MeanFunction: R6 Class representing a mean function function and data
In samuel-watson/glmmr: Design and analysis for generalised linear mixed models in R
MeanFunction R Documentation
R6 Class representing a mean function function and data
Description
R6 Class representing a mean function function and data
R6 Class representing a mean function function and data
Details
For the generalised linear mixed model
Y \sim F(μ,σ)
μ = h^-1(Xβ + Zγ)
γ \sim MVN(0,D)
this class defines the family F, link function h, and fixed effects design matrix X.
The mean function is defined by a model formula, data, and parameters.
A new instance can be generated with $new(). The class will generate the
relevant matrix X automatically.
Specification of the mean function follows standard model formulae in R.
For example for a stepped-wedge cluster trial model, a typical mean model is
E(y_{ijt}|δ)=β_0 + τ_t + β_1 d_{jt} + z_{ijt}δ where τ_t
are fixed effects for each time period. The formula specification for this would be '~ factor(t) + int'
where 'int' is the name of the variable indicating the treatment.
One can also include non-linear functions of variables in the mean function. These are handled in the analyses
by first-order approximation. Available functions are the same as for the covariance functions
see Covariance. The user can add additional functions by specifying a new function that takes as
an input a named list with elements data and pars, and outputs a matrix with the linearised components.
The function name must begin with 'd', e.g. the function to provide a first order approximation to
the exponential function (see fexp) is named 'dfexp'.
Public fields
formulamodel formula for the fixed effects
dataData frame with data required to build X
familyOne of the family function used in R's glm functions. See family for details
parametersA vector of parameter values for β used for simulating data and calculating
covariance matrix of observations for non-linear models.
randomiseA function that generates a new set of values representing the treatment allocation in an
experimental study
treat_varA string naming the column in data that represents the treatment variable in data. Used
to identify where to replace allocation when randomiser is used.
Xthe fixed effects design matrix
Methods
Public methods
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Method n()
Returns the number of observations
Usage
MeanFunction$n()
Arguments
...ignored
Returns
The number of observations in the model
Examples
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$n()
Method check()
Checks if any changes have been made and updates
Checks if any changes have been made and updates, usually called automatically.
Usage
MeanFunction$check(verbose = TRUE)
Arguments
verboseLogical whether to report if any changes detected.
Returns
NULL
Examples
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$parameters <- c(0,0)
mf1$check()
Method new()
Create a new MeanFunction object
Usage
MeanFunction$new(
formula,
data,
family,
parameters,
verbose = FALSE,
random_function = NULL,
treat_var = NULL
)
Arguments
formulaA formula object that describes the mean function, see Details
dataA data frame containing the covariates in the model, named in the model formula
familyA family object expressing the distribution and link function of the model, see family
parametersA vector with the values of the parameters β to use in data simulation and covariance calculations
verboseLogical indicating whether to report detailed output
random_functionA string naming a function in the global environment that produces a vector of data describing a new
treatment allocation in an experimental model. When used, the output of this function replaces the column of data named by
'treat_var'
treat_varThe name of a column in data (or the name to give a new column) that a random treatment allocation generated
by 'random_function' replaces.
Returns
A MeanFunction object
Examples
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
Method print()
Prints details about the object
Usage
MeanFunction$print()
Arguments
...ignored
Method colnames()
Returns or replaces the column names of the data in the object
Usage
MeanFunction$colnames(names = NULL)
Arguments
namesIf NULL then the function prints the column names, if a vector of names, then it attemps to
replace the current column names of the data
Examples
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$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
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$subset_rows(1:20)
Method subset_cols()
Keeps a subset of the columns of X
All indices not in the provided vector of column numbers will be removed from the fixed effects design
matrix X.
Usage
MeanFunction$subset_cols(index)
Arguments
indexColumns of X to keep
Returns
NULL
Examples
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$subset_cols(1:2)
Method rerandomise()
Generates a new random allocation
If a randomising function has been provided then a new random allocation will
be generated, and will replace the exisitng data at 'treat_var' in the X matrix
Usage
MeanFunction$rerandomise()
Arguments
...ignored
Returns
Nothing is returned, the X matrix is updated
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 <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$n()
## ------------------------------------------------
## Method `MeanFunction$check`
## ------------------------------------------------
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$parameters <- c(0,0)
mf1$check()
## ------------------------------------------------
## Method `MeanFunction$new`
## ------------------------------------------------
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
## ------------------------------------------------
## 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),
family = binomial()
)
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),
family = binomial()
)
mf1$subset_rows(1:20)
## ------------------------------------------------
## Method `MeanFunction$subset_cols`
## ------------------------------------------------
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$subset_cols(1:2)
samuel-watson/glmmr documentation built on July 27, 2022, 10:30 p.m.
