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

corr.evaluate.core | R Documentation |

Compute phenotypic correlations \insertCitepearson_note_1895EvaluateCore between traits, plot correlation matrices as correlograms \insertCitefriendly_corrgrams_2002EvaluateCore and calculate mantel correlation \insertCitelegendre_interpretation_2012EvaluateCore between them to compare entire collection (EC) and core set (CS). \loadmathjax

corr.evaluate.core(data, names, quantitative, qualitative, 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. |

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

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

A list with the following components.

`Correlation Matrix` |
The matrix with phenotypic correlations between traits in EC (below diagonal) and CS (above diagonal). |

`Correologram` |
A correlogram of phenotypic
correlations between traits in EC (below diagonal) and CS (above diagonal)
as a |

`Mantel Correlation` |
A data frame with Mantel correlation coefficient (\mjseqnr) between EC and CS phenotypic correlation matrices, it's p value and significance (*: p \mjseqn\leq 0.01; **: p \mjseqn\leq 0.05; ns: p \mjseqn > 0.05). |

`cor`

,
`cor_pmat`

`ggcorrplot`

, `mantel`

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))) corr.evaluate.core(data = ec, names = "genotypes", quantitative = quant, qualitative = qual, selected = core)

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