Returns a single fitted model object produced by the underlying fitting algorithm from a tidyfit.models frame based on a given row number.
get_model(df, ..., .first_row = TRUE)a tidyfit.models frame created using m(), regress(), classify() and similar methods
arguments passed to dplyr::filter to filter row in 'df' for which the model should be returned. filters can also include columns nested in df$settings.
should the first row be returned if the (filtered) df contains multiple rows
An object of the class associated with the underlying fitting algorithm
This method is a utility to return the object fitted by the underlying algorithm. For instance, when m("lm") is used to create the tidyfit.models frame, the returned object is of class "lm".
get_tidyFit method
# Load data
data("mtcars")
# fit separate models for transmission types
mtcars <- dplyr::group_by(mtcars, am)
fit <- regress(mtcars, mpg ~ ., m("lm"))
# get the model for single row
summary(get_model(fit, am == 0))
#>
#> Call:
#> (function (formula, data, subset, weights, na.action, method = "qr",
#> model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
#> contrasts = NULL, offset, ...)
#> {
#> ret.x <- x
#> ret.y <- y
#> cl <- match.call()
#> mf <- match.call(expand.dots = FALSE)
#> m <- match(c("formula", "data", "subset", "weights", "na.action",
#> "offset"), names(mf), 0L)
#> mf <- mf[c(1L, m)]
#> mf$drop.unused.levels <- TRUE
#> mf[[1L]] <- quote(stats::model.frame)
#> mf <- eval(mf, parent.frame())
#> if (method == "model.frame")
#> return(mf)
#> else if (method != "qr")
#> warning(gettextf("method = '%s' is not supported. Using 'qr'",
#> method), domain = NA)
#> mt <- attr(mf, "terms")
#> y <- model.response(mf, "numeric")
#> w <- as.vector(model.weights(mf))
#> if (!is.null(w) && !is.numeric(w))
#> stop("'weights' must be a numeric vector")
#> offset <- model.offset(mf)
#> mlm <- is.matrix(y)
#> ny <- if (mlm)
#> nrow(y)
#> else length(y)
#> if (!is.null(offset)) {
#> if (!mlm)
#> offset <- as.vector(offset)
#> if (NROW(offset) != ny)
#> stop(gettextf("number of offsets is %d, should equal %d (number of observations)",
#> NROW(offset), ny), domain = NA)
#> }
#> if (is.empty.model(mt)) {
#> x <- NULL
#> z <- list(coefficients = if (mlm) matrix(NA_real_, 0,
#> ncol(y)) else numeric(), residuals = y, fitted.values = 0 *
#> y, weights = w, rank = 0L, df.residual = if (!is.null(w)) sum(w !=
#> 0) else ny)
#> if (!is.null(offset)) {
#> z$fitted.values <- offset
#> z$residuals <- y - offset
#> }
#> }
#> else {
#> x <- model.matrix(mt, mf, contrasts)
#> z <- if (is.null(w))
#> lm.fit(x, y, offset = offset, singular.ok = singular.ok,
#> ...)
#> else lm.wfit(x, y, w, offset = offset, singular.ok = singular.ok,
#> ...)
