nipals {plspm} | R Documentation |
Principal Component Analysis with NIPALS algorithm
nipals(x, nc = 2, scaled = TRUE)
x |
A numeric matrix or data frame. |
nc |
Number of components kept in the results (by default 2) |
scaled |
A logical value indicating whether scaling data is performed (TRUE by default). |
The function nipals
performs Principal Component Analysis of a data matrix that may contain missing data.
An object of class "nipals"
, basically a list with the following elements:
values |
The pseudo eigenvalues. |
scores |
The extracted scores. |
loadings |
The loadings. |
cor.sco |
Correlations between the variables and the scores. |
disto |
Squared distance of the observations to the origin. |
contrib |
Contributions of the observations (rows). |
cos |
Squared cosinus. |
dmod |
Distance to the Model. |
When the analyzed data contain missing values, the help interpretation tools (e.g. cor.sco, disto, contrib, cos, dmod
) may not be meaningful, that is to say, some of the results may not be coherent.
Gaston Sanchez
Tenenhaus, M. (1998) La Regression PLS. Theorie et Pratique. Editions TECHNIP, Paris.
Tenenhaus, M. (2007) Statistique. Methodes pour decrire, expliquer et prevoir. Dunod, Paris.
## Not run: ## example of NIPALS algorithm data(wines) nip1 <- nipals(wines[,-1], nc=5) plot(nip1) ## USArrests data vary nip2 <- nipals(USArrests) plot(nip2) ## End(Not run)