Description Usage Arguments Details Value Author(s) References Examples
Perform Change Point Analysis (CPA) on genotype adjusted means or G-BLUEs time series of a trait
1 | cpa_getOTW(data, h2)
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data |
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h2 |
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Entire temporal data set is partitioned into windows based on the differences in the distribution of trait heritability and phenotypic separability using a change point analysis procedure defined by Matteson et al. (2014).
Return:
Matrix
Genotype adjusted means within the Optimal Time Window or
the duration within an experiment with maximum genotypic and phenotypic diversity.
Soumyashree Kar, Vincent Garin
Matteson, D.S. and James, N.A. (2014). A nonparametric approach for multiple change point analysis of multivariate data. Journal of the American Statistical Association, 109(505), pp.334-345.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | data(SG_PH_data)
SG_PH_data$col_f <- factor(SG_PH_data$col)
SG_PH_data$row_f <- factor(SG_PH_data$row)
SG_PH_data$rep <- factor(SG_PH_data$rep)
SG_PH_data$block <- factor(SG_PH_data$block)
exp_des_data = SG_PH_data[, c("row", "col", "row_f", "col_f","genotype",
"rep", "block")]
## Not run:
op <- SpaTemHTP_proc(exp_des_data, pheno_data = SG_PH_data[, 6:28],
out_det = TRUE, miss_imp = TRUE, sp_adj = TRUE,
random = ~ rep + rep:block + row_f + col_f,
h2_comp = TRUE, plot = TRUE)
data <- op$G_BLUES
# make sure data colnmanes are dd-mm-yyyy Date format compatible
dates <- substr(colnames(data), 2, nchar(colnames(data)))
dates <- str_replace_all(string = dates, pattern = '\\.', replacement = '-')
colnames(data) <- dates
h2 <- op$h2
OTW <- cpa_getOTW(data = data, h2 = h2)
## End(Not run)
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