Description Usage Arguments Details Value Note References Examples

calculating the NOEC and LOEC using Dunnett's test

1 |

`x` |
a numeric vector of experimental concentrations |

`rspn` |
a numeric matrix of experimental responses with at least three replicates. |

`blankC` |
TRUE if rspn contains responses of blank control. The default is FALSE. |

`sigLev` |
the significance level for Dunnett's test. The default is 0.05. |

`alternertive` |
the alternative hypothesis: "U"=upper one-sided test; "B"=two-sided test(default). |

Dunnett's test (Dunnett, 1964) is performed to compare the treatment groups with the
blank controls. The critical constants (store in DTcv) were calculated using step-down
Dunnett test procedure. Three significance level (0.01, 0.05, and 0.1) are supported.
## Q: One dataset has four blank controls (C1, C2, C3, C4) and one treatment has three
replicates (T1, T2, T3),
## another treatment has five replicates (R1, R2, R3, R4, R5), how to arrange the
response matrix (rspn)?
## A: Label the missing values as NA, the response matrix (rspn) can be arranged as follows:

C1 C2 C3 C4 NA

T1 T2 T3 NA NA

R1 R2 R3 R4 R5

The adjustation of critical value for the unequal variances or unequal number of control and replicates is skipped in this program.

`mat ` |
information on Dunnett's test.DT: Dunnett's test values; DTcv: critical values for Dunnett's test at the significance level of sigLev. |

`noec ` |
non-observed effect concentration (NOEC). |

`loec ` |
least-observed effect concentration (LOEC). |

`sigLev ` |
the significance level used in the Dunnett's test. |

`DF ` |
the number of treatments and degree of fredom. |

x a vector of concentrations or levels in an ascending order. response matrix with at least 3 replicates. if the response matrix (rspn) contains blank controls (blankC = TRUE), the blank controls should be allocated in the first tow of rspn matrix. missing values should be labled as NA.

Dunnett, C.W., 1964. New tables for multiple comparisons with a control. Biometrics 30, 482-491.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## example 1
# calcualte the NOEC and LOEC of heavy metal Ni(2+) on the MCF-7 cells at the default significance
# level of 0.05
x <- cytotox$Ni$x
rspn <- cytotox$Ni$y
NOEC(x, rspn)
## example 2
# calcualte the NOEC and LOEC of Neomycin sulfate on the phtotobacteria at the significance
# level of 0.01
x <- antibiotox$NEO$x
rspn <- antibiotox$NEO$y
NOEC(x, rspn, sigLev = 0.01)
``` |

```
$mat
C/Level t critical_value sign
[1,] 3.07119e-06 -0.4008349 2.978 -1
[2,] 4.29966e-06 2.5602677 2.978 -1
[3,] 6.55186e-06 4.9506225 2.978 1
[4,] 9.41830e-06 7.0285893 2.978 1
[5,] 1.39227e-05 9.5968564 2.978 1
[6,] 2.04746e-05 15.9844908 2.978 1
[7,] 3.07119e-05 18.5833842 2.978 1
[8,] 4.29966e-05 23.6243508 2.978 1
[9,] 6.55186e-05 29.1524483 2.978 1
[10,] 9.41830e-05 31.7965624 2.978 1
[11,] 1.39227e-04 32.9589216 2.978 1
[12,] 2.04746e-04 33.6529088 2.978 1
$noec
[1] 4.29966e-06
$loec
[1] 6.55186e-06
$sigLev
[1] 0.05
$DF
[1] 12 26
$mat
C/Level t critical_value sign
[1,] 1.577e-08 0.509263 3.672 -1
[2,] 2.253e-08 1.589741 3.672 -1
[3,] 3.466e-08 2.099806 3.672 -1
[4,] 5.545e-08 2.432897 3.672 -1
[5,] 8.491e-08 2.981837 3.672 -1
[6,] 1.300e-07 3.532144 3.672 -1
[7,] 2.079e-07 7.955151 3.672 1
[8,] 3.119e-07 9.729954 3.672 1
[9,] 4.679e-07 14.549435 3.672 1
[10,] 7.278e-07 20.694929 3.672 1
[11,] 1.126e-06 34.006070 3.672 1
[12,] 1.733e-06 42.948748 3.672 1
$noec
[1] 1.3e-07
$loec
[1] 2.079e-07
$sigLev
[1] 0.01
$DF
[1] 12 26
```

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