Package 'Phenotype' reference manual

Package 'Phenotype'

Title:	A Tool for Phenotypic Data Processing
Description:	Large-scale phenotypic data processing is essential in research. Researchers need to eliminate outliers from the data in order to obtain true and reliable results. Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method can be used to process phenotypic data under different conditions and is widely used in animal and plant breeding. The 'Phenotype' can remove outliers from phenotypic data and performs the best linear unbiased prediction (BLUP), help researchers quickly complete phenotypic data analysis. H.P.Piepho. (2008) <doi:10.1007/s10681-007-9449-8>.
Authors:	Peng Zhao [aut, cre]
Maintainer:	Peng Zhao <[email protected]>
License:	Artistic-2.0
Version:	0.1.0
Built:	2025-02-19 02:57:55 UTC
Source:	https://github.com/biozhp/phenotype

Title:

A Tool for Phenotypic Data Processing

Description:

Large-scale phenotypic data processing is essential in research. Researchers need to eliminate outliers from the data in order to obtain true and reliable results. Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method can be used to process phenotypic data under different conditions and is widely used in animal and plant breeding. The 'Phenotype' can remove outliers from phenotypic data and performs the best linear unbiased prediction (BLUP), help researchers quickly complete phenotypic data analysis. H.P.Piepho. (2008) <doi:10.1007/s10681-007-9449-8>.

Authors:

Peng Zhao [aut, cre]

Maintainer:

Peng Zhao <[email protected]>

License:

Artistic-2.0

Version:

0.1.0

Built:

2025-02-19 02:57:55 UTC

Source:

https://github.com/biozhp/phenotype

Help Index

blup

Description

Performs the Best Linear Unbiased Prediction (BLUP)

Usage

blup(
  x,
  sample = NULL,
  year = NULL,
  loc = NULL,
  rep = NULL,
  phe = NULL,
  fold = 1.5
)
blup(
  x,
  sample = NULL,
  year = NULL,
  loc = NULL,
  rep = NULL,
  phe = NULL,
  fold = 1.5
)

Arguments

`x`	Input phenotype data file.
`sample`	The column name of the sample name in phenotypic data. (Default: NULL)
`year`	The column name of the year in phenotypic data. (Default: NULL)
`loc`	The column name of the location in phenotypic data. (Default: NULL)
`rep`	The column name of the replication in phenotypic data. (Default: NULL)
`phe`	The column name of the phenotypic value in data. (Default: NULL)
`fold`	Fold before inter-quartile range. (Default: 1.5)

Value

Estimate BLUPs for a phenotypic data with outliers removed on a per sample basis.

Author(s)

Peng Zhao <[email protected]>

Examples

data("wheatds")
blup_out <- blup(wheatds, sample = "Line", loc = "Env", rep = "Rep", phe = "DS")
data("wheatds")
blup_out <- blup(wheatds, sample = "Line", loc = "Env", rep = "Rep", phe = "DS")

histplot

Description

Histogram drawing

Usage

histplot(
  x,
  color = "#99d6e1",
  rug_color = "#f79999",
  freq = FALSE,
  lwd = 2,
  rug_lwd = 1,
  main = "",
  xlab = "",
  ylab = "",
  cex.main = 1.5,
  cex.lab = 1.5,
  cex.axis = 1.5,
  breaks = "Sturges",
  ylim = NULL,
  xpos = 0.03,
  ypos = 0,
  cex.text = 1.2
)
histplot(
  x,
  color = "#99d6e1",
  rug_color = "#f79999",
  freq = FALSE,
  lwd = 2,
  rug_lwd = 1,
  main = "",
  xlab = "",
  ylab = "",
  cex.main = 1.5,
  cex.lab = 1.5,
  cex.axis = 1.5,
  breaks = "Sturges",
  ylim = NULL,
  xpos = 0.03,
  ypos = 0,
  cex.text = 1.2
)

