Behavioral 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

---

Masturbation Correlates With Wanting, But Doesn’t Change Marriage Effect For Any Outcome

Want Graphs

bin_graphs_want(sex_data, sex_data$Masturbation, "Masturbation Binary", c("Does Masturbate", "Doesn't Masturbate"),  
                     c("Unmarried, Does", "Married, Does", "Unmarried, Doesn't", "Married, Doesn't"))

Like Graphs

bin_graphs_like(sex_data, sex_data$Masturbation, "Masturbation Binary", c("Does Masturbate", "Doesn't Masturbate"),  
                     c("Unmarried, Does", "Married, Does", "Unmarried, Doesn't", "Married, Doesn't"))

Frequency Graphs

bin_graphs_freq(sex_data, sex_data$Masturbation, "Masturbation Binary", c("Does Masturbate", "Doesn't Masturbate"),  
                     c("Unmarried, Does", "Married, Does", "Unmarried, Doesn't", "Married, Doesn't"))

Want - Only Masturbates

reg_just_mod_i(sex_data$want, sex_data, sex_data$Masturbation, "Masturbates")

	Estimate	Std. Error	t_value	p_value
Intercept	25.713283	0.4207056	61.119	0
Married: True	-4.832764	0.3882385	-12.448	0
Masturbates	3.509673	0.3896280	9.008	0

Want – Full Controls

reg_full_control_gend(sex_data$want, sex_data, sex_data$Masturbation, "Masturbates")

	Estimate	Std. Error	t_value	p_value
Intercept	31.6357836	0.5937128	53.285	0
Married: True	-2.3241751	0.4173741	-5.569	0
Masturbates	3.0708399	0.3757931	8.172	0
Age (years)	-0.1462420	0.0110613	-13.221	0
Duration	-0.0092792	0.0054456	-1.704	0.0885

Like - Only Masturbates

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$Masturbation, "Masturbates")

	Estimate	Std. Error	t_value	p_value
Intercept	37.897542	0.4767215	79.496	0
Married: True	-3.883481	0.4398593	-8.829	0
Masturbates	0.213616	0.4447252	0.48	0.631

Like – Full Controls

reg_full_control_gend(sex_data$totlike, sex_data, sex_data$Masturbation, "Masturbates")

	Estimate	Std. Error	t_value	p_value
Intercept	40.5204466	0.6978172	58.067	0
Married: True	-2.8094235	0.4877010	-5.761	0
Masturbates	0.0061784	0.4441115	0.014	0.9889
Age (years)	-0.0647526	0.0129863	-4.986	0
Duration	-0.0034569	0.0062795	-0.551	0.582

Frequency - Only Masturbates

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$Masturbation, "Masturbates")

	Estimate	Std. Error	t_value	p_value
Intercept	3.5492972	0.0727877	48.762	0
Married: True	-0.7372743	0.0672972	-10.956	0
Masturbates	0.0600030	0.0666479	0.9	0.3681

Frequency – Full Controls

reg_full_control_gend(sex_data$freq_num, sex_data, sex_data$Masturbation, "Masturbates")

	Estimate	Std. Error	t_value	p_value
Intercept	4.7746206	0.1026428	46.517	0
Married: True	-0.2341212	0.0713455	-3.282	0.001
Masturbates	-0.0256017	0.0631884	-0.405	0.6854
Age (years)	-0.0297049	0.0019449	-15.273	0
Duration	-0.0017873	0.0012219	-1.463	0.1437

---

Controlling For Masturbation Frequency Strengthens Marriage Effects Slightly

Graphs

# (6 - desire )because scale is reverse
sex_data$MastFreqNum = 6-  as.numeric(sex_data$MasturbationFreq)

contsmall(sex_data, sex_data$MastFreqNum, "Masturbation Frequency", "")

