Compare the distribution frequencies of qualitative traits between entire collection (EC) and core set (CS) by Chi-squared test for homogeneity (Pearson 1900; Snedecor and Irwin 1933) .
chisquare.evaluate.core(data, names, qualitative, 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 qualitative traits as a character vector.
Character vector with the names of individuals selected in
core collection and present in the names column.
A a data frame with the following columns.
The qualitative trait.
The number of classes in the trait for EC.
The frequency of the classes in the trait for EC.
The number of classes in the trait for CS.
The frequency of the classes in the trait for CS.
The \(\chi^{2}\) test statistic.
The p value for the test statistic.
The significance of the test statistic (*: p \(\leq\) 0.01; **: p \(\leq\) 0.05; ns: p \( > \) 0.05).
Pearson K (1900).
“X. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling.”
The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 50(302), 157--175.
Snedecor G, Irwin MR (1933).
“On the chi-square test for homogeneity.”
Iowa State College Journal of Science, 8, 75--81.
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)))
chisquare.evaluate.core(data = ec, names = "genotypes",
qualitative = qual, selected = core)
#> Trait EC_No.Classes
#> 1 CUAL 5
#> 2 LNGS 3
#> 3 PTLC 5
#> 4 DSTA 5
#> 5 LFRT 5
#> 6 LBTEF 6
#> 7 CBTR 3
#> 8 NMLB 10
#> 9 ANGB 4
#> 10 CUAL9M 5
#> 11 LVC9M 5
#> 12 TNPR9M 5
#> 13 PL9M 3
#> 14 STRP 4
#> 15 STRC 2
#> 16 PSTR 3
#> EC_Classes
#> 1 Dark green(321); Green(2); Green purple(889); Light green(48); Purple(424)
#> 2 Long(741); Medium(791); Short(152)
#> 3 Dark green(20); Green purple(1008); Light green(50); Purple(578); Red(28)
#> 4 Absent(76); Central part(770); Top and bottom(50); Top part(188); Totally pigmented(600)
#> 5 100% leaf retention(1); 25-50% leaf retention(679); 50-75% leaf retention(897); <100% leaf retention(83); <25% leaf retention(24)
#> 6 0(404); 1(250); 2(388); 3(489); 4(140); 5(13)
#> 7 Cream(1048); White(610); Yellow(26)
#> 8 0(397); 1(185); 2(308); 3(506); 4(195); 5(72); 6(15); 7(3); 8(2); 9(1)
#> 9 150-300(468); 450-600(785); 750-900(34); No branching(397)
#> 10 Dark green(566); Green(47); Green purple(435); Light green(33); Purple(603)
#> 11 Dark green(624); Green(14); Green purple(896); Light green(110); Purple(40)
#> 12 1(140); 2(252); 3(301); 4(361); 5(630)
#> 13 Long (25-30cm)(887); Medium (15-20cm)(792); Short (5-10cm)(5)
#> 14 Absent(555); Intermediate(459); Long(43); Short(627)
#> 15 Absent(721); Present(963)
#> 16 Irregular(556); Tending toward horizontal(1125); Tending toward vertical(3)
#> CS_No.Classes
#> 1 4
#> 2 3
#> 3 5
#> 4 5
#> 5 4
#> 6 6
#> 7 3
#> 8 9
#> 9 4
#> 10 5
#> 11 5
#> 12 5
#> 13 2
#> 14 4
#> 15 2
#> 16 2
#> CS_Classes
#> 1 Dark green(31); Green(0); Green purple(89); Light green(6); Purple(42)
#> 2 Long(72); Medium(72); Short(24)
#> 3 Dark green(7); Green purple(99); Light green(12); Purple(43); Red(7)
#> 4 Absent(22); Central part(74); Top and bottom(6); Top part(19); Totally pigmented(47)
#> 5 100% leaf retention(0); 25-50% leaf retention(64); 50-75% leaf retention(88); <100% leaf retention(14); <25% leaf retention(2)
#> 6 0(32); 1(25); 2(37); 3(43); 4(28); 5(3)
#> 7 Cream(94); White(70); Yellow(4)
#> 8 0(30); 1(20); 2(30); 3(54); 4(18); 5(12); 6(2); 7(1); 8(1); 9(0)
#> 9 150-300(54); 450-600(76); 750-900(8); No branching(30)
#> 10 Dark green(51); Green(8); Green purple(48); Light green(5); Purple(56)
#> 11 Dark green(60); Green(4); Green purple(84); Light green(17); Purple(3)
#> 12 1(20); 2(31); 3(31); 4(32); 5(54)
#> 13 Long (25-30cm)(86); Medium (15-20cm)(82); Short (5-10cm)(0)
#> 14 Absent(54); Intermediate(51); Long(6); Short(57)
#> 15 Absent(68); Present(100)
#> 16 Irregular(56); Tending toward horizontal(112); Tending toward vertical(0)
#> chisq_statistic chisq_pvalue chisq_significance
#> 1 0.5046947 0.96840316 ns
#> 2 5.0473183 0.08019198 ns
#> 3 25.8082829 0.00029997 **
#> 4 24.1043807 0.00039996 **
#> 5 3.7515039 0.36426357 ns
#> 6 15.8517808 0.01019898 *
#> 7 2.8705932 0.24557544 ns
#> 8 9.0091683 0.38566143 ns
#> 9 8.3226194 0.03869613 *
#> 10 3.9554501 0.41315868 ns
#> 11 7.2401831 0.11738826 ns
#> 12 5.0840571 0.27137286 ns
#> 13 0.6650546 0.80611939 ns
#> 14 1.6140159 0.66733327 ns
#> 15 0.3416413 0.56564344 ns
#> 16 0.3043632 1.00000000 ns