| Title: | Generalized Linear Mixed Models via Monte Carlo Likelihood Approximation |
|---|---|
| Description: | Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information. |
| Authors: | Christina Knudson [aut, cre], Charles J. Geyer [ctb], Sydney Benson [ctb] |
| Maintainer: | Christina Knudson <[email protected]> |
| License: | GPL-2 |
| Version: | 1.4.5 |
| Built: | 2024-11-22 04:36:55 UTC |
| Source: | https://github.com/cran/glmm |
Tests of the presence of the bacteria H. influenzae in children with otitis media in the Northern Territory of Australia.
data(bacteria)data(bacteria)
A data frame with the following columns:
Presence or absence: a factor with levels n and y.
active/placebo: a factor with levels a and p.
hi/low compliance: a factor with levels hi and lo.
Numeric: week of test.
Subject ID: a factor.
A factor with levels placebo, drug, drug+, a re-coding of ap and hilo.
y reformatted as 0/1 rather than n/y.
Dr. A. Leach tested the effects of a drug on 50 children with a history of otitis media in the Northern Territory of Australia. The children were randomized to the drug or the a placebo, and also to receive active encouragement to comply with taking the drug.
The presence of H. influenzae was checked at weeks 0, 2, 4, 6 and 11: 30 of the checks were missing and are not included in this data frame.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S, Fourth edition. Springer.
data(bacteria)data(bacteria)
Given a scalar eta, this calculates the cumulant and two derivatives for the Bernoulli family. Also checks that the data are entered correctly.
bernoulli.glmm()bernoulli.glmm()
family.glmm |
The family name, as a string. |
link |
The link function (canonical link is required), as a string. |
cum |
The cumulant function. |
cp |
The first derivative of the cumulant function. |
cpp |
The second derivative of the cumulant function. |
checkData |
A function to check that all data are either 0 or 1. |
This function is to be used by the glmm command.
Christina Knudson
eta<--3:3 bernoulli.glmm()$family.glmm bernoulli.glmm()$cum(eta) bernoulli.glmm()$cp(1) bernoulli.glmm()$cpp(2)eta<--3:3 bernoulli.glmm()$family.glmm bernoulli.glmm()$cum(eta) bernoulli.glmm()$cp(1) bernoulli.glmm()$cpp(2)
Given a scalar eta and the number of trials, this calculates the cumulant and two derivatives for the Bernoulli family. Also checks that the data are entered correctly.
binomial.glmm()binomial.glmm()
family.glmm |
The family name, as a string. |
link |
The link function (canonical link is required), as a string. |
cum |
The cumulant function. |
cp |
The first derivative of the cumulant function. |
cpp |
The second derivative of the cumulant function. |
checkData |
A function to check that all data are nonnegative. |
This function is to be used by the glmm command.
Christina Knudson
eta<--3:3 ntrials <- 1 binomial.glmm()$family.glmm binomial.glmm()$cum(eta, ntrials) binomial.glmm()$cp(1, ntrials) binomial.glmm()$cpp(2, ntrials)eta<--3:3 ntrials <- 1 binomial.glmm()$family.glmm binomial.glmm()$cum(eta, ntrials) binomial.glmm()$cp(1, ntrials) binomial.glmm()$cpp(2, ntrials)
This data set contains simulated data from the paper of Booth and Hobert (referenced below) as well as another vector.
data(Booth2)data(Booth2)
A data frame with 3 columns:
Response vector.
Fixed effect model matrix. The matrix has just one column vector.
A categorical vector to be used for part of the random effect model matrix.
A categorical vector to be used for part of the random effect model matrix.
The original data set was generated by Booth and Hobert using a single variance component, a single fixed effect, no intercept, and a logit link. This data set has the z2 vector added purely to illustrate an example with multiple variance components.
Booth, J. G. and Hobert, J. P. (1999) Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm. Journal of the Royal Statistical Society, Series B, 61, 265–285. doi:10.1111/1467-9868.00176.
data(Booth2)data(Booth2)
This data set contains simulated data from the paper of Booth and Hobert referenced below.
data(BoothHobert)data(BoothHobert)
A data frame with 3 columns:
Response vector.
Fixed effect model matrix. The matrix has just one column vector.
