## 22.3 Factor Analysis

Purpose

• Using a few linear combinations of underlying unobservable (latent) traits, we try to describe the covariance relationship among a large number of measured traits

• Similar to PCA, but factor analysis is model based

More details can be found on PSU stat or UMN stat

Let $$\mathbf{y}$$ be the set of $$p$$ measured variables

$$E(\mathbf{y}) = \mathbf{\mu}$$

$$var(\mathbf{y}) = \mathbf{\Sigma}$$

We have

\begin{aligned} \mathbf{y} - \mathbf{\mu} &= \mathbf{Lf} + \epsilon \\ &= \left( \begin{array} {c} l_{11}f_1 + l_{12}f_2 + \dots + l_{tm}f_m \\ \vdots \\ l_{p1}f_1 + l_{p2}f_2 + \dots + l_{pm} f_m \end{array} \right) + \left( \begin{array} {c} \epsilon_1 \\ \vdots \\ \epsilon_p \end{array} \right) \end{aligned}

where

• $$\mathbf{y} - \mathbf{\mu}$$ = the p centered measurements

• $$\mathbf{L}$$ = $$p \times m$$ matrix of factor loadings

• $$\mathbf{f}$$ = unobserved common factors for the population

• $$\mathbf{\epsilon}$$ = random errors (i.e., variation that is not accounted for by the common factors).

We want $$m$$ (the number of factors) to be much smaller than $$p$$ (the number of measured attributes)

Restrictions on the model

• $$E(\epsilon) = \mathbf{0}$$

• $$var(\epsilon) = \Psi_{p \times p} = diag( \psi_1, \dots, \psi_p)$$

• $$\mathbf{\epsilon}, \mathbf{f}$$ are independent

• Additional assumption could be $$E(\mathbf{f}) = \mathbf{0}, var(\mathbf{f}) = \mathbf{I}_{m \times m}$$ (known as the orthogonal factor model) , which imposes the following covariance structure on $$\mathbf{y}$$

\begin{aligned} var(\mathbf{y}) = \mathbf{\Sigma} &= var(\mathbf{Lf} + \mathbf{\epsilon}) \\ &= var(\mathbf{Lf}) + var(\epsilon) \\ &= \mathbf{L} var(\mathbf{f}) \mathbf{L}' + \mathbf{\Psi} \\ &= \mathbf{LIL}' + \mathbf{\Psi} \\ &= \mathbf{LL}' + \mathbf{\Psi} \end{aligned}

Since $$\mathbf{\Psi}$$ is diagonal, the off-diagonal elements of $$\mathbf{LL}'$$ are $$\sigma_{ij}$$, the co variances in $$\mathbf{\Sigma}$$, which means $$cov(y_i, y_j) = \sum_{k=1}^m l_{ik}l_{jk}$$ and the covariance of $$\mathbf{y}$$ is completely determined by the m factors ( $$m <<p$$)

$$var(y_i) = \sum_{k=1}^m l_{ik}^2 + \psi_i$$ where $$\psi_i$$ is the specific variance and the summation term is the i-th communality (i.e., portion of the variance of the i-th variable contributed by the $$m$$ common factors ($$h_i^2 = \sum_{k=1}^m l_{ik}^2$$)

The factor model is only uniquely determined up to an orthogonal transformation of the factors.

Let $$\mathbf{T}_{m \times m}$$ be an orthogonal matrix $$\mathbf{TT}' = \mathbf{T'T} = \mathbf{I}$$ then

\begin{aligned} \mathbf{y} - \mathbf{\mu} &= \mathbf{Lf} + \epsilon \\ &= \mathbf{LTT'f} + \epsilon \\ &= \mathbf{L}^*(\mathbf{T'f}) + \epsilon & \text{where } \mathbf{L}^* = \mathbf{LT} \end{aligned}

and

\begin{aligned} \mathbf{\Sigma} &= \mathbf{LL}' + \mathbf{\Psi} \\ &= \mathbf{LTT'L} + \mathbf{\Psi} \\ &= (\mathbf{L}^*)(\mathbf{L}^*)' + \mathbf{\Psi} \end{aligned}

Hence, any orthogonal transformation of the factors is an equally good description of the correlations among the observed traits.

