Description Usage Arguments Value See Also Examples

View source: R/normalization.R

Normalizes an abundance vector to the desired number of ranks.

1 2 | ```
RADnormalization(input, max_rank, average_over = 1, min_rank = 1,
labels = FALSE, count_data = TRUE, method = "upperlimit")
``` |

`input` |
A vector which contains the abundance values (an abundance vector). |

`max_rank` |
The desired rank to which this method normalizes the input. |

`average_over` |
Number of times, a normalized RAD is created and averaged to produce the result. |

`min_rank` |
The minimum rank to which this method normalizes the input. |

`labels` |
A logical. If |

`count_data` |
A logical. |

`method` |
Sets the stop criterion for normalization. This should be one of "lowerlimit", "middle" or "upperlimit". Method affects the final result. lowerlimit: Samples from species pool one by one, until reaches max_rank. middle: Samples from species pool with random size until the sampled vector has desired ranks (max_rank). upperlimit: Removes from species pool one by one, until reaches max_rank. |

A list of following items:

$norm_rad: Normalized RAD sum up to 1. If `labels = TRUE`

, it would also contain the labels.

$norm_rad_count: A matrix of `average_over`

rows and `max_rank`

columns.
Each row contains one normalized RAD. These normalized RADs are averaged and sum up to 1 in order to make norm_rad

$norm_rad_mean_sd: Standard deviation of the mean for all the ranks in `norm_rad`

.
This vector is created using the values in `norm_rad_count`

$inputs: A list which contains inputs used for creating normalized RADs.

`RADnormalization_matrix`

for normalize an entire otutable,
`representative_point`

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

for study the representative of group of norm rads.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
data("gut_otu_table")
rads <- gut_otu_table
original_rad <- sort(rads[1,],decreasing = TRUE)
#removing zeros
original_rad <- original_rad[original_rad > 0]
plot(original_rad,ylim = c(1,max(original_rad)),log = "xy", xlab = "Rank",ylab = "Abundance",
main = "RAD of first sample",pch = 19,type = "b",cex = 0.5)
print(paste("number of ranks present in the original rad is:",length(original_rad)))
norm_rad <- RADnormalization(input = rads[1,],max_rank = 500,average_over = 50)
points(x = norm_rad$norm_rad * sum(norm_rad$norm_rad_count[1,]) ,pch = 19,cex = 1, type = "l",
col = "blue",lwd = 4)
points(x = norm_rad$norm_rad_count[1,],pch = 19,cex = 1, type = "l",col = "red",lwd = 3)
points(x = norm_rad$norm_rad_count[2,],pch = 19,cex = 1, type = "l",col = "green",lwd = 3)
legend("bottomleft",legend = c("Original RAD","possible norm rad","possible norm rad",
paste("nrad averaged over 50 realizations, times",
sum(norm_rad$norm_rad_count[1,]))),
col = c("black","red","green","blue"),lwd = 2,bty = "n")
``` |

```
[1] "number of ranks present in the original rad is: 1116"
```

RADanalysis documentation built on May 2, 2019, 6:13 a.m.

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