p.adjust.method |
method for multiple testing adjustment. Options are those of the p.adjust.methods of stats:: p.adjust function. Default for this function is "fdr". |
Value
matrice of coefficients, standard errors, p-values and multiple testing adjusted p-values of all variables in the models.
Examples
1 2 3 4 5 6 7 | #Load summary tables of bacterial taxa relative abundance from Bangladesh data
data(taxtab6)
tab6<-as.data.frame(taxtab6)
tl<-colnames(taxtab6)[grep("k__bacteria.p__fusobacteria",colnames(taxtab6))]
taxacom.ex<-taxa.compare(taxtab=tab6[,c("personid","x.sampleid","bf","age.sample",tl)],
propmed.rel="gamlss",comvar="bf",adjustvar="age.sample",
longitudinal="yes",p.adjust.method="fdr")
|
Example output
Loading required package: gamlss
Loading required package: splines
Loading required package: gamlss.data
Attaching package: ‘gamlss.data’
The following object is masked from ‘package:datasets’:
sleep
Loading required package: gamlss.dist
Loading required package: MASS
Loading required package: nlme
Loading required package: parallel
********** GAMLSS Version 5.2-0 **********
For more on GAMLSS look at https://www.gamlss.com/
Type gamlssNews() to see new features/changes/bug fixes.
******************************************************************
Family: c("BEZI", "Zero Inflated Beta")
Call: gamlss::gamlss(formula = stats::as.formula(paste(taxname[i],
paste(c(comvar, adjustvar, "random(personid)"), collapse = "+"),
sep = "~")), family = BEZI, data = testdat, trace = FALSE)
Fitting method: RS()
------------------------------------------------------------------
Mu link function: logit
Mu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.77401 0.30668 -18.827 <2e-16 ***
bfNon_exclusiveBF -0.05981 0.36399 -0.164 0.870
bfNo_BF -0.43882 1.06824 -0.411 0.682
age.sample 0.02862 0.08537 0.335 0.738
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Sigma link function: log
Sigma Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.6285 0.1604 28.85 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Nu link function: logit
Nu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7177 0.1551 11.07 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
NOTE: Additive smoothing terms exist in the formulas:
i) Std. Error for smoothers are for the linear effect only.
ii) Std. Error for the linear terms may not be reliable.
------------------------------------------------------------------
No. of observations in the fit: 322
Degrees of Freedom for the fit: 41.46391
Residual Deg. of Freedom: 280.5361
at cycle: 20
Global Deviance: -377.117
AIC: -294.1892
SBC: -137.6816
******************************************************************
******************************************************************
Family: c("BEZI", "Zero Inflated Beta")
Call: gamlss::gamlss(formula = stats::as.formula(paste(taxname[i],
paste(c(comvar, adjustvar, "random(personid)"), collapse = "+"),
sep = "~")), family = BEZI, data = testdat, trace = FALSE)
Fitting method: RS()
------------------------------------------------------------------
Mu link function: logit
Mu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.77401 0.30668 -18.827 <2e-16 ***
bfNon_exclusiveBF -0.05981 0.36399 -0.164 0.870
bfNo_BF -0.43882 1.06824 -0.411 0.682
age.sample 0.02862 0.08537 0.335 0.738
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Sigma link function: log
Sigma Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.6285 0.1604 28.85 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Nu link function: logit
Nu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7177 0.1551 11.07 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
NOTE: Additive smoothing terms exist in the formulas:
i) Std. Error for smoothers are for the linear effect only.
ii) Std. Error for the linear terms may not be reliable.
------------------------------------------------------------------
No. of observations in the fit: 322
Degrees of Freedom for the fit: 41.46391
Residual Deg. of Freedom: 280.5361
at cycle: 20
Global Deviance: -377.117
AIC: -294.1892
SBC: -137.6816
******************************************************************
******************************************************************
Family: c("BEZI", "Zero Inflated Beta")
Call: gamlss::gamlss(formula = stats::as.formula(paste(taxname[i],
paste(c(comvar, adjustvar, "random(personid)"), collapse = "+"),
sep = "~")), family = BEZI, data = testdat, trace = FALSE)
Fitting method: RS()
------------------------------------------------------------------
Mu link function: logit
Mu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.77401 0.30668 -18.827 <2e-16 ***
bfNon_exclusiveBF -0.05981 0.36399 -0.164 0.870
bfNo_BF -0.43882 1.06824 -0.411 0.682
age.sample 0.02862 0.08537 0.335 0.738
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Sigma link function: log
Sigma Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.6285 0.1604 28.85 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Nu link function: logit
Nu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7177 0.1551 11.07 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
NOTE: Additive smoothing terms exist in the formulas:
i) Std. Error for smoothers are for the linear effect only.
ii) Std. Error for the linear terms may not be reliable.
