Comments (8)
@nivelive please, provide a reproducible example so that I can reproduce the error on my side.
from metan.
setwd("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 |
#> |=========================================================|
library(reprex)
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)
Metan_analysis$ENV <- as.factor(Metan_analysis$ENV)
Metan_analysis$GEN <- as.factor(Metan_analysis$GEN)
Metan_analysis$REP<- as.factor(Metan_analysis$REP)
inspect(Metan_analysis)
#> # A tibble: 13 x 10
#> Variable Class Missing Levels Valid_n Min Median Max Outlier Text
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1 ENV factor No 3 252 NA NA NA NA NA
#> 2 GEN factor No 28 252 NA NA NA NA NA
#> 3 REP factor No 3 252 NA NA NA NA NA
#> 4 DF numeric No - 252 62 75 91 0 NA
#> 5 FLBL numeric No - 252 24.2 39.0 56.3 2 NA
#> 6 FLBW numeric No - 252 0.37 0.92 1.63 0 NA
#> 7 PL numeric No - 252 17.1 29.9 41.9 1 NA
#> 8 PH numeric No - 252 66.5 92.6 147 0 NA
#> 9 FL numeric No - 252 3.07 5.57 10.7 1 NA
#> 10 FW numeric No - 252 0.27 0.87 1.6 0 NA
#> 11 EHL numeric No - 252 3.1 6.53 11.9 0 NA
#> 12 FNME numeric No - 252 2.67 6.67 11.3 0 NA
#> 13 Y numeric No - 252 0.25 1.22 2.61 0 NA
#> Warning: Possible outliers in variable(s) FLBL, PL, FL. Use 'find_outliers()'
#> for more details.
stab <- ge_stats(Metan_analysis, ENV, GEN, REP)
#> Error in rowSums((gamma.n)^2): 'x' must be an array of at least two dimensions
Created on 2023-02-03 with reprex v2.0.2
from metan.
@nivelive In my side, with the example data data_ge
, no error is observed. Without Metan_analysis
I can't reproduce the error.
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 |
#> |=========================================================|
args(ge_stats)
#> function (.data, env, gen, rep, resp, verbose = TRUE, prob = 0.05)
#> NULL
# by default, all numeric variables are analyzed.
# So, the argument 'resp' is not mandatory
model <- ge_stats(data_ge, ENV, GEN, REP)
#> Evaluating trait GY |====================== | 50% 00:00:09 Evaluating trait HM |============================================| 100% 00:00:20
Created on 2023-02-08 with reprex v2.0.2
from metan.
I tried making it into a reproducible example using reprex() but I am not understanding why is it not working. can you please tell me what mistake I m doing so that I can send you the proper reproducible example. I have referred Jenifer Bryan video and as per her instructions I tried to reprex it.
from metan.
setwd("D:/PhD/Ragi")
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 |
#> |=========================================================|
library(readxl)
Metan_analysis <- read_excel("Metan_analysis.xlsx",
col_types = c("text", "text", "text",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric"))
Metan_analysis$REP <- as.factor(Metan_analysis$REP)
Metan_analysis$GEN <- as.factor(Metan_analysis$GEN)
Metan_analysis$ENV <- as.factor(Metan_analysis$ENV)
inspect(Metan_analysis, plot = TRUE, threshold = 28)
#> # A tibble: 13 x 10
#> Variable Class Missing Levels Valid_n Min Median Max Outlier Text
#> <chr> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1 ENV factor No 3 252 NA NA NA NA NA
#> 2 GEN factor No 28 252 NA NA NA NA NA
#> 3 REP factor No 3 252 NA NA NA NA NA
#> 4 DF numeric No - 252 62 75 91 0 NA
#> 5 FLBL numeric No - 252 24.2 39.0 56.3 2 NA
#> 6 FLBW numeric No - 252 0.37 0.92 1.63 0 NA
#> 7 PL numeric No - 252 17.1 29.9 41.9 1 NA
#> 8 PH numeric No - 252 66.5 92.6 147 0 NA
#> 9 FL numeric No - 252 3.07 5.57 10.7 1 NA
#> 10 FW numeric No - 252 0.27 0.87 1.6 0 NA
#> 11 EHL numeric No - 252 3.1 6.53 11.9 0 NA
#> 12 FNME numeric No - 252 2.67 6.67 11.3 0 NA
#> 13 Y numeric No - 252 0.25 1.22 2.61 0 NA
#> Warning: Possible outliers in variable(s) FLBL, PL, FL. Use 'find_outliers()'
#> for more details.