Random effect model matrix. The matrix has just one column vector.
This data set was generated by Booth and Hobert using a single variance component, a single fixed effect, no intercept, and a logit link.
Booth, J. G. and Hobert, J. P. (1999) Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm. Journal of the Royal Statistical Society, Series B, 61, 265–285. doi:10.1111/1467-9868.00176.
data(BoothHobert)data(BoothHobert)
This data set is a reformatted version of cbpp from the lme4 package. Contagious bovine pleuropneumonia (CBPP) is a major disease of cattle in Africa, caused by a mycoplasma. This dataset describes the serological incidence of CBPP in zebu cattle during a follow-up survey implemented in 15 commercial herds located in the Boji district of Ethiopia. The goal of the survey was to study the within-herd spread of CBPP in newly infected herds. Blood samples were quarterly collected from all animals of these herds to determine their CBPP status. These data were used to compute the serological incidence of CBPP (new cases occurring during a given time period). Some data are missing (lost to follow-up).
data(cbpp2)data(cbpp2)
A data frame with 3 columns:
Response vector. 1 if CBPP is observed, 0 otherwise.
A factor with levels 1 to 4.
A factor identifying the herd (1 through 15).
Serological status was determined using a competitive enzyme-linked immuno-sorbent assay (cELISA).
Lesnoff, M., Laval, G., Bonnet, P., Abdicho, S., Workalemahu, A., Kifle, D., Peyraud, A., Lancelot, R., Thiaucourt, F. (2004) Within-herd spread of contagious bovine pleuropneumonia in Ethiopian highlands. Preventive Veterinary Medicine, 64, 27–40. doi:10.1016/j.prevetmed.2004.03.005.
data(cbpp2)data(cbpp2)
A function that extracts the fixed effect coefficients returned from glmm.
## S3 method for class 'glmm' coef(object,...)## S3 method for class 'glmm' coef(object,...)
object |
An object of class |
... |
further arguments passed to or from other methods. |
coefficients |
A vector of coefficients (fixed effects only) |
Christina Knudson
glmm for model fitting.
library(glmm) set.seed(1234) data(salamander) m<-1000 sal<-glmm(Mate~0+Cross,random=list(~0+Female,~0+Male),varcomps.names=c("F","M"), data=salamander,family.glmm=bernoulli.glmm,m=m,debug=TRUE,doPQL=FALSE) coef(sal)library(glmm) set.seed(1234) data(salamander) m<-1000 sal<-glmm(Mate~0+Cross,random=list(~0+Female,~0+Male),varcomps.names=c("F","M"), data=salamander,family.glmm=bernoulli.glmm,m=m,debug=TRUE,doPQL=FALSE) coef(sal)
A function that calculates asymptotic confidence intervals for one or more parameters in a model fitted by by glmm. Confidence intervals can be calculated for fixed effect parameters and variance components using models.
## S3 method for class 'glmm' confint(object, parm, level, ...)## S3 method for class 'glmm' confint(object, parm, level, ...)
object |
An object of class |
parm |
A specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered. |
level |
The confidence level required. |
... |
Additional arguments passed to or from other methods. |
A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labeled as (1-level)/2 and 1-(1-level)/2 in percent. By default, 2.5
Christina Knudson
glmm for model fitting.
library(glmm) data(BoothHobert) set.seed(123) mod.mcml1<-glmm(y~0+x1,list(y~0+z1),varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm,m=1000,doPQL=TRUE) confint(mod.mcml1)library(glmm) data(BoothHobert) set.seed(123) mod.mcml1<-glmm(y~0+x1,list(y~0+z1),varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm,m=1000,doPQL=TRUE) confint(mod.mcml1)
This function fits generalized linear mixed models (GLMMs) by approximating the likelihood with ordinary Monte Carlo, then maximizing the approximated likelihood.
