Retrieve data.frame used to fit models via bnec, or directly
from a bayesnecformula.
formula should be of class bayesnecfit,
bayesmanecfit or bayesnecformula.
# S3 method for class 'bayesnecfit'
model.frame(formula, ...)
# S3 method for class 'bayesmanecfit'
model.frame(formula, model, ...)
# S3 method for class 'bayesnecformula'
model.frame(formula, data, ...)An model object of class bayesnecfit,
bayesmanecfit, or a formula of class
bayesnecformula.
Unused if formula is a bayesnecfit or a
bayesmanecfit. Else, if formula is a
bayesnecformula, additional arguments to be passed to
check_formula.
A valid model string.
A data.frame containing the variables
specified in formula.
If formula is a bayesnecfit or a
bayesmanecfit, a data.frame containing
the data used to fit the model.
If, instead, formula is a bayesnecformula,
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").
If formula is a bayesnecformula and it
contains transformations to variables x and y, these are evaluated and
returned as part of the data.frame.
# \donttest{
library(bayesnec)
# if input is of class `bayesnecfit` or `bayesmanecfit`
model.frame(manec_example, model = "nec4param")
#> y x
#> 1 1.81753716 1.01874617
#> 2 2.31660095 0.81574746
#> 3 1.72290850 0.37133055
#> 4 2.47827471 0.40083228
#> 5 1.86355409 1.29454513
#> 6 2.39344847 1.38979430
#> 7 2.44867487 0.20807618
#> 8 1.53236967 0.63632398
#> 9 2.68271344 0.62667268
#> 10 2.75545171 0.74352161
#> 11 -0.72165471 2.10177950
#> 12 0.74223372 1.75578151
#> 13 1.92652136 0.76176644
#> 14 -0.24585004 1.98744470
#> 15 0.76099900 1.74139013
#> 16 2.15101769 1.08934727
#> 17 2.69104590 0.04505614
#> 18 2.49637234 0.80484962
#> 19 0.19280001 1.92552126
#> 20 2.62941229 1.48297852
#> 21 1.91996822 0.40368936
#> 22 2.20932082 1.18528684
#> 23 2.10261674 0.32513406
#> 24 1.67257283 1.11906119
#> 25 2.42730928 0.26519802
#> 26 1.76470022 0.62633844
#> 27 2.23128899 0.31244457
#> 28 1.71546355 0.12784117
#> 29 1.96525963 0.89823899
#> 30 1.66894887 0.74621947
#> 31 3.00870633 0.85374045
#> 32 2.43285909 0.92205393
#> 33 -0.48778119 1.96856634
#> 34 1.84597419 1.18492596
#> 35 1.92807736 0.37994358
#> 36 1.74175047 0.43551436
#> 37 2.35658367 1.36208723
#> 38 2.48674416 0.75908675
#> 39 2.45232814 0.67545599
#> 40 2.63202300 0.34843701
#> 41 -0.75737944 2.08655140
#> 42 1.66952275 0.23745512
#> 43 1.44637922 0.17133746
#> 44 0.51525902 1.83447390
#> 45 2.29255167 0.03234801
#> 46 2.70966110 0.97118466
#> 47 2.37563422 1.23252515
#> 48 2.16232277 0.69879132
#> 49 2.92560473 0.32238542
#> 50 0.98056568 1.65522764
#> 51 1.95465732 0.59936245
#> 52 2.01082448 0.66544343
#> 53 -0.85674115 2.36795395
#> 54 -6.90675478 3.13776710
#> 55 1.72611555 1.50581926
#> 56 0.02076562 1.78634238
#> 57 2.41890914 0.09778806
#> 58 2.22724962 1.53289699
#> 59 1.67129635 0.35410575
#> 60 1.55513976 1.29098749
#> 61 2.44416405 0.23759447
#> 62 1.89439817 0.54382372
#> 63 2.37042869 0.16967735
#> 64 1.83967579 1.34011564
#> 65 -0.47439313 2.06637640
#> 66 -0.89239448 2.21612584
#> 67 0.79149428 1.73569066
#> 68 1.58836486 0.51879138
#> 69 1.57267826 1.56274476
#> 70 1.59831797 0.24631971
#> 71 1.69118540 1.38092221
#> 72 1.94552283 0.43042725
#> 73 2.18164978 0.04844550
#> 74 2.70687710 0.78149645
#> 75 1.88646056 0.48993624
#> 76 -0.42290590 2.17270628
#> 77 2.22143943 0.47982702
#> 78 1.98709144 0.56961218
#> 79 2.63543591 0.05163864
#> 80 -1.30724886 2.52258634
#> 81 1.42229818 1.59282805
#> 82 2.43861218 0.77733849
#> 83 0.45069003 1.71289428
#> 84 0.64790451 1.71660083
#> 85 2.42482651 1.44024186
#> 86 3.89182030 1.15883062
#> 87 0.87668024 1.65976414
