## # Attaching packages
## <U+2714> ggeffects 1.0.1 <U+2714> sjlabelled 1.1.7
## <U+2714> sjmisc 2.8.6 <U+2714> sjstats 0.18.1
## <U+2714> sjPlot 2.8.7 <U+2714> esc 0.5.1jbm <- lm(ta ~ var1 + var2 + var3, master_v6)
tab_model(jbm, collapse.se = T, show.ci = F, p.style ="stars", title = "Jones-Balance Modeli")| ta | |
|---|---|
| Predictors | Estimates |
| (Intercept) |
-0.11 (0.06) |
| var1 |
0.47 *** (0.01) |
| var2 |
0.09 *** (0.00) |
| var3 |
0.01 ** (0.00) |
| Observations | 28967 |
| R2 / R2 adjusted | 0.788 / 0.788 |
|
|
report(jbm)## We fitted a linear model (estimated using OLS) to predict ta with var1, var2 and var3 (formula: ta ~ var1 + var2 + var3). The model explains a significant and substantial proportion of variance (R2 = 0.79, F(3, 28963) = 35846.55, p < .001, adj. R2 = 0.79). The model's intercept, corresponding to var1 = 0, var2 = 0 and var3 = 0, is at -0.11 (95% CI [-0.22, 5.03e-03], t(28963) = -1.87, p = 0.061). Within this model:
##
## - The effect of var1 is significantly positive (beta = 0.47, 95% CI [0.45, 0.48], t(28963) = 62.57, p < .001; Std. beta = 0.17, 95% CI [0.17, 0.18])
## - The effect of var2 is significantly positive (beta = 0.09, 95% CI [0.08, 0.09], t(28963) = 70.94, p < .001; Std. beta = 0.81, 95% CI [0.79, 0.84])
## - The effect of var3 is significantly positive (beta = 0.01, 95% CI [5.36e-03, 0.02], t(28963) = 3.17, p < .01; Std. beta = 0.04, 95% CI [0.01, 0.06])
##
## Standardized parameters were obtained by fitting the model on a standardized version of the dataset.