------------------------------------------------------------------
No. of observations in the fit: 322
Degrees of Freedom for the fit: 41.46391
Residual Deg. of Freedom: 280.5361
at cycle: 20
Global Deviance: -377.117
AIC: -294.1892
SBC: -137.6816
******************************************************************
******************************************************************
Family: c("BEZI", "Zero Inflated Beta")
Call: gamlss::gamlss(formula = stats::as.formula(paste(taxname[i],
paste(c(comvar, adjustvar, "random(personid)"), collapse = "+"),
sep = "~")), family = BEZI, data = testdat, trace = FALSE)
Fitting method: RS()
------------------------------------------------------------------
Mu link function: logit
Mu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.78117 0.32065 -18.029 <2e-16 ***
bfNon_exclusiveBF -0.06259 0.36574 -0.171 0.864
bfNo_BF -0.44786 1.06816 -0.419 0.675
age.sample 0.02855 0.08629 0.331 0.741
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Sigma link function: log
Sigma Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.6510 0.1622 28.67 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Nu link function: logit
Nu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.7419 0.1565 11.13 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
NOTE: Additive smoothing terms exist in the formulas:
i) Std. Error for smoothers are for the linear effect only.
ii) Std. Error for the linear terms may not be reliable.
------------------------------------------------------------------
No. of observations in the fit: 322
Degrees of Freedom for the fit: 40.49761
Residual Deg. of Freedom: 281.5024
at cycle: 20
Global Deviance: -366.568
AIC: -285.5728
SBC: -132.7125
******************************************************************
******************************************************************
Family: c("BEZI", "Zero Inflated Beta")
Call: gamlss::gamlss(formula = stats::as.formula(paste(taxname[i],
paste(c(comvar, adjustvar, "random(personid)"), collapse = "+"),
sep = "~")), family = BEZI, data = testdat, trace = FALSE)
Fitting method: RS()
------------------------------------------------------------------
Mu link function: logit
Mu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.76889 0.37948 -15.202 <2e-16 ***
bfNon_exclusiveBF -0.11675 0.42666 -0.274 0.785
bfNo_BF -0.51402 1.09430 -0.470 0.639
age.sample 0.03977 0.09919 0.401 0.689
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Sigma link function: log
Sigma Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.6293 0.1898 24.39 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
Nu link function: logit
Nu Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.104 0.179 11.75 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
------------------------------------------------------------------
NOTE: Additive smoothing terms exist in the formulas:
i) Std. Error for smoothers are for the linear effect only.
ii) Std. Error for the linear terms may not be reliable.
------------------------------------------------------------------
No. of observations in the fit: 322
Degrees of Freedom for the fit: 32.7031
Residual Deg. of Freedom: 289.2969
at cycle: 20
Global Deviance: -244.1
AIC: -178.6938
SBC: -55.2543
******************************************************************
Warning messages:
1: In RS() : Algorithm RS has not yet converged
2: In summary.gamlss(gamlss::gamlss(stats::as.formula(paste(taxname[i], :
summary: vcov has failed, option qr is used instead
3: In RS() : Algorithm RS has not yet converged
4: In summary.gamlss(gamlss::gamlss(stats::as.formula(paste(taxname[i], :
summary: vcov has failed, option qr is used instead
5: In RS() : Algorithm RS has not yet converged
6: In summary.gamlss(gamlss::gamlss(stats::as.formula(paste(taxname[i], :
summary: vcov has failed, option qr is used instead
7: In RS() : Algorithm RS has not yet converged
8: In summary.gamlss(gamlss::gamlss(stats::as.formula(paste(taxname[i], :
summary: vcov has failed, option qr is used instead
9: In RS() : Algorithm RS has not yet converged
10: In summary.gamlss(gamlss::gamlss(stats::as.formula(paste(taxname[i], :
summary: vcov has failed, option qr is used instead
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.