Calculate relative tolerance of some phenotype(s) relative to control

Often in bellwether experiments we are curious about the effect of some treatment vs control. For certain routes in analysing the data this requires considering phenotypes as relative differences compared to a control. Note that the conjugate function can also be useful in considering the relative tolerance to stress between groups and that growth models are another suggested way to test relative tolerance questions.

Usage

relativeTolerance(
  df,
  phenotypes = NULL,
  grouping = NULL,
  control = NULL,
  controlGroup = NULL,
  traitCol = "trait",
  valueCol = "value"
)

Arguments

df: Dataframe to use, this can be in long or wide format.
phenotypes: A character vector of column names for the phenotypes that should be compared against control.
grouping: A character vector of column names that identify groups in the data. These groups will be calibrated separately, with the exception of the group that identifies a control within the greater hierarchy. Note that for levels of grouping where the control group does not exist the output will be NA.
control: A column name for the variable to be used to select the control observations. If left NULL (the default) then this will be taken as the first string in the group argument.
controlGroup: The level of the control variable to compare groups against.
traitCol: Column with phenotype names, defaults to "trait". This should generally not need to be changed from the default. If this and valueCol are present in colnames(df) then the data is assumed to be in long format.
valueCol: Column with phenotype values, defaults to "value". This should generally not need to be changed from the default.

Value

A dataframe with relative tolerance columns added.

Examples

# \donttest{
sv <- read.pcv(
  "https://raw.githubusercontent.com/joshqsumner/pcvrTestData/main/pcv4-single-value-traits.csv",
  reader = "fread"
)
sv$genotype <- substr(sv$barcode, 3, 5)
sv$genotype <- ifelse(sv$genotype == "002", "B73",
  ifelse(sv$genotype == "003", "W605S",
    ifelse(sv$genotype == "004", "MM", "Mo17")
  )
)
sv$fertilizer <- substr(sv$barcode, 8, 8)
sv$fertilizer <- ifelse(sv$fertilizer == "A", "100",
  ifelse(sv$fertilizer == "B", "50", "0")
)

sv <- bw.time(sv,
  plantingDelay = 0, phenotype = "area_pixels",
  cutoff = 10, timeCol = "timestamp", group = c("barcode", "rotation"), plot = FALSE
)
phenotypes <- colnames(sv)[19:35]
phenoForm <- paste0("cbind(", paste0(phenotypes, collapse = ", "), ")")
groupForm <- "DAS+DAP+barcode+genotype+fertilizer"
form <- as.formula(paste0(phenoForm, "~", groupForm))
sv <- aggregate(form, data = sv, mean, na.rm = TRUE)
sv <- bw.outliers(sv,
  phenotype = "area_pixels",
  group = c("DAS", "genotype", "fertilizer"),
  plotgroup = c("barcode")
)$data
#> Warning: 16 groupings had all observations removed

pixels_per_cmsq <- 42.5^2 # pixel per cm^2
sv$area_cm2 <- sv$area_pixels / pixels_per_cmsq
sv$height_cm <- sv$height_pixels / 42.5

df <- sv
phenotypes <- c("area_cm2", "height_cm")
grouping <- c("fertilizer", "genotype", "DAS")
controlGroup <- "100"
control <- "fertilizer"

rt <- relativeTolerance(df, phenotypes, grouping, control, controlGroup)
head(rt)
#>   fertilizer genotype DAS phenotype    mu_rel se_rel     mu_trt      se_trt
#> 1          0      B73   0  area_cm2 1.6666667     NA 0.09411765 0.012918767
#> 2          0      B73   0 height_cm 7.7500000     NA 0.36470588 0.024956710
#> 3          0       MM   0  area_cm2 1.1657303     NA 0.11487889 0.017812240
#> 4         50       MM   0  area_cm2 0.6685393     NA 0.06588235          NA
#> 5          0       MM   0 height_cm 0.6250000     NA 0.29411765 0.008318903
#> 6         50       MM   0 height_cm 0.1000000     NA 0.04705882          NA
#>   mu_control se_control
#> 1 0.05647059         NA
#> 2 0.04705882         NA
#> 3 0.09854671         NA
#> 4 0.09854671         NA
#> 5 0.47058824         NA
#> 6 0.47058824         NA
sapply(rt, function(c) sum(is.na(c)))
#> fertilizer   genotype        DAS  phenotype     mu_rel     se_rel     mu_trt 
#>          0          0          0          0         24         52          0 
#>     se_trt mu_control se_control 
#>         22         24         42 
# }