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

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```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
[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.