Package for multi-environment trial analysis
Home Page: https://tiagoolivoto.github.io/metan/
License: GNU General Public License v3.0
My homepage is done with Hugo and based on Academic theme. My publication page is inspired on Walmes Zeviani's page. I have used portions of his code to generate the plot.
Congratulation for developing such a wonderful package.
I think I spotted a type in the labels of plot_scores(AMMI_model, type = 2). Both labels are PC1. I think x-axis label should be PC1 and y-axis label should be PC2.
library(metan)
AMMI_model <- performs_ammi(data_ge,
env = ENV,
gen = GEN,
rep = REP,
resp = GY,
verbose = FALSE)
plot_scores(AMMI_model, type = 2)
Hello, thank you for developing such a great package, I'm running into a problem in the plot function when I run it on the output of gge() function. The issue is lines pointing to the individual genotypes in the graph, I have more than 100 lines, and when I try to plot them, those lines make the figure very messy, the issue was solved in plot_scores for ammi analysis by using the command repel= FALSE but there is no such option in plot() command. Could you please help me with that? I have attached the figure. I have tried changing font size and shape size, but couldn't make these lines go away
Thank you
I found few outliers in my data using find_outlier function of METAN package. How can I remove these outliers?
Brief description of the problem
# insert reprex hereDear Tiago,
Sorry to say that I don't know how to prepare "reprex". But I will be precise.
PROBLEM :-
Same code of "metan" to get MGIDI is running on one dataset (df.xlsx, attached) , but not on similar second dataset (df2.xlsx, attached).
The code I used is pasted here. It generates following message after line "$$$" in code is run. :::
"Error in cor(means, use = use) : no complete element pairs
In addition: Warning message:
Missing values observed in the table of means. Using complete observations to compute the correlation matrix. "
CODE :-
getwd()
setwd("C:/Users/hp/Desktop/MGIDI")
library(metan)
library(tidyverse)
library(tidyr)
library(dplyr)
library(rio)
df <- import("df2.xlsx", sheet=1)
str(df)
df <- df %>% mutate(across(where(is_character),as_factor))
str(df)
mod <-
gamem(df,
gen = GEN,
rep = REP,
resp = c('T1', 'T2','T3','T4','T5','T6','T7'),
verbose = F)
print(mod)
gmd(mod, "blupg")
$$$ mgidi_ind <- mgidi(mod, mineval=1,SI=33,ideotype = c("h, h, h, h, h, h, h"))
gmd(mgidi_ind, "MGIDI")
p1 <- plot(mgidi_ind, type = "contribution" )
p2 <- plot(mgidi_ind, SI=33,type = "index", title = "MGIDI for Control")
p2 + p1
DATA FILES ATTACHED::-
Hope you will consider my limitation and help.
Regards.
Please briefly describe your problem and what output you expect. If you have a question, please don't use this form. Instead, ask on https://stackoverflow.com/ or https://community.rstudio.com/.
Please include a minimal reproducible example (AKA a reprex). If you've never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.
using my own data set, I noticed a mislabeling of the ENV and GEN factors in the legend of the Which Won Where Biplot.
# insert reprex here
Error in s$u[, 1:minimo] : subscript out of bounds
In addition: Warning messages:
1: Expected 2 pieces. Additional pieces discarded in 48 rows [37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, ...].
2: Data imputation used to fill the GxE matrix
Could you help me?.
Thank you in advance,
Adelina.
[metan.xlsx](https://github.com/TiagoOlivoto/metan/files/4451456/metan.xlsx)_
I make my AMMI analysis with metan but the results don´t coincide with agricolae and with the program in SAS. The PC are negative when should be positive and vice versa.
I don´t know what is happening, could be my mistake on the code?
Grettings, i hope your answer.
Please briefly describe your problem and what output you expect. If you have a question, please don't use this form. Instead, ask on https://stackoverflow.com/ or https://community.rstudio.com/.
Please include a minimal reproducible example (AKA a reprex). If you've never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.
