error_x ignores user provided value; defaults to 10 always

Using the example for coef plots here, I find that no matter what values are provided o error_x = ~ ... , the plot always shows error bars at 10% of the point-estimate.

Brief description of the problem

library(plotly)
#> Loading required package: ggplot2
#> 
#> Attaching package: 'plotly'
#> The following object is masked from 'package:ggplot2':
#> 
#>     last_plot
#> The following object is masked from 'package:stats':
#> 
#>     filter
#> The following object is masked from 'package:graphics':
#> 
#>     layout
library(ggplot2)

fit <- lm(mpg ~ cyl + disp + wt, mtcars)
summary(fit)
#> 
#> Call:
#> lm(formula = mpg ~ cyl + disp + wt, data = mtcars)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -4.4035 -1.4028 -0.4955  1.3387  6.0722 
#> 
#> Coefficients:
#>              Estimate Std. Error t value Pr(>|t|)    
#> (Intercept) 41.107678   2.842426  14.462 1.62e-14 ***
#> cyl         -1.784944   0.607110  -2.940  0.00651 ** 
#> disp         0.007473   0.011845   0.631  0.53322    
#> wt          -3.635677   1.040138  -3.495  0.00160 ** 
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 2.595 on 28 degrees of freedom
#> Multiple R-squared:  0.8326, Adjusted R-squared:  0.8147 
#> F-statistic: 46.42 on 3 and 28 DF,  p-value: 5.399e-11

# Plots Don't Agree. Plotly shows incoprrect error bars
goodplot <- GGally::ggcoef(fit, sort = "ascending")
#> Registered S3 method overwritten by 'GGally':
#>   method from   
#>   +.gg   ggplot2
badplot <- fit %>% broom::tidy() %>%
        mutate(term = forcats::fct_reorder(term, estimate)) %>%
        plot_ly(x = ~ estimate, y = ~ term) %>%
        add_markers(
                error_x = ~ list(value = std.error),
                color = I("black"),
                hoverinfo = "x"
        )

^{Created on 2020-03-02 by the reprex package (v0.3.0)}

Inizia dalla chiamata a add_markers() del reprex e dal relativo mapping error_x = ~ list(value = std.error), quindi segui il modo in cui l’interfaccia R gestisce i valori delle barre di errore forniti dall’utente. Riproduci il grafico usando il modello mtcars e conferma che vengano visualizzati gli errori standard forniti invece dei valori predefiniti del 10%.