This function does not cover lavaan models (NULL is returned).
model_get_response(model)
# S3 method for default
model_get_response(model)
# S3 method for glm
model_get_response(model)
# S3 method for glmerMod
model_get_response(model)
# S3 method for model_fit
model_get_response(model)a model object
Other model_helpers:
model_compute_terms_contributions(),
model_get_assign(),
model_get_coefficients_type(),
model_get_contrasts(),
model_get_model(),
model_get_model_frame(),
model_get_model_matrix(),
model_get_n(),
model_get_nlevels(),
model_get_offset(),
model_get_pairwise_contrasts(),
model_get_response_variable(),
model_get_terms(),
model_get_weights(),
model_get_xlevels(),
model_identify_variables(),
model_list_contrasts(),
model_list_higher_order_variables(),
model_list_terms_levels(),
model_list_variables()
lm(hp ~ mpg + factor(cyl) + disp:hp, mtcars) %>%
model_get_response()
#> Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive
#> 110 110 93 110
#> Hornet Sportabout Valiant Duster 360 Merc 240D
#> 175 105 245 62
#> Merc 230 Merc 280 Merc 280C Merc 450SE
#> 95 123 123 180
#> Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental
#> 180 180 205 215
#> Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla
#> 230 66 52 65
#> Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
#> 97 150 150 245
#> Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa
#> 175 66 91 113
#> Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E
#> 264 175 335 109
mod <- glm(
response ~ stage * grade + trt,
gtsummary::trial,
family = binomial,
contrasts = list(stage = contr.sum, grade = contr.treatment(3, 2), trt = "contr.SAS")
)
mod %>% model_get_response()
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
#> 0 1 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0
#> 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 37 38 39 40 41
#> 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 1 0 0 0
#> 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61
#> 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 1 0 1 1 0
#> 62 63 64 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82
#> 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1
#> 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102
#> 0 0 0 1 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0
#> 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123
#> 0 0 1 0 0 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0
#> 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 140 141 142 143 144
#> 0 0 1 1 0 0 0 1 0 0 0 1 0 1 1 1 1 1 0 1
#> 145 146 147 148 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165
#> 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0
#> 166 167 168 169 170 171 172 173 174 175 176 177 178 180 181 182 183 184 185 186
#> 0 0 1 0 1 0 1 0 1 1 1 0 1 0 1 0 0 1 0 1
#> 187 188 189 190 191 192 193 194 196 197 198 199 200
#> 0 0 0 0 0 0 0 0 0 1 1 0 0
#> attr(,"label")
#> [1] "Tumor Response"
mod <- glm(
Survived ~ Class * Age + Sex,
data = Titanic %>% as.data.frame(),
weights = Freq,
family = binomial
)
mod %>% model_get_response()
#> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
#> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 0 1 1
#> 27 28 29 30 31 32
#> 1 1 1 1 1 1
d <- dplyr::as_tibble(Titanic) %>%
dplyr::group_by(Class, Sex, Age) %>%
dplyr::summarise(
n_survived = sum(n * (Survived == "Yes")),
n_dead = sum(n * (Survived == "No"))
)
#> `summarise()` has grouped output by 'Class', 'Sex'. You can override using the
#> `.groups` argument.
mod <- glm(cbind(n_survived, n_dead) ~ Class * Age + Sex, data = d, family = binomial, y = FALSE)
mod %>% model_get_response()
#> 1 2 3 4 5 6 7
#> 0.97222222 1.00000000 0.32571429 1.00000000 0.86021505 1.00000000 0.08333333
#> 8 9 10 11 12 13 14
#> 1.00000000 0.46060606 0.45161290 0.16233766 0.27083333 0.86956522 0.00000000
#> 15 16
#> 0.22273782 0.00000000