See the codes and error message:
gmd(MTSI_index2, "FA") %>% print_table()
Error in get_model_data(x, what, type, verbose) :
Invalid class in object x. See ?get_model_data for more information.
# insert reprex hereI want to include Anthesis silking interval (ASI) as a covariate to grain yield while calculating BLUP values and plotting them.
gamem_met(Data, env = Loc_Code,
gen = ENTRY, rep = REP,
block = BLOCK, random= "gen", prob = 0.05, resp = "Grain Yield", verbose = TRUE)
How to include covariate ASI in this code
Hello!
The function runs well only with variable GW.
library(openxlsx)
library(metan)
#> Registered S3 method overwritten by 'GGally':
#> method from
#> +.gg ggplot2
#> |=========================================================|
#> | Multi-Environment Trial Analysis (metan) v1.16.0 |
#> | Author: Tiago Olivoto |
#> | Type 'citation('metan')' to know how to cite metan |
#> | Type 'vignette('metan_start')' for a short tutorial |
#> | Visit 'https://bit.ly/pkgmetan' for a complete tutorial |
#> |=========================================================|
Data_numeric1 <- read.xlsx("C:/Users/stupk/Documents/R/Example.xlsx", "Lines", colNames = TRUE)
Data_numeric1$YEAR <- as.factor(Data_numeric1$YEAR)
Data_numeric1$GEN <- as.factor(Data_numeric1$GEN)
Data_numeric1$REP <- as.factor(Data_numeric1$REP)
stab_ind <- ge_stats(Data_numeric1, env = YEAR, gen = GEN,rep = REP, resp = c(HIGH, BUSH, SAFE,GY))
#> Error in rowSums((gamma.n)^2): 'x' must be a matrix with at least two dimensions
stab_ind <- ge_stats(Data_numeric1, env = YEAR, gen = GEN,rep = REP, resp = GW)
#> Evaluating trait GW |============================================| 100% 00:00:05 Created on 2022-03-15 by the reprex package (v2.0.1)
[Example.xlsx](https://github.com/TiagoOlivoto/metan/files/8249601/Example.xlsx)
pcoeff <- path_coeff_mat(cor_mat, resp = TGW)
Error in solve.default(cor.x, cor.y) :
system is computationally singular: reciprocal condition number = 8.72837e-18
Hi,
First, thanks a lot for the package and especially for MGIDI algorithm.
I applied mgidi function both on blup data, with trait values being centered on 0 for the whole population. Hence, I have negative values for the traits. When I output the sel_dif table, I find for some traits negative values in the column SDperc, despite a positive selection differential.
As an example, this is my sel_dif table:
Every time Xo (average value of the trait for the whole population) is negative, the resulting SDperc has the wrong sign.
The explanation is in the code of the MGIDI function L.302.
I think it would be more correct to use the absolute value for Xo in the denominator.
Thanks again,
Charlotte
I think gge must have arguments rep and block similar to anova_ind(), anova_joint(), performs_ammi(), waas() and waasb(), to analyze data from trials conducted in an alpha-lattice design.
Please briefly describe your problem and what output you expect. If you have a question, please don't use this form. Instead, ask on https://stackoverflow.com/ or https://community.rstudio.com/.
Please include a minimal reproducible example (AKA a reprex). If you've never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.
Brief description of the problem
# insert reprex herePlease briefly describe your problem and what output you expect. If you have a question, please don't use this form. Instead, ask on https://stackoverflow.com/ or https://community.rstudio.com/.
Please include a minimal reproducible example (AKA a reprex). If you've never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.
