Psycho-physiological Factors

rm(list = ls())
setwd("~/Downloads")
library(foreign)
sex_data <- read.spss("sex_data.sav", use.value.label=TRUE, to.data.frame=TRUE)

Libraries/themes

library(ggplot2)
library(tidyverse)
library(gridExtra)
library(finalfit)
library(kableExtra)
library(xtable)
jrothsch_theme <-  theme_bw() + 
  theme(text = element_text(size = 10, face = "bold", color = "deepskyblue4"),panel.grid = element_blank(),axis.text = element_text(size = 10, color = "gray13"), axis.title = element_text(size = 10, color = "red"), legend.text = element_text(colour="Black", size=10), legend.title = element_text(colour="Black", size=7), plot.subtitle = element_text(size=14, face="italic", color="black"))

---

Removing obserations without our key variables

sex_data <- sex_data[!is.na(sex_data$status),]
sex_data <- sex_data[!is.na(sex_data$age),]
sex_data <- sex_data[!is.na(sex_data$Years_PrimaryPartner),]
sex_data <- sex_data[!is.na(sex_data$MALE),]

---

Making variables

sex_data <- sex_data %>%
  mutate(married = status == "Married")

sex_data <- sex_data %>%
  mutate(married_gender = ifelse(married, 
                                 ifelse(MALE == "Male", "Married Male", "Married Female"),
                                 ifelse(MALE == "Male", "Unmarried Male", "Unmarried Female ")))

  married_num =   length(sex_data$married[sex_data$married == T])
  unmarried_num =   length(sex_data$married[sex_data$married == T])
  
  sex_data <- sex_data %>% mutate(freq_num =  as.numeric(ifelse(sexfreq == "At least once per day", 6, 
                                ifelse(sexfreq == "3-4 times per week", 5,
                                  ifelse(sexfreq == 'At least once a week', 4,
                                       ifelse(sexfreq == 'At least once per month', 3, 
                                              ifelse(sexfreq =='At least once per year', 2,
                                                     ifelse( sexfreq =="Less than once a year", 1, 0))))))))
  
  
  female <- sex_data %>%
  filter(sex_data$MALE == "Female")

---

Binary graphs

bin_graphs_want <- function(df, mod, modname, modlabs,  interaction_labs){
  
  
  df2 = df[!is.na(df$want) & !is.na(mod),]
  mod = mod[!is.na(df$want) & !is.na(mod)]
  
    df2$mod_married = interaction(df2$married, mod)
     
    age <- ggplot(data = df2, aes(x = age, y = want, color = mod))  +
      geom_smooth() +
      labs(title = paste("Effect Of", modname, "On", "Want", " -- Age"), x = "Age", y = "Want", color = "") + 
      scale_x_continuous(limits = c(18, 80)) +
      scale_color_discrete(labels = modlabs) + 
      jrothsch_theme
  
  sum_inter <- df2 %>%
      group_by( mod_married) %>%
      summarize(DV = mean(want))
    
    mod_married <- ggplot(data = sum_inter,  aes(x = mod_married, y = DV)) +
    geom_bar(stat = 'identity', position = 'identity', fill = "Black") + 
      scale_x_discrete(labels = interaction_labs) + 
      labs(title = paste0("Interaction Between ", modname, "And Marriage"), y = "Want", x = "") +
      jrothsch_theme
    
    
    grid.arrange(age, mod_married)
    
}

bin_graphs_like <- function(df, mod, modname, modlabs, interaction_labs){
  

  
  df2 = df[!is.na(df$totlike) & !is.na(mod),]
  mod = mod[!is.na(df$totlike) & !is.na(mod)]
  
    df2$mod_married = interaction(df2$married, mod)
     
    age <- ggplot(data = df2, aes(x = age, y = totlike, color = mod))  +
      geom_smooth() +
      labs(title = paste("Effect Of", modname, "On", "Like", " -- Age"), x = "Age", y = "Like", color = "") + 
      scale_x_continuous(limits = c(18, 80)) +
      scale_color_discrete(labels = modlabs) + 
      jrothsch_theme
  
  sum_inter <- df2 %>%
      group_by( mod_married) %>%
      summarize(DV = mean(totlike))
    
    mod_married <- ggplot(data = sum_inter,  aes(x = mod_married, y = DV)) +
    geom_bar(stat = 'identity', position = 'identity', fill = "black") +
         scale_x_discrete(labels = interaction_labs) + 
      labs(title = paste0("Interaction Between ", modname, "And Marriage"), y = "Like", x = "") +
      jrothsch_theme
    
    
    grid.arrange(age, mod_married)
    
}
bin_graphs_freq <- function(df, mod, modname, modlabs, interaction_labs){
  

  
  df2 = df[!is.na(df$freq_num) & !is.na(mod),]
  mod = mod[!is.na(df$freq_num) & !is.na(mod)]
  
    df2$mod_married = interaction(df2$married, mod)
     
    age <- ggplot(data = df2, aes(x = age, y = freq_num, color = mod))  +
      geom_smooth() +
      labs(title = paste("Effect Of", modname, "On", "Frequency", " -- Age"), x = "Age", y = "Frequency", color = "") + 
      scale_x_continuous(limits = c(18, 80)) +
      scale_color_discrete(labels = modlabs) + 
      jrothsch_theme
  
  sum_inter <- df2 %>%
      group_by(mod_married) %>%
      summarize(DV = mean(freq_num))
    
    mod_married <- ggplot(data = sum_inter,  aes(x = mod_married, y = DV)) +
    geom_bar(stat = 'identity', position = 'identity', fill = "black") +    
      scale_x_discrete(labels = interaction_labs) + 
      labs(title = paste0("Interaction Between ", modname, "And Marriage"), y = "Frequency", x = "") +
      jrothsch_theme
    
    
    grid.arrange(age, mod_married)
    