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| MeanFunction | R Documentation |
R6 Class representing a mean function function and data
Description
R6 Class representing a mean function function and data
R6 Class representing a mean function function and data
Details
For the generalised linear mixed model
Y \sim F(μ,σ)
μ = h^-1(Xβ + Zγ)
γ \sim MVN(0,D)
this class defines the family F, link function h, and fixed effects design matrix X. The mean function is defined by a model formula, data, and parameters. A new instance can be generated with $new(). The class will generate the relevant matrix X automatically.
Specification of the mean function follows standard model formulae in R. For example for a stepped-wedge cluster trial model, a typical mean model is E(y_{ijt}|δ)=β_0 + τ_t + β_1 d_{jt} + z_{ijt}δ where τ_t are fixed effects for each time period. The formula specification for this would be '~ factor(t) + int' where 'int' is the name of the variable indicating the treatment.
One can also include non-linear functions of variables in the mean function. These are handled in the analyses by first-order approximation. Available functions are the same as for the covariance functions see Covariance. The user can add additional functions by specifying a new function that takes as an input a named list with elements data and pars, and outputs a matrix with the linearised components. The function name must begin with 'd', e.g. the function to provide a first order approximation to the exponential function (see fexp) is named 'dfexp'.
Public fields
formulamodel formula for the fixed effects
dataData frame with data required to build X
familyOne of the family function used in R's glm functions. See family for details
parametersA vector of parameter values for β used for simulating data and calculating covariance matrix of observations for non-linear models.
randomiseA function that generates a new set of values representing the treatment allocation in an experimental study
treat_varA string naming the column in data that represents the treatment variable in data. Used to identify where to replace allocation when randomiser is used.
Xthe fixed effects design matrix
Methods
Public methods
Method n()
Returns the number of observations
Usage
MeanFunction$n()
Arguments
...ignored
Returns
The number of observations in the model
Examples
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$n()
Method check()
Checks if any changes have been made and updates
Checks if any changes have been made and updates, usually called automatically.
Usage
MeanFunction$check(verbose = TRUE)
Arguments
verboseLogical whether to report if any changes detected.
Returns
NULL
Examples
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$parameters <- c(0,0)
mf1$check()
Method new()
Create a new MeanFunction object
Usage
MeanFunction$new( formula, data, family, parameters, verbose = FALSE, random_function = NULL, treat_var = NULL )
Arguments
formulaA formula object that describes the mean function, see Details
dataA data frame containing the covariates in the model, named in the model formula
familyA family object expressing the distribution and link function of the model, see family
parametersA vector with the values of the parameters β to use in data simulation and covariance calculations
verboseLogical indicating whether to report detailed output
random_functionA string naming a function in the global environment that produces a vector of data describing a new treatment allocation in an experimental model. When used, the output of this function replaces the column of data named by 'treat_var'
treat_varThe name of a column in data (or the name to give a new column) that a random treatment allocation generated by 'random_function' replaces.
Returns
A MeanFunction object
Examples
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
Method print()
Prints details about the object
Usage
MeanFunction$print()
Arguments
...ignored
Method colnames()
Returns or replaces the column names of the data in the object
Usage
MeanFunction$colnames(names = NULL)
Arguments
namesIf NULL then the function prints the column names, if a vector of names, then it attemps to replace the current column names of the data
Examples
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$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
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$subset_rows(1:20)
Method subset_cols()
Keeps a subset of the columns of X
All indices not in the provided vector of column numbers will be removed from the fixed effects design matrix X.
Usage
MeanFunction$subset_cols(index)
Arguments
indexColumns of X to keep
Returns
NULL
Examples
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$subset_cols(1:2)
Method rerandomise()
Generates a new random allocation
If a randomising function has been provided then a new random allocation will be generated, and will replace the exisitng data at 'treat_var' in the X matrix
Usage
MeanFunction$rerandomise()
Arguments
...ignored
Returns
Nothing is returned, the X matrix is updated
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 <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$n()
## ------------------------------------------------
## Method `MeanFunction$check`
## ------------------------------------------------
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$parameters <- c(0,0)
mf1$check()
## ------------------------------------------------
## Method `MeanFunction$new`
## ------------------------------------------------
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
## ------------------------------------------------
## 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),
family = binomial()
)
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),
family = binomial()
)
mf1$subset_rows(1:20)
## ------------------------------------------------
## Method `MeanFunction$subset_cols`
## ------------------------------------------------
df <- nelder(~(cl(4)*t(5)) > ind(5))
df$int <- 0
df[df$cl <= 5, 'int'] <- 1
mf1 <- MeanFunction$new(formula = ~ int ,
data=df,
parameters = c(-1,1),
family = binomial()
)
mf1$subset_cols(1:2)
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