glmm(fixed, random, varcomps.names, data, family.glmm, m, varcomps.equal, weights=NULL, doPQL = TRUE,debug=FALSE, p1=1/3,p2=1/3, p3=1/3, rmax=1000,iterlim=1000, par.init, zeta=5, cluster=NULL)glmm(fixed, random, varcomps.names, data, family.glmm, m, varcomps.equal, weights=NULL, doPQL = TRUE,debug=FALSE, p1=1/3,p2=1/3, p3=1/3, rmax=1000,iterlim=1000, par.init, zeta=5, cluster=NULL)
fixed |
an object of class " |
random |
an object of class " |
varcomps.names |
The names of the distinct variance components in order of |
data |
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which |
family.glmm |
The name of the family. Must be class |
m |
The desired Monte Carlo sample size. See a note under "Details." |
varcomps.equal |
An optional vector with elements 1 through the number of distinct variance components. Denotes variance components are to be set equal by assigning them the same integer. The length of varcomps.equal must be equal to the length of the list of random effects formulas. If omitted, varcomps.equal assumes no variance component should be set equal. |
weights |
An optional vector with length equal to the length of the response vector. This argument makes the specified observations more or less informative for the model. See a note under "Details." |
doPQL |
logical. If |
debug |
logical. If |
p1 |
A probability for mixing the random effects generated from three distributions. |
p2 |
A probability for mixing the random effects generated from three distributions. |
p3 |
A probability for mixing the random effects generated from three distributions. |
rmax |
The maximum allowed trust region radius. This may be set very large. If set small, the algorithm traces a steepest ascent path. This is an argument for |
iterlim |
A positive integer specifying the maximum number of trust iterations to be performed before the trust program is terminated. This is an argument for |
par.init |
An optional argument. A single vector that specifies the initial values of the fixed effects and variance components. The parameters should be inputted in the order that |
zeta |
A scalar that specifies the degrees of freedom for the t-distribution from which random effects are generated. |
cluster |
An optional argument. A cluster created by the user to enable computations to be done in parallel. See "Details" for more information on the default value. |
Let be a vector of fixed effects and let be a vector of random effects. Let and be design matrices for the fixed and random effects, respectively. The random effects are assumed to be normally distributed with mean 0 and variance matrix , where is diagonal with entries from the unknown vector . Letting be the link function, . If the response type is Bernoulli or Binomial, then the logit function is the link; if the response type is Poisson, then the natural logarithm is the link function.
Models for glmm are specified symbolically. A typical fixed effects model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with duplicates removed.
A specification of the form first:second indicates the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.
The terms in the formula will be re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on: to avoid this, pass a terms object as the formula.
If you choose binomial.glmm as the family.glmm, then your response should be a two-column matrix: the first column reports the number of successes and the second reports the number of failures.
The random effects for glmm are also specified symbolically. The random effects model specification is typically a list. Each element of the random list has the form response ~ 0 + term. The 0 centers the random effects at 0. If you want your random effects to have a nonzero mean, then include that term in the fixed effects. Each variance component must have its own formula in the list.
To set some variance components equal to one another, use the varcomps.equal argument. The argument varcomps.equal should be a vector whose length is equal to the length of the random effects list. The vector should contain positive integers, and the first element of the varcomps.equal should be 1. To set variance components equal to one another, assign the same integer to the corresponding elements of varcomps.equal. For example, to set the first and second variance components equal to each other, the first two elements of varcomps.equal should be 1. If varcomps.equal is omitted, then the variance components are assumed to be distinct.
Each distinct variance component should have a name. The length of varcomps.names should be equal to the number of distinct variance components. If varcomps.equal is omitted, then the length of varcomps.names should be equal to the length of random.
The package uses a relevance-weighted log density weighting scheme, similar to that described in Hu and Zidek (1997). This means that the weights argument functions in the following manner: a model built for a data set with three observations (obs. 1, obs. 2, obs. 3) and weighting scheme 1, 1, 2 gives the same results as the model built for a data set with four observations (obs. 1, obs. 2, obs. 3, obs. 3 - the last two observations are identical) and weighting scheme 1, 1, 1, 1.
Monte Carlo likelihood approximation relies on an importance sampling distribution. Though infinitely many importance sampling distributions should yield the correct MCMLEs eventually, the importance sampling distribution used in this package was chosen to reduce the computation cost. When doPQL is TRUE, the importance sampling distribution relies on PQL estimates (as calculated in this package). When doPQL is FALSE, the random effect estimates in the distribution are taken to be 0, the fixed effect estimates are taken to be 0, and the variance component estimates are taken to be 1.
