MeanFunction: For the generalised linear mixed model
In glmmrBase: Generalised Linear Mixed Models in R
MeanFunction R Documentation
For the generalised linear mixed model
Description
For the generalised linear mixed model
For the generalised linear mixed model
Details
Y \sim F(\mu,\sigma)
\mu = h^-1(X\beta + Z\gamma)
\gamma \sim MVN(0,D)
this class defines the fixed effects design matrix X.
The mean function is defined by a model formula, data, and parameters.
A new instance can be generated with $new(). The class will generate the
relevant matrix X automatically. See glmmrBase for a
detailed guide on model specification.
Specification of the mean function follows standard model formulae in R.
For example for a stepped-wedge cluster trial model, a typical mean model is
E(y_{ijt}|\delta)=\beta_0 + \tau_t + \beta_1 d_{jt} + z_{ijt}\delta where \tau_t
are fixed effects for each time period. The formula specification for this would be '~ factor(t) + int'
where 'int' is the name of the variable indicating the treatment.
One can also include non-linear functions of variables in the mean function, and name the parameters.
The resulting X matrix is then a matrix of first-order partial derivatives. For example, one can
specify '~ int + b_1*exp(b_2*x)'.
Using 'update_parameters()' is the preferred way of updating the parameters of the
mean or covariance objects as opposed to direct assignment, e.g. 'self$parameters <- c(...)'.
The function calls check functions to automatically update linked matrices with the new parameters.
Public fields
formulamodel formula for the fixed effects
dataData frame with data required to build X
parametersA vector of parameter values for \beta used for simulating data and calculating
covariance matrix of observations for non-linear models.
offsetAn optional vector specifying the offset values
Xthe fixed effects design matrix
Methods
Public methods
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Method n()
Returns the number of observations
Usage
MeanFunction$n()
Arguments
...ignored
Returns
The number of observations in the model
Examples
\dontshow{
setParallel(FALSE) # for the CRAN check
}
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 2, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1)
)
mf1$n()
Method new()
Create a new MeanFunction object
Usage
MeanFunction$new(
formula,
data,
parameters = NULL,
offset = NULL,
verbose = FALSE
)
Arguments
formulaA formula object that describes the mean function, see Details
data(Optional) A data frame containing the covariates in the model, named in the model formula
parameters(Optional) A vector with the values of the parameters \beta to use in data simulation and covariance calculations.
If the parameters are not specified then they are initialised to 0.
offsetA vector of offset values (optional)
verboseLogical indicating whether to report detailed output
Returns
A MeanFunction object
Examples
\dontshow{
setParallel(FALSE) # for the CRAN check
}
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 2, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
)
Method print()
Prints details about the object
Usage
MeanFunction$print()
Arguments
...ignored
Method update_parameters()
Updates the model parameters
Usage
MeanFunction$update_parameters(parameters)
Arguments
parametersA vector of parameters for the mean function.
verboseLogical indicating whether to provide more detailed feedback
Method colnames()
Returns or replaces the column names of the data in the object
Usage
MeanFunction$colnames(names = NULL)
Arguments
namesIf NULL then the function prints the column names, if a vector of names, then it attempts to
replace the current column names of the data
Examples
\dontshow{
setParallel(FALSE) # for the CRAN check
}
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1)
)
mf1$colnames(c("cluster","time","individual","treatment"))
mf1$colnames()
Method subset_rows()
Keeps a subset of the data and removes the rest
All indices not in the provided vector of row numbers will be removed from both the data and fixed effects
design matrix X.
Usage
MeanFunction$subset_rows(index)
Arguments
indexRows of the data to keep
Returns
NULL
Examples
\dontshow{
setParallel(FALSE) # for the CRAN check
}
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1)
)
mf1$subset_rows(1:20)
Method linear_predictor()
Returns the linear predictor
Returns the linear predictor, X * beta
Usage
MeanFunction$linear_predictor()
Returns
A vector
Method any_nonlinear()
Returns a logical indicating whether the mean function contains non-linear functions of model parameters.
Mainly used internally.
Usage
MeanFunction$any_nonlinear()
Returns
None. Called for effects
Method clone()
The objects of this class are cloneable with this method.
Usage
MeanFunction$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `MeanFunction$n`
## ------------------------------------------------
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 2, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1)
)
mf1$n()
## ------------------------------------------------
## Method `MeanFunction$new`
## ------------------------------------------------
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 2, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
)
## ------------------------------------------------
## Method `MeanFunction$colnames`
## ------------------------------------------------
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1)
)
mf1$colnames(c("cluster","time","individual","treatment"))
mf1$colnames()
## ------------------------------------------------
## Method `MeanFunction$subset_rows`
## ------------------------------------------------
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1)
)
mf1$subset_rows(1:20)
glmmrBase documentation built on April 3, 2025, 10:44 p.m.
