diversity.evaluate.core: Diversity Indices
In EvaluateCore: Quality Evaluation of Core Collections
View source: R/diversity.evaluate.core.R
diversity.evaluate.core R Documentation
Diversity Indices
Description
Compute the following diversity indices and perform corresponding statistical
tests to compare the phenotypic diversity for qualitative traits between
entire collection (EC) and core set (CS).
Simpson's and
related indices
Simpson's Index (\mjseqnd)
\insertCitesimpson_measurement_1949,peet_measurement_1974EvaluateCore
Simpson's Index of Diversity or Gini's Diversity Index or Gini-Simpson
Index or Nei's Diversity Index or Nei's Variation Index (\mjseqnD)
\insertCitegini_variabilita_1912,gini_variabilita_1912-2,greenberg_measurement_1956,berger_diversity_1970,nei_analysis_1973,peet_measurement_1974EvaluateCore
Maximum Simpson's Index of Diversity or Maximum Nei's
Diversity/Variation Index (\mjseqnD_max)
\insertCitehennink_interpretation_1990EvaluateCore
Simpson's
Reciprocal Index or Hill's \mjseqnN_2 (\mjseqnD_R)
\insertCitewilliams_patterns_1964,hill_diversity_1973EvaluateCore
Relative Simpson's Index of Diversity or Relative Nei's
Diversity/Variation Index (\mjseqnD')
\insertCitehennink_interpretation_1990EvaluateCore
Shannon-Weaver and related indices
Shannon or
Shannon-Weaver or Shannon-Weiner Diversity Index (\mjseqnH)
\insertCiteshannon_mathematical_1949,peet_measurement_1974EvaluateCore
Maximum Shannon-Weaver Diversity Index (\mjseqnH_max)
\insertCitehennink_interpretation_1990EvaluateCore
Relative
Shannon-Weaver Diversity Index or Shannon Equitability Index (\mjseqnH')
\insertCitehennink_interpretation_1990EvaluateCore
McIntosh
Diversity Index
McIntosh Diversity Index (\mjseqnD_Mc)
\insertCitemcintosh_index_1967,peet_measurement_1974EvaluateCore
\loadmathjax
Usage
diversity.evaluate.core(data, names, qualitative, selected, base = 2, R = 1000)
Arguments
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
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 names column.
base
The logarithm base to be used for computation of Shannon-Weaver
Diversity Index (\mjseqnI). Default is 2.
R
The number of bootstrap replicates. Default is 1000.
Value
A list with three data frames as follows.
simpson
- Trait
The qualitative trait.
- EC_No.Classes
The number of classes in the trait for EC.
- CS_No.Classes
The number of classes in the trait for CS.
- EC_d
The Simpson's Index (\mjseqnd) for EC.
- EC_D
The Simpson's Index of Diversity (\mjseqnD) for EC.
- EC_D.max
The Maximum Simpson's Index of Diversity
(\mjseqnD_max) for EC.
- EC_D.inv
The Simpson's Reciprocal
Index (\mjseqnD_R) for EC.
- EC_D.rel
The Relative
Reciprocal Index (\mjseqnD') for EC.
- EC_d.V
The variance
of \mjseqnd for EC according to
\insertCitesimpson_measurement_1949EvaluateCore.
- EC_d.boot.V
The bootstrap variance of \mjseqnd for EC.
- CS_d
The Simpson's Index (\mjseqnd) for CS.
- CS_D
The Simpson's Index of Diversity (\mjseqnD) for CS.
- CS_D.max
The Maximum Simpson's Index of Diversity
(\mjseqnD_max) for CS.
- CS_D.inv
The Simpson's Reciprocal
Index (\mjseqnD_R) for CS.
- CS_D.rel
The Relative
Reciprocal Index (\mjseqnD') for CS.
- CS_d.V
The variance
of \mjseqnd for CS according to
\insertCitesimpson_measurement_1949EvaluateCore.
- CS_d.boot.V
The bootstrap variance of \mjseqnd for CS.
- d.t.df
The degrees of freedom for t test.
- d.t.stat
The t statistic.
