Run the analyses on the data
study_analyse(study)
study_analyze(study)A study list object with class scivrs_study
A study object with class scivrs_study
s <- study() %>%
add_hypothesis("H1", "Petal width and length will be positively correlated.") %>%
add_analysis("A1", cor.test(dat$Petal.Width, dat$Petal.Length)) %>%
add_criterion("sig", "p.value", "<", 0.05) %>%
add_criterion("pos", "estimate", ">", 0) %>%
add_eval("corroboration", "sig & pos",
"Petal width is significantly and positively correlated to length") %>%
add_eval("falsification", "sig & !pos",
"Petal width is significantly and negatively correlated to length") %>%
add_data("dat", iris) %>%
study_analyse()
#> Sepal.Length set to dataType float
#> Sepal.Width set to dataType float
#> Petal.Length set to dataType float
#> Petal.Width set to dataType float
#> Species set to dataType string
#> Hypothesis H1: Petal width and length will be positively correlated.
#>
#> Criterion sig:
#> * p.value < 0.05 is TRUE
#> * p.value = 0.000
#>
#> Criterion pos:
#> * estimate > 0 is TRUE
#> * estimate = 0.963
#>
#> Conclusion: corroborate
#> * Corroborate (sig & pos): TRUE
#> * Falsify (sig & !pos): FALSE
s
#> Demo Study
#> ----------
#>
#> * Hypotheses: H1
#> * Data: dat
#> * Analyses: A1
#>
#> Hypothesis H1: Petal width and length will be positively correlated.
#>
#> Criterion sig:
#> * p.value < 0.05 is TRUE
#> * p.value = 0.000
#>
#> Criterion pos:
#> * estimate > 0 is TRUE
#> * estimate = 0.963
#>
#> Conclusion: corroborate
#> * Corroborate (sig & pos): TRUE
#> * Falsify (sig & !pos): FALSE