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For following code performing principal component analysis:

> prcomp(iris[1:4], scale=TRUE)
Standard deviations:
[1] 1.7083611 0.9560494 0.3830886 0.1439265

Rotation:
                    PC1         PC2        PC3        PC4
Sepal.Length  0.5210659 -0.37741762  0.7195664  0.2612863
Sepal.Width  -0.2693474 -0.92329566 -0.2443818 -0.1235096
Petal.Length  0.5804131 -0.02449161 -0.1421264 -0.8014492
Petal.Width   0.5648565 -0.06694199 -0.6342727  0.5235971

Will the standard biplot() in R be plotted for Rotations or for Loadings (rotation values multiplied by standard deviation for that component - also seen in above output)? (see comments by @amoeba on Conclusions from output of a principal component analysisConclusions from output of a principal component analysis)

For factor analysis, I presume it will be Loadings:

> factanal(mtcars,2)

Call:
factanal(x = mtcars, factors = 2)

Uniquenesses:
  mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb 
0.167 0.070 0.096 0.143 0.298 0.168 0.150 0.256 0.171 0.246 0.386 

Loadings:
     Factor1 Factor2
mpg   0.686  -0.602 
cyl  -0.629   0.731 
disp -0.730   0.609 
hp   -0.337   0.862 
drat  0.807  -0.225 
wt   -0.810   0.420 
qsec -0.162  -0.908 
vs    0.291  -0.812 
am    0.907         
gear  0.860   0.125 
carb          0.783 

               Factor1 Factor2
SS loadings      4.494   4.357
Proportion Var   0.409   0.396
Cumulative Var   0.409   0.805

Test of the hypothesis that 2 factors are sufficient.
The chi square statistic is 68.57 on 34 degrees of freedom.
The p-value is 0.000405

For following code performing principal component analysis:

> prcomp(iris[1:4], scale=TRUE)
Standard deviations:
[1] 1.7083611 0.9560494 0.3830886 0.1439265

Rotation:
                    PC1         PC2        PC3        PC4
Sepal.Length  0.5210659 -0.37741762  0.7195664  0.2612863
Sepal.Width  -0.2693474 -0.92329566 -0.2443818 -0.1235096
Petal.Length  0.5804131 -0.02449161 -0.1421264 -0.8014492
Petal.Width   0.5648565 -0.06694199 -0.6342727  0.5235971

Will the standard biplot() in R be plotted for Rotations or for Loadings (rotation values multiplied by standard deviation for that component - also seen in above output)? (see comments by @amoeba on Conclusions from output of a principal component analysis)

For factor analysis, I presume it will be Loadings:

> factanal(mtcars,2)

Call:
factanal(x = mtcars, factors = 2)

Uniquenesses:
  mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb 
0.167 0.070 0.096 0.143 0.298 0.168 0.150 0.256 0.171 0.246 0.386 

Loadings:
     Factor1 Factor2
mpg   0.686  -0.602 
cyl  -0.629   0.731 
disp -0.730   0.609 
hp   -0.337   0.862 
drat  0.807  -0.225 
wt   -0.810   0.420 
qsec -0.162  -0.908 
vs    0.291  -0.812 
am    0.907         
gear  0.860   0.125 
carb          0.783 

               Factor1 Factor2
SS loadings      4.494   4.357
Proportion Var   0.409   0.396
Cumulative Var   0.409   0.805

Test of the hypothesis that 2 factors are sufficient.
The chi square statistic is 68.57 on 34 degrees of freedom.
The p-value is 0.000405

For following code performing principal component analysis:

> prcomp(iris[1:4], scale=TRUE)
Standard deviations:
[1] 1.7083611 0.9560494 0.3830886 0.1439265

Rotation:
                    PC1         PC2        PC3        PC4
Sepal.Length  0.5210659 -0.37741762  0.7195664  0.2612863
Sepal.Width  -0.2693474 -0.92329566 -0.2443818 -0.1235096
Petal.Length  0.5804131 -0.02449161 -0.1421264 -0.8014492
Petal.Width   0.5648565 -0.06694199 -0.6342727  0.5235971

Will the standard biplot() in R be plotted for Rotations or for Loadings (rotation values multiplied by standard deviation for that component - also seen in above output)? (see comments by @amoeba on Conclusions from output of a principal component analysis)

