New to component analysis -- FAMD(df) in R -- "Error : arguments must have same length"

[Omkar Kulkarni]

Hello, 

I am new to the component analysis and with my basic understanding I could gather, 

if we have both 'continuous' and 'categorical' variables we can use Factor analysis of Mixed Data. 

In my case, I have a dataset with 31 variables. 

14 -- continuous

17 -- categorical. 

My ultimate goal is to predict G3 -- a  continuous variable. 

But using FAMD in R, I wanted to have an exploratory analysis. 

1. Whats the mistake I am using in using FAMD() in R. Please see below for output. 

2. Is FAMD a good/easy method for exploratory analysis in the first place? If no, what is? 

Error

> FAMD(student.n, graph = TRUE)

Error in aggregate.data.frame(as.data.frame(x), ...) : 

  arguments must have same length

In addition: Warning messages:

1: In pca$var$coord[ind.quali, , drop = FALSE]/sqrt(prop) :

  longer object length is not a multiple of shorter object length

2: In coord.quali.var^2/dist2 :

  longer object length is not a multiple of shorter object length

3: In vtest/sqrt(nombre) :

  longer object length is not a multiple of shorter object length

> 

Summary of dataframe

str(student.n)

'data.frame': 395 obs. of  31 variables:

 $ school    : Factor w/ 2 levels "GP","MS": 1 1 1 1 1 1 1 1 1 1 ...

 $ sex       : Factor w/ 2 levels "F","M": 1 1 1 1 1 2 2 1 2 2 ...

 $ age       : int  18 17 15 15 16 16 16 17 15 15 ...

 $ address   : Factor w/ 2 levels "R","U": 2 2 2 2 2 2 2 2 2 2 ...

 $ famsize   : Factor w/ 2 levels "GT3","LE3": 1 1 2 1 1 2 2 1 2 1 ...

 $ Pstatus   : Factor w/ 2 levels "A","T": 1 2 2 2 2 2 2 1 1 2 ...

 $ Medu      : int  4 1 1 4 3 4 2 4 3 3 ...

 $ Fedu      : int  4 1 1 2 3 3 2 4 2 4 ...

 $ Mjob      : Factor w/ 5 levels "at_home","health",..: 1 1 1 2 3 4 3 3 4 3 ...

 $ Fjob      : Factor w/ 5 levels "at_home","health",..: 5 3 3 4 3 3 3 5 3 3 ...

 $ reason    : Factor w/ 4 levels "course","home",..: 1 1 3 2 2 4 2 2 2 2 ...

 $ guardian  : Factor w/ 3 levels "father","mother",..: 2 1 2 2 1 2 2 2 2 2 ...

 $ traveltime: int  2 1 1 1 1 1 1 2 1 1 ...

 $ studytime : int  2 2 2 3 2 2 2 2 2 2 ...

 $ failures  : int  0 0 3 0 0 0 0 0 0 0 ...

 $ schoolsup : Factor w/ 2 levels "no","yes": 2 1 2 1 1 1 1 2 1 1 ...

 $ famsup    : Factor w/ 2 levels "no","yes": 1 2 1 2 2 2 1 2 2 2 ...

 $ paid      : Factor w/ 2 levels "no","yes": 1 1 2 2 2 2 1 1 2 2 ...

 $ activities: Factor w/ 2 levels "no","yes": 1 1 1 2 1 2 1 1 1 2 ...

 $ nursery   : Factor w/ 2 levels "no","yes": 2 1 2 2 2 2 2 2 2 2 ...

 $ higher    : Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 2 2 2 2 ...

 $ internet  : Factor w/ 2 levels "no","yes": 1 2 2 2 1 2 2 1 2 2 ...

 $ romantic  : Factor w/ 2 levels "no","yes": 1 1 1 2 1 1 1 1 1 1 ...

 $ famrel    : int  4 5 4 3 4 5 4 4 4 5 ...

 $ freetime  : int  3 3 3 2 3 4 4 1 2 5 ...

 $ goout     : int  4 3 2 2 2 2 4 4 2 1 ...

 $ Dalc      : int  1 1 2 1 1 1 1 1 1 1 ...

 $ Walc      : int  1 1 3 1 2 2 1 1 1 1 ...

 $ health    : int  3 3 3 5 5 5 3 1 1 5 ...

 $ absences  : int  6 4 10 2 4 10 0 6 0 0 ...

 $ G3        : int  6 6 10 15 10 15 11 6 19 15 ...

Cheers,

Omkar 

[François Husson]

Hi

1) It is difficult to see your problem. Can you send me your dataset and the lines of code you use.
2) Yes FAMd is nice to explore a data set with both continuous and categorical variables.

FH