Arguments

`x`	Input phenotype data.
`color`	The color of histogram.
`rug_color`	The color of rug under the histogram.
`freq`	If FALSE, the histogram graphic is a representation of frequencies; if TRUE, the histogram graphic is a representation of probability densitie. (Default: FALSE)
`lwd`	The line width of histogram. (Default: 2)
`rug_lwd`	The line width of rug under the histogram. (Default: 1)
`main`	The title of plot.
`xlab`	The X axis labels.
`ylab`	The Y axis labels
`cex.main`	The magnification to be used for title. (Default: 1.5)
`cex.lab`	The magnification to be used for axis labels. (Default: 1.5)
`cex.axis`	The magnification to be used for axis annotation. (Default: 1.5)
`breaks`	The number of bars in the histogram.
`ylim`	Y axis ranges.
`xpos`	The horizontal position of the pvalue label. (Default: 0.03)
`ypos`	The vertical position of the pvalue label. (Default: 0)
`cex.text`	The magnification to be used for pvalue labels. (Default: 1.2)

Value

Histogram and p-value of Shapiro-Wilk Normality Test.

Author(s)

Peng Zhao <[email protected]>

Examples

data("wheatds")
inlier <- outlier(wheatds, sample = "Line", loc = "Env", rep = "Rep", phe = "DS", mode = "blup")
stat_out <- stat(x = inlier, sample = "Sample", phe = "inlier")
histplot(x = stat_out$mean)
data("wheatds")
inlier <- outlier(wheatds, sample = "Line", loc = "Env", rep = "Rep", phe = "DS", mode = "blup")
stat_out <- stat(x = inlier, sample = "Sample", phe = "inlier")
histplot(x = stat_out$mean)

outlier

Description

Remove outliers from phenotypic data

Usage

outlier(
  x,
  sample = NULL,
  year = NULL,
  loc = NULL,
  rep = NULL,
  phe = NULL,
  fold = 1.5,
  mode = "normal"
)
outlier(
  x,
  sample = NULL,
  year = NULL,
  loc = NULL,
  rep = NULL,
  phe = NULL,
  fold = 1.5,
  mode = "normal"
)

Arguments

`x`	Input phenotype data file.
`sample`	The column name of the sample name in phenotypic data. (Default: NULL)
`year`	The column name of the year in phenotypic data. (Default: NULL)
`loc`	The column name of the location in phenotypic data. (Default: NULL)
`rep`	The column name of the replication in phenotypic data. (Default: NULL)
`phe`	The column name of the phenotypic value in data. (Default: NULL)
`fold`	Fold before inter-quartile range. (Default: 1.5)
`mode`	Type of input phenotypic data. "normal" means normal data, "blup" means data containing year/location/repetition. (Default: "normal")

Value

phenotypic data with outliers removed.

Author(s)

Peng Zhao <[email protected]>

Examples

data("wheatds")
inlier <- outlier(wheatds, sample = "Line", loc = "Env", rep = "Rep", phe = "DS", mode = "blup")
data("wheatds")
inlier <- outlier(wheatds, sample = "Line", loc = "Env", rep = "Rep", phe = "DS", mode = "blup")

stat

Description

Calculate statistical indicators of phenotypic data

Usage

stat(x, sample = NULL, phe = NULL)
stat(x, sample = NULL, phe = NULL)

Arguments

`x`	Input phenotype data file.
`sample`	The column name of the sample name in phenotypic data. (Default: NULL)
`phe`	The column name of the phenotypic value in data. (Default: NULL)

Value

Mean, median, standard deviation, standard error of phenotypic data for each sample.

Author(s)

Peng Zhao <[email protected]>

Examples

data("wheatds")
inlier <- outlier(wheatds, sample = "Line", loc = "Env", rep = "Rep", phe = "DS", mode = "blup")
stat_out <- stat(x = inlier, sample = "Sample", phe = "inlier")
data("wheatds")
inlier <- outlier(wheatds, sample = "Line", loc = "Env", rep = "Rep", phe = "DS", mode = "blup")
stat_out <- stat(x = inlier, sample = "Sample", phe = "inlier")

Stripe rust disease severity (leaf areas infected, DS) of the wheat RIL population

Description

Stripe rust disease severity (leaf areas infected, DS) of the wheat RIL population in Yangling, Tianshui, Jiangyou.

Usage

data("wheatds")
data("wheatds")

Format

A data frame containing samples, environments, repetitions, and disease severity of the wheat RIL population.

Examples

data("wheatds")
data("wheatds")