Brief description of the problem
#Each time i use the print_table I get: "Error in print_table(.) : could not find function "print_table"
gen_mod <- gamem(data_g, GEN, REP,
resp = c(ED, CL, CD, KW, TKW, NKR))
get_model_data(gen_mod, "blupg") %>% print_table()
Prepare for release:
devtools::build_readme()urlchecker::url_check()devtools::check(remote = TRUE, manual = TRUE)devtools::check_win_devel()rhub::check_for_cran()revdepcheck::revdep_check(num_workers = 4)cran-comments.mdSubmit to CRAN:
usethis::use_version('minor')devtools::submit_cran()Wait for CRAN...
usethis::use_github_release()usethis::use_dev_version()Please briefly describe your problem and what output you expect. If you have a question, please don't use this form. Instead, ask on https://stackoverflow.com/ or https://community.rstudio.com/.
Please include a minimal reproducible example (AKA a reprex). If you've never heard of a reprex before, start by reading https://www.tidyverse.org/help/#reprex.
Brief description of the problem
# insert reprex heresetwd("D:/PhD/Ragi")
library(readxl)
library(metan)
#> Registered S3 method overwritten by 'GGally':
#> method from
#> +.gg ggplot2
#> |=========================================================|
#> | Multi-Environment Trial Analysis (metan) v1.17.0 |
#> | Author: Tiago Olivoto |
#> | Type 'citation('metan')' to know how to cite metan |
#> | Type 'vignette('metan_start')' for a short tutorial |
#> | Visit 'https://bit.ly/pkgmetan' for a complete tutorial |
#> |=========================================================|
Metan_analysis <- read_excel("Metan_analysis.xlsx",
col_types = c("text", "text", "text",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric"))
View(Metan_analysis)
stab <- ge_stats(Metan_analysis, ENV, GEN, REP)
#> Error in rowSums((gamma.n)^2): 'x' must be an array of at least two dimensionsCreated on 2023-02-01 with reprex v2.0.2
Hi,
when calculating shukla stability using the Shukla() function, I ran into a problem where the results change depending on the subset of my data. I'm not sure if it's desired to take the initial number of levels from the data frame, as in line 55 with as.factor(), or if there's something wrong with using factor() instead to solve this problem?
Line 55 in 526b462
Here is a reproducible example with the provided sample data:
out1 <- Shukla(data_ge2[data_ge2$ENV != "A1", ], #subsetting one environment
env = ENV,
gen = GEN,
rep = REP,
resp = PH)
data_ge3 <- data_ge2[data_ge2$ENV != "A1", ] #subsetting the same environment
data_ge3$ENV <- factor(data_ge3$ENV) #but relevel the factor
out2 <- Shukla(data_ge3,
env = ENV,
gen = GEN,
rep = REP,
resp = PH)
print(out1)
print(out2)
And here the results:
> print(out1)
Variable PH
---------------------------------------------------------------------------
Shukla stability variance
---------------------------------------------------------------------------
# A tibble: 13 × 6
GEN Y ShuklaVar rMean rShukaVar ssiShukaVar
<fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 H1 2.59 0.0383 1 7 8
2 H10 2.16 0.0172 13 3 16
3 H11 2.27 0.0230 10 4 14
4 H12 2.35 0.0760 8 11 19
5 H13 2.46 0.0845 7 13 20
6 H2 2.54 0.0586 2 9 11
7 H3 2.48 0.0820 5 12 17
8 H4 2.48 0.0479 4 8 12
9 H5 2.48 0.0159 6 2 8
10 H6 2.49 0.0352 3 6 9
11 H7 2.28 0.0258 9 5 14
12 H8 2.20 0.0607 11 10 21
13 H9 2.18 -0.000876 12 1 13
> print(out2)
Variable PH
---------------------------------------------------------------------------
Shukla stability variance
---------------------------------------------------------------------------
# A tibble: 13 × 6
GEN Y ShuklaVar rMean rShukaVar ssiShukaVar
<fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 H1 2.59 0.0574 1 7 8
2 H10 2.16 0.0258 13 3 16
3 H11 2.27 0.0345 10 4 14
4 H12 2.35 0.114 8 11 19
5 H13 2.46 0.127 7 13 20
6 H2 2.54 0.0880 2 9 11
7 H3 2.48 0.123 5 12 17
8 H4 2.48 0.0719 4 8 12
9 H5 2.48 0.0238 6 2 8
10 H6 2.49 0.0528 3 6 9
11 H7 2.28 0.0387 9 5 14
12 H8 2.20 0.0910 11 10 21
13 H9 2.18 -0.00131 12 1 13
I want to include Anthesis silking interval (ASI) as a covariate to grain yield while calculating BLUP values and plotting them.