}

---

Small continuous graphs

contsmall <- function(df, mod, modname, modlabs){

  df2 = df[!is.na(df$want) & !is.na(df$totlike) & !is.na(df$freq_num ) & !is.na(mod) ,]
  mod = mod[!is.na(df$want) & !is.na(df$totlike) & !is.na(df$freq_num ) & !is.na(mod)]
  
want <- ggplot(df2, aes(x=mod, y=  want)) +
  stat_summary_bin(fun.y='mean', bins=20,
                    size=2, geom='point', mapping = aes(group = married, color = married)) +
  geom_smooth(method='lm', se = F, aes(color = married)) + jrothsch_theme +
  labs(title = paste0("Interaction between marriage and ", modname ), x = modname, y = "Want")
  
like <- ggplot(df2, aes(x=mod, y = totlike)) +
  stat_summary_bin(fun.y='mean', bins=20,
                    size=2, geom='point', mapping = aes(group = married, color = married)) +
  geom_smooth(method='lm', se = F, aes(color = married)) + jrothsch_theme +
    labs( x = modname, y = "Like")


freq <- ggplot(df2, aes(x=mod, y=  freq_num)) +
  stat_summary_bin(fun.y='mean', bins=20,
                    size=2, geom='point', mapping = aes(group = married, color = married)) +
  geom_smooth(method='lm', se = F, aes(color = married)) + jrothsch_theme +
      labs( x = modname, y = "Frequency")

  

grid.arrange(want, like, freq)

}

---

Regressions – Standard

###################################################################################################################################
#REgressions
#########################################################################################################################
  reg_no_mod <- function(dv, df){
      dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married, data = .)

  
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True")
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  }


###########################################################################################################################
  reg_no_mod_i <- function(dv, df){

  dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + age + married*age, data = .)

  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: TRUE", "Age",  "Married X Age")
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  }

##################################################################################################################

  reg_no_mod_control <- function(dv, df){

  dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + age +  Years_PrimaryPartner + MALE, data = .)

  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: TRUE", "Age",  "Duration", "MALE")
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  }

##################################################################################################################

  reg_just_mod_i <- function(dv, df, mod, modname){
    
      dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + mod, data = .)

  
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname)
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  }
  
#############################################################################################################################################################################################################################################################
  reg_full_control <- function(dv, df, mod, modname){
     
     dv <- as.numeric(dv)
       tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner + MALE, data = .)
  
      
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration", "Male")
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  
    
  }


 


#############################################################################################################################################################################################################################################################
  reg_full_control_i <- function(dv, df, mod, modname){
    
          dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner + MALE + married*age, data = .)

  
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration", "Male", "Married X Age")
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
  }

---

Subsets where controls can’t be used – e.g. one gender subsets

     reg_full_control_gend <- function(dv, df, mod, modname){

    
      dv <- as.numeric(dv)
      
       tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner, data = .)
  
      
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration" )
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  
    
     }

 reg_full_control_gend_i <- function(dv, df, mod, modname){
    
          dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner + age*married, data = .)

  
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration", "Married X Age")
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
  }

---

Looking at modertators interaction with marriage

  ireg_just_mod_i <- function(dv, df, mod, modname){
    
      dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + mod + married*mod, data = .)

  
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, paste("Married X", modname))
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  }
  
#############################################################################################################################################################################################################################################################
  ireg_full_control <- function(dv, df, mod, modname){
     
     dv <- as.numeric(dv)
       tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner + MALE + married*mod, data = .)
  
      
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration", "Male",  paste("Married X", modname))
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  
    
  }


 


#############################################################################################################################################################################################################################################################
  ireg_full_control_i <- function(dv, df, mod, modname){
    
          dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner + MALE + age*married + married*mod, data = .)

  
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration", "Male", "Married X Age",  paste("Married X", modname))
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
  }
  
  
  
   
    
    #############################################################################################################################################################################################################################################################
   #For subsets wheere we can't use all controls
    
    #############################################################################################################################################################################################################################################################
    
     ireg_full_control_gend <- function(dv, df, mod, modname){

    
      dv <- as.numeric(dv)
      
       tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner + married*mod, data = .)
  
