README.md

In khellton/zibppca:

ZIBP-PCA: Zero-inflated Bivariate Poisson Principal Component analysis

This vignette describes how to use the R package zibppca to perform Zero-inflated Bivariate Poisson Principal Component analysis for Neuro-Psychiatric Inventory (NPI) items.

Installation

You can install the R package zibppca directly from GitHub, after installing the package devtools. The help description files can be accessed by using ?

#Install and load the devtools package
install.packages("devtools")
library(devtools)

# Install the zibppca package directly from GitHub
install_github("khellton/zibppca")

#Load the zibppca package
library(zibppca)

#Check the help file
?zibppca

Analyzing NPI items

First, load the NPI data either from SPSS or csv file and create a data.frame. Then the first 10 patients should look something like this:

#>    Delusions_NPI1 Hallucinations_NPI2 Aggression_NPI3 Depression_NPI4
#> 1               0                   0               0               6
#> 2               0                   0               0               0
#> 3               6                   0               2               0
#> 4               0                   0               0               0
#> 5               4                   0               0               4
#> 6               0                   0               0               0
#> 7               0                   0               0               0
#> 8              12                   0               3               0
#> 9               0                   0               0              12
#> 10              8                   6               8               0
#>    Anxiety_NPI5 Euphoria_NPI6 Apathy_NPI7 Disinhibition_NPI8
#> 1             2             6           6                 12
#> 2             0             0           8                  0
#> 3             0             8           0                  6
#> 4             0             8           0                  8
#> 5             0             0           8                  0
#> 6             0             6           8                  8
#> 7             3             0          12                  0
#> 8             0             0           0                  0
#> 9             0             0          12                  4
#> 10            0             0           9                 12
#>    Irritability_NPI9 MotorDisturbance_NPI10 Sleep_NPI11 Appetite_NPI12
#> 1                  0                      1           0              0
#> 2                  0                      0           0              0
#> 3                  6                      0          12              0
#> 4                  0                      4           9              0
#> 5                  0                      0           0              4
#> 6                  0                      8           0             12
#> 7                  0                      0           0              4
#> 8                  8                      0           0              0
#> 9                  1                      0           0              3
#> 10                 6                     12          12              0

The distribution of the different items can be visually inspected by histograms, or clustered box plots, for instance as seen below for Delusions and Aggression.

Principal component analysis

The zibppca package has the following outline

library(zibppca)
#Run the main command 
result <- zibppca(data,scale. = TRUE)
#> [1] "Analyzing data with 12 variables and 830 observations"

### Loadings of the first 3 components
result$loadings[,1:3]
#>                                 [,1]          [,2]          [,3]
#> Delusions_NPI1         -0.2622601240  0.4821305118 -0.3798866955
#> Hallucinations_NPI2    -0.1082992701  0.5120023629 -0.5008639915
#> Aggression_NPI3        -0.6164949410 -0.1098594931  0.0080981797
#> Depression_NPI4        -0.0423469611  0.4352356548  0.5584055258
#> Anxiety_NPI5           -0.0782833592  0.4634346828  0.5067744951
#> Euphoria_NPI6          -0.0124559932 -0.0488332654  0.0351436861
#> Apathy_NPI7            -0.0018804366  0.0633776167  0.1126235691
#> Disinhibition_NPI8     -0.3117465700 -0.2535016300  0.1336287956
#> Irritability_NPI9      -0.6588558291 -0.1354960146  0.0655665155
#> MotorDisturbance_NPI10 -0.0081733914 -0.0104434701  0.0066161937
#> Sleep_NPI11            -0.0009260809 -0.0003347656  0.0002063836
#> Appetite_NPI12         -0.0002378082 -0.0001637063  0.0002709173

### Scree plot of eigenvalues
result$eigenvalues 
#>  [1] 1.5116877 1.0954316 1.0692296 1.0046937 1.0000139 0.9999985 0.9999799
#>  [8] 0.9998962 0.9502895 0.9220619 0.8894493 0.6550727
plot(result$eigenvalues,main = 'Scree plot',xlab='Eigenvalues')

### Plot patient scores 
plot(result$scores[,1],result$scores[,2],main = 'Score plot',xlab='1. component',ylab='2. component')

khellton/zibppca documentation built on May 28, 2019, 8:39 a.m.