This package's importance sampling distribution is a mixture of three distributions: a t centered at 0 with scale matrix determined by the PQL estimates of the variance components and with zeta degrees of freedom, a normal distribution centered at the PQL estimates of the random effects and with a variance matrix containing the PQL estimates of the variance components, and a normal distribution centered at the PQL estimates of the random effects and with a variance matrix based on the Hessian of the penalized log likelihood. The first component is included to guarantee the gradient of the MCLA has a central limit theorem. The second component is included to mirror our best guess of the distribution of the random effects. The third component is included so that the numerator and the denominator are similar when calculating the MCLA value.
The Monte Carlo sample size m should be chosen as large as possible. You may want to run the model a couple times to begin to understand the variability inherent to Monte Carlo. There are no hard and fast rules for choosing m, and more research is needed on this area. For a general idea, I believe the BoothHobert model produces stable enough estimates at and the salamander model produces stable enough estimates at , as long as doPQL is TRUE.
To decrease the computation time involved with larger Monte Carlo sample sizes, a parallel computing component has been added to this package. Generally, the larger the Monte Carlo sample size m, the greater the benefit of utilizing additional cores. By default, glmm will create a cluster that uses a single core. This forces all computations to be done sequentially rather than simultaneously.
To see the summary of the model, use summary().
glmm returns an object of class glmm is a list containing at least the following components:
beta |
A vector of the Monte Carlo maximum likelihood estimates (MCMLEs) for the fixed effects. |
nu |
A vector of the Monte Carlo maximum likelihood estimates for the variance components. |
loglike.value |
The Monte Carlo log likelihood evaluated at the MCMLEs |
loglike.gradient |
The Monte Carlo log likelihood gradient vector at the MCMLEs |
loglike.hessian |
The Monte Carlo log likelihood Hessian matrix at the MCMLEs |
mod.mcml |
A list containing the weighted (if applicable) fixed effect design matrix, the list of weighted (if applicable) random effect design matrices, the weighted (if applicable) response, and the number of trials (for the Binomial family). |
call |
The call. |
fixedcall |
The fixed effects call. |
randcall |
The random effects call. |
x |
The unweighted design matrix for the fixed effects. |
y |
The unweighted response vector. |
z |
The unweighted design matrix for the random effects. |
weights |
The weights vector. |
family.glmm |
The name of the family. Must be class |
varcomps.names |
The vector of names for the distinct variance components. |
varcomps.equal |
The vector denoting equal variance components. |
umat |
A matrix with |
pvec |
A vector containing |
beta.pql |
PQL estimate of |
nu.pql |
PQL estimate of |
u.pql |
PQL predictions of the random effects. |
zeta |
The number of degrees of freedom used in the t component of the importance sampling distribution. |
cluster |
A cluster created by the user for use during the approximation of the log-likelihood value, gradient and hessian. |
debug |
If |
The function summary (i.e., summary.glmm) can
be used to obtain or print a summary of the results. The generic accessor function coef (i.e., coef.glmm) can be used to extract the coefficients.
Christina Knudson
Geyer, C. J. (1994) On the convergence of Monte Carlo maximum likelihood calculations. Journal of the Royal Statistical Society, Series B, 61, 261–274. doi:10.1111/j.2517-6161.1994.tb01976.x.
Geyer, C. J. and Thompson, E. (1992) Constrained Monte Carlo maximum likelihood for dependent data. Journal of the Royal Statistical Society, Series B, 54, 657–699. doi:10.1111/j.2517-6161.1992.tb01443.x.
Knudson, C. (2016). Monte Carlo likelihood approximation for generalized linear mixed models. PhD thesis, University of Minnesota. http://hdl.handle.net/11299/178948
Knudson, C., Benson, S., Geyer, J., Jones, G. (2021) Likelihood-based inference for generalized linear mixed models: Inference with the R package glmm. Stat, 10:e339. doi:10.1002/sta4.339.
Sung, Y. J. and Geyer, C. J. (2007) Monte Carlo likelihood inference for missing data models. Annals of Statistics, 35, 990–1011. doi:10.1214/009053606000001389.
