imhr.r33._model¶

Classes

class imhr.r33._model.Model(isLibrary=False)[source]¶

Bases: object

Run statistical models for analysis.

Methods

Methods

anova(config, y, f, df, csv, path, effects)	Run analysis of variance model using rpy2, seaborn and pandas.
lmer(config, y, f, df, exclude, csv, path, …)	Run linear mixed regression model, using rpy2, seaborn and pandas.
logistic(config, y, f, df, me, exclude, csv, …)	Run logistic regression model, using rpy2, seaborn and pandas.

anova(config, y, f, df, csv, path, effects)	Run analysis of variance model using rpy2, seaborn and pandas.
lmer(config, y, f, df, exclude, csv, path, …)	Run linear mixed regression model, using rpy2, seaborn and pandas.
logistic(config, y, f, df, me, exclude, csv, …)	Run logistic regression model, using rpy2, seaborn and pandas.

classmethod anova(config, y, f, df, csv, path, effects, is_html=True)[source]¶

Run analysis of variance model using rpy2, seaborn and pandas.

Parameters:	y : str Response variable. f : str Formula to use for analysis. df : pandas.DataFrame Pandas dataframe of raw data. f : str R-compatiable formula. csv : str Name of generated CSV file to run analysis in R. path : str The directory path to save the generated files. effects : list List of main effects. is_html : bool Whether html should be generated.
Returns:	model : rpy2.robjects.methods.RS4 Python representation of an R instance of class ‘S4’. df_anova : pandas.DataFrame Pandas dataframe of model output. get_anova : str R script to run model. html : str HTML output.

Notes

Resources

• https://rpsychologist.com/r-guide-longitudinal-lme-lmer
• https://sites.ualberta.ca/~lkgray/uploads/7/3/6/2/7362679/slides_-anova_assumptions.pdf
• https://rpubs.com/tmcurley/twowayanova
• https://rstudio-pubs-static.s3.amazonaws.com/158708_78d414c091fc47bd99f6f75e3bd8f4cb.html
• https://m-clark.github.io/docs/mixedModels/anovamixed.html
• http://dwoll.de/rexrepos/posts/anovaMixed.html
• https://stats.stackexchange.com/questions/247582/repeated-measures-anova-in-r-errorsubject-vs-errorsubject-day
• https://cran.r-project.org/web/packages/afex/vignettes/afex_anova_example.html#post-hoc-contrasts-and-plotting
• http://www.let.rug.nl/nerbonne/teach/rema-stats-meth-seminar/presentations/Wieling-MixedModels-2011.pdf

Definition:

A test that allows one to make comparisons between the means of multiple groups of data, where two independent variables are considered.

Assumptions of ANOVA

1. Normal distribution (normality)

   • Short: Samples are drawn from a normally distributed population (Q-Q Plot, Shapiro-Wilks Test)
   • Detailed Definition: Residuals in data are normally distributed.

2. Homogeneity of variance (homoscedasticity)

   • Short: Variances are equal (or similar).
   • Detailed: Varience for a DV is constant across the sample. (residual vs fitted plot, Scale-Location plot, Levene’s test)

3. Independent observations

   • Samples have been drawn independently of each other. No analysis needed.

Hypothesis Interpretation

• Null: The means of all levels of an IV groups are equal.
• Alternative: The mean of at least level of an IV is different.

classmethod lmer(config, y, f, df, exclude, csv, path, effects, is_html=True)[source]¶

Run linear mixed regression model, using rpy2, seaborn and pandas.

Parameters:

y : str Response variable. f : list of str Formula to use for analysis. df : pandas.DataFrame Pandas dataframe of raw data. f : str R-compatiable formula. exclude : list List of participants to be excluded. csv : str Name of generated CSV file to run analysis in R. path : str The directory path to save the generated files. effects : list List of main effects. is_html : bool Whether html should be generated.

Returns:

model : rpy2.robjects.methods.RS4 Python representation of an R instance of class ‘S4’. df_lmer : pandas.DataFrame Pandas dataframe of model output. get_lmer : str R script to run model. html : str HTML output.

Notes

Resources

• https://rpsychologist.com/r-guide-longitudinal-lme-lmer
• https://stackoverflow.com/questions/47686227/poisson-regression-in-statsmodels-and-r
• https://tsmatz.wordpress.com/2017/08/30/glm-regression-logistic-poisson-gaussian-gamma-tutorial-with-r/
• https://stats.stackexchange.com/questions/311556/help-interpreting-count-data-glmm-using-lme4-glmer-and-glmer-nb-negative-binom

classmethod logistic(config, y, f, df, me, exclude, csv, path, is_html=True)[source]¶

Run logistic regression model, using rpy2, seaborn and pandas.

Parameters:	y : str Response variable. f : str Formula to use for analysis. df : pandas.DataFrame Pandas dataframe of raw data. me : list of str List of main effects. exclude : list List of participants to be excluded. csv : str Name of generated CSV file to run analysis in R. path : str The directory path to save the generated files. is_html : bool Whether html should be generated.
Returns:	model : rpy2.robjects.methods.RS4 Python representation of an R instance of class ‘S4’. df_logit : pandas.DataFrame Pandas dataframe of model output. get_logit : str R script to run model. html : str HTML output.

Notes

Resources

• https://rpsychologist.com/r-guide-longitudinal-lme-lmer
• https://stats.idre.ucla.edu/r/dae/mixed-effects-logistic-regression/
• https://www.statisticssolutions.com/assumptions-of-logistic-regression/