Let $$\mathbf{y} = \mathbf{Cx}$$ , where $$\mathbf{C}$$ is any diagonal matrix, then $$\mathbf{L}_y = \mathbf{CL}_x$$ and $$\mathbf{\Psi}_y = \mathbf{C\Psi}_x\mathbf{C}$$

Hence, we can see that factor analysis is also invariant to changes in scale

### 22.3.1 Methods of Estimation

To estimate $$\mathbf{L}$$

#### 22.3.1.1 Principal Component Method

Spectral decomposition

\begin{aligned} \mathbf{\Sigma} &= \lambda_1 \mathbf{a}_1 \mathbf{a}_1' + \dots + \lambda_p \mathbf{a}_p \mathbf{a}_p' \\ &= \mathbf{A\Lambda A}' \\ &= \sum_{k=1}^m \lambda+k \mathbf{a}_k \mathbf{a}_k' + \sum_{k= m+1}^p \lambda_k \mathbf{a}_k \mathbf{a}_k' \\ &= \sum_{k=1}^m l_k l_k' + \sum_{k=m+1}^p \lambda_k \mathbf{a}_k \mathbf{a}_k' \end{aligned}

where $$l_k = \mathbf{a}_k \sqrt{\lambda_k}$$ and the second term is not diagonal in general.

Assume

$\psi_i = \sigma_{ii} - \sum_{k=1}^m l_{ik}^2 = \sigma_{ii} - \sum_{k=1}^m \lambda_i a_{ik}^2$

then

$\mathbf{\Sigma} \approx \mathbf{LL}' + \mathbf{\Psi}$

To estimate $$\mathbf{L}$$ and $$\Psi$$ , we use the expected eigenvalues and eigenvectors from $$\mathbf{S}$$ or $$\mathbf{R}$$

• The estimated factor loadings don’t change as the number of actors increases

• The diagonal elements of $$\hat{\mathbf{L}}\hat{\mathbf{L}}' + \hat{\mathbf{\Psi}}$$ are equal to the diagonal elements of $$\mathbf{S}$$ and $$\mathbf{R}$$, but the covariances may not be exactly reproduced

• We select $$m$$ so that the off-diagonal elements close to the values in $$\mathbf{S}$$ (or to make the off-diagonal elements of $$\mathbf{S} - \hat{\mathbf{L}} \hat{\mathbf{L}}' + \hat{\mathbf{\Psi}}$$ small)

#### 22.3.1.2 Principal Factor Method

Consider modeling the correlation matrix, $$\mathbf{R} = \mathbf{L} \mathbf{L}' + \mathbf{\Psi}$$ . Then

$\mathbf{L} \mathbf{L}' = \mathbf{R} - \mathbf{\Psi} = \left( \begin{array} {cccc} h_1^2 & r_{12} & \dots & r_{1p} \\ r_{21} & h_2^2 & \dots & r_{2p} \\ \vdots & \vdots & \ddots & \vdots \\ r_{p1} & r_{p2} & \dots & h_p^2 \end{array} \right)$

where $$h_i^2 = 1- \psi_i$$ (the communality)

Suppose that initial estimates are available for the communalities, $$(h_1^*)^2,(h_2^*)^2, \dots , (h_p^*)^2$$, then we can regress each trait on all the others, and then use the $$r^2$$ as $$h^2$$

The estimate of $$\mathbf{R} - \mathbf{\Psi}$$ at step k is

$(\mathbf{R} - \mathbf{\Psi})_k = \left( \begin{array} {cccc} (h_1^*)^2 & r_{12} & \dots & r_{1p} \\ r_{21} & (h_2^*)^2 & \dots & r_{2p} \\ \vdots & \vdots & \ddots & \vdots \\ r_{p1} & r_{p2} & \dots & (h_p^*)^2 \end{array} \right) = \mathbf{L}_k^*(\mathbf{L}_k^*)'$

where

$\mathbf{L}_k^* = (\sqrt{\hat{\lambda}_1^*\hat{\mathbf{a}}_1^* , \dots \hat{\lambda}_m^*\hat{\mathbf{a}}_m^*})$

and

$\hat{\psi}_{i,k}^* = 1 - \sum_{j=1}^m \hat{\lambda}_i^* (\hat{a}_{ij}^*)^2$

we used the spectral decomposition on the estimated matrix $$(\mathbf{R}- \mathbf{\Psi})$$ to calculate the $$\hat{\lambda}_i^* s$$ and the $$\mathbf{\hat{a}}_i^* s$$