Hu, F. and Zidek, J. V. (2001) The relevance weighted likelihood with applications. In: Ahmed, S. E. and Reid N. (eds). Empirical Bayes and Likelihood Inference. Lecture Notes in Statistics, 148, 211–235. Springer, New York. doi:10.1007/978-1-4613-0141-7_13.
#First, using the basic Booth and Hobert dataset #to fit a glmm with a logistic link, one variance component, #one fixed effect, and an intercept of 0. The Monte Carlo #sample size is 100 to save time. library(glmm) data(BoothHobert) set.seed(1234) mod.mcml1 <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm, m=100, doPQL=TRUE) mod.mcml1$beta mod.mcml1$nu summary(mod.mcml1) coef(mod.mcml1) # This next example has crossed random effects but we assume # all random effects have the same variance. The Monte Carlo #sample size is 100 to save time. #data(salamander) #set.seed(1234) #onenu <- glmm(Mate~Cross, random=list(~0+Female,~0+Male), #varcomps.names=c("only"), varcomps.equal=c(1,1), data=salamander, #family.glmm=bernoulli.glmm, m=100, debug=TRUE, doPQL=TRUE) #summary(onenu)#First, using the basic Booth and Hobert dataset #to fit a glmm with a logistic link, one variance component, #one fixed effect, and an intercept of 0. The Monte Carlo #sample size is 100 to save time. library(glmm) data(BoothHobert) set.seed(1234) mod.mcml1 <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm, m=100, doPQL=TRUE) mod.mcml1$beta mod.mcml1$nu summary(mod.mcml1) coef(mod.mcml1) # This next example has crossed random effects but we assume # all random effects have the same variance. The Monte Carlo #sample size is 100 to save time. #data(salamander) #set.seed(1234) #onenu <- glmm(Mate~Cross, random=list(~0+Female,~0+Male), #varcomps.names=c("only"), varcomps.equal=c(1,1), data=salamander, #family.glmm=bernoulli.glmm, m=100, debug=TRUE, doPQL=TRUE) #summary(onenu)
A function that calculates the Monte Carlo log likelihood evaluated at the the Monte Carlo maximum likelihood estimates returned from glmm.
## S3 method for class 'glmm' logLik(object,...)## S3 method for class 'glmm' logLik(object,...)
object |
An object of class |
... |
further arguments passed to or from other methods. |
logLik |
The variance-covariance matrix for the parameter estimates |
Christina Knudson
glmm for model fitting.
library(glmm) data(BoothHobert) set.seed(1234) mod <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm, m=100) logLik(mod)library(glmm) data(BoothHobert) set.seed(1234) mod <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm, m=100) logLik(mod)
A function that calculates the Monte Carlo standard error for the Monte Carlo maximum likelihood estimates returned from glmm.
mcse(object)mcse(object)
object |
An object of class |
With maximum likelihood performed by Monte Carlo likelihood approximation, there are two sources of variability: there is variability from sample to sample and from Monte Carlo sample (of generated random effects) to Monte Carlo sample. The first source of variability (from sample to sample) is measured using standard error, which appears with the point estimates in the summary tables. The second source of variability is due to the Monte Carlo randomness, and this is measured by the Monte Carlo standard error.
A large Monte Carlo standard error indicates the Monte Carlo sample size m is too small.
mcse |
The Monte Carlo standard errors for the Monte Carlo maximum likelihood estimates returned from |
Christina Knudson
Geyer, C. J. (1994) On the convergence of Monte Carlo maximum likelihood calculations. Journal of the Royal Statistical Society, Series B, 61, 261–274.
Knudson, C. (2016) Monte Carlo likelihood approximation for generalized linear mixed models. PhD thesis, University of Minnesota. http://hdl.handle.net/11299/178948
glmm for model fitting.
library(glmm) data(BoothHobert) set.seed(1234) mod <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm, m=100, doPQL=TRUE) mcse(mod)library(glmm) data(BoothHobert) set.seed(1234) mod <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm, m=100, doPQL=TRUE) mcse(mod)
A function that calculates the Monte Carlo variance covariance matrix for the Monte Carlo maximum likelihood estimates returned from glmm.
mcvcov(object)mcvcov(object)
object |
An object of class |
With maximum likelihood performed by Monte Carlo likelihood approximation, there are two sources of variability: there is variability from sample to sample and from Monte Carlo sample (of generated random effects) to Monte Carlo sample. The first source of variability (from sample to sample) is measured using standard error, which appears with the point estimates in the summary tables. The second source of variability is due to the Monte Carlo randomness, and this is measured by the Monte Carlo standard error.