#> }
#> class(z) <- c(if (mlm) "mlm", "lm")
#> z$na.action <- attr(mf, "na.action")
#> z$offset <- offset
#> z$contrasts <- attr(x, "contrasts")
#> z$xlevels <- .getXlevels(mt, mf)
#> z$call <- cl
#> z$terms <- mt
#> if (model)
#> z$model <- mf
#> if (ret.x)
#> z$x <- x
#> if (ret.y)
#> z$y <- y
#> if (!qr)
#> z$qr <- NULL
#> z
#> })(formula = mpg ~ ., data = structure(list(mpg = c(21.4, 18.7,
#> 18.1, 14.3, 24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4,
#> 14.7, 21.5, 15.5, 15.2, 13.3, 19.2), cyl = c(6, 8, 6, 8, 4, 4,
#> 6, 6, 8, 8, 8, 8, 8, 8, 4, 8, 8, 8, 8), disp = c(258, 360, 225,
#> 360, 146.7, 140.8, 167.6, 167.6, 275.8, 275.8, 275.8, 472, 460,
#> 440, 120.1, 318, 304, 350, 400), hp = c(110, 175, 105, 245, 62,
#> 95, 123, 123, 180, 180, 180, 205, 215, 230, 97, 150, 150, 245,
#> 175), drat = c(3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,
#> 3.07, 3.07, 3.07, 2.93, 3, 3.23, 3.7, 2.76, 3.15, 3.73, 3.08),
#> wt = c(3.215, 3.44, 3.46, 3.57, 3.19, 3.15, 3.44, 3.44, 4.07,
#> 3.73, 3.78, 5.25, 5.424, 5.345, 2.465, 3.52, 3.435, 3.84,
#> 3.845), qsec = c(19.44, 17.02, 20.22, 15.84, 20, 22.9, 18.3,
#> 18.9, 17.4, 17.6, 18, 17.98, 17.82, 17.42, 20.01, 16.87,
#> 17.3, 15.41, 17.05), vs = c(1, 0, 1, 0, 1, 1, 1, 1, 0, 0,
#> 0, 0, 0, 0, 1, 0, 0, 0, 0), gear = c(3, 3, 3, 3, 4, 4, 4,
#> 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3), carb = c(1, 2, 1, 4,
#> 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 2, 4, 2)), class = "data.frame", row.names = c(NA,
#> -19L)), weights = NULL, model = TRUE, x = FALSE, y = FALSE)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -2.0346 -1.0493 0.3835 0.6959 2.2395
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 8.64345 21.51122 0.402 0.6972
#> cyl -0.53391 1.12741 -0.474 0.6471
#> disp -0.02025 0.01744 -1.162 0.2753
#> hp 0.06223 0.04607 1.351 0.2097
#> drat 0.59159 3.01195 0.196 0.8486
#> wt 1.95413 2.23132 0.876 0.4039
#> qsec -0.88432 0.75840 -1.166 0.2736
#> vs 0.73891 2.51186 0.294 0.7753
#> gear 8.65416 3.89565 2.221 0.0534 .
#> carb -4.81050 1.90037 -2.531 0.0322 *
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 1.804 on 9 degrees of freedom
#> Multiple R-squared: 0.8893, Adjusted R-squared: 0.7785
#> F-statistic: 8.031 on 9 and 9 DF, p-value: 0.00238
#>
# get model by row number
summary(get_model(fit, dplyr::row_number() == 2))
#>
#> Call:
#> (function (formula, data, subset, weights, na.action, method = "qr",
#> model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE,
#> contrasts = NULL, offset, ...)
#> {
#> ret.x <- x
#> ret.y <- y
#> cl <- match.call()
#> mf <- match.call(expand.dots = FALSE)
#> m <- match(c("formula", "data", "subset", "weights", "na.action",
#> "offset"), names(mf), 0L)
#> mf <- mf[c(1L, m)]
#> mf$drop.unused.levels <- TRUE
#> mf[[1L]] <- quote(stats::model.frame)
#> mf <- eval(mf, parent.frame())
#> if (method == "model.frame")
#> return(mf)
#> else if (method != "qr")
#> warning(gettextf("method = '%s' is not supported. Using 'qr'",
#> method), domain = NA)
#> mt <- attr(mf, "terms")
#> y <- model.response(mf, "numeric")
#> w <- as.vector(model.weights(mf))
#> if (!is.null(w) && !is.numeric(w))
#> stop("'weights' must be a numeric vector")
#> offset <- model.offset(mf)
#> mlm <- is.matrix(y)
#> ny <- if (mlm)
#> nrow(y)
#> else length(y)
#> if (!is.null(offset)) {
#> if (!mlm)
#> offset <- as.vector(offset)
#> if (NROW(offset) != ny)
#> stop(gettextf("number of offsets is %d, should equal %d (number of observations)",
#> NROW(offset), ny), domain = NA)
#> }
#> if (is.empty.model(mt)) {
#> x <- NULL
#> z <- list(coefficients = if (mlm) matrix(NA_real_, 0,
#> ncol(y)) else numeric(), residuals = y, fitted.values = 0 *
#> y, weights = w, rank = 0L, df.residual = if (!is.null(w)) sum(w !=
#> 0) else ny)
#> if (!is.null(offset)) {
#> z$fitted.values <- offset
#> z$residuals <- y - offset
#> }
#> }
#> else {
#> x <- model.matrix(mt, mf, contrasts)
#> z <- if (is.null(w))
#> lm.fit(x, y, offset = offset, singular.ok = singular.ok,
#> ...)
