gbp
fits Bayesian hierarchical models using the Uniform distribution on the second level variance component (variance of the prior distribution), which enables good frequentist repeated sampling properties.
1 2 3 
y 
a (k by 1) vector of k groups' sample means for Gaussian or of each group's number of successful trials for Binomial and Poisson data, where k is the number of groups (or units) in a dataset. 
se.or.n 
a (k by 1) vector composed of the standard errors of all groups for Gaussian or of each group's total number of trials for Binomial and Poisson data. 
covariates 
(optional) a matrix of covariate(s) each column of which corresponds to one covariate. 
mean.PriorDist 
(optional) a numeric value for the secondlevel mean parameter, i.e. the mean of prior distribution, if you know this value a priori. 
model 
a character string indicating which hierarchical model to fit. "gaussian" for Gaussian data, "poisson" for Poisson, and "binomial" for Binomial. Default is "gaussian" 
intercept 

confidence.lvl 
a 
n.AR 
Only for Binomial model. If 
n.AR.factor 
Only for Binomial model. If 
trial.scale 
A scale used in the trial distribution of α. If resultant weight has too mamy 0's, scale should be smaller than before. If resultant weight has too few 0's, scale should be larger than before. If there are relatively huge weights, scale should be larger than before. Default is NA. 
save.result 
Only for Binomial model with the acceptancerejection sampling. If 
normal.CI 
Only applicable for Gaussian data. If 
t 
Nonnegative constant to determine the hyperprior distribution of r for the Binomial model with the acceptancerejection method. If t is positive, then the hyperprior distribution of r is proper, otherwise improper. dr/(t + r)^{u+1}. 
u 
A positive constant to determine the hyperprior distribution of r for the Binomial model with the acceptancerejection method. dr/(t + r)^{u+1}. 
gbp
fits a hierarchical model whose firstlevel hierarchy is a distribution of observed data and secondlevel is a conjugate prior distribution on the firstlevel parameter. To be specific, for Normal data, gbp
constructs a twolevel NormalNormal multilevel model. V_j (=σ^2/n_j) is assumed to be known or to be accurately estimated, and subscript j indicates jth group
(or unit) in a dataset.
(y_j  θ_j) ~ indep N(θ_j, σ_j^2)
(θ_j  μ0_j, A) ~ indep N(μ0_j, A)
μ0_j = x_j'β
for j = 1, …, k, where k is the number of groups (units) in a dataset.
For Poisson data, gbp
builds a twolevel PoissonGamma multilevel model. A square bracket below indicates [mean, variance] of distribution, a constant multiplied to the notation representing Gamma distribution (Gam) is a scale, and y_j = z_j / n_j.
(z_j  θ_j) ~ indep Pois(n_jθ_j)
(θ_j  r, μ0_j) ~ indep Gam(rμ0_j) / r ~ indep Gam[μ0_j, μ0_j / r]
log(μ0_j) = x_j'β
for j = 1, …, k, where k is the number of groups (units) in a dataset.
For Binomial data, gbp
sets a twolevel BinomialBeta multilevel model. A square bracket below indicates [mean, variance] of distribution and y_j = z_j / n_j.
(z_j  θ_j) ~ indep Bin(n_j, θ_j)
(θ_j  r, μ0_j) ~ indep Beta(rμ0_j, r(1  μ0_j)) ~ indep Beta[μ0_j, μ0_j(1  μ0_j) / (r + 1)]
logit(μ0_j) = x_j'β
for j = 1, …, k, where k is the number of groups (units) in a dataset.
For reference, based on the above notations, the Uniform prior distribution on the second level variance component (variance of the prior distribution) is dA for Gaussian and d(1 / r) (= dr / r^2) for Binomial and Poisson data. The second level variance component can be interpreted as variation among the firstlevel parameters (θ_j) or variance of ensemble information.
Under this setting, the argument y
in gbp
is a (k by 1) vector of k groups' sample means (y_j's in the description of NormalNormal model above) for Gaussian or of each group's number of successful trials (z_j's) for Binomial and Poisson data, where k is the number of groups (or units) in a dataset.
The argument se.or.n
is a (k by 1) vector composed of the standard errors (V_j's) of all groups for Gaussian or of each group's total number of trials (n_j's) for Binomial and Poisson data.
As for two optional arguments, covariates
and mean.PriorDist
, there are three feasible
combinations of them to run gbp
. The first situation is when we do not have any covariate and do not
know a mean of the prior distribution (μ0) a priori. In this case, assigning none of two
optional arguments, such as "gbp(z, n, model = "binomial")
", will lead to a correct model. gbp
will automatically fit a regression with only an intercept term to estimate a common mean of the prior
distribution (exchangeability).