      
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration",  paste("Married X", modname))
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r",  "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
    
  
    
     }

 ireg_full_control_gend_i <- function(dv, df, mod, modname){
    
          dv <- as.numeric(dv)
  tx1 <- df %>% lm(dv ~ married + mod + age + Years_PrimaryPartner + age*married + married*mod, data = .)

  
  tx1 <- xtable(tx1)
  colnames(tx1)[colnames(tx1)=="Pr(>|t|)"] <- "p_value"
    colnames(tx1)[colnames(tx1)=="t value"] <- "t_value"

  
  tx1 <- tx1 %>%
    mutate(p_value = round(p_value, 4)) %>%
    mutate(t_value = round(t_value, 3)) %>%
    mutate(p_value= cell_spec(p_value, "html", background = ifelse(p_value < .01, "gold", "white"))) %>%
    mutate(t_value= cell_spec(t_value, "html", background = ifelse(t_value < -2.5, "#FF6347", 
                                                                   ifelse(t_value > 2.5, "lightgreen", "white"))))

  
  rownames(tx1) <- c("Intercept", "Married: True", modname, "Age (years)", "Duration", "Married X Age",  paste("Married X", modname))
  
kable(tx1, row.names= T, align=c("l", "l", "r", "r", "r")  ,
            booktabs=TRUE, escape = F) %>% 
    kable_styling(font_size=8) 
  }

Marriage on Want/Age/Like without mediators, with or without controls, for comparison

Want - Marriage

reg_no_mod(sex_data$want, sex_data )

	Estimate	Std. Error	t_value	p_value
Intercept	28.927462	0.2358428	122.656	0
Married: True	-5.062436	0.2804827	-18.049	0

Like - Marriage

reg_no_mod(sex_data$totlike, sex_data )

	Estimate	Std. Error	t_value	p_value
Intercept	38.524460	0.2654955	145.104	0
Married: True	-3.954296	0.3168241	-12.481	0

Frequency - Marriage

reg_no_mod(sex_data$freq_num, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	3.6641623	0.0408283	89.746	0
Married: True	-0.6595098	0.0480844	-13.716	0

Want - Marriage, controls

reg_no_mod_control(sex_data$want, sex_data )

	Estimate	Std. Error	t_value	p_value
Intercept	33.6515234	0.3616805	93.042	0
Married: TRUE	-2.4498192	0.2836310	-8.637	0
Age	-0.1788330	0.0079330	-22.543	0
Duration	-0.0164498	0.0049803	-3.303	0.001
MALE	4.5428634	0.2430139	18.694	0

Like - Marriage, controls

reg_no_mod_control(sex_data$totlike, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	40.9088281	0.4368889	93.637	0
Married: TRUE	-2.6911441	0.3412552	-7.886	0
Age	-0.0852356	0.0095901	-8.888	0
Duration	-0.0098875	0.0058960	-1.677	0.0936
MALE	1.9027594	0.2944406	6.462	0

Frequency - Marriage, controls

reg_no_mod_control(sex_data$freq_num, sex_data)

	Estimate	Std. Error	t_value	p_value
Intercept	4.8698702	0.0640338	76.052	0
Married: TRUE	-0.1485242	0.0488622	-3.04	0.0024
Age	-0.0334322	0.0014244	-23.471	0
Duration	-0.0032137	0.0010801	-2.975	0.0029
MALE	0.2339988	0.0411009	5.693	0

---

Menopause doesn’t remove marriage effect, and any effect is likely due to age.

Want Graphs

bin_graphs_want(female, female$postmeno, "Menopause Status", c("Pre Menopause", "Post Menopause"),  
                     c("Unmarried, Pre", "Married, Pre", "Unmarried, Post", "Married, Post"))

Like Graphs

bin_graphs_like(female, female$postmeno, "Menopause Status", c("Pre Menopause", "Post Menopause"),  
                     c("Unmarried, Pre", "Married, Pre", "Unmarried, Post", "Married, Post"))

Frequency Graphs

bin_graphs_freq(female, female$postmeno, "Menopause Status", c("Pre Menopause", "Post Menopause"),  
                     c("Unmarried, Pre", "Married, Pre", "Unmarried, Post", "Married, Post"))

Want - Only Menopause

reg_just_mod_i(female$want, female, female$postmeno, "Menopause")

	Estimate	Std. Error	t_value	p_value
Intercept	27.820409	0.7494864	37.119	0
Married: True	-5.305510	0.7624238	-6.959	0
Menopause	-4.303908	0.6492579	-6.629	0

Want – Full Controls

reg_full_control_gend(female$want, female, female$postmeno, "Menopause")

	Estimate	Std. Error	t_value	p_value
Intercept	33.6748401	1.2119710	27.785	0
Married: True	-4.3213696	0.7725891	-5.593	0
Menopause	-0.7868144	0.8512848	-0.924	0.3556
Age (years)	-0.1575124	0.0261521	-6.023	0
Duration	-0.0070232	0.0080199	-0.876	0.3814

Like - Only Menopause

reg_just_mod_i(female$totlike, female, female$postmeno, "Menopause")

	Estimate	Std. Error	t_value	p_value
Intercept	38.917864	0.9591764	40.574	0
Married: True	-4.903643	0.9751163	-5.029	0
Menopause	-2.936073	0.8399695	-3.495	5e-04

Like – Full Controls

reg_full_control_gend(female$totlike, female, female$postmeno, "Menopause")

	Estimate	Std. Error	t_value	p_value
Intercept	41.5508777	1.5897668	26.136	0
Married: True	-4.5731014	1.0079795	-4.537	0
Menopause	-1.3677219	1.1346274	-1.205	0.2284
Age (years)	-0.0721802	0.0347618	-2.076	0.0382
Duration	0.0027988	0.0101813	0.275	0.7835

