Welcome to Psycho's for Julia documentation.
The package is not released yet. Help for its development is very much appreciated.
pkg> add https://github.com/neuropsychology/Psycho.jl.git
Psycho's primary goal is to fill the gap between Julia's output and the formatted result description of your manuscript, with the automated use of best practices guidelines, ensuring standardization and quality of results reporting. It also provides useful tools and functions for psychologists, neuropsychologists and neuroscientists for their everyday data analyses.
using GLM, Psycho # Simulate some data data = simulate_data_correlation([[0.3], [0.1]]) # Standardize the results standardize!(data) # Describe the data report(data)
The data contains 200 observations of the following variables: - y (Mean = 0 ± 1.0 [-2.92, 3.07]) - Var1 (Mean = 0 ± 1.0 [-2.35, 3.25]) - Group (1FD, 50.0%; 2HA, 50.0%)
using GLM # Fit a Linear Model model = lm(@formula(y ~ Var1 * Group), data) # Report the results results = report(model)
We fitted a linear regression to predict y with Var1 and Group (Formula: y ~ 1 + Var1 + Group + Var1 & Group). The model's explanatory power (R²) is of 0.05 (adj. R² = 0.04). The model's intercept is at 0.0. Within this model: - Var1 is significant (Coef = 0.3, t(196) = 3.05, 95% CI [0.11; 0.49], p < .01) and can be considered as small (Std. Coef = 0.3). - Group: 2HA is not significant (Coef = 0, t(196) = 0, 95% CI [-0.27; 0.27], p > .1) and can be considered as very small (Std. Coef = 0). - Var1 & Group: 2HA is not significant (Coef = -0.2, t(196) = -1.44, 95% CI [-0.47; 0.07], p > .1) and can be considered as very small (Std. Coef = -0.2).