descstat <- desc_stat(Metan_analysis, stats = "all")
anova<- anova_ind(Metan_analysis, env= ENV, gen = GEN, rep = REP)
#> Evaluating trait DF |==== | 10% 00:00:00 Evaluating trait FLBL |======== | 20% 00:00:00 Evaluating trait FLBW |============= | 30% 00:00:00 Evaluating trait PL |================== | 40% 00:00:00 Evaluating trait PH |====================== | 50% 00:00:00 Evaluating trait FL |========================== | 60% 00:00:00 Evaluating trait FW |=============================== | 70% 00:00:00 Evaluating trait EHL |================================== | 80% 00:00:00 Evaluating trait FNME |====================================== | 90% 00:00:00 Evaluating trait Y |=============================================| 100% 00:00:00
anova_joint <- anova_joint(Metan_analysis, env= ENV, gen = GEN, rep = REP)
#> variable DF
#> ---------------------------------------------------------------------------
#> Joint ANOVA table
#> ---------------------------------------------------------------------------
#> Source Df Sum Sq Mean Sq F value Pr(>F)
#> ENV 2.00 11353.7 5676.86 1788.21 3.83e-111
#> REP(ENV) 6.00 21.7 3.62 1.14 3.42e-01
#> GEN 27.00 3069.6 113.69 35.81 8.47e-55
#> GEN:ENV 54.00 753.8 13.96 4.40 1.59e-13
#> Residuals 162.00 514.3 3.17 NA NA
#> CV(%) 2.33 NA NA NA NA
#> MSR+/MSR- 14.87 NA NA NA NA
#> OVmean 76.38 NA NA NA NA
#> ---------------------------------------------------------------------------
#>
#> variable FLBL
#> ---------------------------------------------------------------------------
#> Joint ANOVA table
#> ---------------------------------------------------------------------------
#> Source Df Sum Sq Mean Sq F value Pr(>F)
#> ENV 2.00 1114 557.1 38.14 2.67e-14
#> REP(ENV) 6.00 612 101.9 6.98 1.29e-06
#> GEN 27.00 2539 94.1 6.44 7.87e-15
#> GEN:ENV 54.00 957 17.7 1.21 1.79e-01
#> Residuals 162.00 2366 14.6 NA NA
#> CV(%) 9.90 NA NA NA NA
#> MSR+/MSR- 1.86 NA NA NA NA
#> OVmean 38.59 NA NA NA NA
#> ---------------------------------------------------------------------------
#>
#> variable FLBW
#> ---------------------------------------------------------------------------
#> Joint ANOVA table
#> ---------------------------------------------------------------------------
#> Source Df Sum Sq Mean Sq F value Pr(>F)
#> ENV 2.000 10.066 5.033 360.46 2.24e-60
#> REP(ENV) 6.000 0.622 0.104 7.43 4.88e-07
#> GEN 27.000 5.356 0.198 14.21 2.84e-30
#> GEN:ENV 54.000 2.914 0.054 3.86 1.71e-11
#> Residuals 162.000 2.262 0.014 NA NA
#> CV(%) 12.771 NA NA NA NA
#> MSR+/MSR- 2.267 NA NA NA NA
#> OVmean 0.925 NA NA NA NA
#> ---------------------------------------------------------------------------
#>
#> variable PL
#> ---------------------------------------------------------------------------
#> Joint ANOVA table
#> ---------------------------------------------------------------------------
#> Source Df Sum Sq Mean Sq F value Pr(>F)
#> ENV 2.00 2296.2 1148.12 194.134 9.64e-44
#> REP(ENV) 6.00 21.1 3.52 0.595 7.34e-01
#> GEN 27.00 722.3 26.75 4.524 6.31e-10
#> GEN:ENV 54.00 674.8 12.50 2.113 1.71e-04
#> Residuals 162.00 958.1 5.91 NA NA
#> CV(%) 8.25 NA NA NA NA
#> MSR+/MSR- 3.10 NA NA NA NA
#> OVmean 29.46 NA NA NA NA
#> ---------------------------------------------------------------------------