Frequency - Only Menopause

reg_just_mod_i(female$freq_num, female, female$postmeno, "Menopause")

	Estimate	Std. Error	t_value	p_value
Intercept	3.5496615	0.1434452	24.746	0
Married: True	-0.4410055	0.1443420	-3.055	0.0023
Menopause	-0.8174278	0.1219319	-6.704	0

Frequency – Full Controls

reg_full_control_gend(female$freq_num, female, female$postmeno, "Menopause")

	Estimate	Std. Error	t_value	p_value
Intercept	5.0329825	0.2475783	20.329	0
Married: True	-0.1349138	0.1525934	-0.884	0.3769
Menopause	0.0841373	0.1536656	0.548	0.5842
Age (years)	-0.0386300	0.0055250	-6.992	0
Duration	-0.0061284	0.0042411	-1.445	0.1488

---

Sexual Desire Has A Strong Effect, But Doesn’t Interact Strongly With Marriage

Graphs

# (5 - desire )because scale is reverse
sex_data$SexDesNum = 5 - as.numeric(sex_data$SexualDesire)

contsmall(sex_data, sex_data$SexDesNum, "Desire", "")

Want - Only Desire

reg_just_mod_i(sex_data$want, sex_data, sex_data$SexDesNum, "Desire")

	Estimate	Std. Error	t_value	p_value
Intercept	18.647156	0.4734482	39.386	0
Married: True	-4.002852	0.3503039	-11.427	0
Desire	4.876286	0.1940516	25.129	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$SexDesNum, "Desire")

	Estimate	Std. Error	t_value	p_value
Intercept	24.4420191	0.6294354	38.832	0
Married: True	-1.9573497	0.3749207	-5.221	0
Desire	4.1776388	0.1948553	21.44	0
Age (years)	-0.1403201	0.0105117	-13.349	0
Duration	-0.0094075	0.0049039	-1.918	0.0552
Male	2.3952621	0.3449966	6.943	0

Like - Only Desire

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$SexDesNum,"Desire")

	Estimate	Std. Error	t_value	p_value
Intercept	30.602984	0.5645460	54.208	0
Married: True	-2.864904	0.4165267	-6.878	0
Desire	3.742157	0.2313409	16.176	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$SexDesNum, "Desire")

	Estimate	Std. Error	t_value	p_value
Intercept	32.3017405	0.7817763	41.318	0
Married: True	-2.2376063	0.4627385	-4.836	0
Desire	3.5873083	0.2422867	14.806	0
Age (years)	-0.0394997	0.0129927	-3.04	0.0024
Duration	-0.0034131	0.0059663	-0.572	0.5673
Male	0.3066047	0.4284108	0.716	0.4743

Frequency - Only Desire

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$SexDesNum, "Desire")

	Estimate	Std. Error	t_value	p_value
Intercept	2.6049192	0.0867832	30.016	0
Married: True	-0.6116625	0.0644030	-9.497	0
Desire	0.5116272	0.0361937	14.136	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$SexDesNum, "Desire")

	Estimate	Std. Error	t_value	p_value
Intercept	3.7782237	0.1147580	32.923	0
Married: True	-0.1557964	0.0687353	-2.267	0.0235
Desire	0.4545019	0.0359141	12.655	0
Age (years)	-0.0268896	0.0019727	-13.631	0
Duration	-0.0016330	0.0011773	-1.387	0.1655
Male	-0.0269566	0.0625570	-0.431	0.6666

---

Enjoyment Has A Strong Effect, But Doesn’t Interact Strongly With Marriage

Graphs

# (5 - desire )because scale is reverse
sex_data$SexEnjoyNum = 5 - as.numeric(sex_data$EnjoySex)

contsmall(sex_data, sex_data$SexEnjoyNum, "Enjoyment", "")

Want - Only Enjoyment

reg_just_mod_i(sex_data$want, sex_data, sex_data$SexEnjoyNum, "Enjoyment")

	Estimate	Std. Error	t_value	p_value
Intercept	16.192875	0.4906732	33.001	0
Married: True	-3.940167	0.3367836	-11.699	0
Enjoyment	4.935075	0.1679286	29.388	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$SexEnjoyNum, "Enjoyment")

	Estimate	Std. Error	t_value	p_value
Intercept	22.0290395	0.6105196	36.082	0
Married: True	-1.7136855	0.3574128	-4.795	0
Enjoyment	4.4609243	0.1633201	27.314	0
Age (years)	-0.1486891	0.0098929	-15.03	0
Duration	-0.0093570	0.0046705	-2.003	0.0452
Male	2.5321332	0.3237855	7.82	0

Like - Only Enjoyment

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$SexEnjoyNum,"Enjoyment")

	Estimate	Std. Error	t_value	p_value
Intercept	26.529810	0.5739821	46.221	0
Married: True	-2.566266	0.3924580	-6.539	0
Enjoyment	4.670185	0.1962675	23.795	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$SexEnjoyNum, "Enjoyment")