A large entry in Monte Carlo variance covariance matrix indicates the Monte Carlo sample size m is too small.
The square root of the diagonal elements represents the Monte Carlo standard errors. The off-diagonal entries represent the Monte Carlo covariance.
mcvcov |
The Monte Carlo variance covariance matrix for the Monte Carlo maximum likelihood estimates returned from |
Christina Knudson
Geyer, C. J. (1994) On the convergence of Monte Carlo maximum likelihood calculations. Journal of the Royal Statistical Society, Series B, 61, 261–274. doi:10.1111/j.2517-6161.1994.tb01976.x.
Knudson, C. (2016) Monte Carlo likelihood approximation for generalized linear mixed models. PhD thesis, University of Minnesota. http://hdl.handle.net/11299/178948
glmm for model fitting.
library(glmm) data(BoothHobert) set.seed(1234) mod <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm, m=100, doPQL=TRUE) mcvcov(mod)library(glmm) data(BoothHobert) set.seed(1234) mod <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm, m=100, doPQL=TRUE) mcvcov(mod)
Subjects responded to the question 'Within the past 12 months, how many people have you known personally that were victims of homicide?'
data(murder)data(murder)
A data frame with the following columns:
The number of homicide victims known personally by the subject.
a factor with levels black and white.
a dummy variable to indicate whether the subject was black.
a dummy variable to indicate whether the subject was white.
Agresti, A. (2002) Categorical Data Analysis, Second edition. Wiley.
data(murder)data(murder)
Given a scalar eta, this calculates the cumulant and two derivatives for the Poisson family. Also checks that the data are entered correctly.
poisson.glmm()poisson.glmm()
family.glmm |
The family name, as a string. |
link |
The link function (canonical link is required). |
cum |
The cumulant function. |
cp |
The first derivative of the cumulant function. |
cpp |
The second derivative of the cumulant function. |
checkData |
A function to check that all data are nonnegative integers. |
This function is to be used by the glmm command.
Christina Knudson
poisson.glmm()$family.glmm poisson.glmm()$cum(2) poisson.glmm()$cp(2) poisson.glmm()$cpp(2)poisson.glmm()$family.glmm poisson.glmm()$cum(2) poisson.glmm()$cp(2) poisson.glmm()$cpp(2)
Data on life history traits for the invasive California wild radish Raphanus sativus.
data(radish2)data(radish2)
A data frame with the following columns:
Categorical. Experimental site where plant was grown. Two sites in this dataset.
Categorical. Blocked nested within site.
Categorical. Region from which individuals were obtained: northern, coastal California (N) or southern, inland California (S).
Categorical. Wild population nested within region.
Categorical. Gives node of graphical model corresponding to each component of resp. This is useful for life history analysis (see aster package).
Response vector.
Categorical. Indicates individual plants.
These data are a subset of data previously analyzed using fixed effect
aster methods (R function aster) in the following.
Ridley, C. E. and Ellstrand, N. C. (2010). Rapid evolution of morphology and adaptive life history in the invasive California wild radish (Raphanus sativus) and the implications for management. Evolutionary Applications, 3, 64–76.
These data are a subset of data previously analyzed using random effect
aster methods (R function reaster) in the following.
Geyer, C. J., Ridley, C. E., Latta, R. G., Etterson, J. R., and Shaw, R. G. (2013) Local Adaptation and Genetic Effects on Fitness: Calculations for Exponential Family Models with Random Effects. Annals of Applied Statistics, 7, 1778–1795. doi:10.1214/13-AOAS653.
data(radish2)data(radish2)
This data set presents the outcome of an experiment conducted at the University of Chicago in 1986 to study interbreeding between populations of mountain dusky salamanders (McCullagh and Nelder, 1989, Section 14.5).
data(salamander)data(salamander)
A data frame with the following columns:
Whether the salamanders mated (1) or did not mate (0).