Want - Only Masturbation Frequency

reg_just_mod_i(sex_data$want, sex_data, sex_data$MastFreqNum, "Masturbation Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	23.828069	0.6476797	36.79	0
Married: True	-4.261849	0.4556931	-9.352	0
Masturbation Frequency	1.894929	0.1870059	10.133	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$MastFreqNum, "Masturbation Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	29.9168012	0.8782980	34.062	0
Married: True	-2.4248481	0.4897959	-4.951	0
Masturbation Frequency	1.2841080	0.1921966	6.681	0
Age (years)	-0.1399572	0.0142508	-9.821	0
Duration	-0.0113385	0.0073318	-1.546	0.1222
Male	2.1480876	0.4725876	4.545	0

Like - Only Masturbation Frequency

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$MastFreqNum,"Masturbation Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	37.6440001	0.7469046	50.4	0
Married: True	-4.3094520	0.5236447	-8.23	0
Masturbation Frequency	0.2654942	0.2149702	1.235	0.217

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$MastFreqNum, "Masturbation Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	41.0565500	1.0312607	39.812	0
Married: True	-3.2370843	0.5757667	-5.622	0
Masturbation Frequency	-0.0610910	0.2263510	-0.27	0.7873
Age (years)	-0.0775992	0.0167022	-4.646	0
Duration	-0.0088210	0.0085021	-1.038	0.2997
Male	1.0413703	0.5556341	1.874	0.0611

Frequency - Only Masturbation Frequency

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$MastFreqNum, "Masturbation Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	3.3551771	0.1126418	29.786	0
Married: True	-0.7589831	0.0796163	-9.533	0
Masturbation Frequency	0.1027011	0.0331603	3.097	0.002

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$MastFreqNum, "Masturbation Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	4.5689407	0.1518618	30.086	0
Married: True	-0.1413652	0.0908218	-1.557	0.1198
Masturbation Frequency	-0.0007456	0.0329361	-0.023	0.9819
Age (years)	-0.0234004	0.0028664	-8.164	0
Duration	-0.0163969	0.0035609	-4.605	0
Male	0.1266775	0.0802366	1.579	0.1146

---

Generally, Faking Orgasm Doesn’t Account for Marriage Effect, But Frequent Fakers Dislike Sex Whether Or Not They Are Married

Graphs

sex_data$FakeOrgNum = as.numeric(sex_data$FakeOrgasm)

contsmall(sex_data, sex_data$FakeOrgNum, "Orgasm Faking Frequency", "")

Want - Only Orgasm Faking

reg_just_mod_i(sex_data$want, sex_data, sex_data$FakeOrgNum, "Orgasm Faking Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	29.746179	0.4542020	65.491	0
Married: True	-5.445533	0.3903286	-13.951	0
Orgasm Faking Frequency	-1.021436	0.2383273	-4.286	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$FakeOrgNum, "Orgasm Faking Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	35.3801796	0.6210873	56.965	0
Married: True	-2.6370829	0.4075799	-6.47	0
Orgasm Faking Frequency	-1.0339689	0.2254665	-4.586	0
Age (years)	-0.1911471	0.0112611	-16.974	0
Duration	-0.0061278	0.0053458	-1.146	0.2518
Male	4.1453354	0.3671072	11.292	0

Like - Only Orgasm Faking

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$FakeOrgNum,"Orgasm Faking Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	41.685952	0.4969993	83.875	0
Married: True	-4.209796	0.4266210	-9.868	0
Orgasm Faking Frequency	-2.590407	0.2603343	-9.95	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$FakeOrgNum, "Orgasm Faking Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	44.6589220	0.7216664	61.883	0
Married: True	-2.9397474	0.4728434	-6.217	0
Orgasm Faking Frequency	-2.6565030	0.2621482	-10.134	0
Age (years)	-0.0910808	0.0130586	-6.975	0
Duration	0.0014874	0.0061243	0.243	0.8081
Male	1.3912883	0.4273255	3.256	0.0011

Frequency - Only Orgasm Faking

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$FakeOrgNum, "Orgasm Faking Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	3.4731091	0.0787276	44.116	0
Married: True	-0.7243116	0.0669104	-10.825	0
Orgasm Faking Frequency	0.0898590	0.0420751	2.136	0.0328