After updating the values of $$(\hat{h}_i^*)^2 = 1 - \hat{\psi}_{i,k}^*$$ we will use them to form a new $$\mathbf{L}_{k+1}^*$$ via another spectral decomposition. Repeat the process

Notes:

• The matrix $$(\mathbf{R} - \mathbf{\Psi})_k$$ is not necessarily positive definite

• The principal component method is similar to principal factor if one considers the initial communalities are $$h^2 = 1$$

• if $$m$$ is too large, some communalities may become larger than 1, causing the iterations to terminate. To combat, we can

• fix any communality that is greater than 1 at 1 and then continues.

• continue iterations regardless of the size of the communalities. However, results can be outside fo the parameter space.

#### 22.3.1.3 Maximum Likelihood Method

Since we need the likelihood function, we make the additional (critical) assumption that

• $$\mathbf{y}_j \sim N(\mathbf{\mu},\mathbf{\Sigma})$$ for $$j = 1,..,n$$

• $$\mathbf{f} \sim N(\mathbf{0}, \mathbf{I})$$

• $$\epsilon_j \sim N(\mathbf{0}, \mathbf{\Psi})$$

and restriction

• $$\mathbf{L}' \mathbf{\Psi}^{-1}\mathbf{L} = \mathbf{\Delta}$$ where $$\mathbf{\Delta}$$ is a diagonal matrix. (since the factor loading matrix is not unique, we need this restriction).

Notes:

• Finding MLE can be computationally expensive

• we typically use other methods for exploratory data analysis

• Likelihood ratio tests could be used for testing hypotheses in this framework (i.e., Confirmatory Factor Analysis)

### 22.3.2 Factor Rotation

$$\mathbf{T}_{m \times m}$$ is an orthogonal matrix that has the property that

$\hat{\mathbf{L}} \hat{\mathbf{L}}' + \hat{\mathbf{\Psi}} = \hat{\mathbf{L}}^*(\hat{\mathbf{L}}^*)' + \hat{\mathbf{\Psi}}$

where $$\mathbf{L}^* = \mathbf{LT}$$

This means that estimated specific variances and communalities are not altered by the orthogonal transformation.

Since there are an infinite number of choices for $$\mathbf{T}$$, some selection criterion is necessary

For example, we can find the orthogonal transformation that maximizes the objective function

$\sum_{j = 1}^m [\frac{1}{p}\sum_{i=1}^p (\frac{l_{ij}^{*2}}{h_i})^2 - \{\frac{\gamma}{p} \sum_{i=1}^p (\frac{l_{ij}^{*2}}{h_i})^2 \}^2]$

where $$\frac{l_{ij}^{*2}}{h_i}$$ are “scaled loadings”, which gives variables with small communalities more influence.

Different choices of $$\gamma$$ in the objective function correspond to different orthogonal rotation found in the literature;

1. Varimax $$\gamma = 1$$ (rotate the factors so that each of the $$p$$ variables should have a high loading on only one factor, but this is not always possible).

2. Quartimax $$\gamma = 0$$

3. Equimax $$\gamma = m/2$$

4. Parsimax $$\gamma = \frac{p(m-1)}{p+m-2}$$

5. Promax: non-orthogonal or olique transformations

6. Harris-Kaiser (HK): non-orthogonal or oblique transformations

### 22.3.3 Estimation of Factor Scores

Recall

$(\mathbf{y}_j - \mathbf{\mu}) = \mathbf{L}_{p \times m}\mathbf{f}_j + \epsilon_j$

If the factor model is correct then

$var(\epsilon_j) = \mathbf{\Psi} = diag (\psi_1, \dots , \psi_p)$

Thus we could consider using weighted least squares to estimate $$\mathbf{f}_j$$ , the vector of factor scores for the j-th sampled unit by

\begin{aligned} \hat{\mathbf{f}} &= (\mathbf{L}'\mathbf{\Psi}^{-1} \mathbf{L})^{-1} \mathbf{L}' \mathbf{\Psi}^{-1}(\mathbf{y}_j - \mathbf{\mu}) \\ & \approx (\mathbf{L}'\mathbf{\Psi}^{-1} \mathbf{L})^{-1} \mathbf{L}' \mathbf{\Psi}^{-1}(\mathbf{y}_j - \mathbf{\bar{y}}) \end{aligned}

