moved older regression files to extra
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159
extra/logistic_regression_v1.R
Executable file
159
extra/logistic_regression_v1.R
Executable file
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#!/usr/bin/Rscript
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getwd()
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setwd('~/git/mosaic_2020/')
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getwd()
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########################################################################
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# TASK: Run regression analysis
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# npa
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########################################################################
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#=================================================================================
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# TO DO:
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# Simple stats b/w obesity and non-obesity to consider including in reg analysis
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# Include NPA statistically sign params
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# Rerun graphs and plots without asthma?
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#=================================================================================
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#====================
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# Input: source data
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#====================
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source("data_extraction_formatting_clinical.R")
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# quick sanity checks
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table(clinical_df_ics$ia_exac_copd==1 & clinical_df_ics$asthma == 1)
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table(fp_adults$ia_exac_copd==1 & fp_adults$asthma == 1)
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table(fp_adults_na$ia_exac_copd==1 & fp_adults_na$asthma == 1)
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table(clinical_df_ics$asthma)
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#--------------------
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# Data reassignment
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#--------------------
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my_data = clinical_df_ics
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my_data_na = clinical_df_ics_na
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table(my_data$ia_exac_copd==1 & my_data$asthma == 1)
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table(my_data_na$ia_exac_copd==1 & my_data_na$asthma == 1)
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# clear variables
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rm(fp_adults, fp_adults_na)
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#########################################################################
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if ( names(which(lapply(my_data, class) == "character")) == "mosaic" ){
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cat("Character class for 1 column only:", "mosaic")
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}else{
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cat("More than one character class detected: Resolve!")
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quit()
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}
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#============================
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# Identifying column types: Reg data
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#===========================
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cols_to_omit = c("mosaic", "flustat", "onset_2_initial", "ia_exac_copd")
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my_reg_data = my_data[!colnames(my_data)%in%cols_to_omit]
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my_vars = colnames(my_reg_data)
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lapply(my_reg_data, class)
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numerical_vars = c("age"
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, "vl_pfu_ul_npa1"
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, "los"
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#, "onset2final"
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#, "onsfindeath"
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#, "o2_sat_admis")
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)
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na_count = sapply(my_reg_data, function(x) sum(is.na(x)));na_count
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vars_to_factor = my_vars[!my_vars%in%numerical_vars]
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#int_vars <- lapply(my_reg_data, class) == "integer"
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#int_num_vars <- lapply(my_reg_data, class)%in%c("integer", "numeric")
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#int_num_vars
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#table(int_num_vars)
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# convert to factor
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lapply(my_reg_data, class)
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#my_reg_data[, vars_to_factor] <- lapply(my_reg_data[, vars_to_factor], as.factor)
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my_reg_data[vars_to_factor] <- lapply(my_reg_data[vars_to_factor], as.factor)
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factor_vars <- colnames(my_reg_data)[lapply(my_reg_data, class) == "factor"]
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table(factor_vars)
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# check again
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lapply(my_reg_data, class)
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#factor_vars_keep = names(which(lapply(my_data, nlevels) >2))
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#factor_vars_keep
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################################################################
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#=================
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# reg data prepare
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#=================
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pv1 = "death"
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pv2 = "t1_resp_recoded"
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reg_params1 = factor_vars[!factor_vars%in%pv1]
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reg_params1 = my_vars[!my_vars%in%pv1]
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#reg_params2 = my_vars[!my_vars%in%c(pv1, pv2)]
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for(i in reg_params1) {
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#print (i)
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#p_form = as.formula(paste("death ~ ", i ,sep = ""))
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p_form = as.formula(paste("death ~ obesity + ", i ,sep = ""))
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#p_form = as.formula(paste0( pv1, "~ ", i))
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print(p_form)
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model_reg = glm(p_form , family = binomial, data = my_reg_data)
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print(summary(model_reg))
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print(exp(cbind(OR = coef(model_reg), confint(model_reg))))
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#print (PseudoR2(model_reg))
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print(nobs(model_reg))
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cat("=================================================================================\n")
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}
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for(i in numerical_vars) {
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#print (i)
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p_form = as.formula(paste("death ~ ", i ,sep = ""))
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#p_form = as.formula(paste0( pv1, "~ ", i))
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print(p_form)
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model_reg = glm(p_form , family = binomial, data = my_reg_data)
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print(summary(model_reg))
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print(exp(cbind(OR = coef(model_reg), confint(model_reg))))
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#print (PseudoR2(model_reg))
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print(nobs(model_reg))
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cat("=================================================================================\n")
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}
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##################################################
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full_mod = glm(death ~ obesity +
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gender +
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age_bins +
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los +
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#ethnicity +
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onset_initial_bin +
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o2_sat_bin +
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com_noasthma +
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obesity +
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#ia_cxr +
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smoking +
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#sfluv +
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#h1n1v
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max_resp_score +
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T1_resp_score +
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, family = "binomial", data = my_reg_data)
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summary(full_mod)
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182
extra/logistic_regression_v2.R
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182
extra/logistic_regression_v2.R
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@ -0,0 +1,182 @@
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#!/usr/bin/Rscript
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getwd()
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setwd('~/git/mosaic_2020/')
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getwd()
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########################################################################
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# TASK: Run regression analysis
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# npa
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########################################################################
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#=================================================================================
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# TO DO:
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# Simple stats b/w obesity and non-obesity to consider including in reg analysis
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# Include NPA statistically sign params
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# Rerun graphs and plots without asthma?