- d.t.pvalue
The p
value for t test.
- d.t.significance
The significance of t
test for t-test
- d.boot.z.df
The degrees of freedom for
bootstrap z score.
- d.boot.z.stat
The bootstrap z score.
- d.boot.z.pvalue
The p value of z score.
- d.boot.z.significance
The significance of z score.
shannon
- Trait
The qualitative
trait.
- EC_No.Classes
The number of classes in the trait for
EC.
- CS_No.Classes
The number of classes in the trait for
CS.
- EC_I
The Shannon-Weaver Diversity Index (\mjseqnI) for
EC.
- EC_I.max
The Maximum Shannon-Weaver Diversity Index
(\mjseqnI_max) for EC.
- EC_I.rel
The Relative
Shannon-Weaver Diversity Index (\mjseqnI') for EC.
- EC_I.V
The variance of \mjseqnI for EC according to
\insertCitehutcheson_test_1970EvaluateCore.
- EC_I.boot.V
The bootstrap variance of \mjseqnI for EC.
- CS_I
The Shannon-Weaver Diversity Index (\mjseqnI) for CS.
- CS_I.max
The Maximum Shannon-Weaver Diversity Index
(\mjseqnI_max) for CS.
- CS_I.rel
The Relative
Shannon-Weaver Diversity Index (\mjseqnI') for CS.
- CS_I.V
The variance of \mjseqnI for CS according to
\insertCitehutcheson_test_1970EvaluateCore.
- CS_I.boot.V
The bootstrap variance of \mjseqnI for CS.
- I.t.stat
The t statistic.
- I.t.df
The degrees
of freedom for t test.
- I.t.pvalue
The p value for t test.
- I.t.significance
The significance of t test for t-test
- I.boot.z.df
The degrees of freedom for bootstrap z score.
- I.boot.z.stat
The bootstrap z score.
- I.boot.z.pvalue
The p value of z score.
- I.boot.z.significance
The significance of z score.
mcintosh
- EC_No.Classes
The number of
classes in the trait for EC.
- CS_No.Classes
The number of
classes in the trait for CS.
- EC_D.Mc
The McIntosh Index
(\mjseqnD_Mc) for EC.
- CS_D.Mc
The McIntosh Index
(\mjseqnD_Mc) for CS.
- M.boot.z.stat
The bootstrap z
score.
- M.boot.z.df
The degrees of freedom for bootstrap z
score.
- M.boot.z.pvalue
The p value of z score.
- M.boot.z.significance
The significance of z score.
Details
The diversity indices and the corresponding statistical
tests implemented in diversity.evaluate.core are as follows.
Simpson's and related indices
Simpson's index (\mjseqnd)
which estimates the probability that two accessions randomly selected will
belong to the same phenotypic class of a trait, is computed as follows
\insertCitesimpson_measurement_1949,peet_measurement_1974EvaluateCore.
\mjsdeqnd = \sum_i = 1^kp_i^2
Where, \mjseqnp_i denotes the proportion/fraction/frequency of
accessions in the \mjseqnith phenotypic class for a trait and \mjseqnk
is the number of phenotypic classes for the trait.
The value of \mjseqnd can range from 0 to 1 with 0 representing maximum
diversity and 1, no diversity.
\mjseqnd is subtracted from 1 to give Simpson's index of diversity
(\mjseqnD)
\insertCitegreenberg_measurement_1956,berger_diversity_1970,peet_measurement_1974,hennink_interpretation_1990EvaluateCore
originally suggested by
\insertCitegini_variabilita_1912,gini_variabilita_1912-2;textualEvaluateCore
and described in literature as Gini's diversity index or Gini-Simpson
index. It is the same as Nei's diversity index or Nei's variation index
\insertCitenei_analysis_1973,hennink_interpretation_1990EvaluateCore.
Greater the value of \mjseqnD, greater the diversity with a range from 0
to 1.
\mjsdeqnD = 1 - d
The maximum value of \mjseqnD, \mjseqnD_max occurs when accessions
are uniformly distributed across the phenotypic classes and is computed as
follows \insertCitehennink_interpretation_1990EvaluateCore.
