For example:
If i create something with kmeans, how can i tell what this has, if i need to subset something from it?
Similar question if I create other functions such as glm
Is the best method using summary(fuction)
You can see the code for a function by running the function name with no parentheses after it. You can inspect the formals(), body(), and enviornment() of a function with the respective calls:
glm
#> function (formula, family = gaussian, data, weights, subset,
#> na.action, start = NULL, etastart, mustart, offset, control = list(...),
#> model = TRUE, method = "glm.fit", x = FALSE, y = TRUE, singular.ok = TRUE,
#> contrasts = NULL, ...)
#> {
#> call <- match.call()
#> if (is.character(family))
#> family <- get(family, mode = "function", envir = parent.frame())
#> if (is.function(family))
#> family <- family()
#> if (is.null(family$family)) {
#> print(family)
#> stop("'family' not recognized")
#> }
#> if (missing(data))
#> data <- environment(formula)
#> mf <- match.call(expand.dots = FALSE)
#> m <- match(c("formula", "data", "subset", "weights", "na.action",
#> "etastart", "mustart", "offset"), names(mf), 0L)
#> mf <- mf[c(1L, m)]
#> mf$drop.unused.levels <- TRUE
#> mf[[1L]] <- quote(stats::model.frame)
#> mf <- eval(mf, parent.frame())
#> if (identical(method, "model.frame"))
#> return(mf)
#> if (!is.character(method) && !is.function(method))
#> stop("invalid 'method' argument")
#> if (identical(method, "glm.fit"))
#> control <- do.call("glm.control", control)
#> mt <- attr(mf, "terms")
#> Y <- model.response(mf, "any")
#> if (length(dim(Y)) == 1L) {
#> nm <- rownames(Y)
#> dim(Y) <- NULL
#> if (!is.null(nm))
#> names(Y) <- nm
#> }
#> X <- if (!is.empty.model(mt))
#> model.matrix(mt, mf, contrasts)
#> else matrix(, NROW(Y), 0L)
#> weights <- as.vector(model.weights(mf))
#> if (!is.null(weights) && !is.numeric(weights))
#> stop("'weights' must be a numeric vector")
#> if (!is.null(weights) && any(weights < 0))
#> stop("negative weights not allowed")
#> offset <- as.vector(model.offset(mf))
#> if (!is.null(offset)) {
#> if (length(offset) != NROW(Y))
#> stop(gettextf("number of offsets is %d should equal %d (number of observations)",
#> length(offset), NROW(Y)), domain = NA)
#> }
#> mustart <- model.extract(mf, "mustart")
#> etastart <- model.extract(mf, "etastart")
#> fit <- eval(call(if (is.function(method)) "method" else method,
#> x = X, y = Y, weights = weights, start = start, etastart = etastart,
#> mustart = mustart, offset = offset, family = family,
#> control = control, intercept = attr(mt, "intercept") >
#> 0L, singular.ok = singular.ok))
#> if (length(offset) && attr(mt, "intercept") > 0L) {
#> fit2 <- eval(call(if (is.function(method)) "method" else method,
#> x = X[, "(Intercept)", drop = FALSE], y = Y, weights = weights,
#> offset = offset, family = family, control = control,
#> intercept = TRUE))
#> if (!fit2$converged)
#> warning("fitting to calculate the null deviance did not converge -- increase 'maxit'?")
#> fit$null.deviance <- fit2$deviance
#> }
#> if (model)
#> fit$model <- mf
#> fit$na.action <- attr(mf, "na.action")
#> if (x)
#> fit$x <- X
#> if (!y)
#> fit$y <- NULL
#> fit <- c(fit, list(call = call, formula = formula, terms = mt,
#> data = data, offset = offset, control = control, method = method,
#> contrasts = attr(X, "contrasts"), xlevels = .getXlevels(mt,
#> mf)))
#> class(fit) <- c(fit$class, c("glm", "lm"))
#> fit
#> }
#> <bytecode: 0x7f854090bd60>
#> <environment: namespace:stats>
formals(glm)
#> $formula
#>
#>
#> $family
#> gaussian
#>
#> $data
#>
#>
#> $weights
#>
#>
#> $subset
#>
#>
#> $na.action
#>
#>
#> $start
#> NULL
#>
#> $etastart
#>
#>
#> $mustart
#>
#>
#> $offset
#>
#>
#> $control
#> list(...)