#> 88 -3.27854955 3.22051966
#> 89 2.46633324 0.18394507
#> 90 2.13714590 0.92604417
#> 91 2.16738296 0.58364533
#> 92 2.65975450 0.80851766
#> 93 1.84621881 1.06954478
#> 94 -0.86117420 2.15534832
#> 95 0.96084129 1.59495735
#> 96 1.98523295 0.41964511
#> 97 1.51921728 0.60667725
#> 98 -0.15391721 1.89292590
#> 99 1.70259760 1.14816796
#> 100 -1.00846203 2.22702839
nec4param <- pull_out(manec_example, "nec4param")
#> Pulling out model(s): nec4param
model.frame(nec4param)
#> y x
#> 1 1.81753716 1.01874617
#> 2 2.31660095 0.81574746
#> 3 1.72290850 0.37133055
#> 4 2.47827471 0.40083228
#> 5 1.86355409 1.29454513
#> 6 2.39344847 1.38979430
#> 7 2.44867487 0.20807618
#> 8 1.53236967 0.63632398
#> 9 2.68271344 0.62667268
#> 10 2.75545171 0.74352161
#> 11 -0.72165471 2.10177950
#> 12 0.74223372 1.75578151
#> 13 1.92652136 0.76176644
#> 14 -0.24585004 1.98744470
#> 15 0.76099900 1.74139013
#> 16 2.15101769 1.08934727
#> 17 2.69104590 0.04505614
#> 18 2.49637234 0.80484962
#> 19 0.19280001 1.92552126
#> 20 2.62941229 1.48297852
#> 21 1.91996822 0.40368936
#> 22 2.20932082 1.18528684
#> 23 2.10261674 0.32513406
#> 24 1.67257283 1.11906119
#> 25 2.42730928 0.26519802
#> 26 1.76470022 0.62633844
#> 27 2.23128899 0.31244457
#> 28 1.71546355 0.12784117
#> 29 1.96525963 0.89823899
#> 30 1.66894887 0.74621947
#> 31 3.00870633 0.85374045
#> 32 2.43285909 0.92205393
#> 33 -0.48778119 1.96856634
#> 34 1.84597419 1.18492596
#> 35 1.92807736 0.37994358
#> 36 1.74175047 0.43551436
#> 37 2.35658367 1.36208723
#> 38 2.48674416 0.75908675
#> 39 2.45232814 0.67545599
#> 40 2.63202300 0.34843701
#> 41 -0.75737944 2.08655140
#> 42 1.66952275 0.23745512
#> 43 1.44637922 0.17133746
#> 44 0.51525902 1.83447390
#> 45 2.29255167 0.03234801
#> 46 2.70966110 0.97118466
#> 47 2.37563422 1.23252515
#> 48 2.16232277 0.69879132
#> 49 2.92560473 0.32238542
#> 50 0.98056568 1.65522764
#> 51 1.95465732 0.59936245
#> 52 2.01082448 0.66544343
#> 53 -0.85674115 2.36795395
#> 54 -6.90675478 3.13776710
#> 55 1.72611555 1.50581926
#> 56 0.02076562 1.78634238
#> 57 2.41890914 0.09778806
#> 58 2.22724962 1.53289699
#> 59 1.67129635 0.35410575
#> 60 1.55513976 1.29098749
#> 61 2.44416405 0.23759447
#> 62 1.89439817 0.54382372
#> 63 2.37042869 0.16967735
#> 64 1.83967579 1.34011564
#> 65 -0.47439313 2.06637640
#> 66 -0.89239448 2.21612584
#> 67 0.79149428 1.73569066
#> 68 1.58836486 0.51879138
#> 69 1.57267826 1.56274476
#> 70 1.59831797 0.24631971
#> 71 1.69118540 1.38092221
#> 72 1.94552283 0.43042725
#> 73 2.18164978 0.04844550
#> 74 2.70687710 0.78149645
#> 75 1.88646056 0.48993624
#> 76 -0.42290590 2.17270628
#> 77 2.22143943 0.47982702
#> 78 1.98709144 0.56961218
#> 79 2.63543591 0.05163864
#> 80 -1.30724886 2.52258634
#> 81 1.42229818 1.59282805
#> 82 2.43861218 0.77733849
#> 83 0.45069003 1.71289428
#> 84 0.64790451 1.71660083
#> 85 2.42482651 1.44024186
#> 86 3.89182030 1.15883062
#> 87 0.87668024 1.65976414
#> 88 -3.27854955 3.22051966
#> 89 2.46633324 0.18394507
#> 90 2.13714590 0.92604417
#> 91 2.16738296 0.58364533
#> 92 2.65975450 0.80851766
#> 93 1.84621881 1.06954478
#> 94 -0.86117420 2.15534832
#> 95 0.96084129 1.59495735
#> 96 1.98523295 0.41964511
#> 97 1.51921728 0.60667725
#> 98 -0.15391721 1.89292590
#> 99 1.70259760 1.14816796
#> 100 -1.00846203 2.22702839
# if input is of class `bayesnecformula`
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 a
#> 5 1.159091e+00 9.444444 100 b
#> 6 2.058113e-01 11.555556 100 b
#> 7 3.654441e-02 13.666667 100 a
#> 8 6.488923e-03 15.777778 100 a
#> 9 1.152191e-03 17.888889 100 b
#> 10 2.045861e-04 20.000000 100 b
# }