| Title: | Intraclass Correlation Coefficient for Count Data |
| Version: | 1.1.2 |
| Author: | Josep L. Carrasco <jlcarrasco@ub.edu> |
| Maintainer: | Josep L. Carrasco <jlcarrasco@ub.edu> |
| Depends: | R (≥ 4.0) |
| Imports: | glmmTMB, ggplot2, Deriv, gridExtra, VGAM, dplyr |
| Suggests: | knitr, rmarkdown |
| Description: | Estimates the intraclass correlation coefficient (ICC) for count data to assess repeatability (intra-methods concordance) and concordance (between-method concordance). In the concordance setting, the ICC is equivalent to the concordance correlation coefficient estimated by variance components. The ICC is estimated using the estimates from generalized linear mixed models. The within-subjects distributions considered are: Poisson; Negative Binomial with additive and proportional extradispersion; Zero-Inflated Poisson; and Zero-Inflated Negative Binomial with additive and proportional extradispersion. The statistical methodology used to estimate the ICC with count data can be found in Carrasco (2010) <doi:10.1111/j.1541-0420.2009.01335.x>. |
| VignetteBuilder: | knitr |
| Encoding: | UTF-8 |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | no |
| Packaged: | 2024-02-28 13:27:41 UTC; jlcarrasco |
| Repository: | CRAN |
| Date/Publication: | 2024-02-28 14:40:02 UTC |
CD34+ count cell data
Description
A dataset containing CD34+ count cell data obtained by two methods
Usage
AF
Format
A data frame with 40 rows and 3 variables:
- y
CD34+ counts
- id
Subject
- met
Method
References
Fornas, O., Garcia, J., and Petriz, J. (2000). Flow cytometry counting of CD34+ cells in whole blood. Nature Medicine 6, 833–836.
Dispersion test for GLMM
Description
Dispersion test for GLMM
Usage
DispersionTest(x)
Arguments
x |
An object of class GOF generated by GOF_check function. |
Details
The function prints the dispersion of sample randomized quantile residuals (RQR) and the simulated P-value.
Value
A vector with the sample RQR dispersion and the P-value.
See Also
Examples
# Poisson model. Repeatability setting.
iccpois<-icc_counts(EPP,y="Social",id="id")
iccpois.gof<-GOF_check(iccpois)
DispersionTest(iccpois.gof)
Sparrow fledglings paternity data
Description
A dataset containing the counts of fledglings a male had in every breeding season
Usage
EPP
Format
A data frame with 193 rows and 3 variables:
- Social
Fledglings counts
- id
Subject identifier
- Year
Breeding season
References
Schroeder, J., Burke, T., Mannarelli, M. E., Dawson, D. A., & Nakagawa, S. (2012). Maternal effects and heritability of annual productivity. Journal of Evolutionary Biology, 25, 149– 156.
Goodness of fit for GLMM
Description
Assessment of goodness of fit for GLMM
Usage
GOF_check(x, nsim = 100, alpha = 0.05)
Arguments
x |
An object of clas iccc. |
nsim |
Number of simulations to run. Default is set to 100. |
alpha |
Level of significance |
Details
Randomized quantile residuals are computed for the fitted model. Simulations based on the fitted model are generated and the model is refitted to each simulated dataset. Envelopes for RQR are built as the appropriate quantile (in relation to the level fo significance) of RQR from the refitted models. Additionally, a test for dispersion and zero inflation are carried out by comparing the RQR dispersion and the number of zeros from the original model and data to those from the refitted models and simulated data.
Value
An object of class GOF for which method plot is available. A list with the following components:
-
plot_env. Plot of RQR envelopes with the original RQR.
-
plot_var. Plot of the simulated RQR dispersion.
-
plot_zi. Plot of the count of zeros in the simulated datasets.
-
res_var. Dispersion of RQR from the original sample.
