added prediction.R to do logistic regression
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scripts/prediction.R
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scripts/prediction.R
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#!/usr/bin/env Rscript
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#########################################################
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# TASK: prediction
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#=======================================================================
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# working dir and loading libraries
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getwd()
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setwd("~/git/LSHTM_analysis/scripts")
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getwd()
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source("combining_dfs_plotting.R")
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#source("plotting/corr_data.R")
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#=======
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# output
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#=======
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####################################################################
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# end of loading libraries and functions
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####################################################################
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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table(merged_df2$mutation_info)
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merged_df2$mutation_info_labels = ifelse(merged_df2$mutation_info == dr_muts_col, 1, 0)
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table(merged_df2$mutation_info_labels)
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table(merged_df3$mutation_info)
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merged_df3$mutation_info_labels = ifelse(merged_df3$mutation_info == dr_muts_col, 1, 0)
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table(merged_df3$mutation_info_labels)
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# lig
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table(merged_df3$mutation_info)
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merged_df3$mutation_info_labels = ifelse(merged_df3$mutation_info == dr_muts_col, 1, 0)
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table(merged_df3$mutation_info_labels)
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table(merged_df3$mutation_info)
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merged_df3$mutation_info_labels = ifelse(merged_df3$mutation_info == dr_muts_col, 1, 0)
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table(merged_df3$mutation_info_labels)
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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###############################################################################
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model_ind = glm(mutation_info_labels ~ or_mychisq
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, data = merged_df2
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, family = "binomial")
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summary(model_ind)
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nobs(model_ind)
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#=============
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# try loop
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#=============
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my_ivs = c("or_mychisq", "or_kin", "pval_fisher", "af", "duet_stability_change", "duet_scaled")
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for( i in my_ivs){
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cat ("===============================\n")
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cat(i)
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cat ("\n===============================\n")
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print(summary(glm(mutation_info_labels ~ eval(parse(text=i))
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, data = merged_df2
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, family = "binomial")))
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}
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###############################################################################
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model1 = glm(mutation_info_labels ~ or_mychisq
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, data = merged_df2
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, family = "binomial"); summary(model1)
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model2 = glm(mutation_info_labels ~ or_kin
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, data = merged_df2
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, family = "binomial"); summary(model2)
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model3 = glm(mutation_info_labels ~ pval_fisher
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, data = merged_df2
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, family = "binomial"); summary(model3)
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model4 = glm(mutation_info_labels ~ af
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, data = merged_df2
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, family = "binomial"); summary(model4)
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model5 = glm(mutation_info_labels ~ duet_stability_change
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, data = merged_df2
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, family = "binomial"); summary(model5)
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model6 = glm(mutation_info_labels ~ duet_scaled
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, data = merged_df2
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, family = "binomial"); summary(model6)
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###############################################################################
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#========================================
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# loop and output in df: individually
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#========================================
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my_ivs = c("or_mychisq", "or_kin", "pval_fisher", "af"
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, "rsa"
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, "rd_values"
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, "kd_values"
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, "duet_stability_change"
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, "duet_scaled"
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, "duet_outcome"
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, "ddg"
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, "foldx_scaled"
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, "foldx_outcome")
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ps_logistic_df2 = data.frame()
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for( i in my_ivs){
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print(i)
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df = data.frame(var_name = NA
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, number_samples = NA
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, beta = NA
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, odds_ratio = NA
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, pvalue = NA
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, se = NA
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, zvalue = NA
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, ci_lower = NA
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, ci_upper = NA)
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model = glm(mutation_info_labels ~ eval(parse(text=i))
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, data = merged_df2
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, family = "binomial")
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var_name = i
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number_samples = nobs(model)
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beta_logistic = summary(model)$coefficients[2,1]; beta_logistic
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or_logistic = exp(summary(model)$coefficients[2,1])
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pval_logistic = summary(model)$coefficients[2,4]
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se_logistic = summary(model)$coefficients[2,2]
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zval_logistic = summary(model)$coefficients[2,3]
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ci_mod = exp(confint(model))[2,]
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ci_lower_logistic = ci_mod[["2.5 %"]]
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ci_upper_logistic = ci_mod[["97.5 %"]]
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print(c(var_name, beta_logistic, or_logistic, pval_logistic, se_logistic, zval_logistic, ci_mod))
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df$var_name = var_name
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df$number_samples = number_samples
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df$beta = beta_logistic
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df$odds_ratio = or_logistic
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df$pvalue = pval_logistic
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df$se = se_logistic
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df$zvalue = zval_logistic
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df$ci_lower = ci_lower_logistic
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df$ci_upper = ci_upper_logistic
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print(df)
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ps_logistic_df2 = rbind(ps_logistic_df2, df)
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}
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#--------------------
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# formatting df
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#--------------------
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ps_logistic_df2$data_source = "df2"
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# adding pvalue_signif
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ps_logistic_df2$pvalue_signif = ps_logistic_df2$pvalue
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str(ps_logistic_df2$pvalue_signif)
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ps_logistic_df2 = dplyr::mutate(ps_logistic_df2
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, pvalue_signif = case_when(pvalue_signif == 0.05 ~ "."