\mjsdeqnD_max = 1 - \frac1k
Reciprocal of \mjseqnd gives the Simpson's reciprocal index
(\mjseqnD_R)
\insertCitewilliams_patterns_1964,hennink_interpretation_1990EvaluateCore
and can range from 1 to \mjseqnk. This was also described in
\insertCitehill_diversity_1973;textualEvaluateCore as (\mjseqnN_2).
\mjsdeqnD_R = \frac1d
Relative Simpson's index of diversity or Relative Nei's diversity/variation
index (\mjseqnH') \insertCitehennink_interpretation_1990EvaluateCore
is defined as follows \insertCitepeet_measurement_1974EvaluateCore.
\mjsdeqnD' = \fracDD_max
Differences in Simpson's diversity index for qualitative traits of EC and
CS can be tested by a t-test using the associated variance estimate
described in \insertCitesimpson_measurement_1949;textualEvaluateCore
\insertCitelyons_c20_1978EvaluateCore.
The t statistic is computed as follows.
\mjsdeqnt = \fracd_EC - d_CS\sqrtV_d_EC + V_d_CS
Where, the variance of \mjseqnd (\mjseqnV_d) is,
\mjsdeqnV_d = \frac4N(N-1)(N-2)\sum_i=1^k(p_i)^3 +
2N(N-1)\sum_i=1^k(p_i)^2 - 2N(N-1)(2N-3) \left(
\sum_i=1^k(p_i)^2 \right)^2[N(N-1)]^2
The associated degrees of freedom is computed as follows.
\mjsdeqndf = (k_EC - 1) + (k_CS - 1)
Where, \mjseqnk_EC and \mjseqnk_CS are the number of phenotypic
classes in the trait for EC and CS respectively.
Shannon-Weaver and related indices
An index of information
\mjseqnH, was described by
\insertCiteshannon_mathematical_1949;textualEvaluateCore as follows.
\mjsdeqnH = -\sum_i=1^kp_i \log_2(p_i)
\mjseqnH is described as Shannon or Shannon-Weaver or Shannon-Weiner
diversity index in literature.
Alternatively, \mjseqnH is also computed using natural logarithm instead
of logarithm to base 2.
\mjsdeqnH = -\sum_i=1^kp_i \ln(p_i)
The maximum value of \mjseqnH (\mjseqnH_max) is \mjseqn\ln(k). This
value occurs when each phenotypic class for a trait has the same proportion
of accessions.
\mjsdeqnH_max = \log_2(k)\;\; \textrmOR \;\; H_max = \ln(k)
The relative Shannon-Weaver diversity index or Shannon equitability index
(\mjseqnH') is the Shannon diversity index (\mjseqnI) divided by the
maximum diversity (\mjseqnH_max).
\mjsdeqnH' = \fracHH_max
Differences in Shannon-Weaver diversity index for qualitative traits of EC
and CS can be tested by Hutcheson t-test
\insertCitehutcheson_test_1970EvaluateCore.
The Hutcheson t statistic is computed as follows.
\mjsdeqnt = \fracH_EC - H_CS\sqrtV_H_EC + V_H_CS
Where, the variance of \mjseqnH (\mjseqnV_H) is,
\mjsdeqnV_H = \frac\sum_i=1^kn_i(\log_2n_i)^2
\frac(\sum_i=1^k\log_2n_i)^2NN^2
\mjsdeqn\textrmOR
\mjsdeqnV_H = \frac\sum_i=1^kn_i(\lnn_i)^2
\frac(\sum_i=1^k\lnn_i)^2NN^2
The associated degrees of freedom is approximated as follows.
\mjsdeqndf = \frac(V_H_EC +
V_H_CS)^2\fracV_H_EC^2N_EC +
\fracV_H_CS^2N_CS
McIntosh Diversity Index
A similar index of diversity was
described by \insertCitemcintosh_index_1967;textualEvaluateCore as
follows (\mjseqnD_Mc) \insertCitepeet_measurement_1974EvaluateCore.