-
pval_var. Proportion of simulated RQR dispersion that are greater than the original dispersion that can be interpreted as a simulated P-value to check the goodness of fit on dispersion.
-
zero_count. Count of zeros in the original sample.
-
pval_zi. Proportion of simulated zero count that are greater than that of the original sample. It can be interpreted as a simulated P-value to check the hypothesis of zero-inflation.
See Also
plot.GOF(), DispersionTest(),ZeroTest()
Examples
# Poisson model. Repeatability setting.
iccpois<-icc_counts(EPP,y="Social",id="id")
GOF_check(iccpois)
# Zero-inflated Poisson model. Repeatability setting
icczip<-icc_counts(EPP,y="Social",id="id",fam="zip")
GOF_check(icczip)
Tick counts data
Description
A dataset containing the counts of ticks in Grimsö area
Usage
Grimso
Format
A data frame with 520 rows and 3 variables:
- Tot
Tick counts
- TransectID
Subject identifier
- Round
Identifier of repeated measurements
References
Kjellander, P.L., Aronsson, M., Bergvall, U.A. et al. (2021). Validating a common tick survey method: cloth-dragging and line transects. Exp Appl Acarol 83, 131–146.
Prints the intraclass correlation coefficient
Description
Prints the intraclass correlation coefficient (ICC)
Usage
ICC(x, digits = getOption("digits"))
Arguments
x |
An object of class iccc |
digits |
Number of digits to print |
Value
A vector with the ICC estimate, its standard error and confidence interval.
See Also
Examples
# Poisson model. Repeatability setting.
iccpois<-icc_counts(Grimso,y="Tot",id="TransectID")
ICC(iccpois)
# Negative Binomial with proportional extra-dispersion. Concordance setting
iccnb2<-icc_counts(AF,y="y",id="id",met="met",type="con",fam="nbinom2")
ICC(iccnb2)
# Zero-inflated Poisson model. Repeatability setting
icczip<-icc_counts(EPP,y="Social",id="id",fam="zip")
ICC(icczip)
GLMM variance components
Description
Prints the GLMM variance components and related parameters to estimate the intraclass correlation coefficient (ICC)
Usage
VarComp(x, digits = getOption("digits"))
Arguments
x |
An object of class iccc |
digits |
Number of digits to print |
Value
A vector with the variance components and related parameters
See Also
Examples
# Poisson model. Repeatability setting.
iccpois<-icc_counts(Grimso,y="Tot",id="TransectID")
VarComp(iccpois)
# Negative Binomial with proportional extra-dispersion. Concordance setting
iccnb2<-icc_counts(AF,y="y",id="id",met="met",type="con",fam="nbinom2")
VarComp(iccnb2)
# Zero-inflated Poisson model. Repeatability setting
icczip<-icc_counts(EPP,y="Social",id="id",fam="zip")
VarComp(icczip)
Zero-Inflation test for GLMM
Description
Zero-Inflation test for GLMM
Usage
ZeroTest(x)
Arguments
x |
An object of class GOF generated by GOF_check function. |
Details
The function prints the count of zeros in the sample and the simulated P-value.
Value
A vector with the zero count and the P-value.
See Also
Examples
# Poisson model. Repeatability setting.
iccpois<-icc_counts(EPP,y="Social",id="id")
iccpois.gof<-GOF_check(iccpois)
ZeroTest(iccpois.gof)
# Zero-inflated Poisson model. Repeatability setting
icczip<-icc_counts(EPP,y="Social",id="id",fam="zip")
icczip.gof<-GOF_check(icczip)
ZeroTest(icczip.gof)
Estimates GLMM. Concordance setting.
