Compute the following metrics to compare quantitative traits of the entire collection (EC) and core set (CS).
Changeable Rate of Maximum (\(CR_{\max}\)) (Wang et al. 2007)
Changeable Rate of Minimum (\(CR_{\min}\)) (Wang et al. 2007)
Changeable Rate of Mean (\(CR_{\mu}\)) (Wang et al. 2007)
cr.evaluate.core(data, names, quantitative, selected)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.
Name of column with the individual names as a character string.
Name of columns with the quantitative traits as a character vector.
Character vector with the names of individuals selected in
core collection and present in the names column.
The \(CR\) value.
The Coincidence Rate of Range (\(CR\)) is computed as follows.
\[CR = \left ( \frac{1}{n} \sum_{i=1}^{n} \frac{R_{CS_{i}}}{R_{EC_{i}}} \right ) \times 100\]
Where, \(R_{CS_{i}}\) is the range of the \(i\)th trait in the CS, \(R_{EC_{i}}\) is the range of the \(i\)th trait in the EC and \(n\) is the total number of traits.
A representative CS should have a \(CR\) value no less than 70% (Diwan et al. 1995) or 80% (Hu et al. 2000) .
The Changeable Rate of Maximum (\(CR_{\max}\)) is computed as follows.
\[CR_{\max} = \left ( \frac{1}{n} \sum_{i=1}^{n} \frac{\max_{CS_{i}}}{\max_{EC_{i}}} \right ) \times 100\]
Where, \(\max_{CS_{i}}\) is the maximum value of the \(i\)th trait in the CS, \(\max_{EC_{i}}\) is the maximum value of the \(i\)th trait in the EC and \(n\) is the total number of traits.
The Changeable Rate of Minimum (\(CR_{\min}\)) is computed as follows.
\[CR_{\min} = \left ( \frac{1}{n} \sum_{i=1}^{n} \frac{\min_{CS_{i}}}{\min_{EC_{i}}} \right ) \times 100\]
Where, \(\min_{CS_{i}}\) is the minimum value of the \(i\)th trait in the CS, \(\min_{EC_{i}}\) is the minimum value of the \(i\)th trait in the EC and \(n\) is the total number of traits.
The Changeable Rate of Mean (\(CR_{\mu}\)) is computed as follows.
\[CR_{\mu} = \left ( \frac{1}{n} \sum_{i=1}^{n} \frac{\mu_{CS_{i}}}{\mu_{EC_{i}}} \right ) \times 100\]
Where, \(\mu_{CS_{i}}\) is the mean value of the \(i\)th trait in the CS, \(\mu_{EC_{i}}\) is the mean value of the \(i\)th trait in the EC and \(n\) is the total number of traits.
NaN or Inf values for \[CR_{\min}\] occurs when the
minimum values for some of the traits are zero.
Diwan N, McIntosh MS, Bauchan GR (1995).
“Methods of developing a core collection of annual Medicago species.”
Theoretical and Applied Genetics, 90(6), 755–761.
Hu J, Zhu J, Xu HM (2000).
“Methods of constructing core collections by stepwise clustering with three sampling strategies based on the genotypic values of crops.”
Theoretical and Applied Genetics, 101(1), 264–268.
Wang J, Hu J, Zhang C, Zhang S (2007).
“Assessment on evaluating parameters of rice core collections constructed by genotypic values and molecular marker information.”
Rice Science, 14(2), 101–110.
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)))
cr.evaluate.core(data = ec, names = "genotypes",
quantitative = quant, selected = core)
#> CR CR_Max CR_Min CR_Mean
#> 89.23084 93.78085 NaN 112.80763