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#' Added variable plot data
#'
#' Data for generating the added variable plots.
#'
#' @param model An object of class \code{lm}.
#'
#' @examples
#' model <- lm(mpg ~ disp + hp + wt, data = mtcars)
#' ols_prep_avplot_data(model)
#'
#' @importFrom stats model.frame residuals as.formula
#'
#' @export
#'
ols_prep_avplot_data <- function(model) {
m1 <- as.data.frame(model.frame(model))[1]
m2 <- as.data.frame(model.matrix(model))[, -1]
as.data.frame(cbind(m1, m2))
}
#' Regress predictor on other predictors
#'
#' Regress a predictor in the model on all the other predictors.
#'
#' @param data A \code{data.frame}.
#' @param i A numeric vector (indicates the predictor in the model).
#'
#' @examples
#' model <- lm(mpg ~ disp + hp + wt, data = mtcars)
#' data <- ols_prep_avplot_data(model)
#' ols_prep_regress_x(data, 1)
#'
#' @importFrom stats lsfit
#'
#' @export
#'
ols_prep_regress_x <- function(data, i) {
x <- remove_columns(data, i)
y <- select_columns(data, i)
lsfit(x, y)$residuals
}
#' Regress y on other predictors
#'
#' Regress y on all the predictors except the ith predictor.
#'
#' @param data A \code{data.frame}.
#' @param i A numeric vector (indicates the predictor in the model).
#'
#' @examples
#' model <- lm(mpg ~ disp + hp + wt, data = mtcars)
#' data <- ols_prep_avplot_data(model)
#' ols_prep_regress_y(data, 1)
#'
#' @export
#'
ols_prep_regress_y <- function(data, i) {
x <- remove_columns(data, i)
y <- select_columns(data)
lsfit(x, y)$residuals
}
#' Cooks' D plot data
#'
#' Prepare data for cook's d bar plot.
#'
#' @param model An object of class \code{lm}.
#'
#'
#' @examples
#' model <- lm(mpg ~ disp + hp + wt, data = mtcars)
#' ols_prep_cdplot_data(model)
#'
#' @export
#'
ols_prep_cdplot_data <- function(model) {
cd <- NULL
color <- NULL
cooksd <- cooks.distance(model)
n <- length(cooksd)
obs <- seq_len(n)
ckd <- data.frame(obs = obs, cd = cooksd)
ts <- 4 / n
cooks_max <- max(cooksd)
ckd$color <- ifelse(ckd$cd >= ts, "outlier", "normal")
ckd$fct_color <- ordered(factor(ckd$color), levels = c("normal", "outlier"))
# ckd %<>%
# mutate(
# color = ifelse(cd >= ts, "outlier", "normal"),
# fct_color = color %>%
# factor() %>%
# ordered(levels = c("normal", "outlier"))
# )
maxx <- cooks_max * 0.01 + cooks_max
list(ckd = ckd, maxx = maxx, ts = ts)
}
#' Cooks' D outlier observations
#'
#' Identify outliers in cook's d plot.
#'
#' @param k Cooks' d bar plot data.
#'
#' @examples
#' model <- lm(mpg ~ disp + hp + wt, data = mtcars)
#' k <- ols_prep_cdplot_data(model)
#' ols_prep_outlier_obs(k)
#'
#' @export
#'
ols_prep_outlier_obs <- function(k) {
ckd <- NULL
color <- NULL
obs <- NULL
data <- k$ckd
data$txt <- ifelse(data$color == "outlier", data$obs, NA)
return(data)
}
#' Cooks' d outlier data
#'
#' Outlier data for cook's d bar plot.
#'
#' @param k Cooks' d bar plot data.
#'
#' @examples
#' model <- lm(mpg ~ disp + hp + wt, data = mtcars)
#' k <- ols_prep_cdplot_data(model)
#' ols_prep_cdplot_outliers(k)
#'
#' @export
#'
ols_prep_cdplot_outliers <- function(k) {
color <- NULL
ckd <- NULL
obs <- NULL
cd <- NULL
result <- k$ckd[k$ckd$color == "outlier", c("obs", "cd")]
names(result) <- c("observation", "cooks_distance")
return(result)
}
#' DFBETAs plot data
#'
#' Prepares the data for dfbetas plot.
#'
#' @param d A \code{tibble} or \code{data.frame} with dfbetas.
#' @param threshold The threshold for outliers.
