Format PLS Model Output as LaTeX or Console Tables
pls.summary.RdFormats and displays Partial Least Squares (PLS) model output from pls.regression()
as either LaTeX tables (for PDF rendering) or console-friendly output.
Arguments
- x
A list returned by
pls.regression()(class"pls") containing PLS model components.- ...
Further arguments passed to or from methods (unused).
- include.scores
Logical. Whether to include score matrices (T and U). Default is
TRUE.- latex
Logical. If
TRUE, produces LaTeX output (for PDF rendering). IfFALSE, prints to console. Default isFALSE.
Value
When latex = TRUE, returns a knitr::asis_output object (LaTeX code).
When latex = FALSE, prints formatted tables to the console and returns NULL.
Examples
# Load example data
data(mtcars)
# Prepare data for PLS regression
X <- mtcars[, c("wt", "hp", "disp")]
Y <- mtcars[, "mpg", drop = FALSE]
# Fit PLS model with 2 components
pls.fit <- pls.regression(X, Y, n.components = 2)
# Print a console-formatted summary
pls.summary(pls.fit, latex = FALSE)
#>
#>
#> Table: X Weights (W)
#>
#> -------- --------
#> -0.6025 -0.7868
#> -0.5390 0.2773
#> -0.5886 0.5515
#> -------- --------
#>
#>
#> Table: X Loadings (P)
#>
#> -------- --------
#> -5.1515 -2.0903
#> -4.8773 1.9874
#> -5.3940 0.3199
#> -------- --------
#>
#>
#> Table: Y Weights (C)
#>
#> x
#> ---
#> 1
#> 1
#>
#>
#> Table: Y Loadings (Q)
#>
#> --- ---
#> 1 1
#> --- ---
#>
#>
#> Table: Regression Scalars (b)
#>
#> Component Estimate
#> ---------- ---------
#> 1 5.0114
#> 2 0.5793
#>
#>
#> Table: Regression Coefficients (Original Scale)
#>
#> Estimate
#> ----- ---------
#> wt -2.9704
#> hp -0.0144
#> disp -0.0156
#>
#>
#> Table: X Scores (T)
#>
#> -------- --------
#> 0.1114 0.0201
#> 0.0937 -0.0684
#> 0.1748 0.0026
#> 0.0180 -0.0085
#> -0.1093 0.2026
#> 0.0231 -0.1614
#> -0.1801 0.2697
#> 0.1214 -0.2787
#> 0.0982 -0.2227
#> 0.0392 -0.2298
#> 0.0392 -0.2298
#> -0.1124 -0.1607
#> -0.0889 -0.0427
#> -0.0923 -0.0601
#> -0.3206 -0.1741
#> -0.3351 -0.2402
#> -0.3322 -0.2250
#> 0.2226 -0.0522
#> 0.2770 0.1229
#> 0.2528 0.0591
#> 0.1548 -0.0193
#> -0.0704 0.0586
#> -0.0570 0.0627
#> -0.1934 0.1579
#> -0.1586 0.1347
#> 0.2407 0.0403
#> 0.1825 0.0842
#> 0.2199 0.2913
#> -0.1644 0.4227
#> 0.0516 0.0450
#> -0.2280 0.3070
#> 0.1220 -0.1078
#> -------- --------
#>
#>
#> Table: Y Scores (U)
#>
#> -------- --------
#> 0.1509 -0.4072
#> 0.1509 -0.3189
#> 0.4495 -0.4266
#> 0.2173 0.1271
#> -0.2307 0.3171
#> -0.3303 -0.4459
#> -0.9608 -0.0584
#> 0.7150 0.1065
#> 0.4495 -0.0426
#> -0.1478 -0.3440
#> -0.3801 -0.5763
#> -0.6124 -0.0491
#> -0.4630 -0.0176
#> -0.8115 -0.3487
#> -1.6079 -0.0011
#> -1.6079 0.0714
#> -0.8944 0.7704
#> 2.0424 0.9269
#> 1.7105 0.3225
#> 2.2913 1.0246
#> 0.2338 -0.5421
#> -0.7617 -0.4090
#> -0.8115 -0.5256
#> -1.1267 -0.1575
#> -0.1478 0.6472
#> 1.1962 -0.0103
#> 0.9805 0.0660
#> 1.7105 0.6088
#> -0.7119 0.1119
#> -0.0648 -0.3236
#> -0.8446 0.2982
#> 0.2173 -0.3941
#> -------- --------
#>
#>
#> Table: Variance Explained by Components (X)
#>
#> Latent.Vector Explained.Variance Cumulative
#> -------------- ------------------- -----------
#> 1 85.3995% 85.3995%
#> 2 9.0554% 94.4549%
#>
#>
#> Table: Variance Explained by Components (Y)
#>
#> Latent.Vector Explained.Variance Cumulative
#> -------------- ------------------- -----------
#> 1 81.0145% 81.0145%
#> 2 1.0825% 82.0969%