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) .
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
namescolumn.- na.omit
logical. If
TRUE, missing values (NA) are ignored and not included as a distinct factor level for analysis. Default isTRUE.
Value
A a data frame with the following columns.
- Trait
The qualitative trait.
- EC_No.Classes
The number of classes in the trait for EC.
- EC_Classes
The frequency of the classes in the trait for EC.
- CS_No.Classes
The number of classes in the trait for CS.
- CS_Classes
The frequency of the classes in the trait for CS.
- chisq_statistic
The \(\chi^{2}\) test statistic.
- chisq_pvalue
The p value for the test statistic.
- chisq_significance
The significance of the test statistic (*: p \(\leq\) 0.01; **: p \(\leq\) 0.05; ns: p \( > \) 0.05).
References
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.
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)))
chisquare.evaluate.core(data = ec, names = "genotypes",
qualitative = qual, selected = core)
#> Trait Count EC_No.Classes
#> 1 CUAL 1684 5
#> 2 LNGS 1684 3
#> 3 PTLC 1684 5
#> 4 DSTA 1684 5
#> 5 LFRT 1684 5
#> 6 LBTEF 1684 6
#> 7 CBTR 1684 3
#> 8 NMLB 1684 10
#> 9 ANGB 1684 4
#> 10 CUAL9M 1684 5
#> 11 LVC9M 1684 5
#> 12 TNPR9M 1684 5
#> 13 PL9M 1684 3
#> 14 STRP 1684 4
#> 15 STRC 1684 2
#> 16 PSTR 1684 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 0.96920308 ns
#> 2 5.0473183 0.07839216 ns
#> 3 25.8082829 0.00039996 **
#> 4 24.1043807 0.00009999 **
#> 5 3.7515039 0.36366363 ns
#> 6 15.8517808 0.00859914 **
#> 7 2.8705932 0.24817518 ns
#> 8 9.0091683 0.38416158 ns
#> 9 8.3226194 0.03959604 *
#> 10 3.9554501 0.41075892 ns
#> 11 7.2401831 0.11618838 ns
#> 12 5.0840571 0.27517248 ns
#> 13 0.6650546 0.80361964 ns
#> 14 1.6140159 0.66543346 ns
#> 15 0.3416413 0.57124288 ns
#> 16 0.3043632 1.00000000 ns