The second situation is when we have covariate(s) and do not know a mean of the prior distribution (μ0) a priori. In this case, assigning a matrix, X, each column of which corresponds to one covariate, such as "gbp(z, n, X, model = "poisson")
", will lead to a correct model. Default of gbp
is to fit a regression including an intercept term to estimate a mean of the prior distribution. Double exchangeability will hold in this case.
The last case is when we know a mean of the prior distribution (μ0) a priori. Now, we do
not need to estimate regression coefficients at all because we know a true value of μ0 (strong assumption).
Designating this value into the argument of gbp
like
"gbp(y, se, mean.PriorDist = 3)
" is enough to account for it. For reference,
mean.PriorDist
has a stronger priority than covariates
, which means that when both
arguments are designated, gbp
will fit a hierarchical model using the known mean of prior distribution, mean.PriorDist
.
gbp
returns an object of class "gbp
" which provides three relevant functions plot
, print
, and summary
.
An object of class gbp
comprises of:
sample.mean 
sample mean of each group (or unit) 
se 
if Gaussian data, standard error of sample mean in each group (or unit) 
n 
if Binomial and Poisson data, total number of trials of each group (or unit) 
prior.mean 
numeric if entered, NA if not entered 
prior.mean.hat 
estimate of prior mean by a regression if prior mean is not assigned a priori 
shrinkage 
shrinkage estimate of each group (adjusted posterior mean) 
sd.shrinkage 
posterior standard deviation of shrinkage 
post.mean 
posterior mean of each group 
post.sd 
posterior standard deviation of each group 
post.intv.low 
lower bound of 100*confidence.lvl% posterior interval (quantile of posterior distribution) 
post.intv.upp 
upper bound of 100*confidence.lvl% posterior interval (quantile of posterior distribution) 
model 
"gaussian" for Gaussian, "poisson" for Poisson, and "binomial" for Binomial data 
X 
a covariate vector or matrix if designated. NA if not 
beta.new 
regression coefficient estimates 
beta.var 
estimated variance matrix of regression coefficient 
intercept 
whether TRUE or FALSE 
a.new 
a posterior mode of α defined as log(A) for Gaussian or log(1 / r) for Binomial and Poisson data. Practical meaning (variation of ensemble information) of estimating α will appear in 
a.var 
posterior variance of α 
confidence.lvl 
confidence level based on which confidence interval is constructed 
weight 
(only for Binomial model) weights for acceptancerejection method 
p 
(only for Binomial model) posterior samples of p based on the acceptancerejection method, if this method was used. This is a (k by nsim) matrix. Each row contains posteiror samples of each random effect. 
alpha 
(only for Binomial model) posterior samples of alpha based on the acceptancerejection method, if this method was used 
beta 
(only for Binomial model) posterior samples of beta based on the acceptancerejection method, if this method was used 
accept.rate 
(only for Binomial model) the acceptance rate of the acceptancerejection method, if this method was used 
n.AR 
(Only for Binomial model) If 
n.AR.factor 
(only for Binomial model) If 
Hyungsuk Tak, Joseph Kelly, and Carl Morris
Morris, C. and Lysy, M. (2012). Shrinkage Estimation in Multilevel Normal Models. Statistical Science. 27. 115134.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184  # Loading datasets
data(schools)
y < schools$y
se < schools$se
# Arbitrary covariate for schools data
x2 < rep(c(1, 0, 1, 2), 2)
# baseball data where z is Hits and n is AtBats
z < c(18, 17, 16, 15, 14, 14, 13, 12, 11, 11, 10, 10, 10, 10, 10, 9, 8, 7)
n < c(45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45)
# One covariate: 1 if a player is an outfielder and 0 otherwise
x1 < c(1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0)
################################################################
# Gaussian Regression Interactive Multilevel Modeling (GRIMM) #
################################################################
####################################################################################
# If we do not have any covariate and do not know a mean of the prior distribution #
####################################################################################
g < gbp(y, se, model = "gaussian")
g
print(g, sort = FALSE)
summary(g)
plot(g)
plot(g, sort = FALSE)
### when we want to simulate pseudo datasets considering the estimated values
### as true ones.
gcv < coverage(g, nsim = 10)
### gcv$coverageRB, gcv$coverage10, gcv$average.coverageRB, gcv$average.coverage10,
### gcv$minimum.coverageRB, gcv$minimum.coverage10, gcv$raw.resultRB, gcv$raw.result10.
### when we want to simulate pseudo datasets based on different values of A
### and a regression coefficient (intercept),
### not using estimated values as true ones.
gcv < coverage(g, A.or.r = 9, reg.coef = 10, nsim = 10)
##################################################################################
# If we have one covariate and do not know a mean of the prior distribution yet, #
##################################################################################
g < gbp(y, se, x2, model = "gaussian")
g
print(g, sort = FALSE)
summary(g)
plot(g)
plot(g, sort = FALSE)
### when we want to simulate pseudo datasets considering the estimated values
### as true ones.
gcv < coverage(g, nsim = 10)
