Run analysis

Run the analyses on the data

study_analyse(study)

study_analyze(study)

Arguments

study	A study list object with class scivrs_study

Value

A study object with class scivrs_study

Examples

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