#### 22.3.3.1 The Regression Method

Alternatively, we can use the regression method to estimate the factor scores

Consider the joint distribution of $$(\mathbf{y}_j - \mathbf{\mu})$$ and $$\mathbf{f}_j$$ assuming multivariate normality, as in the maximum likelihood approach. then,

$\left( \begin{array} {c} \mathbf{y}_j - \mathbf{\mu} \\ \mathbf{f}_j \end{array} \right) \sim N_{p + m} \left( \left[ \begin{array} {cc} \mathbf{LL}' + \mathbf{\Psi} & \mathbf{L} \\ \mathbf{L}' & \mathbf{I}_{m\times m} \end{array} \right] \right)$

when the $$m$$ factor model is correct

Hence,

$E(\mathbf{f}_j | \mathbf{y}_j - \mathbf{\mu}) = \mathbf{L}' (\mathbf{LL}' + \mathbf{\Psi})^{-1}(\mathbf{y}_j - \mathbf{\mu})$

notice that $$\mathbf{L}' (\mathbf{LL}' + \mathbf{\Psi})^{-1}$$ is an $$m \times p$$ matrix of regression coefficients

Then, we use the estimated conditional mean vector to estimate the factor scores

$\mathbf{\hat{f}}_j = \mathbf{\hat{L}}'(\mathbf{\hat{L}}\mathbf{\hat{L}}' + \mathbf{\hat{\Psi}})^{-1}(\mathbf{y}_j - \mathbf{\bar{y}})$

Alternatively, we could reduce the effect of possible incorrect determination fo the number of factors $$m$$ by using $$\mathbf{S}$$ as a substitute for $$\mathbf{\hat{L}}\mathbf{\hat{L}}' + \mathbf{\hat{\Psi}}$$ then

$\mathbf{\hat{f}}_j = \mathbf{\hat{L}}'\mathbf{S}^{-1}(\mathbf{y}_j - \mathbf{\bar{y}})$

where $$j = 1,\dots,n$$

### 22.3.4 Model Diagnostic

• Plots

• Check for outliers (recall that $$\mathbf{f}_j \sim iid N(\mathbf{0}, \mathbf{I}_{m \times m})$$)

• Check for multivariate normality assumption

• Use univariate tests for normality to check the factor scores

### 22.3.5 Application

In the psych package,

• h2 = the communalities

• u2 = the uniqueness

• com = the complexity

library(psych)
library(tidyverse)
## Load the data from the psych package
data(Harman.5)
Harman.5
#>         population schooling employment professional housevalue
#> Tract1        5700      12.8       2500          270      25000
#> Tract2        1000      10.9        600           10      10000
#> Tract3        3400       8.8       1000           10       9000
#> Tract4        3800      13.6       1700          140      25000
#> Tract5        4000      12.8       1600          140      25000
#> Tract6        8200       8.3       2600           60      12000
#> Tract7        1200      11.4        400           10      16000
#> Tract8        9100      11.5       3300           60      14000
#> Tract9        9900      12.5       3400          180      18000
#> Tract10       9600      13.7       3600          390      25000
#> Tract11       9600       9.6       3300           80      12000
#> Tract12       9400      11.4       4000          100      13000

# Correlation matrix
cor_mat <- cor(Harman.5)
cor_mat
#>              population  schooling employment professional housevalue
#> population   1.00000000 0.00975059  0.9724483    0.4388708 0.02241157
#> schooling    0.00975059 1.00000000  0.1542838    0.6914082 0.86307009
#> employment   0.97244826 0.15428378  1.0000000    0.5147184 0.12192599
#> professional 0.43887083 0.69140824  0.5147184    1.0000000 0.77765425
#> housevalue   0.02241157 0.86307009  0.1219260    0.7776543 1.00000000

