Description Usage Arguments Value Author(s) See Also Examples

View source: R/analyze.stanreg.R

Analyze stanreg objects.

1 2 3 4 |

`x` |
A stanreg model. |

`CI` |
Credible interval bounds. |

`index` |
Index of effect existence to report. Can be 'overlap' or 'ROPE'. |

`ROPE_bounds` |
Bounds ot the ROPE. If NULL and effsize is TRUE, than the ROPE. will have default values c(-0.1, 0.1) and computed on the standardized posteriors. |

`effsize` |
Compute Effect Sizes according to Cohen (1988). For linear models only. |

`effsize_rules` |
Grid for effect size interpretation. See interpret_d. |

`...` |
Arguments passed to or from other methods. |

Contains the following indices:

the Median of the posterior distribution of the parameter (can be used as a point estimate, similar to the beta of frequentist models).

the Median Absolute Deviation (MAD), a robust measure of dispertion (could be seen as a robust version of SD).

the Credible Interval (CI) (by default, the 90% CI; see Kruschke, 2018), representing a range of possible parameter.

the Maximum Probability of Effect (MPE), the probability that the effect is positive or negative (depending on the median’s direction).

the Overlap (O), the percentage of overlap between the posterior distribution and a normal distribution of mean 0 and same SD than the posterior. Can be interpreted as the probability that a value from the posterior distribution comes from a null distribution.

the ROPE, the proportion of the 95% CI of the posterior distribution that lies within the region of practical equivalence.

"get_R2.stanreg" "bayes_R2.stanreg"

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
## Not run:
library(psycho)
library(rstanarm)
data <- attitude
fit <- rstanarm::stan_glm(rating ~ advance + privileges, data=data)
results <- analyze(fit, effsize=TRUE)
summary(results)
print(results)
plot(results)
fit <- rstanarm::stan_glmer(Sepal.Length ~ Sepal.Width + (1|Species), data=iris)
results <- analyze(fit)
summary(results)
fit <- rstanarm::stan_glm(Sex ~ Adjusting,
data=psycho::affective, family="binomial")
results <- analyze(fit)
summary(results)
fit <- rstanarm::stan_glmer(Sex ~ Adjusting + (1|Salary),
data=psycho::affective, family="binomial")
results <- analyze(fit)
summary(results)
## End(Not run)
``` |

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