Extracting Output from Principal Function in Psych Package as a Data Frame

psych: principal - loadings components

This was partly answered, but since it is my package, I will give a somewhat more complete answer.

The summary table of the PCA or FA factor loadings tables is calculated in the print function. It it is returned (invisibly by print). However, it is available as the Vaccounted object.

i.e. summary table of the PCA or FA output

set.seed(0)
x <- replicate(8, rnorm(10))
pca.x <- principal(x, nf=4, rotate="varimax")
p <- print(pca.x)

round(p$Vaccounted,2)   #shows the summary of the loadings table
                       PC1  PC3  PC4  PC2
SS loadings           2.32 1.93 1.37 1.34
Proportion Var        0.29 0.24 0.17 0.17
Cumulative Var        0.29 0.53 0.70 0.87
Proportion Explained  0.33 0.28 0.20 0.19
Cumulative Proportion 0.33 0.61 0.81 1.00

This works for the fa function as well.

psych - Getting factor loadings as data.frame for LaTeX export

When you look at fac$loading, you see that it is a S3 object. Removing the class attribute gives you a matrix which can then be passed to xtable:

str(fac$loadings)
class(fac$loadings)

xtable(unclass(fac$loadings))

psych - Getting cumulative Variance from fa loadings

After running str(fac) it seems that you can get the data you're interested in with

df <- fac$Vaccounted
> df
                            ML1        ML2       ML3       ML4        ML5
SS loadings           4.4291169 2.42278894 1.5623383 1.3308373 0.96561620
Proportion Var        0.1581827 0.08652818 0.0557978 0.0475299 0.03448629
Cumulative Var        0.1581827 0.24471092 0.3005087 0.3480386 0.38252492
Proportion Explained  0.4135227 0.22620272 0.1458671 0.1242531 0.09015437
Cumulative Proportion 0.4135227 0.63972545 0.7855925 0.9098456 1.00000000

Getting data frame from loadings of factor analysis (fa function in psych)

x <- c(1, 2, 1, 2)
y <- c(1, 2, 3, 4)
z <- c(4, 3, 2, 1)
df <- data.frame(x, y, z)

fit <- psych::fa(df, nfactors = 2)

x <- fit$loadings

via stats:::print.loadings:

Lambda <- unclass(x)
p <- nrow(Lambda)
factors <- ncol(Lambda)

vx <- colSums(x^2)
varex <- rbind(`SS loadings` = vx)

if (is.null(attr(x, "covariance"))) {
  varex <- rbind(varex, `Proportion Var` = vx/p)
  if (factors > 1) 
    varex <- rbind(varex, `Cumulative Var` = cumsum(vx/p))
}

tibble::rownames_to_column(as.data.frame(varex), "x")
##                x       MR1          MR2
## 1    SS loadings 2.1954555 3.000000e-30
## 2 Proportion Var 0.7318185 1.000000e-30
## 3 Cumulative Var 0.7318185 7.318185e-01

And, for the first table:

cutoff <- 0.1 # (the default for the `print.loadings()` function)
Lambda <- unclass(x)
p <- nrow(Lambda)
fx <- setNames(Lambda, NULL)
fx[abs(Lambda) < cutoff] <- NA_real_
fx <- as.data.frame(fx)
rownames(fx) <- NULL
fx
##          MR1 MR2
## 1  0.4476761  NA
## 2  0.9987596  NA
## 3 -0.9987596  NA

Export Cronbach Alpha Results - R

The output can be saved as a txt file this way. You can also subset the object created with the alpha function using the $ operator to get only the information you are interested in.

setwd("~/Desktop")
out <- psych::alpha(d)
capture.output(out,file = "alpha.txt")

Export describe.by (package psych) as csv file in R

Function describeBy() (this should be used instead of descrive.by() that is deprecated) produces list of data frame and therefore can't be written to file with write.table().

library(psych)    
estatistica <- describeBy(pag,list(pag$Jogo))
estatistica

: A
       var n mean   sd median trimmed  mad min max range skew kurtosis   se
Jogo*    1 3 1.00 0.00      1    1.00 0.00   1   1     0  NaN      NaN 0.00
Pais*    2 3 2.00 1.00      2    2.00 1.48   1   3     2 0.00    -2.33 0.58
Numero   3 3 2.67 2.89      1    2.67 0.00   1   6     5 0.38    -2.33 1.67
-------------------------------------------------------------------- 
: B
       var n mean   sd median trimmed  mad min max range skew kurtosis   se
Jogo*    1 3 2.00 0.00      2    2.00 0.00   2   2     0  NaN      NaN 0.00
Pais*    2 3 2.00 1.00      2    2.00 1.48   1   3     2 0.00    -2.33 0.58
Numero   3 3 3.33 3.21      2    3.33 1.48   1   7     6 0.34    -2.33 1.86

To solve this problem one way would be to put all list elements into one data frame with do.call() and rbind() and then write to file. This will make data frame where group names will be added before original variable names.

estatistica2<-do.call("rbind",estatistica)
estatistica2

         var n mean   sd median trimmed  mad min max range skew kurtosis   se
A.Jogo*    1 3 1.00 0.00    1.0    1.00 0.00   1   1     0  NaN      NaN 0.00
A.Pais*    2 3 2.00 1.00    2.0    2.00 1.48   1   3     2 0.00    -2.33 0.58
A.Numero   3 3 2.67 2.89    1.0    2.67 0.00   1   6     5 0.38    -2.33 1.67
B.Jogo*    1 3 2.00 0.00    2.0    2.00 0.00   2   2     0  NaN      NaN 0.00
B.Pais*    2 3 2.00 1.00    2.0    2.00 1.48   1   3     2 0.00    -2.33 0.58
B.Numero   3 3 3.33 3.21    2.0    3.33 1.48   1   7     6 0.34    -2.33 1.86
C.Jogo*    1 2 3.00 0.00    3.0    3.00 0.00   3   3     0  NaN      NaN 0.00
C.Pais*    2 2 2.50 0.71    2.5    2.50 0.74   2   3     1 0.00    -2.75 0.50
C.Numero   3 2 1.50 0.71    1.5    1.50 0.74   1   2     1 0.00    -2.75 0.50
D.Jogo*    1 2 4.00 0.00    4.0    4.00 0.00   4   4     0  NaN      NaN 0.00
D.Pais*    2 2 2.50 0.71    2.5    2.50 0.74   2   3     1 0.00    -2.75 0.50
D.Numero   3 2 3.50 0.71    3.5    3.50 0.74   3   4     1 0.00    -2.75 0.50

Selecting factor loadings above threshold in R

After consulting various online sources I have made the following changes in my function. So now it incrementally writes the output from the loop.

write.factors <- function(loadings, cutoff_p = 0.295, cutoff_n = -0.295, file_name = "factors.csv"){
  f <- data.frame(unclass(loadings))
  sink(file_name)
  for(c in 1:ncol(f)){
    variables <- rownames(f)
    ff <- data.frame(variables, f[,c])
    colnames(ff)[2] <- colnames(f)[c]
    nd <- subset(ff, ff[,2] > cutoff_p | ff[,2] < cutoff_n)
    nd <- droplevels(nd)
    write.csv(nd)
    cat('____________________________')
    cat('\n')
  }
  sink()
}
write.factors(fa$loadings)