Thanks to your support I can now estimate my PCA model and get the following output (MultivariateStats):

```
PCA(indim = 1599, outdim = 3, principalratio = 0.99857)
```

The methods offered by PCA seem a little rudimentary to me. I’m just trying to explore the advantages of Julia over R and get in R this output:

```
> PCA_Modell <- principal(Rotwein_o_Q, 4)
> print(PCA_Modell, cut = 0.5, sort = TRUE, digits = 2)
Principal Components Analysis
Call: principal(r = Rotwein_o_Q, nfactors = 4)
Standardized loadings (pattern matrix) based upon correlation matrix
item RC1 RC2 RC3 RC4 h2 u2 com
fixed.acidity 1 0.91 0.85 0.15 1.1
density 8 0.78 0.80 0.20 1.6
citric.acid 3 0.75 0.81 0.19 1.9
pH 9 -0.73 0.60 0.40 1.2
free.sulfur.dioxide 6 0.88 0.80 0.20 1.1
total.sulfur.dioxide 7 0.88 0.79 0.21 1.1
residual.sugar 4 0.39 0.61 2.5
alcohol 11 0.78 0.69 0.31 1.3
volatile.acidity 2 -0.72 0.64 0.36 1.5
chlorides 5 0.80 0.73 0.27 1.3
sulphates 10 0.76 0.68 0.32 1.4
RC1 RC2 RC3 RC4
SS loadings 2.87 1.80 1.67 1.44
Proportion Var 0.26 0.16 0.15 0.13
Cumulative Var 0.26 0.42 0.58 0.71
Proportion Explained 0.37 0.23 0.21 0.19
Cumulative Proportion 0.37 0.60 0.81 1.00
Mean item complexity = 1.4
Test of the hypothesis that 4 components are sufficient.
The root mean square of the residuals (RMSR) is 0.09
with the empirical chi square 1473 with prob < 3.2e-303
```

Does Julia offer a similar edition? Because the loadings, SS loadings and Cumulative Var (and more information about the model) are important for model evaluation.

Thank you for your support and

best regards,

Günter