For factor analysis, I presume it will be Loadings:

> factanal(mtcars,2)

Call:
factanal(x = mtcars, factors = 2)

Uniquenesses:
  mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb 
0.167 0.070 0.096 0.143 0.298 0.168 0.150 0.256 0.171 0.246 0.386 

Loadings:
     Factor1 Factor2
mpg   0.686  -0.602 
cyl  -0.629   0.731 
disp -0.730   0.609 
hp   -0.337   0.862 
drat  0.807  -0.225 
wt   -0.810   0.420 
qsec -0.162  -0.908 
vs    0.291  -0.812 
am    0.907         
gear  0.860   0.125 
carb          0.783 

               Factor1 Factor2
SS loadings      4.494   4.357
Proportion Var   0.409   0.396
Cumulative Var   0.409   0.805

Test of the hypothesis that 2 factors are sufficient.
The chi square statistic is 68.57 on 34 degrees of freedom.
The p-value is 0.000405
Post Closed as "Duplicate" by amoeba, kjetil b halvorsen♦, gung - Reinstate Monica, John, Glen_b
clarified the question, based on comments
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amoeba
amoeba
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Biplot to plot Does biplot() function in R use rotations or loadings to plot arrows?

For following code performing principal component analysis:

> prcomp(iris[1:4], scale=TRUE)
Standard deviations:
[1] 1.7083611 0.9560494 0.3830886 0.1439265

Rotation:
                    PC1         PC2        PC3        PC4
Sepal.Length  0.5210659 -0.37741762  0.7195664  0.2612863
Sepal.Width  -0.2693474 -0.92329566 -0.2443818 -0.1235096
Petal.Length  0.5804131 -0.02449161 -0.1421264 -0.8014492
Petal.Width   0.5648565 -0.06694199 -0.6342727  0.5235971

Will the standard biplotbiplot() in R be plotted for Rotations or for Loadings (rotation values multiplied by standard deviation for that component  - also seen in above output)? (see comments by @amoeba on Conclusions from output of a principal component analysis)

For factor analysis, I presume it will be Loadings:

> factanal(mtcars,2)

Call:
factanal(x = mtcars, factors = 2)

Uniquenesses:
  mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb 
0.167 0.070 0.096 0.143 0.298 0.168 0.150 0.256 0.171 0.246 0.386 

Loadings:
     Factor1 Factor2
mpg   0.686  -0.602 
cyl  -0.629   0.731 
disp -0.730   0.609 
hp   -0.337   0.862 
drat  0.807  -0.225 
wt   -0.810   0.420 
qsec -0.162  -0.908 
vs    0.291  -0.812 
am    0.907         
gear  0.860   0.125 
carb          0.783 

               Factor1 Factor2
SS loadings      4.494   4.357
Proportion Var   0.409   0.396
Cumulative Var   0.409   0.805

Test of the hypothesis that 2 factors are sufficient.
The chi square statistic is 68.57 on 34 degrees of freedom.
The p-value is 0.000405

Biplot to plot rotations or loadings?

For following code performing principal component analysis:

> prcomp(iris[1:4], scale=TRUE)
Standard deviations:
[1] 1.7083611 0.9560494 0.3830886 0.1439265

Rotation:
                    PC1         PC2        PC3        PC4
Sepal.Length  0.5210659 -0.37741762  0.7195664  0.2612863
Sepal.Width  -0.2693474 -0.92329566 -0.2443818 -0.1235096
Petal.Length  0.5804131 -0.02449161 -0.1421264 -0.8014492
Petal.Width   0.5648565 -0.06694199 -0.6342727  0.5235971

Will the standard biplot be plotted for Rotations or for Loadings (rotation values multiplied by standard deviation for that component- also seen in above output)? (see comments by @amoeba on Conclusions from output of a principal component analysis)

For factor analysis, I presume it will be Loadings:

> factanal(mtcars,2)

Call:
factanal(x = mtcars, factors = 2)

Uniquenesses:
  mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb 
0.167 0.070 0.096 0.143 0.298 0.168 0.150 0.256 0.171 0.246 0.386 