\mjsdeqnD_Mc = \fracN - \sqrt\sum_i=1^kn_i^2N - \sqrtN
Where, \mjseqnn_i denotes the number of accessions in the \mjseqnith
phenotypic class for a trait and \mjseqnN is the total number of
accessions so that \mjseqnp_i = n_i/N.
Testing for difference with bootstrapping
Bootstrap statistics
are employed to test the difference between the Simpson, Shannon-Weaver and
McIntosh indices for qualitative traits of EC and CS
\insertCitesolow_simple_1993EvaluateCore.
If \mjseqnI_EC and \mjseqnI_CS are the diversity indices with the
original number of accessions, then random samples of the same size as the
original are repeatedly generated (with replacement) \mjseqnR times and
the corresponding diversity index is computed for each sample.
\mjsdeqnI_EC^* = \lbrace H_EC_1, H_EC_, \cdots, H_EC_R
\rbrace
\mjsdeqnI_CS^* = \lbrace H_CS_1, H_CS_, \cdots, H_CS_R
\rbrace
Then the bootstrap null sample \mjseqnI_0 is computed as follows.
\mjsdeqn\Delta^* = I_EC^* - I_CS^*
\mjsdeqnI_0 = \Delta^* - \overline\Delta^*
Where, \mjseqn\overline\Delta^* is the mean of \mjseqn\Delta^*.
Now the original difference in diversity indices (\mjseqn\Delta_0 =
I_EC - I_CS) is tested against mean of bootstrap null sample
(\mjseqnI_0) by a z test. The z score test statistic is computed as
follows.
\mjsdeqnz = \frac\Delta_0 - \overlineH_0\sqrtV_H_0
Where, \mjseqn\overlineH_0 and \mjseqnV_H_0 are the mean and
variance of the bootstrap null sample \mjseqnH_0.
The corresponding degrees of freedom is estimated as follows.
\mjsdeqndf = (k_EC - 1) + (k_CS - 1)
References
\insertAllCited
See Also
shannon, diversity,
boot
Examples
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)))
diversity.evaluate.core(data = ec, names = "genotypes",
qualitative = qual, selected = core)
EvaluateCore documentation built on July 3, 2022, 5:06 p.m.
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| diversity.evaluate.core | R Documentation |
Diversity Indices
Description
Compute the following diversity indices and perform corresponding statistical tests to compare the phenotypic diversity for qualitative traits between entire collection (EC) and core set (CS).
Simpson's and related indices
Simpson's Index (\mjseqnd) \insertCitesimpson_measurement_1949,peet_measurement_1974EvaluateCore
Simpson's Index of Diversity or Gini's Diversity Index or Gini-Simpson Index or Nei's Diversity Index or Nei's Variation Index (\mjseqnD) \insertCitegini_variabilita_1912,gini_variabilita_1912-2,greenberg_measurement_1956,berger_diversity_1970,nei_analysis_1973,peet_measurement_1974EvaluateCore
Maximum Simpson's Index of Diversity or Maximum Nei's Diversity/Variation Index (\mjseqnD_max) \insertCitehennink_interpretation_1990EvaluateCore
Simpson's Reciprocal Index or Hill's \mjseqnN_2 (\mjseqnD_R) \insertCitewilliams_patterns_1964,hill_diversity_1973EvaluateCore
Relative Simpson's Index of Diversity or Relative Nei's Diversity/Variation Index (\mjseqnD') \insertCitehennink_interpretation_1990EvaluateCore
Shannon-Weaver and related indices
Shannon or Shannon-Weaver or Shannon-Weiner Diversity Index (\mjseqnH) \insertCiteshannon_mathematical_1949,peet_measurement_1974EvaluateCore
Maximum Shannon-Weaver Diversity Index (\mjseqnH_max) \insertCitehennink_interpretation_1990EvaluateCore
Relative Shannon-Weaver Diversity Index or Shannon Equitability Index (\mjseqnH') \insertCitehennink_interpretation_1990EvaluateCore
McIntosh Diversity Index
McIntosh Diversity Index (\mjseqnD_Mc) \insertCitemcintosh_index_1967,peet_measurement_1974EvaluateCore
Usage
diversity.evaluate.core(data, names, qualitative, selected, base = 2, R = 1000)
Arguments
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 |
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 |
base |
The logarithm base to be used for computation of Shannon-Weaver Diversity Index (\mjseqnI). Default is 2. |
R |
The number of bootstrap replicates. Default is 1000. |
Value
A list with three data frames as follows.