gamem_met(Data, env = Loc_Code,
gen = ENTRY, rep = REP,
block = BLOCK, random= "gen", prob = 0.05, resp = "Grain Yield", verbose = TRUE)
How to include covariate ASI in this code
I apologize for the inconvenience, but, metan has been removed from CRAN temporally because a check problem not fixed in time. Formerly available versions can be obtained from the archive.
A new patch release that fixes the problem was just submitted and is awaiting a manual inspection by a CRAN's team member.
You can download the latest version (v1.5.1) of metan from Gighub with
devtools::install_github("TiagoOlivoto/metan")
Hi,
I am testing a sample dataset that has the same format as the oat dataset as demonstrated. This was a RCBD design with 3 replicates
str(dataset)
'data.frame': 791 obs. of 4 variables:
$ ENV : Factor w/ 13 levels "2017_P","2017_S",..: 10 10 10 10 10 10 10 10 10 10 ...
$ GEN : Factor w/ 30 levels "8xp","XR_A0",..: 23 24 30 2 10 26 11 3 12 27 ...
$ BLOCK: int 1 1 1 1 1 1 1 1 1 1 ...
$ GY : num 233 225 248 259 255 ...
When I use cv_blup, I get the following error message:
BLUP_model = cv_blup(dataset,
resp = GY,
gen = GEN,
env = ENV,
rep = BLOCK,
nboot = 200,
random="gen")
Error: Column error must be length 262 (the number of rows) or one, not 264
In addition: Warning message:
In pred - testing$Y :
longer object length is not a multiple of shorter object length
When I rerun it the command multiple times without changing anything else, the first line of the error message changes each time. For example:
Error: Column error must be length 261 (the number of rows) or one, not 265
Error: Column error must be length 262 (the number of rows) or one, not 266
Error: Column error must be length 262 (the number of rows) or one, not 263
Error: Column error must be length 262 (the number of rows) or one, not 266
Error: Column error must be length 262 (the number of rows) or one, not 265
Error: Column error must be length 261 (the number of rows) or one, not 267
Error: Column error must be length 262 (the number of rows) or one, not 265
Do you know what is happening here?
Thanks!
Some functions argument do not take custom names as given in the following example
library(metan)
data("data_ge")
data_ge
# A tibble: 420 x 5
ENV GEN REP GY HM
<fct> <fct> <fct> <dbl> <dbl>
1 E1 G1 1 2.17 44.9
2 E1 G1 2 2.50 46.9
3 E1 G1 3 2.43 47.8
4 E1 G2 1 3.21 45.2
5 E1 G2 2 2.93 45.3
6 E1 G2 3 2.56 45.5
7 E1 G3 1 2.77 46.7
8 E1 G3 2 3.62 43.2
9 E1 G3 3 2.28 47.8
10 E1 G4 1 2.36 47.9
# … with 410 more rows
>
> anova_joint(
+ .data = data_ge
+ , env = ENV
+ , gen = GEN
+ , rep = REP
+ , resp = GY
+ )
variable GY
---------------------------------------------------------------------------
Joint ANOVA table
---------------------------------------------------------------------------
Source Df Sum Sq Mean Sq F value Pr(>F)
ENV 13.00 279.57 21.5057 62.33 0.00e+00
REP(ENV) 28.00 9.66 0.3451 3.57 3.59e-08
GEN 9.00 13.00 1.4439 14.93 2.19e-19
GEN:ENV 117.00 31.22 0.2668 2.76 1.01e-11
Residuals 252.00 24.37 0.0967 NA NA
CV(%) 11.63 NA NA NA NA
MSR+/MSR- 6.71 NA NA NA NA
OVmean 2.67 NA NA NA NA
---------------------------------------------------------------------------
All variables with significant (p < 0.05) genotype-vs-environment interaction
Done!
