170 lines
5.1 KiB
R
170 lines
5.1 KiB
R
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source("function_trans_cols.R")
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if (trans_vars==TRUE){
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# If trans_vars flag is TRUE, transform specified variables
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dta<-trans_cols(dta_backup,sqrts=sqrt_vars,log1ps = log1p_vars_all)
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} else {dta<-dta_backup}
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library(dplyr)
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source("function_back_trans.R")
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## =============================================================================
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## Loop
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## =============================================================================
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bm_list<-list()
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for (i in 1:length(outs)){
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## Bivariate
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dta_l<-dta|>
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dplyr::select(all_of(c("active_treat",vars,outs[i])))
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sel<-dta_l|>
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sapply(is.factor)
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# i=1
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biv<-dta_l|>
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tbl_uvregression(data=_,
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y=outs[i],
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method=lm,
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label = lab_sel(labels_all,vars),
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show_single_row=colnames(dta_l)[sel],
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estimate_fun = ~style_sigfig(.x,digits = 3),
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pvalue_fun = ~style_pvalue(.x, digits = 3)
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) |>
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add_global_p()|>
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bold_p() #|>
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# bold_labels() |>
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# italicize_levels()
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# ## What follows is the pragmatic transformation and reformatting
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#
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# ## Transforming log1p() to expm1()
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# biv$table_body$estimate<-expm1(biv$table_body$estimate)
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# biv$table_body$conf.low<-expm1(biv$table_body$conf.low)
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# biv$table_body$conf.high<-expm1(biv$table_body$conf.high)
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#
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# ## Transforming sqrt() to pase_0^2
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# biv$table_body$estimate[biv$table_body$variable=="pase_0"]<-
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# -biv$table_body$estimate[biv$table_body$variable=="pase_0"]^2
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#
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# low<-biv$table_body$conf.low[biv$table_body$variable=="pase_0"]^2
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# high<-biv$table_body$conf.high[biv$table_body$variable=="pase_0"]^2
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#
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# biv$table_body$conf.low[biv$table_body$variable=="pase_0"]<-(-low)
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# biv$table_body$conf.high[biv$table_body$variable=="pase_0"]<-(-high)
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#
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# ## Transforming log1p() to expm1()
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# biv$table_body$estimate[biv$table_body$variable=="nihss_0"]<-
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# expm1(biv$table_body$estimate[biv$table_body$variable=="nihss_0"])
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#
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# low<-expm1(biv$table_body$conf.low[biv$table_body$variable=="nihss_0"])
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# high<-expm1(biv$table_body$conf.high[biv$table_body$variable=="nihss_0"])
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#
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# biv$table_body$conf.low[biv$table_body$variable=="nihss_0"]<-low
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# biv$table_body$conf.high[biv$table_body$variable=="nihss_0"]<-high
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#
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# ## New confidence intervals
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# # biv$table_body$estimate<-format(biv$table_body$estimate, drop0trailing = F,digits =2)
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# biv$table_body$ci<-paste(formatC(biv$table_body$conf.low, digits = 3, format = "f"),
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# formatC(biv$table_body$conf.high, digits = 3, format = "f"),
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# sep=", ")
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## multivariate
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mul<-dta_l |>
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lm(formula(paste(c(outs[i],"."),collapse="~")),
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data = _) |>
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tbl_regression(label = lab_sel(labels_all,vars),
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show_single_row=colnames(dta_l)[sel],
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estimate_fun = ~style_sigfig(.x,digits = 3),
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pvalue_fun = ~style_pvalue(.x, digits = 3)
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add_n() |>
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add_global_p() |>
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bold_p() #|>
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# bold_labels() |>
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# italicize_levels()
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if (trans_back==TRUE){
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ls<-lapply(list(biv,mul), back_trans, outm = "log1p" ,sqrts = "pase_0",log1ps = "nihss_0")
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} else {ls<-list(biv,mul)}
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## Merge
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biv_mul<-tbl_merge(
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tbls = ls,
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tab_spanner = c("**Bivariate linear regression**",
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"**Multivariate linear regression**")
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)
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bm_list[[i]]<-biv_mul
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}
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## =============================================================================
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## Big merge
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## =============================================================================
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if (trans_back==TRUE){tab_span<-c("**One month follow up [TRANS t/r]**",
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"**Six months follow up [TRANS t/r]**")
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} else {tab_span<-c("**One month follow up**",
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"**Six months follow up**")}
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bm_16_tbl<-tbl_merge(
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tbls = bm_list,
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tab_spanner = c("**One month follow up**",
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"**Six months follow up**")
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)
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bm_16_tbl
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bm_16_tbl_rtf <- file("bm_16_tbl_trans_back.RTF", "w")
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writeLines(bm_16_tbl%>%as_gt()%>%as_rtf(), bm_16_tbl_rtf)
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close(bm_16_tbl_rtf)
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bm_16_tbl %>% # build gtsummary table
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as_gt() %>% # convert to gt table
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gt::gtsave( # save table as image
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filename = "bm_16_tbl_trans_back.png"
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)
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## Tests and visualisation
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## Box-Cox power transformation performs comparably to logarithmic transformation. The latter is much easier to explain.
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library(MASS)
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dta_bc<-dta_backup|>
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dplyr::select(all_of(c("mdi_6_newobs_enr",vars)))|>
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mutate(pase_0=sqrt(pase_0),
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mdi_6_newobs_enr=mdi_6_newobs_enr+1)#|>
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# na.omit()
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bc<-boxcox(mdi_6_newobs_enr~.,data=dta_bc)
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lambda <- bc$x[which.max(bc$y)]
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## Q-Q plots to compare the two different approaches, and the non-transformed
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q1 <- qqnorm(lm(((mdi_6_newobs_enr^lambda-1)/lambda) ~ .,data=dta_bc)$residuals)
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q2 <- qqnorm(lm(log(mdi_6_newobs_enr) ~ .,data=dta_bc)$residuals)
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library(patchwork)
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plot(q1); plot(q2)
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## Histograms for reference
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h1 <- hist(dta$pase_0,40); hist(sqrt(dta$pase_0),40)
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h2 <- hist(expm1(dta$mdi_6_newobs_enr),40); hist((dta$mdi_6_newobs_enr),40) ## Observed MDI, and log transformed MDI
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plot(h1); plot(h2)
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