## Principal Component Method with Correlation
cor_pca <- prcomp(Harman.5, scale = T)
# eigen values
cor_results <- data.frame(eigen_values = cor_pca$sdev ^ 2) cor_results <- cor_results %>% mutate( proportion = eigen_values / sum(eigen_values), cumulative = cumsum(proportion), number = row_number() ) cor_results #> eigen_values proportion cumulative number #> 1 2.87331359 0.574662719 0.5746627 1 #> 2 1.79666009 0.359332019 0.9339947 2 #> 3 0.21483689 0.042967377 0.9769621 3 #> 4 0.09993405 0.019986811 0.9969489 4 #> 5 0.01525537 0.003051075 1.0000000 5 # Scree plot of Eigenvalues scree_gg <- ggplot(cor_results, aes(x = number, y = eigen_values)) + geom_line(alpha = 0.5) + geom_text(aes(label = number)) + scale_x_continuous(name = "Number") + scale_y_continuous(name = "Eigenvalue") + theme_bw() scree_gg  screeplot(cor_pca, type = 'lines')  ## Keep 2 factors based on scree plot and eigenvalues factor_pca <- principal(Harman.5, nfactors = 2, rotate = "none") factor_pca #> Principal Components Analysis #> Call: principal(r = Harman.5, nfactors = 2, rotate = "none") #> Standardized loadings (pattern matrix) based upon correlation matrix #> PC1 PC2 h2 u2 com #> population 0.58 0.81 0.99 0.012 1.8 #> schooling 0.77 -0.54 0.89 0.115 1.8 #> employment 0.67 0.73 0.98 0.021 2.0 #> professional 0.93 -0.10 0.88 0.120 1.0 #> housevalue 0.79 -0.56 0.94 0.062 1.8 #> #> PC1 PC2 #> SS loadings 2.87 1.80 #> Proportion Var 0.57 0.36 #> Cumulative Var 0.57 0.93 #> Proportion Explained 0.62 0.38 #> Cumulative Proportion 0.62 1.00 #> #> Mean item complexity = 1.7 #> Test of the hypothesis that 2 components are sufficient. #> #> The root mean square of the residuals (RMSR) is 0.03 #> with the empirical chi square 0.29 with prob < 0.59 #> #> Fit based upon off diagonal values = 1 # factor 1 = overall socioeconomic health # factor 2 = contrast of the population and employment against school and house value ## Ssquared multiple correlation (SMC) prior, no rotation factor_pca_smc <- fa( Harman.5, nfactors = 2, fm = "pa", rotate = "none", SMC = TRUE ) factor_pca_smc #> Factor Analysis using method = pa #> Call: fa(r = Harman.5, nfactors = 2, rotate = "none", SMC = TRUE, fm = "pa") #> Standardized loadings (pattern matrix) based upon correlation matrix #> PA1 PA2 h2 u2 com #> population 0.62 0.78 1.00 -0.0027 1.9 #> schooling 0.70 -0.53 0.77 0.2277 1.9 #> employment 0.70 0.68 0.96 0.0413 2.0 #> professional 0.88 -0.15 0.80 0.2017 1.1 #> housevalue 0.78 -0.60 0.96 0.0361 1.9 #> #> PA1 PA2 #> SS loadings 2.76 1.74 #> Proportion Var 0.55 0.35 #> Cumulative Var 0.55 0.90 #> Proportion Explained 0.61 0.39 #> Cumulative Proportion 0.61 1.00 #> #> Mean item complexity = 1.7 #> Test of the hypothesis that 2 factors are sufficient. #> #> df null model = 10 with the objective function = 6.38 with Chi Square = 54.25 #> df of the model are 1 and the objective function was 0.34 #> #> The root mean square of the residuals (RMSR) is 0.01 #> The df corrected root mean square of the residuals is 0.03 #> #> The harmonic n.obs is 12 with the empirical chi square 0.02 with prob < 0.88 #> The total n.obs was 12 with Likelihood Chi Square = 2.44 with prob < 0.12 #> #> Tucker Lewis Index of factoring reliability = 0.596 #> RMSEA index = 0.336 and the 90 % confidence intervals are 0 0.967 #> BIC = -0.04 #> Fit based upon off diagonal values = 1 ## SMC prior, Promax rotation factor_pca_smc_pro <- fa( Harman.5, nfactors = 2, fm = "pa", rotate = "Promax", SMC = TRUE ) factor_pca_smc_pro #> Factor Analysis using method = pa #> Call: fa(r = Harman.5, nfactors = 2, rotate = "Promax", SMC = TRUE, #> fm = "pa") #> Standardized loadings (pattern matrix) based upon correlation matrix #> PA1 PA2 h2 u2 com #> population -0.11 1.02 1.00 -0.0027 1.0 #> schooling 0.90 -0.11 0.77 0.2277 1.0 #> employment 0.02 0.97 0.96 0.0413 1.0 #> professional 0.75 0.33 0.80 0.2017 1.4 #> housevalue 1.01 -0.14 0.96 0.0361 1.0 #> #> PA1 PA2 #> SS loadings 2.38 2.11 #> Proportion Var 0.48 0.42 #> Cumulative Var 0.48 0.90 #> Proportion Explained 0.53 0.47 #> Cumulative Proportion 0.53 1.00 #> #> With factor correlations of #> PA1 PA2 #> PA1 1.00 0.25 #> PA2 0.25 1.00 #> #> Mean item complexity = 1.1 #> Test of the hypothesis that 2 factors are sufficient. #> #> df null model = 10 with the objective function = 6.38 with Chi Square = 54.25 #> df of the model are 1 and the objective function was 0.34 #> #> The root mean square of the residuals (RMSR) is 0.01 #> The df corrected root mean square of the residuals is 0.03 #> #> The harmonic n.obs is 12 with the empirical chi square 0.02 with prob < 0.88 #> The total n.obs was 12 with Likelihood Chi Square = 2.44 with prob < 0.12 #> #> Tucker Lewis Index of factoring reliability = 0.596 #> RMSEA index = 0.336 and the 90 % confidence intervals are 0 0.967 #> BIC = -0.04 #> Fit based upon off diagonal values = 1 ## SMC prior, varimax rotation factor_pca_smc_var <- fa( Harman.5, nfactors = 2, fm = "pa", rotate = "varimax", SMC = TRUE ) ## Make a data frame of the loadings for ggplot2 factors_df <- bind_rows( data.frame( y = rownames(factor_pca_smc$loadings),
unclass(factor_pca_smc$loadings) ), data.frame( y = rownames(factor_pca_smc_pro$loadings),
unclass(factor_pca_smc_pro$loadings) ), data.frame( y = rownames(factor_pca_smc_var$loadings),
),
.id = "Rotation"
)
flag_gg <- ggplot(factors_df) +
geom_vline(aes(xintercept = 0)) +
geom_hline(aes(yintercept = 0)) +
geom_point(aes(
x = PA2,
y = PA1,
col = y,
shape = y
), size = 2) +
scale_x_continuous(name = "Factor 2", limits = c(-1.1, 1.1)) +
scale_y_continuous(name = "Factor1", limits = c(-1.1, 1.1)) +
facet_wrap("Rotation", labeller = labeller(Rotation = c(
"1" = "Original", "2" = "Promax", "3" = "Varimax"
))) +
coord_fixed(ratio = 1) # make aspect ratio of each facet 1