Variable GY
---------------------------------------------------------------------------
$anova
Source Df Sum Sq Mean Sq F value
1 ENV 13.000000 279.573552 21.50565785 62.325457
2 REP(ENV) 28.000000 9.661516 0.34505416 3.568548
3 GEN 9.000000 12.995044 1.44389374 14.932741
4 GEN:ENV 117.000000 31.219565 0.26683389 2.759595
5 Residuals 252.000000 24.366674 0.09669315 NA
6 CV(%) 11.627790 NA NA NA
7 MSR+/MSR- 6.708789 NA NA NA
8 OVmean 2.674242 NA NA NA
Pr(>F)
1 0.000000e+00
2 3.593191e-08
3 2.190118e-19
4 1.005191e-11
5 NA
6 NA
7 NA
8 NA
$model
Call:
aov(formula = mean ~ GEN + ENV + GEN:ENV + ENV/REP, data = data)
Terms:
GEN ENV GEN:ENV ENV:REP
Sum of Squares 12.99504 279.57355 31.21956 9.66152
Deg. of Freedom 9 13 117 28
Residuals
Sum of Squares 24.36667
Deg. of Freedom 252
Residual standard error: 0.3109552
Estimated effects may be unbalanced
$augment
# A tibble: 420 x 11
ENV GEN REP mean hat sigma fitted resid
<fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
1 E1 G1 1 2.17 0.4 0.311 2.42 -0.255
2 E1 G1 2 2.50 0.400 0.311 2.40 0.101
3 E1 G1 3 2.43 0.4 0.311 2.27 0.154
4 E1 G2 1 3.21 0.400 0.311 2.96 0.249
5 E1 G2 2 2.93 0.400 0.312 2.94 -0.00492
6 E1 G2 3 2.56 0.400 0.311 2.81 -0.244
7 E1 G3 1 2.77 0.4 0.311 2.95 -0.176
8 E1 G3 2 3.62 0.400 0.306 2.92 0.696
9 E1 G3 3 2.28 0.4 0.309 2.80 -0.521
10 E1 G4 1 2.36 0.4 0.311 2.65 -0.286
# … with 410 more rows, and 3 more variables: stdres <dbl>,
# se.fit <dbl>, factors <chr>
$details
# A tibble: 10 x 2
Parameters mean
<chr> <chr>
1 Mean "2.67"
2 SE "NA"
3 SD "NA"
4 CV "NA"
5 Min "0.67 (G10 in E11)"
6 Max "5.09 (G8 in E5)"
7 MinENV "E11 (1.37)"
8 MaxENV "E3 (4.06)"
9 MinGEN "G10 (2.47) "
10 MaxGEN "G8 (3) "
---------------------------------------------------------------------------
>
> library(tidyverse)
> tb1 <-
+ data_ge %>%
+ mutate(
+ Env = ENV
+ , Gen = GEN
+ , Rep = REP
+ ) %>%
+ select(Env, Gen, Rep, GY)
>
> tb1
# A tibble: 420 x 4
Env Gen Rep GY
<fct> <fct> <fct> <dbl>
1 E1 G1 1 2.17
2 E1 G1 2 2.50
3 E1 G1 3 2.43
4 E1 G2 1 3.21
5 E1 G2 2 2.93
6 E1 G2 3 2.56
7 E1 G3 1 2.77
8 E1 G3 2 3.62
9 E1 G3 3 2.28
10 E1 G4 1 2.36
# … with 410 more rows
>
> anova_joint(
+ .data = tb1
+ , env = Env
+ , gen = Gen
+ , rep = Rep
+ , resp = GY
+ )
Error: Can't subset columns that don't exist.
x The columns `ENV`, `GEN`, and `REP` don't exist.
Run `rlang::last_error()` to see where the error occurred.
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