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, pvalue_signif <=0.0001 ~ '****'
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, pvalue_signif <=0.001 ~ '***'
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, pvalue_signif <=0.01 ~ '**'
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, pvalue_signif <0.05 ~ '*'
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, TRUE ~ 'ns'))
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###############################################################################
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#========================================
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# loop and output in df: adjusted
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#========================================
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my_ivs = c("or_mychisq"
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, "rsa", "rd_values", "kd_values"
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, "duet_stability_change", "duet_scaled", "duet_outcome"
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, "ddg", "foldx_scaled", "foldx_outcome")
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model_adjusted = glm(mutation_info_labels ~ or_mychisq + rsa + rd_values + kd_values +
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duet_stability_change + duet_scaled + duet_outcome + ddg + foldx_scaled + foldx_outcome
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, data = merged_df2
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, family = "binomial")
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var_names = model_adjusted$terms
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number_samples = nobs(model_adjusted)
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summary(model_adjusted)
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ci_mod = exp(confint(model_adjusted))
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my_ivs_adjusted = names(model_adjusted$coefficients)
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ps_logistic_adjusted_df2 = data.frame(var_name = NA
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, number_samples = NA
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, beta = NA
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, odds_ratio = NA
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, pvalue = NA
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, se = NA
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, zvalue = NA
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, ci_lower = NA
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, ci_upper = NA)
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for (i in my_ivs_adjusted){
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print(i)
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ps_logistic_adjusted_df2$var_name = i
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ps_logistic_adjusted_df2$number_samples = number_samples
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ps_logistic_adjusted_df2$beta = model_adjusted$coefficients[[i]]
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ps_logistic_adjusted_df2$odds_ratio = exp(model_adjusted$coefficients[[i]])
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ps_logistic_adjusted_df2$pvalue =
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ps_logistic_adjusted_df2$se =
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ps_logistic_adjusted_df2$zvalue =
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ps_logistic_adjusted_df2$ci_lower = ci_mod[[i]]
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ps_logistic_adjusted_df2$ci_upper =
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}
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ci_lower_logistic = ci_mod[["2.5 %"]]
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ci_upper_logistic = ci_mod[["97.5 %"]]
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pval_logistic = summary(model_adjusted)$coefficients[2,4]
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se_logistic = summary(model_adjusted)$coefficients[2,2]
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zval_logistic = summary(model_adjusted)$coefficients[2,3]
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ci_mod = exp(confint(model_adjusted))[2,]
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ci_lower_logistic = ci_mod[["2.5 %"]]
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ci_upper_logistic = ci_mod[["97.5 %"]]
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df$var_name = var_name
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df$number_samples = number_samples
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df$beta = beta_logistic
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df$odds_ratio = or_logistic
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df$pvalue = pval_logistic
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df$se = se_logistic
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df$zvalue = zval_logistic
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df$ci_lower = ci_lower_logistic
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df$ci_upper = ci_upper_logistic
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