Commit ⋅ jinseob2kim/jstable

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R/forestglm.R changed.

80.00% 82.14% -3.67%

R/forestcox.R changed.

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DESCRIPTION has changed.

@@ -60,7 +60,9 @@

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  possible_modely <- purrr::possibly(function(x){purrr::map_dbl(x, .[["y"]], 1)}, NA)
  possible_rowone <- purrr::possibly(function(x){x[2, ]}, NA)
  

  xlabel <- setdiff(as.character(formula)[[3]], "+")[1]
  ncoef <- ifelse(any(class(data) == "survey.design"), ifelse(length(levels(data$variables[[xlabel]])) <= 2, 1,  length(levels(data$variables[[xlabel]])) - 1),
                  ifelse(length(levels(data[[xlabel]])) <= 2, 1,  length(levels(data[[xlabel]])) - 1))
  var_cov <- setdiff(var_cov, c(as.character(formula[[3]]), var_subgroup))
  family.svyglm <- gaussian()
  if (family  == "binomial") family.svyglm <- quasibinomial()

@@ -116,14 +118,13 @@

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      data$variables[[var_subgroup]] %>% table %>% names -> label_val
      label_val %>% purrr::map(~possible_svyglm(formula, design = subset(data, get(var_subgroup) == .), x = TRUE, family = family.svyglm)) -> model
      xlev <- survey::svyglm(formula, design = data)$xlevels
      xlabel <- setdiff(as.character(formula)[[3]], "+")[1]
      pvs_int <- possible_svyglm(as.formula(gsub(xlabel, paste(xlabel, "*", var_subgroup, sep=""), deparse(formula))), design = data, family = family.svyglm) %>% summary %>% coefficients
      pv_int <- round(pvs_int[nrow(pvs_int), ncol(pvs_int)], decimal.pvalue)
      #if (!is.null(xlev) & length(xlev[[1]]) != 2) stop("Categorical independent variable must have 2 levels.")
      model.int <- survey::svyglm(as.formula(gsub(xlabel, paste(xlabel, "*", var_subgroup, sep=""), deparse(formula))), design = data, family = family.svyglm)
      
      
      if (length(label_val) > 2 | length(xlev) > 2){
        model.int <- survey::svyglm(as.formula(gsub(xlabel, paste(xlabel, "*", var_subgroup, sep=""), deparse(formula))), design = data, family = family.svyglm)
      if (sum(grepl(":", names(coef(model.int)))) > 1){
        pv_anova <- anova(model.int, method = "Wald")
        pv_int <- round(pv_anova[[length(pv_anova)]][[7]], decimal.pvalue)
      }

@@ -133,13 +134,12 @@

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      data %>% subset(!is.na(get(var_subgroup))) %>% group_split(get(var_subgroup)) %>% purrr::map(~possible_glm(formula, data = ., x= T, family = family)) -> model
      data %>% subset(!is.na(get(var_subgroup))) %>% select(var_subgroup) %>% table %>% names -> label_val
      xlev <- stats::glm(formula, data = data)$xlevels
      xlabel <- setdiff(as.character(formula)[[3]], "+")[1]
      model.int <- possible_glm(as.formula(gsub(xlabel, paste(xlabel, "*", var_subgroup, sep=""), deparse(formula))), data = data, family = family)  
      pvs_int <- model.int %>% summary %>% coefficients
      pv_int <- round(pvs_int[nrow(pvs_int), ncol(pvs_int)], decimal.pvalue)
      #if (!is.null(xlev) & length(xlev[[1]]) != 2) stop("Categorical independent variable must have 2 levels.")
      
      if (length(label_val) > 2 | length(xlev) > 2){
      if (sum(grepl(":", names(coef(model.int)))) > 1){
        pv_anova <- anova(model.int, test = "Chisq")
        pv_int <- round(pv_anova[nrow(pv_anova), 5], decimal.pvalue)
      }