	Estimate	Std. Error	t_value	p_value
Intercept	28.2380293	0.7503679	37.632	0
Married: True	-1.8756361	0.4368953	-4.293	0
Enjoyment	4.5887135	0.2008934	22.842	0
Age (years)	-0.0408328	0.0121019	-3.374	8e-04
Duration	-0.0036877	0.0056276	-0.655	0.5124
Male	0.1451025	0.3978127	0.365	0.7153

Frequency - Only Enjoyment

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$SexEnjoyNum, "Enjoyment")

	Estimate	Std. Error	t_value	p_value
Intercept	2.4514043	0.0928991	26.388	0
Married: True	-0.6100057	0.0641409	-9.51	0
Enjoyment	0.4749193	0.0322321	14.734	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$SexEnjoyNum, "Enjoyment")

	Estimate	Std. Error	t_value	p_value
Intercept	3.6381487	0.1157157	31.44	0
Married: True	-0.1388800	0.0682496	-2.035	0.042
Enjoyment	0.4408183	0.0312785	14.093	0
Age (years)	-0.0283410	0.0019380	-14.624	0
Duration	-0.0011378	0.0011684	-0.974	0.3303
Male	0.0018551	0.0611802	0.03	0.9758

---

Masturbation Orgasm Enjoyment Has A Positive Effect, But Doesn’t Account For Marriage Effect

Graphs

# (5 - desire )because scale is reverse
sex_data$MastOrgNum= as.numeric(sex_data$MasturbationOrgasm)

contsmall(sex_data, sex_data$MastOrgNum, "Masturbation Orgasm Satisfaction", "")

Want - Only Masturbation Enjoyment

reg_just_mod_i(sex_data$want, sex_data, sex_data$MastOrgNum, "Masturbation Orgasm Satisfaction")

	Estimate	Std. Error	t_value	p_value
Intercept	23.8541722	0.9152326	26.064	0
Married: True	-4.8092933	0.4590795	-10.476	0
Masturbation Orgasm Satisfaction	0.8482631	0.1280314	6.625	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$MastOrgNum, "Masturbation Orgasm Satisfaction")

	Estimate	Std. Error	t_value	p_value
Intercept	28.4886412	1.0772789	26.445	0
Married: True	-2.6059747	0.4896120	-5.323	0
Masturbation Orgasm Satisfaction	0.7978845	0.1244737	6.41	0
Age (years)	-0.1554759	0.0138270	-11.244	0
Duration	-0.0135746	0.0072982	-1.86	0.0631
Male	3.4221777	0.4625076	7.399	0

Like - Only Masturbation ENjoyment

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$MastOrgNum,"Masturbation Orgasm Satisfaction")

	Estimate	Std. Error	t_value	p_value
Intercept	35.6800127	1.0323338	34.562	0
Married: True	-4.2784736	0.5158773	-8.294	0
Masturbation Orgasm Satisfaction	0.4251481	0.1446775	2.939	0.0034

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$MastOrgNum, "Masturbation Orgasm Satisfaction")

	Estimate	Std. Error	t_value	p_value
Intercept	38.0353428	1.2616197	30.148	0
Married: True	-3.2172197	0.5733830	-5.611	0
Masturbation Orgasm Satisfaction	0.3846776	0.1466499	2.623	0.0088
Age (years)	-0.0692634	0.0161885	-4.279	0
Duration	-0.0086168	0.0084393	-1.021	0.3074
Male	1.1528404	0.5420863	2.127	0.0336

Frequency - Only Masturbation Enjoyment

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$MastOrgNum, "Masturbation Orgasm Satisfaction")

	Estimate	Std. Error	t_value	p_value
Intercept	2.9017329	0.1538718	18.858	0
Married: True	-0.7302272	0.0781658	-9.342	0
Masturbation Orgasm Satisfaction	0.1142339	0.0216544	5.275	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$MastOrgNum, "Masturbation Orgasm Satisfaction")

	Estimate	Std. Error	t_value	p_value
Intercept	3.9566666	0.1820058	21.739	0
Married: True	-0.1213259	0.0906225	-1.339	0.1809
Masturbation Orgasm Satisfaction	0.0823007	0.0207977	3.957	1e-04
Age (years)	-0.0220462	0.0028197	-7.819	0
Duration	-0.0157764	0.0035621	-4.429	0
Male	0.1670280	0.0779068	2.144	0.0322

---

Orgasm Frequency Doesn’t Interact Strongly With Marriage

Graphs

# (5 - desire )because scale is reverse
sex_data$OrgFreqNum= as.numeric(sex_data$orgasm)

contsmall(sex_data, sex_data$OrgFreqNum, "Orgasm Frequency", "")

Want - Only Orgasm Frequency

reg_just_mod_i(sex_data$want, sex_data, sex_data$OrgFreqNum, "Orgasm Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	17.160239	0.3995932	42.944	0
Married: True	-4.542637	0.2520150	-18.025	0
Orgasm Frequency	3.033123	0.0873044	34.742	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$OrgFreqNum, "Orgasm Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	23.3990326	0.4763179	49.125	0
Married: True	-2.3569892	0.2610402	-9.029	0
Orgasm Frequency	2.6540077	0.0882931	30.059	0
Age (years)	-0.1497854	0.0073795	-20.297	0
Duration	-0.0133419	0.0045783	-2.914	0.0036
Male	2.1070190	0.2384758	8.835	0