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$FakeOrgNum, "Orgasm Faking Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	4.6483460	0.1106267	42.018	0
Married: True	-0.2228745	0.0712392	-3.129	0.0018
Orgasm Faking Frequency	0.0508237	0.0405494	1.253	0.2102
Age (years)	-0.0308910	0.0020260	-15.248	0
Duration	-0.0015573	0.0012184	-1.278	0.2013
Male	0.2108186	0.0638475	3.302	0.001

---

Lube Doesn’t Offset Marriage Effect, But Interacts In Interesting Ways

Graphs

sex_data$LubeNum = as.numeric(sex_data$lube)

contsmall(sex_data, sex_data$LubeNum, "Lube Use Frequency", "")

Want - Only Masturbation Frequency

reg_just_mod_i(sex_data$want, sex_data, sex_data$LubeNum, "Lube Use Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	28.1249776	0.3240067	86.804	0
Married: True	-4.9052735	0.2855114	-17.181	0
Lube Use Frequency	0.4287085	0.1013284	4.231	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$LubeNum, "Lube Use Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	32.5068844	0.4134712	78.619	0
Married: True	-2.4038534	0.2885690	-8.33	0
Lube Use Frequency	0.5442508	0.0940628	5.786	0
Age (years)	-0.1759847	0.0081142	-21.688	0
Duration	-0.0162218	0.0049805	-3.257	0.0011
Male	4.4808710	0.2487751	18.012	0

Like - Only Masturbation Frequency

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$LubeNum,"Lube Use Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	37.5333572	0.3594896	104.407	0
Married: True	-3.9572115	0.3167785	-12.492	0
Lube Use Frequency	0.4554106	0.1124252	4.051	1e-04

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$LubeNum, "Lube Use Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	39.8547714	0.4887817	81.539	0
Married: True	-2.6575331	0.3411295	-7.79	0
Lube Use Frequency	0.5178601	0.1111956	4.657	0
Age (years)	-0.0870408	0.0095922	-9.074	0
Duration	-0.0105475	0.0058877	-1.791	0.0733
Male	1.9009603	0.2940875	6.464	0

Frequency - Only Masturbation Frequency

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$LubeNum, "Lube Use Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	3.4119269	0.0545293	62.571	0
Married: True	-0.6357592	0.0482201	-13.185	0
Lube Use Frequency	0.1312485	0.0169472	7.745	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$LubeNum, "Lube Use Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	4.5288613	0.0712203	63.59	0
Married: True	-0.1462357	0.0488291	-2.995	0.0028
Lube Use Frequency	0.1612277	0.0157072	10.265	0
Age (years)	-0.0328129	0.0014306	-22.937	0
Duration	-0.0032100	0.0010613	-3.025	0.0025
Male	0.2306529	0.0414256	5.568	0

---

Drugs Doesn’t Offset Marriage Effect, But Interacts In Interesting Ways

Graphs

sex_data$DrugNum = as.numeric(sex_data$drugs)

contsmall(sex_data, sex_data$DrugNum, "Potency-Enhacncing Drug Use Frequency", "")

Want - Only Drug Use

reg_just_mod_i(sex_data$want, sex_data, sex_data$DrugNum, "Drug Use Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	29.1259287	0.3034984	95.967	0
Married: True	-4.8888789	0.2862352	-17.08	0
Drug Use Frequency	-0.0563525	0.1366889	-0.412	0.6802

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$DrugNum, "Drug Use Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	33.2787578	0.3897049	85.395	0
Married: True	-2.4113851	0.2894974	-8.33	0
Drug Use Frequency	0.3240472	0.1289948	2.512	0.012
Age (years)	-0.1769679	0.0082276	-21.509	0
Duration	-0.0155739	0.0049935	-3.119	0.0018
Male	4.4680834	0.2497531	17.89	0

Like - Only Drug Use

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$DrugNum,"Drug Use Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	38.6785266	0.3366652	114.887	0
Married: True	-3.9351491	0.3175153	-12.394	0
Drug Use Frequency	-0.1199988	0.1516265	-0.791	0.4287

