Extracting the model frame from a bayesnecformula

Recovers evaluated data.frame given input data and a formula of class bayesnecformula.

# S3 method for bayesnecformula
model.frame(formula, data, ...)

Arguments

formula

A formula of class bayesnecformula.

data

A data.frame containing the variables specified in formula.

...

Additional arguments to be passed to check_formula.

Value

A data.frame with additional attributes detailing the population-level variables (attribute "bnec_pop") (response y, predictor x, and, if binomial a formula, trials) and, if applicable, the group-level variables (attribute "bnec_group").

Details

If the formula contains transformations to variables x and y, these are evaluated and returned as part of the data.frame.

Examples

library(bayesnec)
nec3param <- function(beta, nec, top, x) {
  top * exp(-exp(beta) * (x - nec) *
    ifelse(x - nec < 0, 0, 1))
}

data <- data.frame(x = seq(1, 20, length.out = 10), tr = 100, wght = c(1, 2),
                   group_1 = sample(c("a", "b"), 10, replace = TRUE),
                   group_2 = sample(c("c", "d"), 10, replace = TRUE))
data$y <- nec3param(beta = -0.2, nec = 4, top = 100, data$x)

f_1 <- y ~ crf(x, "nec3param")
f_2 <- "y | trials(tr) ~ crf(sqrt(x), \"nec3param\")"
f_3 <- y | trials(tr) ~ crf(x, "nec3param") + ogl(group_1) + pgl(group_2)
f_4 <- y | trials(tr) ~ crf(x, "nec3param") + (nec + top | group_1)

m_1 <- model.frame(bnf(f_1), data)
attr(m_1, "bnec_pop")
#> y_var x_var 
#>   "y"   "x" 
#> attr(,"na.action")
#> [1] 3
#> attr(,"class")
#> [1] "omit"
model.frame(bnf(f_2), data)
#>               y  sqrt(x) trials(tr)
#> 1  1.000000e+02 1.000000        100
#> 2  1.000000e+02 1.763834        100
#> 3  3.676327e+01 2.285218        100
#> 4  6.527785e+00 2.708013        100
#> 5  1.159091e+00 3.073181        100
#> 6  2.058113e-01 3.399346        100
#> 7  3.654441e-02 3.696846        100
#> 8  6.488923e-03 3.972125        100
#> 9  1.152191e-03 4.229526        100
#> 10 2.045861e-04 4.472136        100
m_3 <- model.frame(bnf(f_3), data)
attr(m_3, "bnec_group")
#> [1] "group_1" "group_2"
model.frame(bnf(f_4), data)
#>               y         x trials(tr) group_1
#> 1  1.000000e+02  1.000000        100       a
#> 2  1.000000e+02  3.111111        100       b
#> 3  3.676327e+01  5.222222        100       b
#> 4  6.527785e+00  7.333333        100       b
#> 5  1.159091e+00  9.444444        100       a
#> 6  2.058113e-01 11.555556        100       a
#> 7  3.654441e-02 13.666667        100       b
#> 8  6.488923e-03 15.777778        100       b
#> 9  1.152191e-03 17.888889        100       b
#> 10 2.045861e-04 20.000000        100       a