simpson |
|
shannon |
|
mcintosh |
|
Details
The diversity indices and the corresponding statistical
tests implemented in diversity.evaluate.core are as follows.
Simpson's and related indices
Simpson's index (\mjseqnd) which estimates the probability that two accessions randomly selected will belong to the same phenotypic class of a trait, is computed as follows \insertCitesimpson_measurement_1949,peet_measurement_1974EvaluateCore.
\mjsdeqnd = \sum_i = 1^kp_i^2
Where, \mjseqnp_i denotes the proportion/fraction/frequency of accessions in the \mjseqnith phenotypic class for a trait and \mjseqnk is the number of phenotypic classes for the trait.
The value of \mjseqnd can range from 0 to 1 with 0 representing maximum diversity and 1, no diversity.
\mjseqnd is subtracted from 1 to give Simpson's index of diversity (\mjseqnD) \insertCitegreenberg_measurement_1956,berger_diversity_1970,peet_measurement_1974,hennink_interpretation_1990EvaluateCore originally suggested by \insertCitegini_variabilita_1912,gini_variabilita_1912-2;textualEvaluateCore and described in literature as Gini's diversity index or Gini-Simpson index. It is the same as Nei's diversity index or Nei's variation index \insertCitenei_analysis_1973,hennink_interpretation_1990EvaluateCore. Greater the value of \mjseqnD, greater the diversity with a range from 0 to 1.
\mjsdeqnD = 1 - d
The maximum value of \mjseqnD, \mjseqnD_max occurs when accessions are uniformly distributed across the phenotypic classes and is computed as follows \insertCitehennink_interpretation_1990EvaluateCore.
\mjsdeqnD_max = 1 - \frac1k
Reciprocal of \mjseqnd gives the Simpson's reciprocal index (\mjseqnD_R) \insertCitewilliams_patterns_1964,hennink_interpretation_1990EvaluateCore and can range from 1 to \mjseqnk. This was also described in \insertCitehill_diversity_1973;textualEvaluateCore as (\mjseqnN_2).
\mjsdeqnD_R = \frac1d
Relative Simpson's index of diversity or Relative Nei's diversity/variation index (\mjseqnH') \insertCitehennink_interpretation_1990EvaluateCore is defined as follows \insertCitepeet_measurement_1974EvaluateCore.
\mjsdeqnD' = \fracDD_max
Differences in Simpson's diversity index for qualitative traits of EC and CS can be tested by a t-test using the associated variance estimate described in \insertCitesimpson_measurement_1949;textualEvaluateCore \insertCitelyons_c20_1978EvaluateCore.
The t statistic is computed as follows.
\mjsdeqnt = \fracd_EC - d_CS\sqrtV_d_EC + V_d_CS
Where, the variance of \mjseqnd (\mjseqnV_d) is,
\mjsdeqnV_d = \frac4N(N-1)(N-2)\sum_i=1^k(p_i)^3 + 2N(N-1)\sum_i=1^k(p_i)^2 - 2N(N-1)(2N-3) \left( \sum_i=1^k(p_i)^2 \right)^2[N(N-1)]^2
The associated degrees of freedom is computed as follows.
\mjsdeqndf = (k_EC - 1) + (k_CS - 1)
Where, \mjseqnk_EC and \mjseqnk_CS are the number of phenotypic classes in the trait for EC and CS respectively.
Shannon-Weaver and related indices
An index of information \mjseqnH, was described by \insertCiteshannon_mathematical_1949;textualEvaluateCore as follows.
\mjsdeqnH = -\sum_i=1^kp_i \log_2(p_i)
\mjseqnH is described as Shannon or Shannon-Weaver or Shannon-Weiner diversity index in literature.