flag_gg


# promax and varimax did a good job to assign trait to a particular factor

factor_mle_1 <- fa(
Harman.5,
nfactors = 1,
fm = "mle",
rotate = "none",
SMC = TRUE
)
factor_mle_1
#> Factor Analysis using method =  ml
#> Call: fa(r = Harman.5, nfactors = 1, rotate = "none", SMC = TRUE, fm = "mle")
#>               ML1    h2     u2 com
#> population   0.97 0.950 0.0503   1
#> schooling    0.14 0.021 0.9791   1
#> employment   1.00 0.995 0.0049   1
#> professional 0.51 0.261 0.7388   1
#> housevalue   0.12 0.014 0.9864   1
#>
#>                 ML1
#> Proportion Var 0.45
#>
#> Mean item complexity =  1
#> Test of the hypothesis that 1 factor is sufficient.
#>
#> df null model =  10  with the objective function =  6.38 with Chi Square =  54.25
#> df of  the model are 5  and the objective function was  3.14
#>
#> The root mean square of the residuals (RMSR) is  0.41
#> The df corrected root mean square of the residuals is  0.57
#>
#> The harmonic n.obs is  12 with the empirical chi square  39.41  with prob <  2e-07
#> The total n.obs was  12  with Likelihood Chi Square =  24.56  with prob <  0.00017
#>
#> Tucker Lewis Index of factoring reliability =  0.022
#> RMSEA index =  0.564  and the 90 % confidence intervals are  0.374 0.841
#> BIC =  12.14
#> Fit based upon off diagonal values = 0.5
#> Measures of factor score adequacy
#>                                                    ML1
#> Correlation of (regression) scores with factors   1.00
#> Multiple R square of scores with factors          1.00
#> Minimum correlation of possible factor scores     0.99

