Power plots with ggplot2

powerPlot generates Power Plots for lm or bestfit objects with ggplot2.

powerPlot(object, ...)

# S3 method for lm
powerPlot(
  object,
  func,
  axis = c("standard", "inverted"),
  smooth = TRUE,
  se = FALSE,
  ...
)

# S3 method for bestfit
powerPlot(object, fit = 1, ...)

# S3 method for lmerMod
powerPlot(object, func, ...)

Arguments

object	object of class lm or bestfit
...	not used.
func	function used to transform the dependent variable
axis	option to plot predicted values on the x axis (inverted) or in the y axis (standard)
smooth	option to add a regression line to the plot
fit	chosen fit

Value

a power plot

Examples

library(sf)
dados <- st_drop_geometry(centro_2015)
dados$padrao <- as.numeric(dados$padrao)
fit <- lm(log(valor) ~ area_total + quartos + suites + garagens +
log(dist_b_mar) + I(1/padrao), dados, subset = -c(31, 39))
powerPlot(fit)
#> `geom_smooth()` using formula 'y ~ x'
powerPlot(fit, se = TRUE)
#> `geom_smooth()` using formula 'y ~ x'
powerPlot(fit, smooth = FALSE)
powerPlot(fit, axis = "inverted")
#> `geom_smooth()` using formula 'y ~ x'
library(ggplot2)
p <- powerPlot(fit, func = "log", axis = "inverted")
p + labs(title = "Poder de Predição", subtitle = "Em milhões de Reais")
#> `geom_smooth()` using formula 'y ~ x'
dados <- st_drop_geometry(centro_2015)
best_fit <- bestfit(valor ~ ., dados)
powerPlot(best_fit, fit = 257)
#> Error in is.data.frame(data): object 'dados' not found
library(lme4)
#> Loading required package: Matrix
#> Registered S3 methods overwritten by 'lme4':
#>   method                          from
#>   cooks.distance.influence.merMod car 
#>   influence.merMod                car 
#>   dfbeta.influence.merMod         car 
#>   dfbetas.influence.merMod        car 
data(centro_2015)
dados <- st_drop_geometry(centro_2015)
Mfit <- lmer(log(valor) ~ area_total + quartos + suites + garagens +
dist_b_mar + (1|padrao), dados)
powerPlot(Mfit)
#> Loading required package: broom.mixed
#> Registered S3 method overwritten by 'broom.mixed':
#>   method      from 
#>   tidy.gamlss broom
#> `geom_smooth()` using formula 'y ~ x'
powerPlot(Mfit, func = "log")
#> `geom_smooth()` using formula 'y ~ x'