imputeBDLs | R Documentation |

Parametric replacement of rounded zeros for compositional data using classical and robust methods based on ilr coordinates with a special choice of balances.

```
imputeBDLs(
x,
maxit = 10,
eps = 0.1,
method = "subPLS",
dl = rep(0.05, ncol(x)),
variation = TRUE,
nPred = NULL,
nComp = "boot",
bruteforce = FALSE,
noisemethod = "residuals",
noise = FALSE,
R = 10,
correction = "normal",
verbose = FALSE,
test = FALSE
)
adjustImputed(xImp, xOrig, wind)
checkData(x, dl)
## S3 method for class 'replaced'
print(x, ...)
```

`x` |
data.frame or matrix |

`maxit` |
maximum number of iterations |

`eps` |
convergency criteria |

`method` |
either "lm", "lmrob" or "pls" |

`dl` |
Detection limit for each variable. zero for variables with variables that have no detection limit problems. |

`variation, ` |
if TRUE those predictors are chosen in each step, who's variation is lowest to the predictor. |

`nPred, ` |
if determined and variation equals TRUE, it fixes the number of predictors |

`nComp` |
if determined, it fixes the number of pls components. If “boot”, the number of pls components are estimated using a bootstraped cross validation approach. |

`bruteforce` |
sets imputed values above the detection limit to the detection limit. Replacement above the detection limit are only exeptionally occur due to numerical instabilities. The default is FALSE! |

`noisemethod` |
adding noise to imputed values. Experimental |

`noise` |
TRUE to activate noise (experimental) |

`R` |
number of bootstrap samples for the determination of pls components. Only important for method “pls”. |

`correction` |
normal or density |

`verbose` |
additional print output during calculations. |

`test` |
an internal test situation (this parameter will be deleted soon) |

`xImp` |
imputed data set |

`xOrig` |
original data set |

`wind` |
index matrix of rounded zeros |

`...` |
further arguments passed through the print function |

Statistical analysis of compositional data including zeros runs into problems, because log-ratios cannot be applied. Usually, rounded zeros are considerer as missing not at random missing values.

The algorithm iteratively imputes parts with rounded zeros whereas in each step (1) compositional data are expressed in pivot coordinates (2) tobit regression is applied (3) the rounded zeros are replaced by the expected values (4) the corresponding inverse ilr mapping is applied. After all parts are imputed, the algorithm starts again until the imputations do not change.

`x ` |
imputed data |

`criteria ` |
change between last and second last iteration |

`iter ` |
number of iterations |

```
maxit
``` |
maximum number of iterations |

`wind` |
index of zeros |

`nComp` |
number of components for method pls |

`method` |
chosen method |

Matthias Templ, method subPLS from Jiajia Chen

Templ, M., Hron, K., Filzmoser, P., Gardlo, A. (2016).
Imputation of rounded zeros for high-dimensional compositional data.
*Chemometrics and Intelligent Laboratory Systems*, 155, 183-190.

Chen, J., Zhang, X., Hron, K., Templ, M., Li, S. (2018).
Regression imputation with Q-mode clustering for rounded zero replacement in high-dimensional compositional data.
*Journal of Applied Statistics*, 45 (11), 2067-2080.

`imputeBDLs`

```
p <- 10
n <- 50
k <- 2
T <- matrix(rnorm(n*k), ncol=k)
B <- matrix(runif(p*k,-1,1),ncol=k)
X <- T %*% t(B)
E <- matrix(rnorm(n*p, 0,0.1), ncol=p)
XE <- X + E
data <- data.frame(pivotCoordInv(XE))
col <- ncol(data)
row <- nrow(data)
DL <- matrix(rep(0),ncol=col,nrow=1)
for(j in seq(1,col,2))
{DL[j] <- quantile(data[,j],probs=0.06,na.rm=FALSE)}
for(j in 1:col){
data[data[,j]<DL[j],j] <- 0
}
## Not run:
# under dontrun because of long exectution time
imp <- imputeBDLs(data,dl=DL,maxit=10,eps=0.1,R=10,method="subPLS")
imp
imp <- imputeBDLs(data,dl=DL,maxit=10,eps=0.1,R=10,method="pls", variation = FALSE)
imp
imp <- imputeBDLs(data,dl=DL,maxit=10,eps=0.1,R=10,method="lm")
imp
imp <- imputeBDLs(data,dl=DL,maxit=10,eps=0.1,R=10,method="lmrob")
imp
data(mcad)
## generate rounded zeros artificially:
x <- mcad
x <- x[1:25, 2:ncol(x)]
dl <- apply(x, 2, quantile, 0.1)
for(i in seq(1, ncol(x), 2)){
x[x[,i] < dl[i], i] <- 0
}
ni <- sum(x==0, na.rm=TRUE)
ni/(ncol(x)*nrow(x)) * 100
dl[seq(2, ncol(x), 2)] <- 0
replaced_lm <- imputeBDLs(x, dl=dl, eps=1, method="lm",
verbose=FALSE, R=50, variation=TRUE)$x
replaced_lmrob <- imputeBDLs(x, dl=dl, eps=1, method="lmrob",
verbose=FALSE, R=50, variation=TRUE)$x
replaced_plsfull <- imputeBDLs(x, dl=dl, eps=1,
method="pls", verbose=FALSE, R=50,
variation=FALSE)$x
## End(Not run)
```

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