factor_mle_2 <- fa(
Harman.5,
nfactors = 2,
fm = "mle",
rotate = "none",
SMC = TRUE
)
factor_mle_2
#> Factor Analysis using method =  ml
#> Call: fa(r = Harman.5, nfactors = 2, rotate = "none", SMC = TRUE, fm = "mle")
#>                ML2  ML1   h2    u2 com
#> population   -0.03 1.00 1.00 0.005 1.0
#> schooling     0.90 0.04 0.81 0.193 1.0
#> employment    0.09 0.98 0.96 0.036 1.0
#> professional  0.78 0.46 0.81 0.185 1.6
#> housevalue    0.96 0.05 0.93 0.074 1.0
#>
#>                        ML2  ML1
#> Proportion Var        0.47 0.43
#> Cumulative Var        0.47 0.90
#> Proportion Explained  0.52 0.48
#> Cumulative Proportion 0.52 1.00
#>
#> Mean item complexity =  1.1
#> Test of the hypothesis that 2 factors are sufficient.
#>
#> df null model =  10  with the objective function =  6.38 with Chi Square =  54.25
#> df of  the model are 1  and the objective function was  0.31
#>
#> The root mean square of the residuals (RMSR) is  0.01
#> The df corrected root mean square of the residuals is  0.05
#>
#> The harmonic n.obs is  12 with the empirical chi square  0.05  with prob <  0.82
#> The total n.obs was  12  with Likelihood Chi Square =  2.22  with prob <  0.14
#>
#> Tucker Lewis Index of factoring reliability =  0.658
#> RMSEA index =  0.307  and the 90 % confidence intervals are  0 0.945
#> BIC =  -0.26
#> Fit based upon off diagonal values = 1
#> Measures of factor score adequacy
#>                                                    ML2  ML1
#> Correlation of (regression) scores with factors   0.98 1.00
#> Multiple R square of scores with factors          0.95 1.00
#> Minimum correlation of possible factor scores     0.91 0.99

factor_mle_3 <- fa(
Harman.5,
nfactors = 3,
fm = "mle",
rotate = "none",
SMC = TRUE
)
factor_mle_3
#> Factor Analysis using method =  ml
#> Call: fa(r = Harman.5, nfactors = 3, rotate = "none", SMC = TRUE, fm = "mle")
#>                ML2  ML1   ML3   h2     u2 com
#> population   -0.12 0.98 -0.11 0.98 0.0162 1.1
#> schooling     0.89 0.15  0.29 0.90 0.0991 1.3
#> employment    0.00 1.00  0.04 0.99 0.0052 1.0
#> professional  0.72 0.52 -0.10 0.80 0.1971 1.9
#> housevalue    0.97 0.13 -0.09 0.97 0.0285 1.1
#>
#>                        ML2  ML1  ML3
#> Proportion Var        0.46 0.45 0.02
#> Cumulative Var        0.46 0.91 0.93
#> Proportion Explained  0.49 0.49 0.02
#> Cumulative Proportion 0.49 0.98 1.00
#>
#> Mean item complexity =  1.2
#> Test of the hypothesis that 3 factors are sufficient.
#>
#> df null model =  10  with the objective function =  6.38 with Chi Square =  54.25
#> df of  the model are -2  and the objective function was  0
#>
#> The root mean square of the residuals (RMSR) is  0
#> The df corrected root mean square of the residuals is  NA
#>
#> The harmonic n.obs is  12 with the empirical chi square  0  with prob <  NA
#> The total n.obs was  12  with Likelihood Chi Square =  0  with prob <  NA
#>
#> Tucker Lewis Index of factoring reliability =  1.318
#> Fit based upon off diagonal values = 1
#> Measures of factor score adequacy
#>                                                    ML2  ML1  ML3
#> Correlation of (regression) scores with factors   0.99 1.00 0.82
#> Multiple R square of scores with factors          0.98 1.00 0.68
#> Minimum correlation of possible factor scores     0.96 0.99 0.36

The output info for the null hypothesis of no common factors is in the statement “The degrees of freedom for the null model ..”

The output info for the null hypothesis that number of factors is sufficient is in the statement “The total number of observations was …”

One factor is not enough, two is sufficient, and not enough data for 3 factors (df of -2 and NA for p-value). Hence, we should use 2-factor model.