adjectives: A Dataset for Factor Analysis

In ACSWR: A Companion Package for the Book "A Course in Statistics with R"

Description

The data set is obtained from Rencher (2002). Here, a 12-year old girl rates 7 of her acquaintances on a differential grade of 1-9 for five adjectives kind, intelligent, happy, likable, and just.

Usage

data(adjectives)

Format

A data frame with 7 observations on the following 6 variables.

People

a factor with levels FATHER FSM1a FSM2 FSM3 MSMb SISTER TEACHER

Kind

a numeric vector

Intelligent

a numeric vector

Happy

a numeric vector

Likeable

a numeric vector

Just

a numeric vector

References

Rencher, A.C. (2002). Methods of Multivariate Analysis, 2e. J. Wiley.

Examples

data(adjectives)
adjectivescor <- cor(adjectives[,-1])
round(adjectivescor,3)
adj_eig <- eigen(adjectivescor)
cumsum(adj_eig$values)/sum(adj_eig$values)
adj_eig$vectors[,1:2]
loadings1 <- adj_eig$vectors[,1]*sqrt(adj_eig$values[1])
loadings2 <- adj_eig$vectors[,2]*sqrt(adj_eig$values[2])
cbind(loadings1,loadings2)
communalities <- (adj_eig$vectors[,1]*sqrt(adj_eig$values[1]))^2+
(adj_eig$vectors[,2]*sqrt(adj_eig$values[2]))^2
round(communalities,3)
specific_variances <- 1-communalities
round(specific_variances,3)
var_acc_factors <- adj_eig$values
round(var_acc_factors,3)
prop_var <- adj_eig$values/sum(adj_eig$values)
round(prop_var,3)
cum_prop <- cumsum(adj_eig$values)/sum(adj_eig$values)
round(cum_prop,3)

Example output

             Kind Intelligent  Happy Likeable  Just
Kind        1.000       0.296  0.881    0.995 0.545
Intelligent 0.296       1.000 -0.022    0.326 0.837
Happy       0.881      -0.022  1.000    0.867 0.130
Likeable    0.995       0.326  0.867    1.000 0.544
Just        0.545       0.837  0.130    0.544 1.000
[1] 0.6526490 0.9603115 0.9938815 1.0000000 1.0000000
          [,1]       [,2]
[1,] 0.5366646 -0.1863665
[2,] 0.2875272  0.6506116
[3,] 0.4342879 -0.4734720
[4,] 0.5374480 -0.1692745
[5,] 0.3896959  0.5377197
     loadings1  loadings2
[1,] 0.9694553 -0.2311480
[2,] 0.5194021  0.8069453
[3,] 0.7845174 -0.5872412
[4,] 0.9708704 -0.2099491
[5,] 0.7039644  0.6669269
[1] 0.993 0.921 0.960 0.987 0.940
[1] 0.007 0.079 0.040 0.013 0.060
[1] 3.263 1.538 0.168 0.031 0.000
[1] 0.653 0.308 0.034 0.006 0.000
[1] 0.653 0.960 0.994 1.000 1.000

ACSWR documentation built on May 2, 2019, 6:53 a.m.