R modelr fit_with 拟合公式列表

fit_with(data, .f, .formulas, ...)

formulas()

# fit_with() is typically used with formulas().
disp_fits <- mtcars %>% fit_with(lm, formulas(~disp,
  additive = ~drat + cyl,
  interaction = ~drat * cyl,
  full = add_predictors(interaction, ~am, ~vs)
))

# The list of fitted models is named after the names of the list of
# formulas:
disp_fits$full
#> 
#> Call:
#> .f(formula = disp ~ drat * cyl + am + vs, data = data)
#> 
#> Coefficients:
#> (Intercept)         drat          cyl           am           vs  
#>     -164.83        27.58        75.84       -38.22       -15.53  
#>    drat:cyl  
#>       -6.97  
#> 

# Additional arguments are passed on to .f
mtcars %>% fit_with(glm, list(am ~ disp), family = binomial)
#> [[1]]
#> 
#> Call:  .f(formula = am ~ disp, family = function (link = "logit") 
#> {
#>     linktemp <- substitute(link)
#>     if (!is.character(linktemp)) 
#>         linktemp <- deparse(linktemp)
#>     okLinks <- c("logit", "probit", "cloglog", "cauchit", "log")
#>     family <- "binomial"
#>     if (linktemp %in% okLinks) 
#>         stats <- make.link(linktemp)
#>     else if (is.character(link)) {
#>         stats <- make.link(link)
#>         linktemp <- link
#>     }
#>     else {
#>         if (inherits(link, "link-glm")) {
#>             stats <- link
#>             if (!is.null(stats$name)) 
#>                 linktemp <- stats$name
#>         }
#>         else {
#>             stop(gettextf("link \"%s\" not available for %s family; available links are %s", 
#>                 linktemp, family, paste(sQuote(okLinks), collapse = ", ")), 
#>                 domain = NA)
#>         }
#>     }
#>     variance <- function(mu) mu * (1 - mu)
#>     validmu <- function(mu) all(is.finite(mu)) && all(mu > 0 & 
#>         mu < 1)
#>     dev.resids <- function(y, mu, wt) .Call(C_binomial_dev_resids, 
#>         y, mu, wt)
#>     aic <- function(y, n, mu, wt, dev) {
#>         m <- if (any(n > 1)) 
#>             n
#>         else wt
#>         -2 * sum(ifelse(m > 0, (wt/m), 0) * dbinom(round(m * 
#>             y), round(m), mu, log = TRUE))
#>     }
#>     simfun <- function(object, nsim) {
#>         ftd <- fitted(object)
#>         n <- length(ftd)
#>         ntot <- n * nsim
#>         wts <- object$prior.weights
#>         if (any(wts%%1 != 0)) 
#>             stop("cannot simulate from non-integer prior.weights")
#>         if (!is.null(m <- object$model)) {
#>             y <- model.response(m)
#>             if (is.factor(y)) {
#>                 yy <- factor(1 + rbinom(ntot, size = 1, prob = ftd), 
#>                   labels = levels(y))
#>                 split(yy, rep(seq_len(nsim), each = n))
#>             }
#>             else if (is.matrix(y) && ncol(y) == 2) {
#>                 yy <- vector("list", nsim)
#>                 for (i in seq_len(nsim)) {
#>                   Y <- rbinom(n, size = wts, prob = ftd)
#>                   YY <- cbind(Y, wts - Y)
#>                   colnames(YY) <- colnames(y)
#>                   yy[[i]] <- YY
#>                 }
#>                 yy
#>             }
#>             else rbinom(ntot, size = wts, prob = ftd)/wts
#>         }
#>         else rbinom(ntot, size = wts, prob = ftd)/wts
#>     }
#>     structure(list(family = family, link = linktemp, linkfun = stats$linkfun, 
#>         linkinv = stats$linkinv, variance = variance, dev.resids = dev.resids, 
#>         aic = aic, mu.eta = stats$mu.eta, initialize = binomInitialize(family), 
#>         validmu = validmu, valideta = stats$valideta, simulate = simfun), 
#>         class = "family")
#> }, data = data)
#> 
#> Coefficients:
#> (Intercept)         disp  
#>      2.6308      -0.0146  
#> 
#> Degrees of Freedom: 31 Total (i.e. Null);  30 Residual
#> Null Deviance:	    43.23 
#> Residual Deviance: 29.73 	AIC: 33.73
#>