Cross between female and male type. A factor with four levels: R/R,R/W,W/R, and W/W. The type of the female salamander is listed first and the male is listed second. Rough Butt is represented by R and White Side is represented by W. For example, Cross=W/R indicates a White Side female was crossed with a Rough Butt male.
Identification number of the male salamander. A factor.
Identification number of the female salamander. A factor.
McCullagh P. and Nelder, J. A. (1989) Generalized Linear Models. Chapman and Hall/CRC.
data(salamander)data(salamander)
A function that calculates the standard error for the Monte Carlo maximum likelihood estimates returned from glmm.
se(object)se(object)
object |
An object of class |
With maximum likelihood performed by Monte Carlo likelihood approximation, there are two sources of variability: there is variability from sample to sample and from Monte Carlo sample (of generated random effects) to Monte Carlo sample. The first source of variability (from sample to sample) is measured using standard error, which appears with the point estimates in the summary tables. The second source of variability is due to the Monte Carlo randomness, and this is measured by the Monte Carlo standard error.
se |
The standard errors for the Monte Carlo maximum likelihood estimates returned from |
Christina Knudson
glmm for model fitting.
mcse for calculating Monte Carlo standard error.
library(glmm) data(BoothHobert) set.seed(1234) mod <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm, m=100, doPQL=TRUE) se(mod)library(glmm) data(BoothHobert) set.seed(1234) mod <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm, m=100, doPQL=TRUE) se(mod)
"summary" method for class glmm objects.
## S3 method for class 'glmm' summary(object, ...) ## S3 method for class 'summary.glmm' print(x, digits = max(3, getOption("digits") - 3), signif.stars = getOption("show.signif.stars"), ...)## S3 method for class 'glmm' summary(object, ...) ## S3 method for class 'summary.glmm' print(x, digits = max(3, getOption("digits") - 3), signif.stars = getOption("show.signif.stars"), ...)
object |
an object of class |
x |
an object of class |
digits |
the number of significant digits to use when printing. |
signif.stars |
logical. If |
... |
further arguments passed to or from other methods. |
The function summary.glmm computes and returns a list of summary statistics of the fitted generalized linear mixed model given in object, using the components (list elements) "call" and "terms" from its argument, plus
coefficients |
a matrix for the fixed effects. The matrix has columns for the estimated coefficient, its standard error, t-statistic and corresponding (two-sided) p-value. |
nucoefmat |
a matrix with columns for the variance components. The matrix has columns for the estimated variance component, its standard error, t-statistic and corresponding (one-sided) p-value. |
x |
the design matrix for the fixed effects. |
z |
the design matrix for the random effects. |
y |
the response vector. |
fixedcall |
the call for the fixed effects. |
randcall |
the call for the random effects. |
family.mcml |
the family used to fit the model. |
call |
the call to |
link |
the canonical link function. |
Christina Knudson
The model fitting function glmm, the generic summary, and the function coefthat extracts the fixed effect coefficients.
A function that extracts the variance components returned from glmm.
varcomps(object,...)varcomps(object,...)
object |
An object of class |
... |
further arguments passed to or from other methods. |
varcomps |
A vector of variance component estimates |
Christina Knudson
glmm for model fitting.
coef.glmm for fixed effects coefficients.
library(glmm) data(BoothHobert) set.seed(1234) mod <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm, m=100, doPQL=TRUE) varcomps(mod)library(glmm) data(BoothHobert) set.seed(1234) mod <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm, m=100, doPQL=TRUE) varcomps(mod)
A function that calculates the variance-covariance matrix for the Monte Carlo maximum likelihood estimates returned from glmm.
## S3 method for class 'glmm' vcov(object,...)## S3 method for class 'glmm' vcov(object,...)
object |
An object of class |
... |
further arguments passed to or from other methods. |
vcov |
The variance-covariance matrix for the parameter estimates |
Christina Knudson
glmm for model fitting.
library(glmm) data(BoothHobert) set.seed(1234) mod <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm, m=100, doPQL=TRUE) vcov(mod)library(glmm) data(BoothHobert) set.seed(1234) mod <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), data=BoothHobert, family.glmm=bernoulli.glmm, m=100, doPQL=TRUE) vcov(mod)