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#=================================================================================
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#====================
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# Input: source data
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#====================
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source("data_extraction_formatting_clinical.R")
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# quick sanity checks
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table(clinical_df_ics$ia_exac_copd==1 & clinical_df_ics$asthma == 1)
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table(fp_adults$ia_exac_copd==1 & fp_adults$asthma == 1)
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table(fp_adults_na$ia_exac_copd==1 & fp_adults_na$asthma == 1)
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table(clinical_df_ics$asthma)
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#--------------------
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# Data reassignment
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#--------------------
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my_data = clinical_df_ics
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my_data_na = clinical_df_ics_na
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table(my_data$ia_exac_copd==1 & my_data$asthma == 1)
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table(my_data_na$ia_exac_copd==1 & my_data_na$asthma == 1)
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# clear variables
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rm(fp_adults, fp_adults_na)
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#########################################################################
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if ( names(which(lapply(my_data, class) == "character")) == "mosaic" ){
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cat("Character class for 1 column only:", "mosaic")
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}else{
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cat("More than one character class detected: Resolve!")
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quit()
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}
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#============================
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# Identifying column types: Reg data
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#===========================
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cols_to_omit = c("mosaic", "flustat", "onset_2_initial", "ia_exac_copd")
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my_reg_data = my_data[!colnames(my_data)%in%cols_to_omit]
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my_vars = colnames(my_reg_data)
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my_vars
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lapply(my_reg_data, class)
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numerical_vars = c("age"
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, "vl_pfu_ul_npa1"
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, "los"
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, "onset2final"
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, "onsfindeath"
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, "o2_sat_admis")
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my_reg_data[numerical_vars] <- lapply(my_reg_data[numerical_vars], as.numeric)
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my_reg_params = my_vars
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na_count = sapply(my_reg_data, function(x) sum(is.na(x)));na_count
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names(na_count)[na_count>0]
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vars_to_factor = my_vars[!my_vars%in%numerical_vars]
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# convert to factor
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lapply(my_reg_data, class)
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my_reg_data[vars_to_factor] <- lapply(my_reg_data[vars_to_factor], as.factor)
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factor_vars <- colnames(my_reg_data)[lapply(my_reg_data, class) == "factor"]
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table(factor_vars)
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# check again
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lapply(my_reg_data, class)
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# all parasm for reg
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my_reg_params = c("age"
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, "vl_pfu_ul_npa1"
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, "los"
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, "onset2final"
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#, "onsfindeath"
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#, "o2_sat_admis"
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#, "death"
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#, "obesity"
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, "sfluv"
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, "h1n1v"
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, "gender"
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, "asthma"
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, "ethnicity"
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, "smoking"
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, "ia_cxr"
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, "max_resp_score"
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, "T1_resp_score"
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, "com_noasthma"
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, "T2_resp_score"
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, "inresp_sev"
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, "steroid"
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, "age_bins"
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, "o2_sat_bin"
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, "onset_initial_bin"
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, "steroid_ics"
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, "t1_resp_recoded")
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################################################################
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#=================
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# reg data prepare
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#=================
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pv1 = "death"
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pv2 = "t1_resp_recoded"
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#reg_params1 = factor_vars[!factor_vars%in%pv1]
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#reg_params_mixed = my_vars[!my_vars%in%pv1]
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#-----------------------------
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# outcome: death
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# data: fp adults
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#-----------------------------
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for(i in my_reg_params) {
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#print (i)
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p_form = as.formula(paste("death ~ obesity + ", i ,sep = ""))
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print(p_form)
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model_reg = glm(p_form , family = binomial, data = my_reg_data)
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print(summary(model_reg))
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print(exp(cbind(OR = coef(model_reg), confint(model_reg))))
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#print (PseudoR2(model_reg))
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print(nobs(model_reg))
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cat("=================================================================================\n")
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}
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#-----------------------------
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# outcome: t1_resp_recoded
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# data: fp adults
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#-----------------------------
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for(i in my_reg_params) {
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#print (i)
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p_form = as.formula(paste("t1_resp_recoded ~ obesity + ", i ,sep = ""))
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print(p_form)
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model_reg = glm(p_form , family = binomial, data = my_reg_data)
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print(summary(model_reg))
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print(exp(cbind(OR = coef(model_reg), confint(model_reg))))
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#print (PseudoR2(model_reg))
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print(nobs(model_reg))
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cat("=================================================================================\n")
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}
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##################################################
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full_mod = glm(death ~ obesity +
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gender +
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age_bins +
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los +
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#ethnicity +
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onset_initial_bin +
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o2_sat_bin +
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com_noasthma +
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obesity +
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#ia_cxr +
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smoking +
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#sfluv +
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#h1n1v
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max_resp_score +
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T1_resp_score +
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, family = "binomial", data = my_reg_data)
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summary(full_mod)
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