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FieldSimR

FieldSimR is an R package for simulating plot data in multi-environment field trials with one or more traits. Its core function generates plot errors that capture:

  • spatial trend,
  • random error (noise), and
  • extraneous variation.

Spatial trend is simulated using bivariate interpolation or a separable first order autoregressive (AR1) process. Random error is simulated using an independent process. Extraneous variation is simulated using random or zig-zag ordering between neighbouring columns and/or rows. The three error components are combined at a user-defined ratio.

Phenotypes can be generated by combining the plot errors with genetic values (e.g., true, simulated, or predicted). FieldSimR provides wrapper functions to simulate correlated genetic values that capture genotype-by-environment (GxE) interaction with the R package AlphaSimR.

Installation

FieldSimR is available on CRAN.

To install use:

install.packages("FieldSimR")

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install.packages('FieldSimR')

Monthly Downloads

410

Version

1.4.0

License

GPL (>= 3)

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Maintainer

Christian Werner

Last Published

August 30th, 2024

Functions in FieldSimR (1.4.0)

gv_df_unstr

Genetic values - Example data frame
field_trial_error

Simulate plot errors in plant breeding field trials
multi_asr_input

Simulate genetic values based on a multiplicative model for GxE interaction - `AlphaSimR` input parameters
make_phenotypes

Generate phenotypes - Combine genetic values and plot errors
compsym_asr_input

Simulate genetic values based on a compound symmetry model for GxE interaction - `AlphaSimR` input parameters
plot_effects

Graphics for plot effects
group_cor_mat

Simulate a reduced rank correlation matrix with multiple groups
multi_asr_output

Simulate genetic values based on a multiplicative model for GxE interaction - Simulation with `AlphaSimR`
compsym_asr_output

Simulate genetic values based on a compound symmetry model for GxE interaction - Simulation with `AlphaSimR`
error_df_bivar

Plot errors - Example data frame
sample_met

Sample environments from a target population
plot_hist

Histogram of values
qq_plot

Q-Q plot
skew_diag_mat

Simulate a skewed diagonal variance matrix
plot_matrix

Graphics for matrices
sample_variogram

Sample variogram
theoretical_variogram

Theoretical variogram
struc_cor_mat

Simulate a structured correlation matrix with reduced rank
rand_diag_mat

Simulate a random diagonal variance matrix
rand_cor_mat

Simulate a random correlation matrix
unstr_asr_input

Simulate genetic values based on an unstructured model for GxE interaction - `AlphaSimR` input parameters
unstr_asr_output

Simulate genetic values based on an unstructured model for GxE interaction - Simulation with `AlphaSimR`