@@ -67,6 +67,11 @@

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  formula.km <- formula
  var_cov <- setdiff(var_cov, c(as.character(formula[[3]]), var_subgroup))
  xlabel <- setdiff(as.character(formula)[[3]], "+")[1]
  
  ncoef <- ifelse(any(class(data) == "survey.design"), ifelse(length(levels(data$variables[[xlabel]])) <= 2, 1,  length(levels(data$variables[[xlabel]])) - 1),
    ifelse(length(levels(data[[xlabel]])) <= 2, 1,  length(levels(data[[xlabel]])) - 1))
  
  if (is.null(var_subgroup)){
    if (!is.null(var_cov)){
      formula <- as.formula(paste0(deparse(formula), " + ", paste(var_cov, collapse = "+")))

@@ -87,27 +92,34 @@

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      #out.kap <- paste(res.kap.times[["n.event"]], " (", round(100 * (1 - res.kap.times[["surv"]]), decimal.percent), ")", sep = "")
    }
    
    Point.Estimate <- round(exp(coef(model)), decimal.hr)[1]
    
    
    Point.Estimate <- round(exp(coef(model)), decimal.hr)[1:ncoef]
    
    #if (length(Point.Estimate) > 1){
    #  stop("Formula must contain 1 independent variable only.")
    #}
    
    
    CI <- round(exp(confint(model)[1, ]), decimal.hr)
    CI <- round(exp(confint(model)[1:ncoef, ]), decimal.hr)
    event <- purrr::map_dbl(model$y, 1) %>% tail(model$n)
    #prop <- round(prop.table(table(event, model$x[, 1]), 2)[2, ] * 100, decimal.percent)
    pv <- round(summary(model)$coefficients[1, "Pr(>|z|)"], decimal.pvalue)
    pv <- round(summary(model)$coefficients[1:ncoef, "Pr(>|z|)"], decimal.pvalue)
    
    out <- data.frame(Variable = "Overall", Count = model$n, Percent = 100, `Point Estimate` = Point.Estimate, Lower = CI[1], Upper = CI[2], check.names = F) %>% 
      mutate(`P value` = ifelse(pv >= 0.001, pv, "<0.001"), `P for interaction` = NA)
             
    if (!is.null(names(prop))){
    if (ncoef <= 2){
      out <- data.frame(Variable = "Overall", Count = model$n, Percent = 100, `Point Estimate` = Point.Estimate, Lower = CI[1], Upper = CI[2], check.names = F) %>% 
        cbind(t(prop)) %>% 
        mutate(`P value` = ifelse(pv >= 0.001, pv, "<0.001"), `P for interaction` = NA) 
    }         
        mutate(`P value` = ifelse(pv >= 0.001, pv, "<0.001"), `P for interaction` = NA)
      
      if (!is.null(names(prop))){
        out <- data.frame(Variable = "Overall", Count = model$n, Percent = 100, `Point Estimate` = Point.Estimate, Lower = CI[1], Upper = CI[2], check.names = F) %>% 
          cbind(t(prop)) %>% 
          mutate(`P value` = ifelse(pv >= 0.001, pv, "<0.001"), `P for interaction` = NA) 
      }     
    } else{
      out <- data.frame(Variable = c("Overall", rep("", length(Point.Estimate))), Subgroup = rep("", length(Point.Estimate) + 1), Levels =  levels(data[[xlabel]]), `Point Estimate` = c("Reference", Point.Estimate), Lower = c("", CI[, 1]), Upper = c("", CI[, 2]), check.names = F) %>% 
        mutate(`P value` = c("", ifelse(pv >= 0.001, pv, "<0.001")), `P for interaction` = NA)
      rownames(out) <- NULL
    }    
    
    return(out)
  } else if (length(var_subgroup) > 1 | any(grepl(var_subgroup, formula))){