### gcv$coverageRB, gcv$coverage10, gcv$average.coverageRB, gcv$average.coverage10,
### gcv$minimum.coverageRB, gcv$minimum.coverage10, gcv$raw.resultRB, gcv$raw.result10.
### when we want to simulate pseudo datasets based on different values of A
### and regression coefficients, not using estimated values
### as true ones. Two values of reg.coef are for beta0 and beta1
gcv < coverage(g, A.or.r = 9, reg.coef = c(10, 1), nsim = 10)
################################################
# If we know a mean of the prior distribution, #
################################################
g < gbp(y, se, mean.PriorDist = 8, model = "gaussian")
g
print(g, sort = FALSE)
summary(g)
plot(g)
plot(g, sort = FALSE)
### when we want to simulate pseudo datasets considering the estimated values
### as true ones.
gcv < coverage(g, nsim = 10)
### gcv$coverageRB, gcv$coverage10, gcv$average.coverageRB, gcv$average.coverage10,
### gcv$minimum.coverageRB, gcv$minimum.coverage10, gcv$raw.resultRB, gcv$raw.result10.
### when we want to simulate pseudo datasets based on different values of A and
### 2nd level mean as true ones, not using estimated values as true ones.
coverage(g, A.or.r = 9, mean.PriorDist = 5, nsim = 10)
###############################################################
# Binomial Regression Interactive Multilevel Modeling (BRIMM) #
###############################################################
####################################################################################
# If we do not have any covariate and do not know a mean of the prior distribution #
####################################################################################
b < gbp(z, n, model = "binomial")
b
print(b, sort = FALSE)
summary(b)
plot(b)
plot(b, sort = FALSE)
### when we want to simulate pseudo datasets considering the estimated values
### as true ones.
bcv < coverage(b, nsim = 10)
### bcv$coverageRB, bcv$coverage10, bcv$average.coverageRB, bcv$average.coverage10,
### bcv$minimum.coverageRB, bcv$minimum.coverage10, bcv$raw.resultRB, bcv$raw.result10.
### when we want to simulate pseudo datasets based on different values of r
### and a regression coefficient (intercept),
### not using estimated values as true ones.
bcv < coverage(b, A.or.r = 60, reg.coef = 1, nsim = 10)
##################################################################################
# If we have one covariate and do not know a mean of the prior distribution yet, #
##################################################################################
b < gbp(z, n, x1, model = "binomial")
b
print(b, sort = FALSE)
summary(b)
plot(b)
plot(b, sort = FALSE)
### when we want to simulate pseudo datasets considering the estimated values
### as true ones.
bcv < coverage(b, nsim = 10)
### bcv$coverageRB, bcv$coverage10, bcv$average.coverageRB, bcv$average.coverage10,
### bcv$minimum.coverageRB, bcv$minimum.coverage10, bcv$raw.resultRB, bcv$raw.result10.
### when we want to simulate pseudo datasets based on different values of r
### and regression coefficients, not using estimated values
### as true ones. Two values of reg.coef are for beta0 and beta1
bcv < coverage(b, A.or.r = 60, reg.coef = c(1, 0), nsim = 10)
################################################
# If we know a mean of the prior distribution, #
################################################
b < gbp(z, n, mean.PriorDist = 0.265, model = "binomial")
b
print(b, sort = FALSE)
summary(b)
plot(b)
plot(b, sort = FALSE)
### when we want to simulate pseudo datasets considering the estimated values
### as true ones.
bcv < coverage(b, nsim = 10)
### bcv$coverageRB, bcv$coverage10, bcv$average.coverageRB, bcv$average.coverage10,
### bcv$minimum.coverageRB, bcv$minimum.coverage10, bcv$raw.resultRB, bcv$raw.result10.
### when we want to simulate pseudo datasets based on different values of r and
### 2nd level mean as true ones, not using estimated values as true ones.
bcv < coverage(b, A.or.r = 60, mean.PriorDist = 0.3, nsim = 10)
##############################################################
# Poisson Regression Interactive Multilevel Modeling (PRIMM) #
##############################################################
################################################
# If we know a mean of the prior distribution, #
################################################
p < gbp(z, n, mean.PriorDist = 0.265, model = "poisson")
p
print(p, sort = FALSE)
summary(p)
plot(p)
plot(p, sort = FALSE)
### when we want to simulate pseudo datasets considering the estimated values
### as true ones.
pcv < coverage(p, nsim = 10)
### pcv$coverageRB, pcv$coverage10, pcv$average.coverageRB, pcv$average.coverage10,
### pcv$minimum.coverageRB, pcv$minimum.coverage10, pcv$raw.resultRB, pcv$raw.result10.
### when we want to simulate pseudo datasets based on different values of r and
### 2nd level mean as true ones, not using estimated values as true ones.
pcv < coverage(p, A.or.r = 60, mean.PriorDist = 0.3, nsim = 10)

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