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NOTE: This R package focuses on the implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods and classical multivariate statistical tools.
Fitting a PLS model:
library(mvdalab)
data(Penta)
mod1 <- plsFit(log.RAI ~., scale = TRUE, data = Penta[, -1],
ncomp = 3, method = "bidiagpls",
validation = "oob")
summary(mod1)
#> Call:
#>
#> plsFit(formula = log.RAI ~ ., ncomp = 3, data = Penta[, -1],
#> method = "bidiagpls", scale = TRUE,
#> validation = "oob")
#>
#> Coefficients:
#> Estimate Bootstrap Error 't value' bias 'bias t value'
#> L3 0.438131130 0.07813105 5.60764388 -0.056287765 -0.72042756
#> S3 -0.339839340 0.08368706 -4.06083517 0.044477379 0.53147262
#> P1 -0.210181974 0.06071820 -3.46159762 0.057955814 0.95450482
#> S1 -0.135884870 0.06997010 -1.94204192 0.019907955 0.28452089
#> P3 0.111249534 0.06854336 1.62305337 0.034862926 0.50862586
#> S2 0.089752422 0.04701730 1.90892350 0.006892843 0.14660228
#> L2 0.071367951 0.04526160 1.57678793 -0.019359275 -0.42771960
#> L4 0.069951677 0.07232330 0.96720806 -0.004522244 -0.06252818
#> L5 0.035696148 0.04552429 0.78411217 0.008591990 0.18873419
#> P4 -0.028238597 0.05462905 -0.51691543 -0.020000349 -0.36611199
#> P2 -0.025167765 0.06283351 -0.40054683 -0.012729431 -0.20258984
#> S4 0.020226747 0.06658432 0.30377644 -0.037592328 -0.56458231
#> L1 0.017465764 0.06489465 0.26914025 -0.006988723 -0.10769335
#> S5 0.010701880 0.04456740 0.24012801 -0.004024037 -0.09029106
#> P5 -0.002811084 0.04681625 -0.06004504 0.003525576 0.07530667
#>
#> Fit Summary:
#>
#> Number of objects = 30
#> Number of predictor variables = 15
#> Method: bidiagpls
#> No. of bootstrap samples = 1000
#> Number of components considered
#> in above parameter estimates = 3
#> R2X = 0.228 0.389 0.485
#> R2Y = 0.691 0.824 0.874
#> Out-of-Bag R2 (per component) = 0.446 0.458 0.354
#> Out-of-Bag PRESS (per component) = 4.335 3.902 4.455
#> Out-of-Bag MSPRESS.632 (per component) = 0.335 0.263 0.286
#> Out-of-Bag RMSPRESS.632 (per component) = 0.578 0.512 0.535PCA via NIPALS.
library(mvdalab)
my.nipals <- pca.nipals(iris[, 1:4], ncomps = 4, tol = 1e-08)
names(my.nipals)
#> [1] "Loadings" "Scores" "Loading.Space" "Score.Space"
my.nipals$Loadings
#> [,1] [,2] [,3] [,4]
#> Sepal.Length 0.36138514 0.65659919 -0.58203416 0.3154592
#> Sepal.Width -0.08452411 0.73015136 0.59793829 -0.3196944
#> Petal.Length 0.85667099 -0.17337204 0.07625627 -0.4798353
#> Petal.Width 0.35828937 -0.07548926 0.54579393 0.7536837
svd(scale(iris[, 1:4], scale = FALSE))$v
#> [,1] [,2] [,3] [,4]
#> [1,] 0.36138659 -0.65658877 0.58202985 0.3154872
#> [2,] -0.08452251 -0.73016143 -0.59791083 -0.3197231
#> [3,] 0.85667061 0.17337266 -0.07623608 -0.4798390
#> [4,] 0.35828920 0.07548102 -0.54583143 0.7536574Traditional Multivariate Mean Vector Comparison.
library(mvdalab)
data(College)
dat1 <- College
#Generate a 'fake' difference of 15 units
dat2 <- College + matrix(rnorm(nrow(dat1) * ncol(dat1), mean = 15),
nrow = nrow(dat1), ncol = ncol(dat1))
Comparison <- MVComp(dat1, dat2, level = .95)
Comparison
#> lower 95 % confidence upper 95 % confidence Significance
#> 1 -47.66009 17.95686 Not Significant
#> 2 -19.69886 -10.07013 Significant
#> 3 -16.88621 -12.75947 SignificantThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.