Loadings:
     Factor1 Factor2
mpg   0.686  -0.602 
cyl  -0.629   0.731 
disp -0.730   0.609 
hp   -0.337   0.862 
drat  0.807  -0.225 
wt   -0.810   0.420 
qsec -0.162  -0.908 
vs    0.291  -0.812 
am    0.907         
gear  0.860   0.125 
carb          0.783 

               Factor1 Factor2
SS loadings      4.494   4.357
Proportion Var   0.409   0.396
Cumulative Var   0.409   0.805

Test of the hypothesis that 2 factors are sufficient.
The chi square statistic is 68.57 on 34 degrees of freedom.
The p-value is 0.000405

Does biplot() function in R use rotations or loadings to plot arrows?

For following code performing principal component analysis:

> prcomp(iris[1:4], scale=TRUE)
Standard deviations:
[1] 1.7083611 0.9560494 0.3830886 0.1439265

Rotation:
                    PC1         PC2        PC3        PC4
Sepal.Length  0.5210659 -0.37741762  0.7195664  0.2612863
Sepal.Width  -0.2693474 -0.92329566 -0.2443818 -0.1235096
Petal.Length  0.5804131 -0.02449161 -0.1421264 -0.8014492
Petal.Width   0.5648565 -0.06694199 -0.6342727  0.5235971

Will the standard biplot() in R be plotted for Rotations or for Loadings (rotation values multiplied by standard deviation for that component  - also seen in above output)? (see comments by @amoeba on Conclusions from output of a principal component analysis)

For factor analysis, I presume it will be Loadings:

> factanal(mtcars,2)

Call:
factanal(x = mtcars, factors = 2)

Uniquenesses:
  mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb 
0.167 0.070 0.096 0.143 0.298 0.168 0.150 0.256 0.171 0.246 0.386 

Loadings:
     Factor1 Factor2
mpg   0.686  -0.602 
cyl  -0.629   0.731 
disp -0.730   0.609 
hp   -0.337   0.862 
drat  0.807  -0.225 
wt   -0.810   0.420 
qsec -0.162  -0.908 
vs    0.291  -0.812 
am    0.907         
gear  0.860   0.125 
carb          0.783 

               Factor1 Factor2
SS loadings      4.494   4.357
Proportion Var   0.409   0.396
Cumulative Var   0.409   0.805

Test of the hypothesis that 2 factors are sufficient.
The chi square statistic is 68.57 on 34 degrees of freedom.
The p-value is 0.000405
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rnso
rnso
  • 10.2k
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  • 123

Biplot to plot rotations or loadings?

For following code performing principal component analysis:

> prcomp(iris[1:4], scale=TRUE)
Standard deviations:
[1] 1.7083611 0.9560494 0.3830886 0.1439265

Rotation:
                    PC1         PC2        PC3        PC4
Sepal.Length  0.5210659 -0.37741762  0.7195664  0.2612863
Sepal.Width  -0.2693474 -0.92329566 -0.2443818 -0.1235096
Petal.Length  0.5804131 -0.02449161 -0.1421264 -0.8014492
Petal.Width   0.5648565 -0.06694199 -0.6342727  0.5235971

Will the standard biplot be plotted for Rotations or for Loadings (rotation values multiplied by standard deviation for that component- also seen in above output)? (see comments by @amoeba on Conclusions from output of a principal component analysis)

For factor analysis, I presume it will be Loadings:

> factanal(mtcars,2)

Call:
factanal(x = mtcars, factors = 2)

Uniquenesses:
  mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb 
0.167 0.070 0.096 0.143 0.298 0.168 0.150 0.256 0.171 0.246 0.386 

Loadings:
     Factor1 Factor2
mpg   0.686  -0.602 
cyl  -0.629   0.731 
disp -0.730   0.609 
hp   -0.337   0.862 
drat  0.807  -0.225 
wt   -0.810   0.420 
qsec -0.162  -0.908 
vs    0.291  -0.812 
am    0.907         
gear  0.860   0.125 
carb          0.783 

               Factor1 Factor2
SS loadings      4.494   4.357
Proportion Var   0.409   0.396
Cumulative Var   0.409   0.805

Test of the hypothesis that 2 factors are sufficient.
The chi square statistic is 68.57 on 34 degrees of freedom.
The p-value is 0.000405