pca_decision: PCA decision

In lemuscanovas/synoptReg: Synoptic Climate Classification and Spatial Regionalization of Environmental Data

PCA decision

Description

pca_decision plots the explained variances against the number of the principal component. In addition, it returns all the information about the PCA performance.

Usage

pca_decision(x, ncomp = 30, norm = T, matrix_mode = "S-mode")

Arguments

x	data.frame. A data.frame with the following variables: x, y, time, value, var, units. See as_synoptReg.
ncomp	integer. Number of principal components to show/retain
norm	logical. Default TRUE. norm = TRUE is recommended for classify two ore more variables.
matrix_mode	character. The mode of matrix to use. Choose between S-mode and T-mode

Value

a list with:

• A list with class princomp containing all the results of the PCA

• A data frame containing the main results of the ncomp selected (standard deviation, proportion of variance and cumulative variance).

• A ggplot2 object to visualize the scree test

Note

To perform the PCA the x must contain more rows than columns. In addition, x cannot contain NA values.

See Also

as_synoptReg

Examples

# Load data (mslp or precp_grid)
data(msl)
data(z500)

# Joining both variables
atmos_data <- dplyr::bind_rows(msl,z500)

# Deciding on the number of PC to retain
info <- pca_decision(atmos_data, norm = TRUE)

lemuscanovas/synoptReg documentation built on March 20, 2024, 8:18 a.m.