Summary method for the pre_factor function

Summary method for the pre_factor function

# S3 method for pre_factor
summary(object, dec = 2, ...)

Arguments

object	Return value from pre_factor
dec	Rounding to use for output
...	further arguments passed to or from other methods

Details

See https://radiant-rstats.github.io/docs/multivariate/pre_factor.html for an example in Radiant

See also

pre_factor to calculate results

plot.pre_factor to plot results

Examples

result <- pre_factor(shopping, "v1:v6")
summary(result)
#> Pre-factor analysis diagnostics
#> Data        : shopping 
#> Variables   : v1, v2, v3, v4, v5, v6 
#> Observations: 20 
#> Correlation : Pearson
#> 
#> Bartlett test
#> Null hyp. : variables are not correlated
#> Alt. hyp. : variables are correlated
#> Chi-square: 66.71 df(15), p.value < .001
#> 
#> KMO test:  0.55 
#> 
#> Variable collinearity:
#>     Rsq  KMO
#> v1 0.89 0.52
#> v2 0.38 0.78
#> v3 0.79 0.59
#> v4 0.59 0.48
#> v5 0.81 0.64
#> v6 0.58 0.43
#> 
#> Fit measures:
#>      Eigenvalues Variance % Cumulative %
#>  PC1        2.66       0.44         0.44
#>  PC2        2.15       0.36         0.80
#>  PC3        0.47       0.08         0.88
#>  PC4        0.43       0.07         0.95
#>  PC5        0.22       0.04         0.99
#>  PC6        0.06       0.01         1.00
pre_factor(computer, "high_end:business") %>% summary()
#> Warning: NaNs produced
#> Error in solve.default(r) : 
#>   system is computationally singular: reciprocal condition number = 8.23843e-18
#> matrix is not invertible, image not found
#> The selected variables are perfectly collinear. Please check the correlations
#> and remove any variable with a correlation of 1 or -1 from the analysis