#'
#' @examples
#' model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
#' dfb <- dfbetas(model)
#' n <- nrow(dfb)
#' threshold <- 2 / sqrt(n)
#' dbetas <- dfb[, 1]
#' df_data <- data.frame(obs = seq_len(n), dbetas = dbetas)
#' ols_prep_dfbeta_data(df_data, threshold)
#'
#' @export
#'
ols_prep_dfbeta_data <- function(d, threshold) {
color <- NULL
obs <- NULL
d$color <- ifelse(((d$dbetas >= threshold) | (d$dbetas <= -threshold)), c("outlier"), c("normal"))
d$fct_color <- ordered(factor(d$color), levels = c("normal", "outlier"))
d$txt <- ifelse(d$color == "outlier", d$obs, NA)
return(d)
# d %>%
# mutate(
# color = ifelse(((d$dbetas >= threshold) | (d$dbetas <= -threshold)),
# c("outlier"), c("normal")),
# fct_color = color %>%
# factor() %>%
# ordered(levels = c("normal", "outlier")),
# txt = ifelse(color == "outlier", obs, NA)
# )
}
#' DFBETAs plot outliers
#'
#' Data for identifying outliers in dfbetas plot.
#'
#' @param d A \code{tibble} or \code{data.frame}.
#'
#' @examples
#' model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
#' dfb <- dfbetas(model)
#' n <- nrow(dfb)
#' threshold <- 2 / sqrt(n)
#' dbetas <- dfb[, 1]
#' df_data <- data.frame(obs = seq_len(n), dbetas = dbetas)
#' d <- ols_prep_dfbeta_data(df_data, threshold)
#' ols_prep_dfbeta_outliers(d)
#'
#' @export
#'
ols_prep_dfbeta_outliers <- function(d) {
color <- NULL
obs <- NULL
dbetas <- NULL
d[d$color == "outlier", c("obs", "dbetas")]
}
#' Deleted studentized residual plot data
#'
#' Generates data for deleted studentized residual vs fitted plot.
#'
#' @param model An object of class \code{lm}.
#'
#' @examples
#' model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
#' ols_prep_dsrvf_data(model)
#'
#' @export
#'
ols_prep_dsrvf_data <- function(model) {
dsr <- NULL
color <- NULL
pred <- fitted(model)
dsresid <- unname(rstudent(model))
n <- length(dsresid)
ds <- data.frame(obs = seq_len(n), dsr = dsresid)
ds$color <- ifelse((abs(ds$dsr) >= 2), "outlier", "normal")
ds$fct_color <- ordered(factor(ds$color), levels = c("normal", "outlier"))
# ds %<>%
# mutate(
# color = ifelse((abs(dsr) >= 2), "outlier", "normal"),
# fct_color = color %>%
# factor() %>%
# ordered(levels = c("normal", "outlier"))
# )
ds2 <- data.frame(obs = seq_len(n),
pred = pred,
dsr = ds$dsr,
color = ds$color,
fct_color = ds$fct_color)
minx <- min(ds2$dsr) - 1
maxx <- max(ds2$dsr) + 1
cminx <- ifelse(minx < -2, minx, -2.5)
cmaxx <- ifelse(maxx > 2, maxx, 2.5)
list(ds = ds2, cminx = cminx, cmaxx = cmaxx)
}
#' Residual fit spread plot data
#'
#' Data for generating residual fit spread plot.
#'
#' @param model An object of class \code{lm}.
#'
#' @examples
#' model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
#' ols_prep_rfsplot_fmdata(model)
#' ols_prep_rfsplot_rsdata(model)
#'
#' @export
#'
ols_prep_rfsplot_fmdata<- function(model) {
predicted <- fitted(model)
pred_m <- mean(predicted)
y <- predicted - pred_m
percenti <- ecdf(y)
x <- percenti(y)
data.frame(x, y)
}
#' @rdname ols_prep_rfsplot_fmdata
#' @export
#'
ols_prep_rfsplot_rsdata <- function(model) {
y <- residuals(model)
residtile <- ecdf(y)
x <- residtile(y)
data.frame(x, y)
}
#' Residual vs regressor plot data
#'
#' Data for generating residual vs regressor plot.
#'
#' @param model An object of class \code{lm}.
#'
#' @examples
#' model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
#' ols_prep_rvsrplot_data(model)
#'
#' @export
#'
ols_prep_rvsrplot_data <- function(model) {
np <- length(coefficients(model)) - 1
dat <- model.frame(model)[, -1]
pnames <- names(coefficients(model))[-1]
list(np = np, dat = dat, pnames = pnames)
}
#' Studentized residual vs leverage plot data
#'
#' Generates data for studentized resiudual vs leverage plot.
#'
#' @param model An object of class \code{lm}.
