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#' @title Plot linear model object
#'
#' @param lm_fit An object of class 'lm'
#' @param format The format of the plot (or a list of plots if format is "list")
#'
#' @examples
#'
#' m1 <- lm(speed ~ dist, data=cars)
#' plot_lm(m1)
#' plot_lm(m1, "2x2")
#' plot_lm(m1, "1x4")
#' plot_lm(m1, "4x1")
#' plot_lm(m1, "list")
#'
#' @export
plot_lm <- function(lm_fit, format="2x2") {
if(!inherits(lm_fit, "lm"))
stop("'lm_fit' must inherit form 'lm'\n")
format <- match.arg(format, c("2x2", "1x4", "4x1", "list"))
dd <- data.frame(fitted_values = predict(lm_fit),
## residuals = resid(lm_fit),
observed_values = predict(lm_fit)+resid(lm_fit),
standardized_residuals = rstandard(lm_fit))
pl1 <- ggplot(dd, aes(x = .data$fitted_values, y = .data$standardized_residuals)) +
geom_point() + geom_hline(yintercept=c(0, -1.96, 1.95))
pl2 <- ggplot(dd, aes(x=.data$fitted_values, y=.data$observed_values)) +
geom_point() + geom_smooth(method="lm", formula=y ~ x, se=FALSE)
pl3 <- ggplot(dd, aes(x=.data$fitted_values, y=sqrt(abs(.data$standardized_residuals)))) + geom_point()
pl4 <- ggplot(dd, aes(sample = .data$standardized_residuals)) + stat_qq() + stat_qq_line() +
labs(y="standardized_residuals", x = "theoretical quantiles")
switch(format,
"2x2"={ cowplot::plot_grid(pl1, pl2, pl3, pl4, nrow=2) },
"1x4"={ cowplot::plot_grid(pl1, pl2, pl3, pl4, nrow=1) },
"4x1"={ cowplot::plot_grid(pl1, pl2, pl3, pl4, nrow=4) },
"list"={ list(pl1=pl1, pl2=pl2, pl3=pl3, pl4=pl4) }
)
}
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