Like - Only Orgasm Frequency

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$OrgFreqNum,"Orgasm Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	24.417086	0.4463002	54.71	0
Married: True	-3.416966	0.2788408	-12.254	0
Orgasm Frequency	3.617943	0.0975657	37.082	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$OrgFreqNum, "Orgasm Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	26.4151109	0.5520139	47.852	0
Married: True	-2.5421131	0.3007581	-8.452	0
Orgasm Frequency	3.7727030	0.1029708	36.639	0
Age (years)	-0.0478062	0.0085128	-5.616	0
Duration	-0.0062815	0.0051968	-1.209	0.2268
Male	-1.5583933	0.2761368	-5.644	0

Frequency - Only Orgasm Frequency

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$OrgFreqNum, "Orgasm Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	2.5688061	0.0729298	35.223	0
Married: True	-0.6122975	0.0467211	-13.105	0
Orgasm Frequency	0.2854054	0.0158894	17.962	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$OrgFreqNum, "Orgasm Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	3.8686373	0.0871396	44.396	0
Married: True	-0.1426012	0.0476117	-2.995	0.0028
Orgasm Frequency	0.2608895	0.0158124	16.499	0
Age (years)	-0.0304286	0.0014041	-21.672	0
Duration	-0.0029655	0.0010478	-2.83	0.0047
Male	-0.0109262	0.0429835	-0.254	0.7994

---

Controlling For Orgasm Satifaction Reduces the MarriedXAge Effect On Like

Graphs

# (5 - desire )because scale is reverse
sex_data$OSatisNum= as.numeric(sex_data$o_satis)

contsmall(sex_data, sex_data$OSatisNum, "Orgasm Satisfaction", "")

Want - Only Orgasm Satisfaction

reg_just_mod_i(sex_data$want, sex_data, sex_data$OSatisNum, "Orgasm Satisfaction")

	Estimate	Std. Error	t_value	p_value
Intercept	12.447766	0.5226481	23.817	0
Married: True	-4.221283	0.2530234	-16.683	0
Orgasm Satisfaction	2.058860	0.0596488	34.516	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$OSatisNum, "Orgasm Satisfaction")

	Estimate	Std. Error	t_value	p_value
Intercept	18.1304975	0.5731339	31.634	0
Married: True	-2.0897140	0.2572007	-8.125	0
Orgasm Satisfaction	1.8528570	0.0561864	32.977	0
Age (years)	-0.1528182	0.0072466	-21.088	0
Duration	-0.0136211	0.0045070	-3.022	0.0025
Male	3.8784567	0.2217693	17.489	0

Like - Only Desire

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$OSatisNum,"Orgasm Satisfaction")

	Estimate	Std. Error	t_value	p_value
Intercept	16.632359	0.5719960	29.078	0
Married: True	-2.939208	0.2715586	-10.823	0
Orgasm Satisfaction	2.724751	0.0653649	41.685	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$OSatisNum, "Orgasm Satisfaction")

	Estimate	Std. Error	t_value	p_value
Intercept	18.6726570	0.6644204	28.104	0
Married: True	-2.1766104	0.2936740	-7.412	0
Orgasm Satisfaction	2.6606840	0.0655776	40.573	0
Age (years)	-0.0510101	0.0082883	-6.154	0
Duration	-0.0070540	0.0050697	-1.391	0.1642
Male	0.9469191	0.2542443	3.724	2e-04

Frequency - Only Desire

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$OSatisNum, "Orgasm Satisfaction")

	Estimate	Std. Error	t_value	p_value
Intercept	1.7723861	0.0933139	18.994	0
Married: True	-0.5637650	0.0460243	-12.249	0
Orgasm Satisfaction	0.2378468	0.0106365	22.361	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$OSatisNum, "Orgasm Satisfaction")

	Estimate	Std. Error	t_value	p_value
Intercept	3.1499045	0.1035681	30.414	0
Married: True	-0.1140092	0.0469062	-2.431	0.0151
Orgasm Satisfaction	0.2067647	0.0100109	20.654	0
Age (years)	-0.0306168	0.0013778	-22.221	0
Duration	-0.0026494	0.0010319	-2.568	0.0103
Male	0.1580627	0.0398160	3.97	1e-04

---

Desire In First 6 Months Doesn’t Account For Marriage Effect

Graphs

# (5 - desire )because scale is reverse
sex_data$Desire6mNum= as.numeric(sex_data$Desire6m)

contsmall(sex_data, sex_data$Desire6mNum, "Desire In First 6 Months", "")

Want - Only Desire First 6 months

reg_just_mod_i(sex_data$want, sex_data, sex_data$Desire6mNum, "Desire In First 6 Months")

	Estimate	Std. Error	t_value	p_value
Intercept	17.096119	0.9031557	18.929	0
Married: True	-5.687971	0.3783083	-15.035	0
Desire In First 6 Months	1.456562	0.1104948	13.182	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$Desire6mNum, "Desire In First 6 Months")

	Estimate	Std. Error	t_value	p_value
Intercept	22.7396530	0.9160375	24.824	0
Married: True	-2.8815693	0.3937859	-7.318	0
Desire In First 6 Months	1.4389073	0.1024854	14.04	0
Age (years)	-0.1876308	0.0108295	-17.326	0
Duration	-0.0084214	0.0051484	-1.636	0.102
Male	4.1107977	0.3514142	11.698	0