@@ -120,14 +132,13 @@

Loading

      data$variables[[var_subgroup]] %>% table %>% names -> label_val
      label_val %>% purrr::map(~possible_svycoxph(formula, design = subset(data, get(var_subgroup) == .), x = TRUE)) -> model
      xlev <- survey::svycoxph(formula, design = data)$xlevels
      xlabel <- setdiff(as.character(formula)[[3]], "+")[1]
      pvs_int <- possible_svycoxph(as.formula(gsub(xlabel, paste0(xlabel, "*", var_subgroup), deparse(formula))), design = data) %>% summary %>% coefficients
      pv_int <- round(pvs_int[nrow(pvs_int), ncol(pvs_int)], decimal.pvalue)
      #if (!is.null(xlev) & length(xlev[[1]]) != 2) stop("Categorical independent variable must have 2 levels.")
      model.int <- survey::svycoxph(as.formula(gsub(xlabel, paste0(xlabel, "*", var_subgroup), deparse(formula))), design = data)
      
      
      if (length(label_val) > 2 | length(xlev) > 2){
        model.int <- survey::svycoxph(as.formula(gsub(xlabel, paste0(xlabel, "*", var_subgroup), deparse(formula))), design = data)
      if (sum(grepl(":", names(coef(model.int)))) > 1){
        model.int$call$formula <- as.formula(gsub(xlabel, paste0(xlabel, "*", var_subgroup), deparse(formula)))
        pv_anova <- survey::regTermTest(model.int, as.formula(paste0("~", xlabel, ":", var_subgroup)))
        pv_int <- round(pv_anova$p[1], decimal.pvalue)
      }

@@ -149,31 +160,34 @@

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      data %>% filter(!is.na(get(var_subgroup))) %>% split(.[[var_subgroup]]) %>% purrr::map(~possible_coxph(formula, data = ., x= T)) -> model
      data %>% filter(!is.na(get(var_subgroup))) %>% select(var_subgroup) %>% table %>% names -> label_val
      xlev <- survival::coxph(formula, data = data)$xlevels
      xlabel <- setdiff(as.character(formula)[[3]], "+")[1]
      model.int <- possible_coxph(as.formula(gsub(xlabel, paste0(xlabel, "*", var_subgroup), deparse(formula))), data = data)  
      pvs_int <- model.int %>% summary %>% coefficients
      pv_int <- round(pvs_int[nrow(pvs_int), ncol(pvs_int)], decimal.pvalue)
      #if (!is.null(xlev) & length(xlev[[1]]) != 2) stop("Categorical independent variable must have 2 levels.")
      
      if (length(label_val) > 2 | length(xlev) > 2){
      if (sum(grepl(":", names(coef(model.int)))) == 1){
        pvs_int <- model.int %>% summary %>% coefficients
        pv_int <- round(pvs_int[nrow(pvs_int), ncol(pvs_int)], decimal.pvalue)
        #if (!is.null(xlev) & length(xlev[[1]]) != 2) stop("Categorical independent variable must have 2 levels.")
        if (!is.numeric(data[[xlabel]])){
          res.kap.times <- data %>% filter(!is.na(get(var_subgroup))) %>% split(.[[var_subgroup]]) %>% purrr::map(~survival::survfit(formula.km, data = .)) %>% purrr::map(~summary(., times = time_eventrate, extend = T))
          prop <- res.kap.times %>% purrr::map(~round(100 * (1 - .[["surv"]]), decimal.percent)) %>% dplyr::bind_cols() %>% t
          colnames(prop) <- xlev[[1]]
        } else{
          prop <- NULL
        }
      } else{
        model.int$call$formula <- as.formula(gsub(xlabel, paste0(xlabel, "*", var_subgroup), deparse(formula)))
        model.int$call$data <- as.name("data")
        pv_anova <- anova(model.int)
        pv_int <- round(pv_anova[nrow(pv_anova), 4], decimal.pvalue)
      }
      
      if (!is.numeric(data[[xlabel]])){
        res.kap.times <- data %>% filter(!is.na(get(var_subgroup))) %>% split(.[[var_subgroup]]) %>% purrr::map(~survival::survfit(formula.km, data = .)) %>% purrr::map(~summary(., times = time_eventrate, extend = T))
        prop <- res.kap.times %>% purrr::map(~round(100 * (1 - .[["surv"]]), decimal.percent)) %>% dplyr::bind_cols() %>% t
        colnames(prop) <- xlev[[1]]
      } else{
        prop <- NULL
      }
 