#>
#> $model
#> [1] TRUE
#>
#> $method
#> [1] "glm.fit"
#>
#> $x
#> [1] FALSE
#>
#> $y
#> [1] TRUE
#>
#> $singular.ok
#> [1] TRUE
#>
#> $contrasts
#> NULL
#>
#> $...
body(glm)
#> {
#> call <- match.call()
#> if (is.character(family))
#> family <- get(family, mode = "function", envir = parent.frame())
#> if (is.function(family))
#> family <- family()
#> if (is.null(family$family)) {
#> print(family)
#> stop("'family' not recognized")
#> }
#> if (missing(data))
#> data <- environment(formula)
#> mf <- match.call(expand.dots = FALSE)
#> m <- match(c("formula", "data", "subset", "weights", "na.action",
#> "etastart", "mustart", "offset"), names(mf), 0L)
#> mf <- mf[c(1L, m)]
#> mf$drop.unused.levels <- TRUE
#> mf[[1L]] <- quote(stats::model.frame)
#> mf <- eval(mf, parent.frame())
#> if (identical(method, "model.frame"))
#> return(mf)
#> if (!is.character(method) && !is.function(method))
#> stop("invalid 'method' argument")
#> if (identical(method, "glm.fit"))
#> control <- do.call("glm.control", control)
#> mt <- attr(mf, "terms")
#> Y <- model.response(mf, "any")
#> if (length(dim(Y)) == 1L) {
#> nm <- rownames(Y)
#> dim(Y) <- NULL
#> if (!is.null(nm))
#> names(Y) <- nm
#> }
#> X <- if (!is.empty.model(mt))
#> model.matrix(mt, mf, contrasts)
#> else matrix(, NROW(Y), 0L)
#> weights <- as.vector(model.weights(mf))
#> if (!is.null(weights) && !is.numeric(weights))
#> stop("'weights' must be a numeric vector")
#> if (!is.null(weights) && any(weights < 0))
#> stop("negative weights not allowed")
#> offset <- as.vector(model.offset(mf))
#> if (!is.null(offset)) {
#> if (length(offset) != NROW(Y))
#> stop(gettextf("number of offsets is %d should equal %d (number of observations)",
#> length(offset), NROW(Y)), domain = NA)
#> }
#> mustart <- model.extract(mf, "mustart")
#> etastart <- model.extract(mf, "etastart")
#> fit <- eval(call(if (is.function(method)) "method" else method,
#> x = X, y = Y, weights = weights, start = start, etastart = etastart,
#> mustart = mustart, offset = offset, family = family,
#> control = control, intercept = attr(mt, "intercept") >
#> 0L, singular.ok = singular.ok))
#> if (length(offset) && attr(mt, "intercept") > 0L) {
#> fit2 <- eval(call(if (is.function(method)) "method" else method,
#> x = X[, "(Intercept)", drop = FALSE], y = Y, weights = weights,
#> offset = offset, family = family, control = control,
#> intercept = TRUE))
#> if (!fit2$converged)
#> warning("fitting to calculate the null deviance did not converge -- increase 'maxit'?")
#> fit$null.deviance <- fit2$deviance
#> }
#> if (model)
#> fit$model <- mf
#> fit$na.action <- attr(mf, "na.action")
#> if (x)
#> fit$x <- X
#> if (!y)
#> fit$y <- NULL
#> fit <- c(fit, list(call = call, formula = formula, terms = mt,
#> data = data, offset = offset, control = control, method = method,
#> contrasts = attr(X, "contrasts"), xlevels = .getXlevels(mt,
#> mf)))
#> class(fit) <- c(fit$class, c("glm", "lm"))
#> fit
#> }
environment(glm)
#> <environment: namespace:stats>
Created on 2019-06-13 by the reprex package (v0.3.0)
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