predict.yai returns NAs for the observed values

[candelas762]

I do not understand the behaviour of the function predict.yai when I apply it to new data. It returns NAs for the observed values. Why is this happening?

I made a reroducible example based on the MoscowMtJoe data.

library(yaImpute)
data("MoscowMtStJoe")
x <- MoscowMtStJoe[, c("EASTING", "NORTHING", "ELEVMEAN",
                       "SLPMEAN", "ASPMEAN", "INTMEAN", "HTMEAN", "CCMEAN")]
x[, 5] <- (1 - cos((x[, 5] - 30) * pi/180))/2
names(x)[5] = "TrASP"
y <- MoscowMtStJoe[, c(1, 9, 12, 14, 18)]

# Create yai object
mal <- yai(x = x, y = y, method = "mahalanobis")

# Create a new dataset with 30 observations based on original data but adding a bit of variance
random = sample(rownames(x), nrow(x))

nd.y = y[random,] +runif(nrow(x), min = 0.1, max = 0.5)
nd.x = x[random,] +runif(nrow(x), min = 0.1, max = 0.5)

nd = cbind(nd.y, nd.x)[1:30,]
rownames(nd) = paste0("n",1:nrow(nd))

# predict
p = predict.yai(object = mal, newdata = nd, observed = T)

head(p)    # predicted
>      ABGR_BA PIPO_BA   PSME_BA     THPL_BA    Total_BA ABGR_BA.o PIPO_BA.o PSME_BA.o THPL_BA.o Total_BA.o
> n1 12.938903       0  1.201908  31.5207887  59.9378177        NA        NA        NA        NA         NA
> n2  2.447804       0 11.993483  30.8633313  56.4527989        NA        NA        NA        NA         NA
> n3  0.000000       0  0.000000   0.3806958   1.4782311        NA        NA        NA        NA         NA
> n4  0.000000       0  0.000000   0.0000000   0.3383963        NA        NA        NA        NA         NA
> n5  0.000000       0  6.746213   0.0000000   7.1332287        NA        NA        NA        NA         NA
> n6 10.357833       0  0.000000 244.8680129 255.4436635        NA        NA        NA        NA         NA

head(nd[,1:5]) # observed
>      ABGR_BA   PIPO_BA    PSME_BA     THPL_BA    Total_BA
> n1 13.2327991 0.2938965  1.4958040  31.8146852  60.2317142
> n2  2.6028360 0.1550319 12.1485148  31.0183632  56.6078308
> n3  0.2097972 0.2097972  0.2097972   0.5904930   1.6880283
> n4  0.1568212 0.1568212  0.1568212   0.1568212   0.4952175
> n5  0.4952800 0.4952800  7.2414930   0.4952800   7.6285087
> n6 10.6680486 0.3102155  0.3102155 245.1782284 255.7538790