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$DrugNum, "Drug Use Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	40.8045045	0.4604050	88.627	0
Married: True	-2.6802903	0.3420179	-7.837	0
Drug Use Frequency	0.0799457	0.1523970	0.525	0.5999
Age (years)	-0.0857264	0.0097202	-8.819	0
Duration	-0.0098698	0.0058994	-1.673	0.0944
Male	1.9093453	0.2950632	6.471	0

Frequency - Only Drug Use

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$DrugNum, "Drug Use Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	3.7344416	0.0514037	72.649	0
Married: True	-0.6275255	0.0485841	-12.916	0
Drug Use Frequency	-0.0313924	0.0230976	-1.359	0.1742

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$DrugNum, "Drug Use Frequency")

	Estimate	Std. Error	t_value	p_value
Intercept	4.7820035	0.0676208	70.718	0
Married: True	-0.1518016	0.0493492	-3.076	0.0021
Drug Use Frequency	0.0727476	0.0218678	3.327	9e-04
Age (years)	-0.0329764	0.0014652	-22.506	0
Duration	-0.0028108	0.0010722	-2.621	0.0088
Male	0.2260370	0.0418982	5.395	0

---

More Previous Sex Partners Decreases Marriage’s Effect On Want, Increases Effect On Like

Graphs

nooutlier_sexpartners <- sex_data[sex_data$howmany < 50,]

contsmall(nooutlier_sexpartners, nooutlier_sexpartners$howmany, "Number Of Previous Sex Partners", "")

Want - Only Sex Partners

reg_just_mod_i(nooutlier_sexpartners$want, nooutlier_sexpartners, nooutlier_sexpartners$howmany, "Sex Partners")

	Estimate	Std. Error	t_value	p_value
Intercept	27.7109286	0.5665457	48.912	0
Married: True	-4.6364867	0.6415684	-7.227	0
Sex Partners	0.1588474	0.0691162	2.298	0.0218

Want – Full Controls

reg_full_control(nooutlier_sexpartners$want, nooutlier_sexpartners, nooutlier_sexpartners$howmany, " Sex Partners")

	Estimate	Std. Error	t_value	p_value
Intercept	30.2471210	1.0530463	28.723	0
Married: True	-1.5196466	0.7502283	-2.026	0.0431
Sex Partners	0.1618124	0.0663974	2.437	0.015
Age (years)	-0.1018521	0.0268054	-3.8	2e-04
Duration	-0.1100553	0.0312582	-3.521	5e-04
Male	4.1140395	0.6596097	6.237	0

Like - Only Sex Partners

reg_just_mod_i(nooutlier_sexpartners$totlike, nooutlier_sexpartners, nooutlier_sexpartners$howmany,"Sex Partners")

	Estimate	Std. Error	t_value	p_value
Intercept	36.9496110	0.6224055	59.366	0
Married: True	-5.2361789	0.7048252	-7.429	0
Sex Partners	-0.0348543	0.0759308	-0.459	0.6463

Like – Full Controls

reg_full_control(nooutlier_sexpartners$totlike,nooutlier_sexpartners, nooutlier_sexpartners$howmany, "Sex Partners")

	Estimate	Std. Error	t_value	p_value
Intercept	38.0222937	1.2131083	31.343	0
Married: True	-3.7555878	0.8642623	-4.345	0
Sex Partners	-0.0339220	0.0764898	-0.443	0.6575
Age (years)	-0.0442488	0.0308797	-1.433	0.1522
Duration	-0.0546437	0.0360094	-1.517	0.1295
Male	1.9214457	0.7598697	2.529	0.0116

Frequency - Only Sex Partners

reg_just_mod_i(nooutlier_sexpartners$freq_num, nooutlier_sexpartners, nooutlier_sexpartners$howmany, "Sex Partners")

	Estimate	Std. Error	t_value	p_value
Intercept	3.6030382	0.0954659	37.742	0
Married: True	-0.6810235	0.1068685	-6.373	0
Sex Partners	-0.0047984	0.0117928	-0.407	0.6842