#>
#> variable PH
#> ---------------------------------------------------------------------------
#> Joint ANOVA table
#> ---------------------------------------------------------------------------
#> Source Df Sum Sq Mean Sq F value Pr(>F)
#> ENV 2.00 60360 30180.0 508.06 1.60e-70
#> REP(ENV) 6.00 1509 251.4 4.23 5.50e-04
#> GEN 27.00 5466 202.4 3.41 7.05e-07
#> GEN:ENV 54.00 3218 59.6 1.00 4.80e-01
#> Residuals 162.00 9623 59.4 NA NA
#> CV(%) 7.87 NA NA NA NA
#> MSR+/MSR- 2.51 NA NA NA NA
#> OVmean 97.94 NA NA NA NA
#> ---------------------------------------------------------------------------
#>
#> variable FL
#> ---------------------------------------------------------------------------
#> Joint ANOVA table
#> ---------------------------------------------------------------------------
#> Source Df Sum Sq Mean Sq F value Pr(>F)
#> ENV 2.00 343.54 171.770 500.85 4.34e-70
#> REP(ENV) 6.00 6.07 1.012 2.95 9.23e-03
#> GEN 27.00 198.95 7.368 21.48 1.58e-40
#> GEN:ENV 54.00 37.81 0.700 2.04 3.23e-04
#> Residuals 162.00 55.56 0.343 NA NA
#> CV(%) 9.96 NA NA NA NA
#> MSR+/MSR- 2.14 NA NA NA NA
#> OVmean 5.88 NA NA NA NA
#> ---------------------------------------------------------------------------
#>
#> variable FW
#> ---------------------------------------------------------------------------
#> Joint ANOVA table
#> ---------------------------------------------------------------------------
#> Source Df Sum Sq Mean Sq F value Pr(>F)
#> ENV 2.000 10.207 5.1033 378.52 8.73e-62
#> REP(ENV) 6.000 0.492 0.0820 6.08 9.11e-06
#> GEN 27.000 2.399 0.0888 6.59 3.41e-15
#> GEN:ENV 54.000 1.870 0.0346 2.57 2.63e-06
#> Residuals 162.000 2.184 0.0135 NA NA
#> CV(%) 14.450 NA NA NA NA
#> MSR+/MSR- 3.611 NA NA NA NA
#> OVmean 0.804 NA NA NA NA
#> ---------------------------------------------------------------------------
#>
#> variable EHL
#> ---------------------------------------------------------------------------
#> Joint ANOVA table
#> ---------------------------------------------------------------------------
#> Source Df Sum Sq Mean Sq F value Pr(>F)
#> ENV 2.00 802.870 401.435 845.198 1.92e-86
#> REP(ENV) 6.00 0.742 0.124 0.261 9.54e-01
#> GEN 27.00 262.922 9.738 20.502 2.65e-39
#> GEN:ENV 54.00 77.340 1.432 3.015 4.06e-08
#> Residuals 162.00 76.943 0.475 NA NA
#> CV(%) 10.12 NA NA NA NA
#> MSR+/MSR- 2.63 NA NA NA NA
#> OVmean 6.81 NA NA NA NA
#> ---------------------------------------------------------------------------
#>
#> variable FNME
#> ---------------------------------------------------------------------------
#> Joint ANOVA table
#> ---------------------------------------------------------------------------
#> Source Df Sum Sq Mean Sq F value Pr(>F)
#> ENV 2.00 416.49 208.244 353.63 7.93e-60
#> REP(ENV) 6.00 6.23 1.039 1.76 1.10e-01
#> GEN 27.00 62.95 2.331 3.96 2.14e-08
#> GEN:ENV 54.00 68.45 1.268 2.15 1.20e-04
#> Residuals 162.00 95.40 0.589 NA NA
#> CV(%) 11.61 NA NA NA NA
#> MSR+/MSR- 2.44 NA NA NA NA
#> OVmean 6.61 NA NA NA NA
#> ---------------------------------------------------------------------------
#>
#> variable Y
#> ---------------------------------------------------------------------------
#> Joint ANOVA table
#> ---------------------------------------------------------------------------