Description
Estimates the GLMM model for concordance setting
Usage
fit_model_con(
data,
fam = c("poisson", "nbinom2", "nbinom1", "zip", "zinb1", "zinb2")
)
Arguments
data |
A data frame containing at least three columns: outcome (named as y), subject identifier (named as id) and method identifier (named as met). |
fam |
Character string. The within-subjects pdf to use. Valid options are: "poisson" (default) for Poisson pdf; "nbinom1" for Negative Binomial pdf with additive extradispersion; "nbinom2" for Negative Binomial pdf with proportional extradispersion; "zip" for zero-inflated Poisson pdf; "zinb1" for zero-inflated nbinom1 pdf; "zinb2" for zero-inflated nbinom2 pdf; |
Value
An object of class glmmTMB with the model estimates.
Estimates GLMM. Repeatability setting.
Description
Estimates the GLMM model for concordance setting
Usage
fit_model_rep(
data,
fam = c("poisson", "nbinom2", "nbinom1", "zip", "zinb1", "zinb2")
)
Arguments
data |
A data frame containing at least two columns: outcome (named as y) and subject identifier (named as id). |
fam |
Character string. The within-subjects pdf to use. Valid options are: "poisson" (default) for Poisson pdf; "nbinom1" for Negative Binomial pdf with additive extradispersion; "nbinom2" for Negative Binomial pdf with proportional extradispersion; "zip" for zero-inflated Poisson pdf; "zinb1" for zero-inflated nbinom1 pdf; "zinb2" for zero-inflated nbinom2 pdf; |
Value
An object of class glmmTMB with the model estimates.
Intraclass correlation coefficient (ICC) for count data
Description
Estimates the intraclass correlation coefficient (ICC) for count data
Usage
icc_counts(
data,
y,
id,
met = NULL,
type = c("rep", "con"),
fam = c("poisson", "nbinom1", "nbinom2", "zip", "zinb1", "zinb2"),
conf = 0.95
)
Arguments
data |
A data frame containing at least two columns: outcome and subject identifier. In case of estimating the ICC for concordance setting, a third column with the method identifier will be needed. |
y |
Character string indicating the name of the outcome column in the dataset. |
id |
Character string indicating the name of the subjects column in the dataset. |
met |
Character string indicating the name of the methods column in the dataset. Only needed in the concordance setting. |
type |
Character string. It chooses the setting in which the ICC should be estimated. Valid values are: |
fam |
Character string. The within-subjects pdf to use. Valid options are: |
conf |
Confidence level for the confidence interval estimation. Default value is set to 95%. |
Details
The intraclass correlation coefficient (ICC) is estimated using the variance components of a generalized linear mixed model (GLMM) (Carrasco, 2010).
The GLMM is estimated using the glmmTMB package (Brooks et al. 2017). The ICC standard error is estimated by applying the delta method (Ver Hoef, 2012) using the variance-covariance matrix of parameters involved in the ICC estimate.
The parameters involved in the estimation of the ICC depends on the within-subjects pdf family chosen: the between-subjects variance, the between-methods variability (in case of concordance analysis), and parameters implicated in the within-subjects family chosen. In all cases the output includes the overall expectation identified as mu; the between-subjects variance named as BSVar (log-scale); the between-methods variability (in case of concordance analysis) named as BMVar (log-scale).
In the Negative Binomial pdf with variance linearly increasing with the mean (Hardin and Hilbe, 2007),
Var(y_i)=\mu_i*(1+r)
and Negative Binomial pdf with variance quadratically increasing with the mean (Hardin and Hilbe, 2007)
Var(y_i)=\mu_i*(1+r*\mu_i)
the extra-dispersion parameter r is included in the output.
For zero-inflated models, the probability of observing an extra zero is included in the output as pi.
Value
An object of class iccc.The output is a list with the following components:
-
model. An object of class glmmTMB. The estimated generalized linear mixed model.
-
ICC. Estimate of the ICC, its standard error and confidence interval.
-
varcomp. Variance components and parameters related to ICC expression.
References
Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Mächler, M. and Bolker, B. M. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal, 9(2), 378–400.
