# Computes representative normalized RAD of a group of normalized RADs.

### Description

Computes representative normalized RAD of a group of normalized RADs.

### Usage

1 2 | ```
representative_RAD(norm_rad, sample_ids = NULL, plot = F, min_rank = 1,
confidence = 0.95, with_conf = TRUE, ...)
``` |

### Arguments

`norm_rad` |
A matrix which contains the normalized RADs (samples in rows). |

`sample_ids` |
Vector of row numbers of the desired group, from which a representative RAD is going to be produced. |

`plot` |
A logical. If |

`min_rank` |
The minimum rank to be considered for making repRADs. |

`confidence` |
Confidence interval of plotted repRAD. Default is 0.9. |

`with_conf` |
A logical. If |

`...` |
Other graphical parameters to use for plotting. This function uses internally the
functions |

### Value

A list of following parameters:

$average: Contains a vector of length equal to the columns of `norm_rad`

. This in the representative normalized RAD which is
the average of normalized RAD of the group.

$population_sd: A vector of length equal to the columns of `norm_rad`

which contains the standard deviation
for each rank.

$standard_error: A vector of length equal to the columns of `norm_rad`

which contains the standard deviation
of the mean for each rank. This vector is the result of `population_sd / sqrt(n)`

,
when n is the number of members of the group (length of `sample_ids`

).

If `plot = TRUE`

, plot of the repRAD is produced and would be added to the previous plot.

If `with_conf = TRUE`

, confidence interval would be added to the repRAD plot.

### See Also

`RADnormalization`

for normalize an abundance vector. This function return more details compared to `RADnormalization_matrix`

,
`RADnormalization_matrix`

for normalize an entire otutable,
`representative_point`

for study the representative of groups of samples in a multi-dimensional scaling plot,

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ```
line_cols <- c("green","red","blue")
sample_classes <- c(1,1,1,1,2,2,3,3,1,1,2,3,3,1,1,2,3,3)
maxrank <- 400
data("gut_nrads")
nrads <- gut_nrads
nrads <- nrads$norm_matrix
#plot nrads
plot(1e10,xlim = c(1,maxrank),ylim = c(2e-5,1),log="xy",
xlab = "rank",ylab = "abundance",cex.lab = 1.5,axes = FALSE)
sfsmisc::eaxis(side = 1,at = c(1,10,100,1000,10000))
sfsmisc::eaxis(side = 2,at = c(1e-4,1e-3,1e-2,1e-1,1),las = 0)
for(i in 1:nrow(nrads)){
points(nrads[i,],type = 'l',col = line_cols[sample_classes[i]],lwd = 0.8)
}
#plot confidence intervals of representative nrads
a <- representative_RAD(norm_rad = nrads,sample_ids = which(sample_classes == 1),
plot = TRUE,confidence = 0.9,with_conf = TRUE,
col = scales::alpha(line_cols[1],0.5),border = NA)
a <- representative_RAD(norm_rad = nrads,sample_ids = which(sample_classes == 2),
plot = TRUE,confidence = 0.9,with_conf = TRUE,
col = scales::alpha(line_cols[2],0.5),border = NA)
a <- representative_RAD(norm_rad = nrads,sample_ids = which(sample_classes == 3),
plot = TRUE,confidence = 0.9,with_conf = TRUE,
col = scales::alpha(line_cols[3],0.5),border = NA)
#plot representative nrads
a <- representative_RAD(norm_rad = nrads,sample_ids = which(sample_classes == 1),
plot = TRUE,with_conf = FALSE,
col = scales::alpha(line_cols[1],0.8),lwd = 4)
a <- representative_RAD(norm_rad = nrads,sample_ids = which(sample_classes == 2),
plot = TRUE,with_conf = FALSE,
col = scales::alpha(line_cols[2],0.8),lwd = 4)
a <- representative_RAD(norm_rad = nrads,sample_ids = which(sample_classes == 3),
plot = TRUE,with_conf = FALSE,
col = scales::alpha(line_cols[3],0.8),lwd = 4)
legend("bottomleft",bty = "n",legend = c("pre Cp","under Cp","post Cp"),
col = line_cols,lwd = 3)
``` |