Alternatively, \mjseqnH is also computed using natural logarithm instead of logarithm to base 2.
\mjsdeqnH = -\sum_i=1^kp_i \ln(p_i)
The maximum value of \mjseqnH (\mjseqnH_max) is \mjseqn\ln(k). This value occurs when each phenotypic class for a trait has the same proportion of accessions.
\mjsdeqnH_max = \log_2(k)\;\; \textrmOR \;\; H_max = \ln(k)
The relative Shannon-Weaver diversity index or Shannon equitability index (\mjseqnH') is the Shannon diversity index (\mjseqnI) divided by the maximum diversity (\mjseqnH_max).
\mjsdeqnH' = \fracHH_max
Differences in Shannon-Weaver diversity index for qualitative traits of EC and CS can be tested by Hutcheson t-test \insertCitehutcheson_test_1970EvaluateCore.
The Hutcheson t statistic is computed as follows.
\mjsdeqnt = \fracH_EC - H_CS\sqrtV_H_EC + V_H_CS
Where, the variance of \mjseqnH (\mjseqnV_H) is,
\mjsdeqnV_H = \frac\sum_i=1^kn_i(\log_2n_i)^2 \frac(\sum_i=1^k\log_2n_i)^2NN^2
\mjsdeqn\textrmOR
\mjsdeqnV_H = \frac\sum_i=1^kn_i(\lnn_i)^2 \frac(\sum_i=1^k\lnn_i)^2NN^2
The associated degrees of freedom is approximated as follows.
\mjsdeqndf = \frac(V_H_EC + V_H_CS)^2\fracV_H_EC^2N_EC + \fracV_H_CS^2N_CS
McIntosh Diversity Index
A similar index of diversity was described by \insertCitemcintosh_index_1967;textualEvaluateCore as follows (\mjseqnD_Mc) \insertCitepeet_measurement_1974EvaluateCore.
\mjsdeqnD_Mc = \fracN - \sqrt\sum_i=1^kn_i^2N - \sqrtN
Where, \mjseqnn_i denotes the number of accessions in the \mjseqnith phenotypic class for a trait and \mjseqnN is the total number of accessions so that \mjseqnp_i = n_i/N.
Testing for difference with bootstrapping
Bootstrap statistics are employed to test the difference between the Simpson, Shannon-Weaver and McIntosh indices for qualitative traits of EC and CS \insertCitesolow_simple_1993EvaluateCore.
If \mjseqnI_EC and \mjseqnI_CS are the diversity indices with the original number of accessions, then random samples of the same size as the original are repeatedly generated (with replacement) \mjseqnR times and the corresponding diversity index is computed for each sample.
\mjsdeqnI_EC^* = \lbrace H_EC_1, H_EC_, \cdots, H_EC_R \rbrace
\mjsdeqnI_CS^* = \lbrace H_CS_1, H_CS_, \cdots, H_CS_R \rbrace
Then the bootstrap null sample \mjseqnI_0 is computed as follows.
\mjsdeqn\Delta^* = I_EC^* - I_CS^*
\mjsdeqnI_0 = \Delta^* - \overline\Delta^*
Where, \mjseqn\overline\Delta^* is the mean of \mjseqn\Delta^*.
Now the original difference in diversity indices (\mjseqn\Delta_0 = I_EC - I_CS) is tested against mean of bootstrap null sample (\mjseqnI_0) by a z test. The z score test statistic is computed as follows.
\mjsdeqnz = \frac\Delta_0 - \overlineH_0\sqrtV_H_0
Where, \mjseqn\overlineH_0 and \mjseqnV_H_0 are the mean and variance of the bootstrap null sample \mjseqnH_0.
The corresponding degrees of freedom is estimated as follows.
\mjsdeqndf = (k_EC - 1) + (k_CS - 1)
References
\insertAllCitedSee Also
shannon, diversity,
boot
Examples
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)))
diversity.evaluate.core(data = ec, names = "genotypes",
qualitative = qual, selected = core)
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