      
    }
    
    model %>% purrr::map_dbl("n", .default = NA) -> Count
    model %>% purrr::map("coefficients", .default = NA) %>% purrr::map_dbl(1) %>% exp %>% round(decimal.hr) -> Point.Estimate
    model %>% purrr::map(possible_confint) %>% purrr::map(possible_rowone) %>% Reduce(rbind, .) %>% exp %>% round(decimal.hr) -> CI
    model %>% purrr::map("coefficients", .default = NA) %>% lapply(function(x){round(exp(x[1:ncoef]), decimal.hr)}) %>% unlist -> Point.Estimate
    #model %>% purrr::map("coefficients", .default = NA) %>% purrr::map_dbl(1) %>% exp %>% round(decimal.hr) -> Point.Estimate
    model %>% purrr::map(possible_confint)  %>% Reduce(rbind, .) %>% exp %>% round(decimal.hr) -> CI
    #model %>% purrr::map(possible_confint) %>% purrr::map(possible_rowone) %>% Reduce(rbind, .) %>% exp %>% round(decimal.hr) -> CI
    #model %>% purrr::map("y") %>% purrr::map(~purrr::map_dbl(., 1)) %>% purrr::map(~tail(., length(.)/2)) -> event
    #purrr::map2(event, model, ~possible_table(.x, .y[["x"]][, 1])) %>% purrr::map(possible_prop.table) %>% purrr::map(~round(., decimal.percent)) %>% Reduce(rbind, .) -> prop
    model %>% purrr::map(possible_pv) %>% purrr::map_dbl(~round(., decimal.pvalue)) -> pv

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R	1,192	1,018	0	174	85.40%
Project Totals (13 files)	1,192	1,018	0	174	85.40%

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60	60		possible_modely <- purrr::possibly(function(x){purrr::map_dbl(x, .[["y"]], 1)}, NA)
61	61		possible_rowone <- purrr::possibly(function(x){x[2, ]}, NA)
62	62
63		-
	63	+	xlabel <- setdiff(as.character(formula)[[3]], "+")[1]
	64	+	ncoef <- ifelse(any(class(data) == "survey.design"), ifelse(length(levels(data$variables[[xlabel]])) <= 2, 1, length(levels(data$variables[[xlabel]])) - 1),
	65	+	ifelse(length(levels(data[[xlabel]])) <= 2, 1, length(levels(data[[xlabel]])) - 1))
64	66		var_cov <- setdiff(var_cov, c(as.character(formula[[3]]), var_subgroup))
65	67		family.svyglm <- gaussian()
66	68		if (family == "binomial") family.svyglm <- quasibinomial()

116	118		data$variables[[var_subgroup]] %>% table %>% names -> label_val
117	119		label_val %>% purrr::map(~possible_svyglm(formula, design = subset(data, get(var_subgroup) == .), x = TRUE, family = family.svyglm)) -> model
118	120		xlev <- survey::svyglm(formula, design = data)$xlevels
119		-	xlabel <- setdiff(as.character(formula)[[3]], "+")[1]
120	121		pvs_int <- possible_svyglm(as.formula(gsub(xlabel, paste(xlabel, "*", var_subgroup, sep=""), deparse(formula))), design = data, family = family.svyglm) %>% summary %>% coefficients
121	122		pv_int <- round(pvs_int[nrow(pvs_int), ncol(pvs_int)], decimal.pvalue)
122	123		#if (!is.null(xlev) & length(xlev[[1]]) != 2) stop("Categorical independent variable must have 2 levels.")
	124	+	model.int <- survey::svyglm(as.formula(gsub(xlabel, paste(xlabel, "*", var_subgroup, sep=""), deparse(formula))), design = data, family = family.svyglm)
123	125
124	126
125		-	if (length(label_val) > 2 \| length(xlev) > 2){
126		-	model.int <- survey::svyglm(as.formula(gsub(xlabel, paste(xlabel, "*", var_subgroup, sep=""), deparse(formula))), design = data, family = family.svyglm)
	127	+	if (sum(grepl(":", names(coef(model.int)))) > 1){
127	128		pv_anova <- anova(model.int, method = "Wald")
128	129		pv_int <- round(pv_anova[[length(pv_anova)]][[7]], decimal.pvalue)
129	130		}

