View source: R/ttest.evaluate.core.R

ttest.evaluate.core | R Documentation |

Test difference between means of entire collection (EC) and core set (CS) for quantitative traits by Student's t test \insertCitestudent_probable_1908EvaluateCore.

ttest.evaluate.core(data, names, quantitative, selected)

`data` |
The data as a data frame object. The data frame should possess one row per individual and columns with the individual names and multiple trait/character data. |

`names` |
Name of column with the individual names as a character string |

`quantitative` |
Name of columns with the quantitative traits as a character vector. |

`selected` |
Character vector with the names of individuals selected in
core collection and present in the |

`Trait` |
The quantitative trait. |

`EC_Min` |
The minimum value of the trait in EC. |

`EC_Max` |
The maximum value of the trait in EC. |

`EC_Mean` |
The mean value of the trait in EC. |

`EC_SE` |
The standard error of the trait in EC. |

`CS_Min` |
The minimum value of the trait in CS. |

`CS_Max` |
The maximum value of the trait in CS. |

`CS_Mean` |
The mean value of the trait in CS. |

`CS_SE` |
The standard error of the trait in CS. |

`ttest_pvalue` |
The p value of the Student's t test for equality of means of EC and CS. |

`ttest_significance` |
The significance of the Student's t test for equality of means of EC and CS. |

`t.test`

data("cassava_CC") data("cassava_EC") ec <- cbind(genotypes = rownames(cassava_EC), cassava_EC) ec$genotypes <- as.character(ec$genotypes) rownames(ec) <- NULL core <- rownames(cassava_CC) quant <- c("NMSR", "TTRN", "TFWSR", "TTRW", "TFWSS", "TTSW", "TTPW", "AVPW", "ARSR", "SRDM") qual <- c("CUAL", "LNGS", "PTLC", "DSTA", "LFRT", "LBTEF", "CBTR", "NMLB", "ANGB", "CUAL9M", "LVC9M", "TNPR9M", "PL9M", "STRP", "STRC", "PSTR") ec[, qual] <- lapply(ec[, qual], function(x) factor(as.factor(x))) ttest.evaluate.core(data = ec, names = "genotypes", quantitative = quant, selected = core)

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