Like - Only Desire First 6 Months

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$Desire6mNum,"Desire In First 6 Months")

	Estimate	Std. Error	t_value	p_value
Intercept	24.642035	0.9992061	24.662	0
Married: True	-4.341071	0.4175127	-10.397	0
Desire In First 6 Months	1.738176	0.1222049	14.223	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$Desire6mNum, "Desire In First 6 Months")

	Estimate	Std. Error	t_value	p_value
Intercept	27.1186707	1.0813486	25.079	0
Married: True	-3.1362111	0.4631881	-6.771	0
Desire In First 6 Months	1.7329313	0.1210803	14.312	0
Age (years)	-0.0819798	0.0127377	-6.436	0
Duration	-0.0033131	0.0059839	-0.554	0.5799
Male	1.7048559	0.4145947	4.112	0

Frequency - Only Desire First 6 Months

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$Desire6mNum, "Desire In First 6 Months")

	Estimate	Std. Error	t_value	p_value
Intercept	2.8656564	0.1587122	18.056	0
Married: True	-0.7568512	0.0667302	-11.342	0
Desire In First 6 Months	0.0949953	0.0194140	4.893	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$Desire6mNum, "Desire In First 6 Months")

	Estimate	Std. Error	t_value	p_value
Intercept	3.9895131	0.1657643	24.067	0
Married: True	-0.2478231	0.0709576	-3.493	5e-04
Desire In First 6 Months	0.0967646	0.0183225	5.281	0
Age (years)	-0.0309254	0.0020102	-15.384	0
Duration	-0.0015657	0.0012095	-1.295	0.1956
Male	0.1734454	0.0627147	2.766	0.0057

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Enjoyment In First 6 Months Doesn’t Account For Marriage Effect

Graphs

# (5 - desire )because scale is reverse
sex_data$Enjoy6mNum= as.numeric(sex_data$Enjoy6m)

contsmall(sex_data, sex_data$Enjoy6mNum, "Enjoyment In First 6 Months", "")

Want - Only Enjoyment First 6 Months

reg_just_mod_i(sex_data$want, sex_data, sex_data$Enjoy6mNum, "Enjoyment In First 6 Months")

	Estimate	Std. Error	t_value	p_value
Intercept	17.600876	0.8749073	20.117	0
Married: True	-5.633394	0.3783057	-14.891	0
Enjoyment In First 6 Months	1.404376	0.1076104	13.051	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$Enjoy6mNum, "Enjoyment In First 6 Months")

	Estimate	Std. Error	t_value	p_value
Intercept	22.9556805	0.8688730	26.42	0
Married: True	-2.7071923	0.3916028	-6.913	0
Enjoyment In First 6 Months	1.4764366	0.0997520	14.801	0
Age (years)	-0.1973365	0.0108076	-18.259	0
Duration	-0.0072448	0.0051256	-1.413	0.1577
Male	4.0211451	0.3501869	11.483	0

Like - Only Enjoyment First 6 Months

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$Enjoy6mNum,"Enjoyment In First 6 Months")

	Estimate	Std. Error	t_value	p_value
Intercept	22.736715	0.9547917	23.813	0
Married: True	-4.342174	0.4097966	-10.596	0
Enjoyment In First 6 Months	2.000748	0.1172859	17.059	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$Enjoy6mNum, "Enjoyment In First 6 Months")

	Estimate	Std. Error	t_value	p_value
Intercept	25.3985017	1.0113144	25.114	0
Married: True	-2.9314170	0.4528799	-6.473	0
Enjoyment In First 6 Months	2.0512033	0.1163932	17.623	0
Age (years)	-0.0965107	0.0125029	-7.719	0
Duration	-0.0018641	0.0058572	-0.318	0.7503
Male	1.5307319	0.4062194	3.768	2e-04

Frequency - Only Enjoyment First 6 Months

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$Enjoy6mNum, "Enjoyment In First 6 Months")

	Estimate	Std. Error	t_value	p_value
Intercept	2.8159234	0.1531233	18.39	0
Married: True	-0.7569702	0.0666090	-11.364	0
Enjoyment In First 6 Months	0.1025751	0.0188500	5.442	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$Enjoy6mNum, "Enjoyment In First 6 Months")

	Estimate	Std. Error	t_value	p_value
Intercept	3.8244912	0.1562899	24.471	0
Married: True	-0.2403842	0.0705246	-3.409	7e-04
Enjoyment In First 6 Months	0.1249421	0.0178052	7.017	0
Age (years)	-0.0319008	0.0020045	-15.914	0
Duration	-0.0013633	0.0012035	-1.133	0.2574
Male	0.1616137	0.0624611	2.587	0.0097

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Being Turned On Before Sex Accounts For Much Of The Sex Effect

Graphs

# (5 - desire )because scale is reverse
sex_data$satis4num= as.numeric(sex_data$satis4)

contsmall(sex_data, sex_data$satis4num, "Turned On Before Physical Contact", "")

Want - Only Pre-Contact Turned On

reg_just_mod_i(sex_data$want, sex_data, sex_data$satis4num, "Turned On Before Physical Contact")

