# That's it, the PCA model has been computed. Now we would like to inspect the results by visualising them. We can do this using the taylor-made plotting function for PCA from the separate [**hoggormPlot** package](https://hoggormplot.readthedocs.io/en/latest/). If we wish to plot the results for component 1 and component 2, we can do this by setting the input argument ``comp=[1, 2]``. The input argument ``plots=[1, 6]`` lets the user define which plots are to be plotted. If this list for example contains value ``1``, the function will generate the scores plot for the model. If the list contains value ``6``, the function will generate a explained variance plot. The hoggormPlot documentation provides a [description of input paramters](https://hoggormplot.readthedocs.io/en/latest/mainPlot.html).
# In[15]:
hop.plot(model, comp=[1, 2],
plots=[1, 6])
# It is also possible to generate the same plots one by one with specific plot functions as shown below.
# In[19]:
hop.loadings(model, line=True)
# ---
# ### Accessing numerical results
# Now that we have visualised the PCA results, we may also want to access the numerical results. Below are some examples. For a complete list of accessible results, please see this part of the documentation.
# In[21]:
# Get scores and store in numpy array
scores = model.X_scores()
# Get scores and store in pandas dataframe with row and column names
# Plot cumulative validated explained variance in X. hop.explainedVariance(model, which=['X']) # In[10]: hop.scores(model) # In[11]: hop.correlationLoadings(model) # In[12]: # Plot X loadings in line plot hop.loadings(model, weights=False, line=True) # In[13]: # Plot regression coefficients hop.coefficients(model, comp=[3]) # --- # ### Accessing numerical results # Now that we have visualised the PCR results, we may also want to access the numerical results. Below are some examples. For a complete list of accessible results, please see this part of the documentation. # In[14]: # Get X scores and store in numpy array
hop.scores(model) # In[11]: hop.correlationLoadings(model) # In[12]: # Plot X loadings in line plot hop.loadings(model, weights=True, line=True) # In[13]: # Plot regression coefficients hop.coefficients(model, comp=3) # --- # ### Accessing numerical results # Now that we have visualised the PLSR results, we may also want to access the numerical results. Below are some examples. For a complete list of accessible results, please see this part of the documentation.