Frequency – Full Controls

reg_full_control(nooutlier_sexpartners$freq_num, nooutlier_sexpartners, nooutlier_sexpartners$howmany, "Sex Partners")

	Estimate	Std. Error	t_value	p_value
Intercept	4.2962107	0.1829015	23.489	0
Married: True	-0.1774571	0.1251693	-1.418	0.1567
Sex Partners	0.0003797	0.0113923	0.033	0.9734
Age (years)	-0.0185144	0.0045396	-4.078	1e-04
Duration	-0.0171612	0.0051101	-3.358	8e-04
Male	0.3779989	0.1108539	3.41	7e-04

---

More Previous Sex Partners Decreases Marriage’s Effect On Want, Increases Effect On Like

Graphs

nooutlier_sexpartners <- sex_data[sex_data$howmany < 50,]

contsmall(nooutlier_sexpartners, nooutlier_sexpartners$howmany, "Number Of Previous Sex Partners", "")

Want - Only Sex Partners

reg_just_mod_i(nooutlier_sexpartners$want, nooutlier_sexpartners, nooutlier_sexpartners$howmany, "Sex Partners")

	Estimate	Std. Error	t_value	p_value
Intercept	27.7109286	0.5665457	48.912	0
Married: True	-4.6364867	0.6415684	-7.227	0
Sex Partners	0.1588474	0.0691162	2.298	0.0218

Want – Full Controls

reg_full_control(nooutlier_sexpartners$want, nooutlier_sexpartners, nooutlier_sexpartners$howmany, " Sex Partners")

	Estimate	Std. Error	t_value	p_value
Intercept	30.2471210	1.0530463	28.723	0
Married: True	-1.5196466	0.7502283	-2.026	0.0431
Sex Partners	0.1618124	0.0663974	2.437	0.015
Age (years)	-0.1018521	0.0268054	-3.8	2e-04
Duration	-0.1100553	0.0312582	-3.521	5e-04
Male	4.1140395	0.6596097	6.237	0

Like - Only Sex Partners

reg_just_mod_i(nooutlier_sexpartners$totlike, nooutlier_sexpartners, nooutlier_sexpartners$howmany,"Sex Partners")

	Estimate	Std. Error	t_value	p_value
Intercept	36.9496110	0.6224055	59.366	0
Married: True	-5.2361789	0.7048252	-7.429	0
Sex Partners	-0.0348543	0.0759308	-0.459	0.6463

Like – Full Controls

reg_full_control(nooutlier_sexpartners$totlike,nooutlier_sexpartners, nooutlier_sexpartners$howmany, "Sex Partners")

	Estimate	Std. Error	t_value	p_value
Intercept	38.0222937	1.2131083	31.343	0
Married: True	-3.7555878	0.8642623	-4.345	0
Sex Partners	-0.0339220	0.0764898	-0.443	0.6575
Age (years)	-0.0442488	0.0308797	-1.433	0.1522
Duration	-0.0546437	0.0360094	-1.517	0.1295
Male	1.9214457	0.7598697	2.529	0.0116

Frequency - Only Sex Partners

reg_just_mod_i(nooutlier_sexpartners$freq_num, nooutlier_sexpartners, nooutlier_sexpartners$howmany, "Sex Partners")

	Estimate	Std. Error	t_value	p_value
Intercept	3.6030382	0.0954659	37.742	0
Married: True	-0.6810235	0.1068685	-6.373	0
Sex Partners	-0.0047984	0.0117928	-0.407	0.6842

Frequency – Full Controls

reg_full_control(nooutlier_sexpartners$freq_num, nooutlier_sexpartners, nooutlier_sexpartners$howmany, "Sex Partners")

	Estimate	Std. Error	t_value	p_value
Intercept	4.2962107	0.1829015	23.489	0
Married: True	-0.1774571	0.1251693	-1.418	0.1567
Sex Partners	0.0003797	0.0113923	0.033	0.9734
Age (years)	-0.0185144	0.0045396	-4.078	1e-04
Duration	-0.0171612	0.0051101	-3.358	8e-04
Male	0.3779989	0.1108539	3.41	7e-04