Provide tidy output from a std_custom object for use in downstream computations
Source:R/standardize_custom.R
tidy.std_custom.RdTidy summarizes information about the components of the standardized regression fit.
Usage
# S3 method for class 'std_custom'
tidy(x, ...)Examples
set.seed(6)
n <- 100
Z <- rnorm(n)
X <- rnorm(n, mean = Z)
Y <- rbinom(n, 1, prob = (1 + exp(X + Z))^(-1))
dd <- data.frame(Z, X, Y)
prob_predict.glm <- function(...) predict.glm(..., type = "response")
x <- standardize(
fitter = "glm",
arguments = list(
formula = Y ~ X * Z,
family = "binomial"
),
predict_fun = prob_predict.glm,
data = dd,
values = list(X = seq(-1, 1, 0.1)),
B = 100,
reference = 0,
contrasts = "difference"
)
#>
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tidy(x)
#> X Estimate lower.0.95 upper.0.95 contrast transform
#> 1 -1.0 0.68877411 0.536707403 0.882255075 none identity
#> 2 -0.9 0.67062009 0.525813648 0.864879358 none identity
#> 3 -0.8 0.65256341 0.520721573 0.844794061 none identity
#> 4 -0.7 0.63468885 0.514816667 0.822014143 none identity
#> 5 -0.6 0.61706694 0.508080952 0.796787359 none identity
#> 6 -0.5 0.59975468 0.492641840 0.769600133 none identity
#> 7 -0.4 0.58279684 0.476460813 0.744746574 none identity
#> 8 -0.3 0.56622750 0.466008766 0.719976812 none identity
#> 9 -0.2 0.55007164 0.450432696 0.699444328 none identity
#> 10 -0.1 0.53434668 0.429345254 0.678849732 none identity
#> 11 0.0 0.51906387 0.408810531 0.658324522 none identity
#> 12 0.1 0.50422954 0.388670917 0.638001823 none identity
#> 13 0.2 0.48984604 0.369157271 0.621476434 none identity
#> 14 0.3 0.47591268 0.347567318 0.607917623 none identity
#> 15 0.4 0.46242632 0.321901089 0.594855155 none identity
#> 16 0.5 0.44938199 0.297725989 0.587484524 none identity
#> 17 0.6 0.43677323 0.275239193 0.582974320 none identity
#> 18 0.7 0.42459251 0.253692137 0.576538798 none identity
#> 19 0.8 0.41283139 0.234773966 0.571133996 none identity
#> 20 0.9 0.40148079 0.221703266 0.560876519 none identity
#> 21 1.0 0.39053110 0.207361355 0.551041083 none identity
#> 22 -1.0 0.16971023 0.040373417 0.308788466 difference identity
#> 23 -0.9 0.15155622 0.035622795 0.285045672 difference identity
#> 24 -0.8 0.13349953 0.031043865 0.253475213 difference identity
#> 25 -0.7 0.11562498 0.026632363 0.221343098 difference identity
#> 26 -0.6 0.09800307 0.022383437 0.192009746 difference identity
#> 27 -0.5 0.08069081 0.018291855 0.162111431 difference identity
#> 28 -0.4 0.06373297 0.014352152 0.130535298 difference identity
#> 29 -0.3 0.04716363 0.010558738 0.098102367 difference identity
#> 30 -0.2 0.03100776 0.006905981 0.065247743 difference identity
#> 31 -0.1 0.01528280 0.003388258 0.032425935 difference identity
#> 32 0.0 0.00000000 0.000000000 0.000000000 difference identity
#> 33 0.1 -0.01483434 -0.031773088 -0.003264277 difference identity
#> 34 0.2 -0.02921783 -0.062855815 -0.006409950 difference identity
#> 35 0.3 -0.04315120 -0.093646417 -0.009442266 difference identity
#> 36 0.4 -0.05663755 -0.123698295 -0.012366334 difference identity
#> 37 0.5 -0.06968189 -0.151915636 -0.015187109 difference identity
#> 38 0.6 -0.08229064 -0.176633031 -0.017909383 difference identity
#> 39 0.7 -0.09447137 -0.199542682 -0.020537779 difference identity
#> 40 0.8 -0.10623249 -0.220740791 -0.023076747 difference identity
#> 41 0.9 -0.11758309 -0.240331840 -0.025530558 difference identity
#> 42 1.0 -0.12853277 -0.258910112 -0.027903299 difference identity