	Estimate	Std. Error	t_value	p_value
Intercept	11.326382	0.3217530	35.202	0
Married: True	-2.452879	0.2101014	-11.675	0
Turned On Before Physical Contact	4.926905	0.0757538	65.038	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$satis4num, "Turned On Before Physical Contact")

	Estimate	Std. Error	t_value	p_value
Intercept	15.9229435	0.4036400	39.448	0
Married: True	-1.0129108	0.2175881	-4.655	0
Turned On Before Physical Contact	4.5599432	0.0756962	60.24	0
Age (years)	-0.1061855	0.0061907	-17.153	0
Duration	-0.0132336	0.0037664	-3.514	4e-04
Male	1.7464225	0.1921945	9.087	0

Like - Only Pre-Contact Turned On

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$satis4num,"Turned On Before Physical Contact")

	Estimate	Std. Error	t_value	p_value
Intercept	18.985722	0.3599119	52.751	0
Married: True	-1.286820	0.2343162	-5.492	0
Turned On Before Physical Contact	5.449207	0.0846786	64.352	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$satis4num, "Turned On Before Physical Contact")

	Estimate	Std. Error	t_value	p_value
Intercept	19.4283996	0.4669062	41.611	0
Married: True	-1.0122283	0.2517480	-4.021	1e-04
Turned On Before Physical Contact	5.5485125	0.0877020	63.265	0
Age (years)	-0.0017742	0.0071579	-0.248	0.8042
Duration	-0.0070453	0.0043255	-1.629	0.1034
Male	-1.4392747	0.2223679	-6.472	0

Frequency - Only Pre-Contact Turned On

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$satis4num, "Turned On Before Physical Contact")

	Estimate	Std. Error	t_value	p_value
Intercept	2.0818769	0.0685215	30.383	0
Married: True	-0.4210280	0.0453740	-9.279	0
Turned On Before Physical Contact	0.4531715	0.0162371	27.91	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$satis4num, "Turned On Before Physical Contact")

	Estimate	Std. Error	t_value	p_value
Intercept	3.3456015	0.0852527	39.243	0
Married: True	-0.0338006	0.0462491	-0.731	0.4649
Turned On Before Physical Contact	0.4017724	0.0158974	25.273	0
Age (years)	-0.0272295	0.0013663	-19.929	0
Duration	-0.0022821	0.0010075	-2.265	0.0235
Male	-0.0223819	0.0403171	-0.555	0.5788

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Comfort Discussing Doesn’t Change The Sex Effect

Graphs

# (5 - desire )because scale is reverse
sex_data$comfortnum= as.numeric(sex_data$comfort)

contsmall(sex_data, sex_data$comfortnum, "Comfort Discussing Sex", "")

Want - Only Comfort

reg_just_mod_i(sex_data$want, sex_data, sex_data$comfortnum, "Comfort Discussing Sex")

	Estimate	Std. Error	t_value	p_value
Intercept	22.038983	0.4781837	46.089	0
Married: True	-4.289796	0.2850571	-15.049	0
Comfort Discussing Sex	1.263746	0.0746269	16.934	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$comfortnum, "Comfort Discussing Sex")

	Estimate	Std. Error	t_value	p_value
Intercept	27.5253786	0.5417739	50.806	0
Married: True	-2.0636434	0.2890627	-7.139	0
Comfort Discussing Sex	1.0721162	0.0701183	15.29	0
Age (years)	-0.1670982	0.0081050	-20.617	0
Duration	-0.0113350	0.0048903	-2.318	0.0205
Male	4.1498890	0.2500652	16.595	0

Like - Only Comfort

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$comfortnum,"Comfort Discussing Sex")

	Estimate	Std. Error	t_value	p_value
Intercept	32.876552	0.5393171	60.96	0
Married: True	-3.464737	0.3215002	-10.777	0
Comfort Discussing Sex	1.017450	0.0841676	12.088	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$comfortnum, "Comfort Discussing Sex")

	Estimate	Std. Error	t_value	p_value
Intercept	35.5261171	0.6483568	54.794	0
Married: True	-2.4036640	0.3459299	-6.948	0
Comfort Discussing Sex	0.9356042	0.0839127	11.15	0
Age (years)	-0.0771634	0.0096995	-7.955	0
Duration	-0.0060454	0.0058524	-1.033	0.3017
Male	1.6058617	0.2992604	5.366	0

Frequency - Only Comfort

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$comfortnum, "Comfort Discussing Sex")

	Estimate	Std. Error	t_value	p_value
Intercept	3.0165581	0.0820622	36.759	0
Married: True	-0.5723779	0.0493282	-11.603	0
Comfort Discussing Sex	0.1227013	0.0128012	9.585	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$comfortnum, "Comfort Discussing Sex")

	Estimate	Std. Error	t_value	p_value
Intercept	4.2759821	0.0939608	45.508	0
Married: True	-0.1271953	0.0501522	-2.536	0.0112
Comfort Discussing Sex	0.1009304	0.0120289	8.391	0
Age (years)	-0.0315795	0.0014637	-21.575	0
Duration	-0.0021007	0.0010731	-1.958	0.0503
Male	0.2034341	0.0427465	4.759	0

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