#> Source Df Sum Sq Mean Sq F value Pr(>F)
#> ENV 2.00 35.90 17.9503 315.21 1.43e-56
#> REP(ENV) 6.00 1.90 0.3168 5.56 2.87e-05
#> GEN 27.00 13.28 0.4919 8.64 7.29e-20
#> GEN:ENV 54.00 7.14 0.1323 2.32 2.55e-05
#> Residuals 162.00 9.23 0.0569 NA NA
#> CV(%) 18.60 NA NA NA NA
#> MSR+/MSR- 1.98 NA NA NA NA
#> OVmean 1.28 NA NA NA NA
#> ---------------------------------------------------------------------------
#>
#> ---------------------------------------------------------------------------
#> Variables with nonsignificant GxE interaction
#> FLBL PH
#> ---------------------------------------------------------------------------
#> Done!
stab <- ge_stats(Metan_analysis, env= ENV, gen = GEN, rep = REP)
#> Error in rowSums((gamma.n)^2): 'x' must be an array of at least two dimensions
Created on 2023-02-08 with reprex v2.0.2
from metan.
In mixed-effect models (e.g., WAASB), traits with p-values equal to 1 for the GEI effects can generate such an error. I suggest you try to run one trait at once and see which one generates the error. It seems that such an error is being caused by a trait with non-significant GEI effects (try removing FLBL and PH) and run again. Since I don't have the raw data to check this out, It is difficult to say the exact source of the error.
from metan.
as per your suggestion I analyzed each trait separately and I could get results for only two traits out of 10. Is this means other traits have non significant GXE interaction?
from metan.
@nivelive If GXE interaction for one trait is 'highly' non-significant (p-value > 0.8, for example) it means that the response of genotypes is very likely to be not dependent on the environmental condition, but this must be interpreted with caution. For example, within 60-70 genotypes, a few (say, 2-3) may have different responses and interact with environments. So, it is always a good choice, to explore the dataset.
Since I don't have the raw data to find the source of the error and this seems to be not a clear bug, I'm closing this now. Feel free to re-open it if you find it necessary.
from metan.
Related Issues (20)
- Release metan 1.16.0 HOT 1
- error while analysis path analysis HOT 7
- Problem with ge_stats HOT 3
- AMMI Analysis HOT 2
- Shukla Stablility with wrong length of levels HOT 1
- print_table(.) : could not find function "print_table" HOT 1
- MGIDI selection differential is false if averaged data is negative HOT 2
- Problem with plot.gge or plot function HOT 2
- Error in corr_coef(data, ..., type = c("linear", "partial"), method = c("pearson", : could not find function "corr_coef" HOT 2
- Error in path_coeff(datt, resp = SYPPI) : could not find function "path_coeff" HOT 2
- Same code of "metan" running on on one dataset, but not on similar second dataset HOT 2
- Inclusion of covariate to dependent trait while calculating BLUP values
- Inclusion of covariate to dependent trait while calculating BLUP values HOT 1
- How to remove outliers in METAN HOT 1
- Hello HOT 2
- metan temporarily out of CRAN HOT 1
- Mislabeling of ENV and GEN in Which Won Where Biplot HOT 6
- rep and block arguments for gge HOT 1
- problem in running your example code
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from metan.