Carrasco, J. (2010). A Generalized Concordance Correlation Coefficient Based on the Variance Components Generalized Linear Mixed Models for Overdispersed Count Data. Biometrics, 66(3), 897-904.
W. Hardin and J. Hilbe. (2007). Generalized Linear Models and Extensions. Stata Press.
Ver Hoef, J.M. (2012) Who Invented the Delta Method?, The American Statistician, 66:2, 124-127,
Examples
# Poisson model. Repeatability setting.
iccpois<-icc_counts(Grimso,y="Tot",id="TransectID")
# Negative Binomial with proportional extra-dispersion. Concordance setting
iccnb2<-icc_counts(AF,y="y",id="id",met="met",type="con",fam="nbinom2")
# Zero-inflated Poisson model. Repeatability setting
icczip<-icc_counts(EPP,y="Social",id="id",fam="zip")
Goodness of fit plots
Description
Draws the plots to assess the goodness of fit
Usage
## S3 method for class 'GOF'
plot(x, type = c("all", "envelope", "dispersion", "zeros"), ...)
Arguments
x |
An object of class GOF generated by GOF_check function. |
type |
Which plot to draw. Values: all (default); envelope for envelopes of randomized quantile residuals; dispersion for plot to assess the dispersion; zeros for plot to assess the zero inflation. |
... |
Ignore |
See Also
Examples
# Poisson model. Repeatability setting.
iccpois<-icc_counts(EPP,y="Social",id="id")
iccpois.gof<-GOF_check(iccpois)
plot(iccpois.gof)
plot(iccpois.gof,type="envelope")
plot(iccpois.gof,type="dispersion")
plot(iccpois.gof,type="zeros")
Bland-Altman plot
Description
Draws the Bland-Altman plot. The differences among pair of data from the same subject is represented on y-axis. The mean of data from the same subject is represented on x-axis. Additionally, a bar plot with the proportions of differences can be drawn.
Usage
plot_BA(data, y, id, rm = NULL, type = c("BA", "bars"))
Arguments
data |
A data frame containing at least two columns: outcome and subject identifier. |
y |
Character string indicating the name of the outcome column in the data set. |
id |
Character string indicating the name of the subjects column in the data set. |
rm |
Optional. Character string indicating the name of column that stands for the repeated measurements from the same subjects in the dataset. Only needed to identify the differences in the Bland-Altman plot. |
type |
Character. Which plot has to be drawn? Default option is Bland-Altman plot ("BA" option). Alternatively, the bar plot of the proportion of the differences can be created ("bars" option). |
Value
A list with the following components:
-
plot. An object of class ggplot. The plot generated.
-
data. An object of class dataframe that contains the data used to generated the plot.
Examples
plot_BA(EPP,y="Social",id="id")
plot_BA(EPP,y="Social",id="id",rm="Year")
plot_BA(EPP,y="Social",id="id",type="bars")
ICC for Negative Binomial with additive extradispersion. Repeatability setting
Description
ICC for Negative Binomial with additive extradispersion. Repeatability setting
Usage
r_NB1(b1, b2, b3)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
Value
Scalar
ICC for Negative Binomial with additive extradispersion. Concordance setting
Description
ICC for Negative Binomial with additive extradispersion. Concordance setting
Usage
r_NB1_2(b1, b2, b3, sb)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
sb |
Between-methods variability |
Value
Scalar
ICC for Negative Binomial with proportional extradispersion. Repeatability setting
Description
ICC for Negative Binomial with proportional extradispersion. Repeatability setting
Usage
r_NB2(b1, b2, b3)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
Value
Scalar
ICC for Negative Binomial with proportional extradispersion. Concordance setting
Description
ICC for Negative Binomial with proportional extradispersion. Concordance setting
Usage
r_NB2_2(b1, b2, b3, sb)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
sb |
Between-methods variability |
Value
Scalar
ICC Poisson model. Repeatability setting
Description
ICC for Poisson model. Repeatability setting
Usage