#> else lm.wfit(x, y, w, offset = offset, singular.ok = singular.ok,
#> ...)
#> }
#> class(z) <- c(if (mlm) "mlm", "lm")
#> z$na.action <- attr(mf, "na.action")
#> z$offset <- offset
#> z$contrasts <- attr(x, "contrasts")
#> z$xlevels <- .getXlevels(mt, mf)
#> z$call <- cl
#> z$terms <- mt
#> if (model)
#> z$model <- mf
#> if (ret.x)
#> z$x <- x
#> if (ret.y)
#> z$y <- y
#> if (!qr)
#> z$qr <- NULL
#> z
#> })(formula = mpg ~ ., data = structure(list(mpg = c(21, 21, 22.8,
#> 32.4, 30.4, 33.9, 27.3, 26, 30.4, 15.8, 19.7, 15, 21.4), cyl = c(6,
#> 6, 4, 4, 4, 4, 4, 4, 4, 8, 6, 8, 4), disp = c(160, 160, 108,
#> 78.7, 75.7, 71.1, 79, 120.3, 95.1, 351, 145, 301, 121), hp = c(110,
#> 110, 93, 66, 52, 65, 66, 91, 113, 264, 175, 335, 109), drat = c(3.9,
#> 3.9, 3.85, 4.08, 4.93, 4.22, 4.08, 4.43, 3.77, 4.22, 3.62, 3.54,
#> 4.11), wt = c(2.62, 2.875, 2.32, 2.2, 1.615, 1.835, 1.935, 2.14,
#> 1.513, 3.17, 2.77, 3.57, 2.78), qsec = c(16.46, 17.02, 18.61,
#> 19.47, 18.52, 19.9, 18.9, 16.7, 16.9, 14.5, 15.5, 14.6, 18.6),
#> vs = c(0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1), gear = c(4,
#> 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 4), carb = c(4, 4, 1, 1,
#> 2, 1, 1, 2, 2, 4, 6, 8, 2)), class = "data.frame", row.names = c(NA,
#> -13L)), weights = NULL, model = TRUE, x = FALSE, y = FALSE)
#>
#> Residuals:
#> 1 2 3 4 5 6 7 8
#> 1.07088 -0.77411 -0.75528 2.69590 0.03134 -1.23282 -0.63755 0.27164
#> 9 10 11 12 13
#> 0.29677 -0.30177 -0.84005 0.57341 -0.39836
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -137.9074 69.1493 -1.994 0.140
#> cyl -1.2813 4.5374 -0.282 0.796
#> disp 0.1799 0.1756 1.024 0.381
#> hp -0.1605 0.1434 -1.119 0.345
#> drat -4.9498 5.4684 -0.905 0.432
#> wt -10.5419 4.9958 -2.110 0.125
#> qsec 8.0950 3.4515 2.345 0.101
#> vs 0.9431 5.0886 0.185 0.865
#> gear 12.3285 6.6603 1.851 0.161
#> carb 4.6885 4.0645 1.154 0.332
#>
#> Residual standard error: 2.078 on 3 degrees of freedom
#> Multiple R-squared: 0.9716, Adjusted R-squared: 0.8864
#> F-statistic: 11.41 on 9 and 3 DF, p-value: 0.03498
#>