133	134		data %>% subset(!is.na(get(var_subgroup))) %>% group_split(get(var_subgroup)) %>% purrr::map(~possible_glm(formula, data = ., x= T, family = family)) -> model
134	135		data %>% subset(!is.na(get(var_subgroup))) %>% select(var_subgroup) %>% table %>% names -> label_val
135	136		xlev <- stats::glm(formula, data = data)$xlevels
136		-	xlabel <- setdiff(as.character(formula)[[3]], "+")[1]
137	137		model.int <- possible_glm(as.formula(gsub(xlabel, paste(xlabel, "*", var_subgroup, sep=""), deparse(formula))), data = data, family = family)
138	138		pvs_int <- model.int %>% summary %>% coefficients
139	139		pv_int <- round(pvs_int[nrow(pvs_int), ncol(pvs_int)], decimal.pvalue)
140	140		#if (!is.null(xlev) & length(xlev[[1]]) != 2) stop("Categorical independent variable must have 2 levels.")
141	141
142		-	if (length(label_val) > 2 \| length(xlev) > 2){
	142	+	if (sum(grepl(":", names(coef(model.int)))) > 1){
143	143		pv_anova <- anova(model.int, test = "Chisq")
144	144		pv_int <- round(pv_anova[nrow(pv_anova), 5], decimal.pvalue)
145	145		}

87	92		#out.kap <- paste(res.kap.times[["n.event"]], " (", round(100 * (1 - res.kap.times[["surv"]]), decimal.percent), ")", sep = "")
88	93		}
89	94
90		-	Point.Estimate <- round(exp(coef(model)), decimal.hr)[1]
91	95
92	96
	97	+	Point.Estimate <- round(exp(coef(model)), decimal.hr)[1:ncoef]
	98	+
93	99		#if (length(Point.Estimate) > 1){
94	100		# stop("Formula must contain 1 independent variable only.")
95	101		#}
96	102
97	103
98		-	CI <- round(exp(confint(model)[1, ]), decimal.hr)
	104	+	CI <- round(exp(confint(model)[1:ncoef, ]), decimal.hr)
99	105		event <- purrr::map_dbl(model$y, 1) %>% tail(model$n)
100	106		#prop <- round(prop.table(table(event, model$x[, 1]), 2)[2, ] * 100, decimal.percent)
101		-	pv <- round(summary(model)$coefficients[1, "Pr(>\|z\|)"], decimal.pvalue)
	107	+	pv <- round(summary(model)$coefficients[1:ncoef, "Pr(>\|z\|)"], decimal.pvalue)
102	108
103		-	out <- data.frame(Variable = "Overall", Count = model$n, Percent = 100, `Point Estimate` = Point.Estimate, Lower = CI[1], Upper = CI[2], check.names = F) %>%
104		-	mutate(`P value` = ifelse(pv >= 0.001, pv, "<0.001"), `P for interaction` = NA)
105		-
106		-	if (!is.null(names(prop))){
	109	+	if (ncoef <= 2){
107	110		out <- data.frame(Variable = "Overall", Count = model$n, Percent = 100, `Point Estimate` = Point.Estimate, Lower = CI[1], Upper = CI[2], check.names = F) %>%
108		-	cbind(t(prop)) %>%
109		-	mutate(`P value` = ifelse(pv >= 0.001, pv, "<0.001"), `P for interaction` = NA)
110		-	}
	111	+	mutate(`P value` = ifelse(pv >= 0.001, pv, "<0.001"), `P for interaction` = NA)
	112	+
	113	+	if (!is.null(names(prop))){
	114	+	out <- data.frame(Variable = "Overall", Count = model$n, Percent = 100, `Point Estimate` = Point.Estimate, Lower = CI[1], Upper = CI[2], check.names = F) %>%
	115	+	cbind(t(prop)) %>%
	116	+	mutate(`P value` = ifelse(pv >= 0.001, pv, "<0.001"), `P for interaction` = NA)
	117	+	}
	118	+	} else{
	119	+	out <- data.frame(Variable = c("Overall", rep("", length(Point.Estimate))), Subgroup = rep("", length(Point.Estimate) + 1), Levels = levels(data[[xlabel]]), `Point Estimate` = c("Reference", Point.Estimate), Lower = c("", CI[, 1]), Upper = c("", CI[, 2]), check.names = F) %>%
	120	+	mutate(`P value` = c("", ifelse(pv >= 0.001, pv, "<0.001")), `P for interaction` = NA)
	121	+	rownames(out) <- NULL
	122	+	}
111	123
112	124		return(out)
113	125		} else if (length(var_subgroup) > 1 \| any(grepl(var_subgroup, formula))){

