flavjack/inti

Tools and Statistical Procedures in Plant Science

Hi, I'm Flavio 👋

• 🔭 I'm currently doing my PhD in plant breeding in UHOH
• 📫 How to reach me: https://lozanoisla.com/

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I'm an agronomist and plant breeder 👨‍🌾. In my free time I work in develop open source app for educational purpose and support young scientist in R+D+i. Founder of ‘inkaverse’ project to develop different procedures and tools used in plant science and experimental designs. The mean aim of the project is to support researchers during the planning and analysis of experiments.

Inkaverse project: https://inkaverse.com/ 🔬

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inti's Issues

fieldbook online using Mozilla Firefox

En mozilla hay un problema cuando usamos el fieldbook online. Este bug lo solucine usando este foros

https://datatables.net/forums/discussion/29006/pdf-and-excel-button-is-not-showing-in-mozilla-ie-safari-in-both-windows-and-mac

Donde, desactive y active el Shockware Flahs y volvieron aparecer los botones.

Mejoras en app- taller UNALM-FIA (22-27 May 2017)

#ToDo Lista de mejoras recogidas del taller de FieldBook y R para la agricultura.

• El ANOVA (anva) debe ser mostrado en una tabla para descargar.

• De la Comparación de Medias, mostrar en una tabla (Mean, CV, MSerror , HSD, cuadrados medios esperados) (out$statistics).

• Agregar un módulo de transformaciones (raíz cuadrada, logaritmo, arcosin).

• En el análisis factorial, sacar un resumen por Factor A y por Factor B. (efectos principales)

• Poner gráfico del PCA del factoextra::pca

• Gráfico de pairs con ggplot2 (Multiples plots para ver correlación y el coeficiente r en cada sub-gráfico).

• En Statistics, colocar por defecto Tukey en Type

• Mostrar debajo del boxplot, una tabla con los outliers.

• Cuadro resumen de los estadísticos básico (media, moda, mediana, min, max,

• En caso de valores perdidos, estimarlos usando el diseno experimental.

Completar los argumentos de las funciones

lm

lm_eqn <- function(x, y, data) {

fml <- as.formula(paste(x,y, sep = " ~ "))

esta mal el orden del modelo, debe ser y ~ x

Multi Traits

Good evening all, thanks for developing this Good package. Just looking at the documentation how can I run heritability for more than one response variable at the same time using the purrr approach. Thanks

BLUEs from H2cal may be wrong if other fixed effects in the model

I fit a model in H2cal that included genotypes but another fixed effect, which I put last in the model. When you do that, this slicing operation gets the wrong rows for the blues data frame:

dplyr::slice((count+1):NROW(.))

In my example, count = 1, but in the fef() output, the other fixed effect is last in the list, so this slicing skips the first genotype and includes the other fixed effect. Workaround is easy for me, just change the order of the terms in the model, but you may want to consider grepping for the geno.name in the fef() output to get the correct indices for slicing to make it more robust.

Just found your package today and think it's awesome, thanks - Jim Holland [email protected]

inti H2cal function errors for metabolite data with heterogeneous concentration values

Hello, I'm running the H2cal function in the context of phenotype treatment for a GWAS analysis as follows:

  cat_h2cal <- H2cal(data = InputDataFrame
                     , trait = "C_001"
                     , gen.name = "ID"
                     , rep.n = 2
                     , fixed.model = "(1|Habitus) + ID"
                     , random.model = "(1|Habitus) + (1|ID)"
                     , emmeans = FALSE
                     , plot_diag = TRUE
                     , outliers.rm = TRUE )

The traits of interest are metabolites, varying greatly in concentration. The function works correctly, generates plots and individuates outliers with some traits, but not with others. In particular, I get this kind of errors for the traits the function can't process:

Errore in lme4::lFormula(formula = C_001 ~ (1 | Habitus) + (1 | ID), :
0 (non-NA) cases
In aggiunta: Messaggi di avvertimento:
1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 26.0806 (tol = 0.002, component 1)
2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model is nearly unidentifiable: very large eigenvalue

• Rescale variables?

I've noticed that if I change the random.model formula term 1|ID with ID, plots are generated. But I'd like to always apply the same model to all my metabolites. If I look at the concentrations of the traits H2Cal is unable to process, I've noticed that troublesome ones have very low concentrations and often many values (with one single exception, >80%), are 0. However, some metabolites with a pretty high 0 frequency (50-60%) are processed correctly.

Also, some of my samples have one replicate, others two.

Can you help me here? How can I handle my dataset in this case? Maybe with a different formula for the random model?
I've found some tips online, about re-scaling variables (for a similar function, called lme4), but I was wondering what the exact cause of my problems could be.

I'm attaching a part of my dataset

SampleToAttach_H2Cal.txt

Here, the troublesome metabolites are C_001, C_002, C_003, C_004, C_009 and C_013.

Thank you in advance

cambiar portada por yupuna

cambio portada ubicado en quipo/bioincuba/marketing e imagen/bioincuba/incubagraria/portada_fieldbook

cita

Adicionar en la herramienta, la manera de citar, Es algo que solitan para la herramienta