linear disriminant function error - arguments must be same length - r

Question

My example dataset:
year <- c("2002","2002","2002","2004","2005","2005","2005","2006", "2006")
FA1 <- c(0.7975030, 1.5032768, 0.8805000, 1.0505961, 1.1379715, 1.1334510, 1.1359434, 0.9614926, 1.2631387)
FA2 <- c(0.7930153, 1.2862355, 0.5633592, 1.0396431, 0.9446277, 1.1944455, 1.086171, 0.767955, 1.2385361)
FA3 <- c(-0.7825210, 0.56415672, -0.9294417, 0.21485071, -0.447953,0.037978, 0.038363, -0.495383, 0.509704)
FA4 <- c(0.38829957,0.34638035,-0.06783007, 0.505020, 0.3158221,0.55505411, 0.42822783, 0.36399347, 0.51352115)
df <- data.frame(year,FA1,FA2,FA3,FA4)
I then select the data I want to use and run a DFA
library(magrittr)
library(DiscriMiner)
yeardf <- df[df$year %in% c(2002, 2005, 2006),]
yeardfd <- linDA(yeardf[,2:4],yeardf$year, validation = "crossval")
But now i get an error telling me the arguments are different lengths.
"Error in table(original = y[test], predicted = pred_class) :
all arguments must have the same length"
I looked at
length(yeardf$year)
dim(yeardf)
And it looks like they are the same.
I also checked for spelling mistakes as that seems to cause this error sometimes.
following up on answer.
The suggested answer works on my example data (which does give me the same error), but I can't quite make it work on my real code.
I first apply the transformation to selected columns in my data.frame. And then I combine the transformed columns with the variables I want to use as groups in my DFA
library(robCompositions)
tFA19 <- cenLR(fadata.PIZ[names(FA19)])[1]
tFA19 <- cbind(fadata.PIZ[1:16],tFA19)
So I think creating my data.frame this way must be leading to my error. I tried to insert stringsAsFactors into my cbind statement, but no luck.

Answer 1

You need ,stringsAsFactors = FALSE in data.frame:
year <- c("2002","2002","2002","2004","2005","2005","2005","2006", "2006")
FA1 <- c(0.7975030, 1.5032768, 0.8805000, 1.0505961, 1.1379715, 1.1334510, 1.1359434, 0.9614926, 1.2631387)
FA2 <- c(0.7930153, 1.2862355, 0.5633592, 1.0396431, 0.9446277, 1.1944455, 1.086171, 0.767955, 1.2385361)
FA3 <- c(-0.7825210, 0.56415672, -0.9294417, 0.21485071, -0.447953,0.037978, 0.038363, -0.495383, 0.509704)
FA4 <- c(0.38829957,0.34638035,-0.06783007, 0.505020, 0.3158221,0.55505411, 0.42822783, 0.36399347, 0.51352115)
df <- data.frame(year,FA1,FA2,FA3,FA4,stringsAsFactors = FALSE)
library(magrittr)
library(DiscriMiner)
yeardf <- df[df$year %in% c(2002, 2005, 2006),]
yeardfd <- linDA(yeardf[,2:4],yeardf$year, validation = "crossval")
yeardfd
Linear Discriminant Analysis
-------------------------------------------
$functions discrimination functions
$confusion confusion matrix
$scores discriminant scores
$classification assigned class
$error_rate error rate
-------------------------------------------
$functions
2002 2005 2006
constant -345 -371 -305
FA1 228 231 213
...