120	132		data$variables[[var_subgroup]] %>% table %>% names -> label_val
121	133		label_val %>% purrr::map(~possible_svycoxph(formula, design = subset(data, get(var_subgroup) == .), x = TRUE)) -> model
122	134		xlev <- survey::svycoxph(formula, design = data)$xlevels
123		-	xlabel <- setdiff(as.character(formula)[[3]], "+")[1]
124	135		pvs_int <- possible_svycoxph(as.formula(gsub(xlabel, paste0(xlabel, "*", var_subgroup), deparse(formula))), design = data) %>% summary %>% coefficients
125	136		pv_int <- round(pvs_int[nrow(pvs_int), ncol(pvs_int)], decimal.pvalue)
126	137		#if (!is.null(xlev) & length(xlev[[1]]) != 2) stop("Categorical independent variable must have 2 levels.")
	138	+	model.int <- survey::svycoxph(as.formula(gsub(xlabel, paste0(xlabel, "*", var_subgroup), deparse(formula))), design = data)
127	139
128		-
129		-	if (length(label_val) > 2 \| length(xlev) > 2){
130		-	model.int <- survey::svycoxph(as.formula(gsub(xlabel, paste0(xlabel, "*", var_subgroup), deparse(formula))), design = data)
	140	+	if (sum(grepl(":", names(coef(model.int)))) > 1){
	141	+	model.int$call$formula <- as.formula(gsub(xlabel, paste0(xlabel, "*", var_subgroup), deparse(formula)))
131	142		pv_anova <- survey::regTermTest(model.int, as.formula(paste0("~", xlabel, ":", var_subgroup)))
132	143		pv_int <- round(pv_anova$p[1], decimal.pvalue)
133	144		}

67	67
68	68		formula.km <- formula
69	69		var_cov <- setdiff(var_cov, c(as.character(formula[[3]]), var_subgroup))
	70	+	xlabel <- setdiff(as.character(formula)[[3]], "+")[1]
	71	+
	72	+	ncoef <- ifelse(any(class(data) == "survey.design"), ifelse(length(levels(data$variables[[xlabel]])) <= 2, 1, length(levels(data$variables[[xlabel]])) - 1),
	73	+	ifelse(length(levels(data[[xlabel]])) <= 2, 1, length(levels(data[[xlabel]])) - 1))
	74	+
70	75		if (is.null(var_subgroup)){
71	76		if (!is.null(var_cov)){
72	77		formula <- as.formula(paste0(deparse(formula), " + ", paste(var_cov, collapse = "+")))

