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Update data and formula for fitting cglmm model

Source: R/data_utils.R
update_formula_and_data.Rd

Update data and formula for fitting cglmm model

Usage

update_formula_and_data(
  data,
  formula,
  family = "gaussian",
  quietly = TRUE,
  dispformula = ~1,
  ziformula = ~0
)

Arguments

data

input data for fitting cglmm model.

formula

model formula, specified by user including amp_acro().

family

the model family.

quietly

controls whether messages from amp_acro are displayed. TRUE by default

dispformula

The formula specifying the dispersion model

ziformula

The formula specifying the zero-inflation model

Value

Returns a list.

Examples

# Use vitamind data but add a "patient" identifier used as a random effect
vitamind2 <- vitamind
vitamind2$patient <- sample(
  LETTERS[1:5],
  size = nrow(vitamind2), replace = TRUE
)

# Use update_formula_and_data() to perform wrangling steps of cglmm()
# without yet fitting the model
data_and_formula <- update_formula_and_data(
  data = vitamind2,
  formula = vit_d ~ X + amp_acro(time,
    group = "X",
    period = 12
  )
)

# print formula from above
data_and_formula$newformula
#> vit_d ~ X + X:main_rrr1 + X:main_sss1
#> <environment: 0x55ecd3330d30>

# fit model while adding random effect to cosinor model formula.
mod <- fit_model_and_process(
  obj = data_and_formula,
  formula = update.formula(
    data_and_formula$newformula, . ~ . + (1 | patient)
  )
)

mod
#> 
#>  Conditional Model 
#> 
#>  Raw formula: 
#> vit_d ~ X + (1 | patient) + X:main_rrr1 + X:main_sss1 
#> 
#>  Raw Coefficients: 
#>              Estimate
#> (Intercept)  29.64388
#> X1            1.93606
#> X0:main_rrr1  0.96588
#> X1:main_rrr1  6.46825
#> X0:main_sss1  6.18892
#> X1:main_sss1  4.82437
#> 
#>  Transformed Coefficients: 
#>             Estimate
#> (Intercept) 29.64388
#> [X=1]        1.93606
#> [X=0]:amp    6.26384
#> [X=1]:amp    8.06925
#> [X=0]:acr    1.41598
#> [X=1]:acr    0.64084
mod$fit # printing the `glmmTMB` model within shows Std.Dev. of random effect
#> Formula:          vit_d ~ X + (1 | patient) + X:main_rrr1 + X:main_sss1
#> Data: newdata
#>      AIC      BIC   logLik df.resid 
#> 1247.800 1274.187 -615.900      192 
#> Random-effects (co)variances:
#> 
#> Conditional model:
#>  Groups   Name        Std.Dev.
#>  patient  (Intercept) 0.4868  
#>  Residual             5.2429  
#> 
#> Number of obs: 200 / Conditional model: patient, 5
#> 
#> Dispersion estimate for gaussian family (sigma^2): 27.5 
#> 
#> Fixed Effects:
#> 
#> Conditional model:
#>  (Intercept)            X1  X0:main_rrr1  X1:main_rrr1  X0:main_sss1  
#>      29.6439        1.9361        0.9659        6.4682        6.1889  
#> X1:main_sss1  
#>       4.8244