r_Pois(b1, b2)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
Value
Scalar
ICC Poisson model. Concordance setting
Description
ICC for Poisson model. Concordance setting
Usage
r_Pois2(b1, b2, sb)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
sb |
Between-methods variability |
Value
Scalar
ICC for Zero-Inflated Negative Binomial with additive extradispersion. Repeatability setting
Description
ICC for Zero-Inflated Negative Binomial with additive extradispersion. Repeatability setting
Usage
r_ZINB1(b1, b2, b3, b4)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
b4 |
Parameter related to extra zero probability in the GLMM model |
Value
Scalar
ICC for Zero-Inflated Negative Binomial with additive extradispersion. Concordance setting
Description
ICC for Zero-Inflated Negative Binomial with additive extradispersion. Concordance setting
Usage
r_ZINB1_2(b1, b2, b3, b4, sb)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
b4 |
Parameter related to extra zero probability in the GLMM model |
sb |
Between-methods variability |
Value
Scalar
ICC for Zero-Inflated Negative Binomial with proportional extradispersion. Repeatability setting
Description
ICC for Zero-Inflated Negative Binomial with proportional extradispersion. Repeatability setting
Usage
r_ZINB2(b1, b2, b3, b4)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
b4 |
Parameter related to extra zero probability in the GLMM model |
Value
Scalar
ICC for Zero-Inflated Negative Binomial with proportional extradispersion. Concordance setting
Description
ICC for Zero-Inflated Negative Binomial with proportional extradispersion. Concordance setting
Usage
r_ZINB2_2(b1, b2, b3, b4, sb)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
b4 |
Parameter related to extra zero probability in the GLMM model |
sb |
Between-methods variability |
Value
Scalar
ICC Zero-Inflated Poisson model. Repeatability setting
Description
ICC for Zero-Inflated Poisson model. Repeatability setting
Usage
r_ZIP(b1, b2, b3)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra zero probability in the GLMM model |
Value
Scalar
ICC Zero-Inflated Poisson model. Concordance setting
Description
ICC for Zero-Inflated Poisson model. Concordance setting
Usage
r_ZIP2(b1, b2, b3, sb)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra zero probability in the GLMM model |
sb |
Between-methods variability |
Value
Scalar
ICC concordance
Description
Estimates the ICC for concordance setting for poisson, nbinom1 and nbinom2 families.
Usage
r_est_con(data, model)
Arguments
data |
A data frame containing at least three columns: outcome (named as y), subject identifier (named as id) and method identifier (named as met). |
model |
A glmmTMB obejct. |
Value
A data frame with ICC estimate, its standard error and variance components.
ICC concordance for zero-inflated models
Description
Estimates the ICC for concordance setting for zero-inflated models (poisson, nbinom1 and nbinom2 families).
Usage
r_est_con_zi(data, model)
Arguments
data |
A data frame containing at least three columns: outcome (named as y), subject identifier (named as id) and method identifier (named as met). |
model |
A glmmTMB obejct. |
Value
A data frame with ICC estimate, its standard error and variance components.
ICC repeatability
Description
Estimates the ICC for repeatability setting for poisson, nbinom1 and nbinom2 families.
Usage
r_est_rep(data, model)
Arguments
data |
A data frame containing at least two columns: outcome (named as y) and subject identifier (named as id). |
model |
A glmmTMB obejct. |
Value
A data frame with ICC estimate, its standard error and variance components.
ICC repeatability for zero-inflated models
Description
Estimates the ICC for repeatability setting for zero-inflated models (poisson, nbinom1 and nbinom2 families).
Usage
r_est_rep_zi(data, model)
Arguments
data |
A data frame containing at least two columns: outcome (named as y) and subject identifier (named as id). |
model |
A glmmTMB obejct. |
Value
A data frame with ICC estimate, its standard error and variance components.