149	160		data %>% filter(!is.na(get(var_subgroup))) %>% split(.[[var_subgroup]]) %>% purrr::map(~possible_coxph(formula, data = ., x= T)) -> model
150	161		data %>% filter(!is.na(get(var_subgroup))) %>% select(var_subgroup) %>% table %>% names -> label_val
151	162		xlev <- survival::coxph(formula, data = data)$xlevels
152		-	xlabel <- setdiff(as.character(formula)[[3]], "+")[1]
153	163		model.int <- possible_coxph(as.formula(gsub(xlabel, paste0(xlabel, "*", var_subgroup), deparse(formula))), data = data)
154		-	pvs_int <- model.int %>% summary %>% coefficients
155		-	pv_int <- round(pvs_int[nrow(pvs_int), ncol(pvs_int)], decimal.pvalue)
156		-	#if (!is.null(xlev) & length(xlev[[1]]) != 2) stop("Categorical independent variable must have 2 levels.")
157		-
158		-	if (length(label_val) > 2 \| length(xlev) > 2){
	164	+	if (sum(grepl(":", names(coef(model.int)))) == 1){
	165	+	pvs_int <- model.int %>% summary %>% coefficients
	166	+	pv_int <- round(pvs_int[nrow(pvs_int), ncol(pvs_int)], decimal.pvalue)
	167	+	#if (!is.null(xlev) & length(xlev[[1]]) != 2) stop("Categorical independent variable must have 2 levels.")
	168	+	if (!is.numeric(data[[xlabel]])){
	169	+	res.kap.times <- data %>% filter(!is.na(get(var_subgroup))) %>% split(.[[var_subgroup]]) %>% purrr::map(~survival::survfit(formula.km, data = .)) %>% purrr::map(~summary(., times = time_eventrate, extend = T))
	170	+	prop <- res.kap.times %>% purrr::map(~round(100 * (1 - .[["surv"]]), decimal.percent)) %>% dplyr::bind_cols() %>% t
	171	+	colnames(prop) <- xlev[[1]]
	172	+	} else{
	173	+	prop <- NULL
	174	+	}
	175	+	} else{
159	176		model.int$call$formula <- as.formula(gsub(xlabel, paste0(xlabel, "*", var_subgroup), deparse(formula)))
160	177		model.int$call$data <- as.name("data")
161	178		pv_anova <- anova(model.int)
162	179		pv_int <- round(pv_anova[nrow(pv_anova), 4], decimal.pvalue)
163		-	}
164		-
165		-	if (!is.numeric(data[[xlabel]])){
166		-	res.kap.times <- data %>% filter(!is.na(get(var_subgroup))) %>% split(.[[var_subgroup]]) %>% purrr::map(~survival::survfit(formula.km, data = .)) %>% purrr::map(~summary(., times = time_eventrate, extend = T))
167		-	prop <- res.kap.times %>% purrr::map(~round(100 * (1 - .[["surv"]]), decimal.percent)) %>% dplyr::bind_cols() %>% t
168		-	colnames(prop) <- xlev[[1]]
169		-	} else{
170	180		prop <- NULL
171	181		}
	182	+
	183	+
172	184		}
173	185
174	186		model %>% purrr::map_dbl("n", .default = NA) -> Count
175		-	model %>% purrr::map("coefficients", .default = NA) %>% purrr::map_dbl(1) %>% exp %>% round(decimal.hr) -> Point.Estimate
176		-	model %>% purrr::map(possible_confint) %>% purrr::map(possible_rowone) %>% Reduce(rbind, .) %>% exp %>% round(decimal.hr) -> CI
	187	+	model %>% purrr::map("coefficients", .default = NA) %>% lapply(function(x){round(exp(x[1:ncoef]), decimal.hr)}) %>% unlist -> Point.Estimate
	188	+	#model %>% purrr::map("coefficients", .default = NA) %>% purrr::map_dbl(1) %>% exp %>% round(decimal.hr) -> Point.Estimate
	189	+	model %>% purrr::map(possible_confint) %>% Reduce(rbind, .) %>% exp %>% round(decimal.hr) -> CI
	190	+	#model %>% purrr::map(possible_confint) %>% purrr::map(possible_rowone) %>% Reduce(rbind, .) %>% exp %>% round(decimal.hr) -> CI
177	191		#model %>% purrr::map("y") %>% purrr::map(~purrr::map_dbl(., 1)) %>% purrr::map(~tail(., length(.)/2)) -> event
178	192		#purrr::map2(event, model, ~possible_table(.x, .y[["x"]][, 1])) %>% purrr::map(possible_prop.table) %>% purrr::map(~round(., decimal.percent)) %>% Reduce(rbind, .) -> prop
179	193		model %>% purrr::map(possible_pv) %>% purrr::map_dbl(~round(., decimal.pvalue)) -> pv

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