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docs: displaying intended graph #262

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Aug 23, 2024
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docs: displaying intended graph #262

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a84afb8
docs(modelisation_approach): graphic ANOVA t-tests
guspan-tanadi Aug 23, 2024
429d039
docs(estimate_response): graphic branch
guspan-tanadi Aug 23, 2024
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2 changes: 1 addition & 1 deletion vignettes/estimate_response.Rmd
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Expand Up @@ -167,7 +167,7 @@
different CI levels (for instance, 69\%, 89\% and 99\%).

```{r}
predicted <- estimate_relation(model, ci = c(0.69, .89, 0.99))

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file=vignettes/estimate_response.Rmd,line=170,col=52,[numeric_leading_zero_linter] Include the leading zero for fractional numeric constants.

iris %>%
ggplot(aes(x = Sepal.Length)) +
Expand Down Expand Up @@ -258,7 +258,7 @@
# anim_save("gganimate_figure.gif", gganimate::animate(p, width = 800, height = 500, type = "cairo"))

if (knitr::is_html_output()) {
knitr::include_graphics("https://github.com/easystats/estimate/raw/master/man/figures/gganimate_figure.gif")
knitr::include_graphics("https://github.com/easystats/estimate/raw/main/man/figures/gganimate_figure.gif")
} else {
iris %>%
ggplot(aes(x = Sepal.Length)) +
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2 changes: 1 addition & 1 deletion vignettes/modelisation_approach.Rmd
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Expand Up @@ -47,7 +47,7 @@
these different tests have not been yet replaced by a unified model.

```{r echo=FALSE, fig.align='center', out.width="50%"}
knitr::include_graphics("https://github.com/easystats/easystats/blob/master/hextools/modelbased/allregressions.png")
knitr::include_graphics("https://github.com/easystats/easystats/raw/main/hextools/modelbased/allregressions.png")
```

The tests mentioned above have major advantages. For instance, they are very easy to
Expand Down Expand Up @@ -97,14 +97,14 @@

```{r}
generate_data <- function(effect = 5, noise = 0.5) {
data <- data.frame()

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file=vignettes/modelisation_approach.Rmd,line=100,col=3,[object_overwrite_linter] 'data' is an exported object from package 'utils'. Avoid re-using such symbols.
n <- 100
for (i in 1:length(effect)) {

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file=vignettes/modelisation_approach.Rmd,line=102,col=13,[seq_linter] Use seq_along(...) instead of 1:length(...), which is likely to be wrong in the empty edge case.
participant <- data.frame(Experimental_Variable = c(seq(-3, 3, length = n / 2), seq(-3, 3, length = n / 2)))
participant$RT <- c(participant$Experimental_Variable[1:(n / 2)]**2 - effect[i], (participant$Experimental_Variable[(n / 2 + 1):n] + effect[i])) + rnorm(n, 0, abs(noise[i]))

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file=vignettes/modelisation_approach.Rmd,line=104,col=121,[line_length_linter] Lines should not be more than 120 characters. This line is 177 characters.
participant$Condition <- rep(c("A", "B"), each = n / 2)
participant$Participant <- paste0("S", i)
data <- rbind(data, participant)

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file=vignettes/modelisation_approach.Rmd,line=107,col=5,[object_overwrite_linter] 'data' is an exported object from package 'utils'. Avoid re-using such symbols.
}
data$RT <- (100 + data$RT) * 10
data
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