RQR for GLMM Computes the randomized quantile residuals for GLMM
Description
RQR for GLMM Computes the randomized quantile residuals for GLMM
Usage
rqr(x)
Arguments
x |
An object of clas iccc. |
Details
Randomized quantile residuals (RQR) are computed for GLMMM. The within-cluster families considered are Poisson, Negative Binomial with additive and proportional extra-dispersion and their zero-inflated extensions. For further details on RQR see Dunn and Smyth (1996) and Feng et al (2020)
Value
A vector with the residuals.
References
Dunn PK, Smyth GK. (1996). Randomized quantile residuals. J Comput Graph Stat.5(3):236–44.
Feng et al. (2020). A comparison of residual diagnosis tools for diagnosing regression models for count data. BMC Medical Research Methodology 20:175
RQR for NegBin1 GLMM Computes the randomized quantile residuals for NegBin1 GLMM
Description
RQR for NegBin1 GLMM Computes the randomized quantile residuals for NegBin1 GLMM
Usage
rqr_nb1(x)
Arguments
x |
An object of clas iccc. |
Value
A vector with the residuals.
RQR for NegBin2 GLMM Computes the randomized quantile residuals for NegBin2 GLMM
Description
RQR for NegBin2 GLMM Computes the randomized quantile residuals for NegBin2 GLMM
Usage
rqr_nb2(x)
Arguments
x |
An object of clas iccc. |
Value
A vector with the residuals.
RQR for Poisson GLMM Computes the randomized quantile residuals for Poisson GLMM
Description
RQR for Poisson GLMM Computes the randomized quantile residuals for Poisson GLMM
Usage
rqr_pois(x)
Arguments
x |
An object of clas iccc. |
Value
A vector with the residuals.
RQR for ZINB1 GLMM Computes the randomized quantile residuals for ZINB1 GLMM
Description
RQR for ZINB1 GLMM Computes the randomized quantile residuals for ZINB1 GLMM
Usage
rqr_zinb1(x)
Arguments
x |
An object of clas iccc. |
Value
A vector with the residuals.
RQR for ZINB2 GLMM Computes the randomized quantile residuals for ZINB2 GLMM
Description
RQR for ZINB2 GLMM Computes the randomized quantile residuals for ZINB2 GLMM
Usage
rqr_zinb2(x)
Arguments
x |
An object of clas iccc. |
Value
A vector with the residuals.
RQR for ZIP GLMM Computes the randomized quantile residuals for ZIP GLMM
Description
RQR for ZIP GLMM Computes the randomized quantile residuals for ZIP GLMM
Usage
rqr_zip(x)
Arguments
x |
An object of clas iccc. |
Value
A vector with the residuals.
ICC standard error for Negative Binomial with a additive extradispersion. Repeatability setting
Description
ICC standard error for Negative Binomial with additive extradispersion. Repeatability setting
Usage
se_r_NB1(b1, b2, b3, CovB)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
CovB |
Variance covariance matrix of b1, b2 and b3 |
Value
Scalar
ICC standard error for Negative Binomial with additive extradispersion. Repeatability setting
Description
ICC standard error for Negative Binomial with additive extradispersion. Repeatability setting
Usage
se_r_NB1_2(b1, b2, b3, sb, CovB, var.sb)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
sb |
Between-methods variability |
CovB |
Variance covariance matrix of b1, b2 and b3 |
var.sb |
Standard error of sb (in variance scale). |
Value
Scalar
ICC standard error for Negative Binomial with proportional extradispersion. Repeatability setting
Description
ICC standard error for Negative Binomial with proportional extradispersion. Repeatability setting
Usage
se_r_NB2(b1, b2, b3, CovB)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
CovB |
Variance covariance matrix of b1, b2 and b3 |
Value
Scalar
ICC standard error for Negative Binomial with proportional extradispersion. Repeatability setting
Description
ICC standard error for Negative Binomial with proportional extradispersion. Repeatability setting
Usage
se_r_NB2_2(b1, b2, b3, sb, CovB, var.sb)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