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| MeanFunction | R Documentation |
For the generalised linear mixed model
Description
For the generalised linear mixed model
For the generalised linear mixed model
Details
Y \sim F(\mu,\sigma)
\mu = h^-1(X\beta + Z\gamma)
\gamma \sim MVN(0,D)
this class defines the fixed effects design matrix X. The mean function is defined by a model formula, data, and parameters. A new instance can be generated with $new(). The class will generate the relevant matrix X automatically. See glmmrBase for a detailed guide on model specification.
Specification of the mean function follows standard model formulae in R.
For example for a stepped-wedge cluster trial model, a typical mean model is
E(y_{ijt}|\delta)=\beta_0 + \tau_t + \beta_1 d_{jt} + z_{ijt}\delta where \tau_t
are fixed effects for each time period. The formula specification for this would be '~ factor(t) + int'
where 'int' is the name of the variable indicating the treatment.
One can also include non-linear functions of variables in the mean function, and name the parameters. The resulting X matrix is then a matrix of first-order partial derivatives. For example, one can specify '~ int + b_1*exp(b_2*x)'.
Using 'update_parameters()' is the preferred way of updating the parameters of the mean or covariance objects as opposed to direct assignment, e.g. 'self$parameters <- c(...)'. The function calls check functions to automatically update linked matrices with the new parameters.
Public fields
formulamodel formula for the fixed effects
dataData frame with data required to build X
parametersA vector of parameter values for
\betaused for simulating data and calculating covariance matrix of observations for non-linear models.offsetAn optional vector specifying the offset values
Xthe fixed effects design matrix
Methods
Public methods
Method n()
Returns the number of observations
Usage
MeanFunction$n()
Arguments
...ignored
Returns
The number of observations in the model
Examples
\dontshow{
setParallel(FALSE) # for the CRAN check
}
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 2, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1)
)
mf1$n()
Method new()
Create a new MeanFunction object
Usage
MeanFunction$new( formula, data, parameters = NULL, offset = NULL, verbose = FALSE )
Arguments
formulaA formula object that describes the mean function, see Details
data(Optional) A data frame containing the covariates in the model, named in the model formula
parameters(Optional) A vector with the values of the parameters
\betato use in data simulation and covariance calculations. If the parameters are not specified then they are initialised to 0.offsetA vector of offset values (optional)
verboseLogical indicating whether to report detailed output
Returns
A MeanFunction object
Examples
\dontshow{
setParallel(FALSE) # for the CRAN check
}
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 2, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
)
Method print()
Prints details about the object
Usage
MeanFunction$print()
Arguments
...ignored
Method update_parameters()
Updates the model parameters
Usage
MeanFunction$update_parameters(parameters)
Arguments
parametersA vector of parameters for the mean function.
verboseLogical indicating whether to provide more detailed feedback
Method colnames()
Returns or replaces the column names of the data in the object
Usage
MeanFunction$colnames(names = NULL)
Arguments
namesIf NULL then the function prints the column names, if a vector of names, then it attempts to replace the current column names of the data
Examples
\dontshow{
setParallel(FALSE) # for the CRAN check
}
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1)
)
mf1$colnames(c("cluster","time","individual","treatment"))
mf1$colnames()
Method subset_rows()
Keeps a subset of the data and removes the rest
All indices not in the provided vector of row numbers will be removed from both the data and fixed effects design matrix X.
Usage
MeanFunction$subset_rows(index)
Arguments
indexRows of the data to keep
Returns
NULL
Examples
\dontshow{
setParallel(FALSE) # for the CRAN check
}
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1)
)
mf1$subset_rows(1:20)
Method linear_predictor()
Returns the linear predictor
Returns the linear predictor, X * beta
Usage
MeanFunction$linear_predictor()
Returns
A vector
Method any_nonlinear()
Returns a logical indicating whether the mean function contains non-linear functions of model parameters. Mainly used internally.
Usage
MeanFunction$any_nonlinear()
Returns
None. Called for effects
Method clone()
The objects of this class are cloneable with this method.
Usage
MeanFunction$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `MeanFunction$n`
## ------------------------------------------------
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 2, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1)
)
mf1$n()
## ------------------------------------------------
## Method `MeanFunction$new`
## ------------------------------------------------
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 2, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
)
## ------------------------------------------------
## Method `MeanFunction$colnames`
## ------------------------------------------------
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1)
)
mf1$colnames(c("cluster","time","individual","treatment"))
mf1$colnames()
## ------------------------------------------------
## Method `MeanFunction$subset_rows`
## ------------------------------------------------
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1)
)
mf1$subset_rows(1:20)
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