#'
#' @examples
#' model <- lm(read ~ write + math + science, data = hsb)
#' ols_prep_rstudlev_data(model)
#'
#'
#' @export
#'
ols_prep_rstudlev_data <- function(model) {
color <- NULL
leverage <- unname(hatvalues(model))
rstudent <- unname(rstudent(model))
k <- length(coefficients(model))
n <- nrow(model.frame(model))
lev_thrsh <- (2 * k) / n
rst_thrsh <- 2
miny <- min(rstudent) - 3
maxy <- max(rstudent) + 3
minx <- min(leverage)
maxx <- ifelse((max(leverage) > lev_thrsh), max(leverage), (lev_thrsh + 0.05))
levrstud <- data.frame(obs = seq_len(n), leverage, rstudent)
levrstud$color1 <- ifelse((levrstud$leverage < lev_thrsh & abs(levrstud$rstudent) < 2), "normal", NA)
levrstud$color2 <- ifelse((levrstud$leverage > lev_thrsh & abs(levrstud$rstudent) < 2), "leverage", NA)
levrstud$color3 <- ifelse((levrstud$leverage < lev_thrsh & abs(levrstud$rstudent) > 2), "outlier", NA)
levrstud$color4 <- ifelse((levrstud$leverage > lev_thrsh & abs(levrstud$rstudent) > 2), "outlier & leverage", NA)
d1 <- levrstud[!is.na(levrstud$color1), c("obs", "leverage", "rstudent", "color1")]
d2 <- levrstud[!is.na(levrstud$color2), c("obs", "leverage", "rstudent", "color2")]
d3 <- levrstud[!is.na(levrstud$color3), c("obs", "leverage", "rstudent", "color3")]
d4 <- levrstud[!is.na(levrstud$color4), c("obs", "leverage", "rstudent", "color4")]
colnames(d1) <- c("obs", "leverage", "rstudent", "color")
colnames(d2) <- c("obs", "leverage", "rstudent", "color")
colnames(d3) <- c("obs", "leverage", "rstudent", "color")
colnames(d4) <- c("obs", "leverage", "rstudent", "color")
out <- rbind(d1, d2, d3, d4)
out$fct_color <- ordered(factor(out$color), levels = c("normal", "leverage", "outlier", "outlier & leverage"))
levdata <- out[order(out$obs), ]
list(levrstud = levdata,
lev_thrsh = lev_thrsh,
minx = minx,
miny = miny,
maxx = maxx,
maxy = maxy
)
}
#' Studentized residual plot data
#'
#' Generates data for studentized residual plot.
#'
#' @param model An object of class \code{lm}.
#'
#' @examples
#' model <- lm(read ~ write + math + science, data = hsb)
#' ols_prep_srplot_data(model)
#'
#' @export
#'
ols_prep_srplot_data<- function(model) {
color <- NULL
dstud <- unname(rstudent(model))
obs <- seq_len(length(dstud))
dsr <- data.frame(obs = obs, dsr = dstud)
dsr$color <- ifelse((abs(dsr$dsr) >= 3), "outlier", "normal")
dsr$fct_color <- ordered(factor(dsr$color), levels = c("normal", "outlier"))
# mutate(
# color = ifelse((abs(dsr) >= 3), "outlier", "normal"),
# fct_color = color %>%
# factor() %>%
# ordered(levels = c("normal", "outlier"))
# )
cminxx <- floor(min(dsr$dsr) - 1)
cmaxxx <- floor(max(dsr$dsr) - 1)
cminx <- ifelse(cminxx > -3, -3, cminxx)
cmaxx <- ifelse(cmaxxx < 3, 3, cmaxxx)
nseq <- seq_len(abs(cminx + 1)) * -1
pseq <- seq_len(cmaxx - 1)
list(cminx = cminx,
cmaxx = cmaxx,
nseq = nseq,
pseq = pseq,
dsr = dsr)
}
#' Standardized residual chart data
#'
#' Generates data for standardized residual chart.
#'
#' @param model An object of class \code{lm}.
#'
#' @examples
#' model <- lm(read ~ write + math + science, data = hsb)
#' ols_prep_srchart_data(model)
#'
#' @export
#'
ols_prep_srchart_data <- function(model) {
color <- NULL
sdres <- rstandard(model)
sdres_out <- abs(sdres) > 2
outlier <- sdres[sdres_out]
obs <- seq_len(length(sdres))
out <- data.frame(obs = obs, sdres = sdres)
out$color <- ifelse(((out$sdres >= 2) | (out$sdres <= -2)), c("outlier"), c("normal"))
out$fct_color <- ordered( factor(out$color), levels = c("normal", "outlier"))
out$txt <- ifelse(out$color == "outlier", out$obs, NA)
return(out)
}
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