sb |
Between-methods variability |
CovB |
Variance covariance matrix of b1, b2 and b3 |
var.sb |
Standard error of sb (in variance scale). |
Value
Scalar
ICC standard error for Poisson model. Repeatability setting
Description
ICC standard error for Poisson model. Repeatability setting
Usage
se_r_Pois(b1, b2, CovB)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
CovB |
Variance covariance matrix of b1 and b2 |
Value
Scalar
ICC standard error for Poisson model. Concordance setting
Description
ICC standard error for Poisson model. Concordance setting
Usage
se_r_Pois2(b1, b2, sb, CovB, var.sb)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
sb |
Between-methods variability |
CovB |
Variance covariance matrix of b1 and b2 |
var.sb |
Standard error of sb (in variance scale). |
Value
Scalar
ICC standard error for Zero-Inflated Negative Binomial with a additive extradispersion. Repeatability setting
Description
ICC standard error for Zero-Inflated Negative Binomial with additive extradispersion. Repeatability setting
Usage
se_r_ZINB1(b1, b2, b3, b4, CovB)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
b4 |
Parameter related to extra zero probability in the GLMM model |
CovB |
Variance covariance matrix of b1, b2, b3 and b4 |
Value
Scalar
ICC standard error for Zero-Inflated Negative Binomial with additive extradispersion. Repeatability setting
Description
ICC standard error for Zero-Inflated Negative Binomial with additive extradispersion. Repeatability setting
Usage
se_r_ZINB1_2(b1, b2, b3, b4, sb, CovB, var.sb)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
b4 |
Parameter related to extra zero probability in the GLMM model |
sb |
Between-methods variability |
CovB |
Variance covariance matrix of b1, b2, b3 and b4 |
var.sb |
Standard error of sb (in variance scale). |
Value
Scalar
ICC standard error for Zero-Inflated Negative Binomial with proportional extradispersion. Repeatability setting
Description
ICC standard error for Zero-Inflated Negative Binomial with proportional extradispersion. Repeatability setting
Usage
se_r_ZINB2(b1, b2, b3, b4, CovB)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
b4 |
Parameter related to extra zero probability in the GLMM model |
CovB |
Variance covariance matrix of b1, b2, b3 and b4 |
Value
Scalar
ICC standard error for Zero-Inflated Negative Binomial with proportional extradispersion. Repeatability setting
Description
ICC standard error for Zero-Inflated Negative Binomial with proportional extradispersion. Repeatability setting
Usage
se_r_ZINB2_2(b1, b2, b3, b4, sb, CovB, var.sb)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra-dispersion in the GLMM model |
b4 |
Parameter related to extra zero probability in the GLMM model |
sb |
Between-methods variability |
CovB |
Variance covariance matrix of b1, b2, b3 and b4 |
var.sb |
Standard error of sb (in variance scale). |
Value
Scalar
ICC standard error for Zero-Inflated Poisson model. Repeatability setting
Description
ICC standard error for Zero-Inflated Poisson model. Repeatability setting
Usage
se_r_ZIP(b1, b2, b3, CovB)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra zero probability in the GLMM model |
CovB |
Variance covariance matrix of b1,b2 and b3 |
Value
Scalar
ICC standard error for Zero-Inflated Poisson model. Concordance setting
Description
ICC standard error for Zero-Inflated Poisson model. Concordance setting
Usage
se_r_ZIP2(b1, b2, b3, sb, CovB, var.sb)
Arguments
b1 |
Intercept of GLMM |
b2 |
Parameter related to between-subjects variance in the GLMM model |
b3 |
Parameter related to extra zero probability in the GLMM model |
sb |
Between-methods variability |
CovB |
Variance covariance matrix of b1, b2 and b3. |
var.sb |
Standard error of sb (in variance scale). |
Value
Scalar