Summary method for the prmap function

Summary method for the prmap function

# S3 method for prmap
summary(object, cutoff = 0, dec = 2, ...)

Arguments

object	Return value from prmap
cutoff	Show only loadings with (absolute) values above cutoff (default = 0)
dec	Rounding to use for output
...	further arguments passed to or from other methods

Details

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

See also

prmap to calculate results

plot.prmap to plot results

Examples

result <- prmap(computer, brand = "brand", attr = "high_end:business")
summary(result)
#> Attribute based brand map
#> Data        : computer 
#> Attributes  : high_end, stylish, modern, value, dated, innovative, business 
#> Dimensions  : 2 
#> Rotation    : varimax
#> Observations: 5 
#> Correlation : Pearson
#> 
#> Brand - Factor scores:
#>           RC1   RC2
#> Apple    1.30  0.09
#> Dell    -0.32 -0.21
#> Gateway -1.19 -0.93
#> HP      -0.52  1.65
#> Sony     0.73 -0.60
#> 
#> Attribute - Factor loadings:
#>              RC1   RC2
#> high_end    0.94 -0.16
#> stylish     0.98  0.06
#> modern      0.99  0.03
#> value      -0.93 -0.28
#> dated      -0.97  0.21
#> innovative  0.86  0.19
#> business    0.02  0.99
#> 
#> Fit measures:
#>               RC1  RC2
#> Eigenvalues  5.35 1.16
#> Variance %   0.76 0.17
#> Cumulative % 0.76 0.93
#> 
#> Attribute communalities:               
#> high_end   0.91
#> stylish    0.97
#> modern     0.97
#> value      0.94
#> dated      0.99
#> innovative 0.77
#> business   0.97
summary(result, cutoff = .3)
#> Attribute based brand map
#> Data        : computer 
#> Attributes  : high_end, stylish, modern, value, dated, innovative, business 
#> Dimensions  : 2 
#> Rotation    : varimax
#> Observations: 5 
#> Correlation : Pearson
#> 
#> Brand - Factor scores:
#>           RC1   RC2
#> Apple    1.30  0.09
#> Dell    -0.32 -0.21
#> Gateway -1.19 -0.93
#> HP      -0.52  1.65
#> Sony     0.73 -0.60
#> 
#> Attribute - Factor loadings:
#>              RC1  RC2
#> high_end    0.94     
#> stylish     0.98     
#> modern      0.99     
#> value      -0.93     
#> dated      -0.97     
#> innovative  0.86     
#> business         0.99
#> 
#> Fit measures:
#>               RC1  RC2
#> Eigenvalues  5.35 1.16
#> Variance %   0.76 0.17
#> Cumulative % 0.76 0.93
#> 
#> Attribute communalities:               
#> high_end   0.91
#> stylish    0.97
#> modern     0.97
#> value      0.94
#> dated      0.99
#> innovative 0.77
#> business   0.97
prmap(
  computer, brand = "brand", attr = "high_end:dated",
  pref = c("innovative","business")
) %>% summary()
#> Attribute based brand map
#> Data        : computer 
#> Attributes  : high_end, stylish, modern, value, dated 
#> Preferences : RC1, RC2 
#> Dimensions  : 2 
#> Rotation    : varimax
#> Observations: 5 
#> Correlation : Pearson
#> 
#> Brand - Factor scores:
#>           RC1   RC2
#> Apple    0.73  0.95
#> Dell     0.77 -1.22
#> Gateway -0.83 -0.93
#> HP      -1.32  0.67
#> Sony     0.66  0.53
#> 
#> Attribute - Factor loadings:
#>            RC1   RC2
#> high_end  0.89  0.44
#> stylish   0.58  0.81
#> modern    0.77  0.62
#> value    -0.44 -0.89
#> dated    -0.82 -0.55
#> 
#> Preference correlations:
#>              RC1  RC2 communalities
#> innovative  0.44 0.72          0.70
#> business   -0.38 0.44          0.34
#> 
#> Fit measures:
#>               RC1  RC2
#> Eigenvalues  2.60 2.33
#> Variance %   0.52 0.47
#> Cumulative % 0.52 0.99
#> 
#> Attribute communalities:             
#> high_end 0.99
#> stylish  0.99
#> modern   0.98
#> value    0.99
#> dated    0.98