Dynamics

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){
  

  df <- sex_data
  mod <- sex_data$children_in_house
  modname <-"Children In House"
  modlabs <- c("Children", "No Children")
  interaction_labs <- c("Unmarried, Children", "Married, Children", "Unmarried, No Children", "Married, No Children")
  df2 = df[!is.na(df$totlike) & !is.na(mod),]
  mod = mod[!is.na(df$totlike) & !is.na(mod)]
  table(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,  size= 1, aes(color = married)) + jrothsch_theme +
    labs( 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,  size= 1, 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

---

Believing Sex Is Important Accounts For Some Of The Marriage Effect

Graphs

sex_data$SexImporNum = as.numeric(sex_data$seximpor)


contsmall(sex_data, sex_data$SexImporNum, "Importance of Sex", "")

Want - Only Importance

reg_just_mod_i(sex_data$want, sex_data, sex_data$SexImporNum, "Importance of Sex")

	Estimate	Std. Error	t_value	p_value
Intercept	13.045487	0.3692273	35.332	0
Married: True	-3.216863	0.2396767	-13.422	0
Importance of Sex	2.380053	0.0486655	48.906	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$SexImporNum, "Importance of Sex")

	Estimate	Std. Error	t_value	p_value
Intercept	16.5953852	0.4837344	34.307	0
Married: True	-2.0302154	0.2510262	-8.088	0
Importance of Sex	2.1750198	0.0485360	44.813	0
Age (years)	-0.0912025	0.0071982	-12.67	0
Duration	-0.0100332	0.0039433	-2.544	0.011
Male	2.8352708	0.2214528	12.803	0

Like - Only Importance

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$SexImporNum,"Importance of Sex")

	Estimate	Std. Error	t_value	p_value
Intercept	25.402354	0.4875021	52.107	0
Married: True	-2.681595	0.3119561	-8.596	0
Importance of Sex	1.989012	0.0643406	30.914	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$SexImporNum, "Importance of Sex")

	Estimate	Std. Error	t_value	p_value
Intercept	25.6459440	0.6568328	39.045	0
Married: True	-2.5313869	0.3388627	-7.47	0
Importance of Sex	1.9710709	0.0665278	29.628	0
Age (years)	-0.0054871	0.0097105	-0.565	0.5721
Duration	-0.0052256	0.0052401	-0.997	0.3187
Male	0.2416187	0.3008386	0.803	0.4219

Frequency - Only Importance

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$SexImporNum, "Importance of Sex")

	Estimate	Std. Error	t_value	p_value
Intercept	1.4760963	0.0691558	21.344	0
Married: True	-0.4445915	0.0455606	-9.758	0
Importance of Sex	0.3434624	0.0091385	37.584	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$SexImporNum, "Importance of Sex")

	Estimate	Std. Error	t_value	p_value
Intercept	2.5542944	0.0910795	28.045	0
Married: True	-0.1358470	0.0476609	-2.85	0.0044
Importance of Sex	0.3082776	0.0090500	34.064	0
Age (years)	-0.0213327	0.0014119	-15.109	0
Duration	-0.0013552	0.0009617	-1.409	0.1589
Male	-0.0346244	0.0415015	-0.834	0.4042

---

Initiating Equally Correlates With Most WLF, But Doesn’t Account For Marriage Effect (regressions pretty useless)

Graphs

sex_data$InitNum = 5 - as.numeric(sex_data$initiate)

contsmall(sex_data, sex_data$InitNum, "Initiating More Than Partner", "")

Want - Only Initiating

reg_just_mod_i(sex_data$want, sex_data, sex_data$InitNum, "Initiating More Than Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	26.058328	0.3425653	76.068	0
Married: True	-5.153207	0.2770681	-18.599	0
Initiating More Than Partner	1.397351	0.1222804	11.427	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$InitNum, "Initiating More Than Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	32.0933649	0.4150560	77.323	0
Married: True	-2.4506415	0.2819908	-8.691	0
Initiating More Than Partner	0.9426112	0.1282448	7.35	0
Age (years)	-0.1772391	0.0078931	-22.455	0
Duration	-0.0184282	0.0049563	-3.718	2e-04
Male	3.6573981	0.2705804	13.517	0

Like - Only Initiating

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$InitNum,"Initiating More Than Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	38.5156024	0.3931978	97.955	0
Married: True	-3.9546820	0.3171102	-12.471	0
Initiating More Than Partner	0.0042946	0.1406012	0.031	0.9756

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$InitNum, "Initiating More Than Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	41.4133554	0.5045660	82.077	0
Married: True	-2.6874619	0.3411509	-7.878	0
Initiating More Than Partner	-0.3119762	0.1562332	-1.997	0.0459
Age (years)	-0.0853923	0.0095874	-8.907	0
Duration	-0.0092394	0.0059030	-1.565	0.1176
Male	2.1969550	0.3291589	6.674	0

Frequency - Only initiating

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$InitNum, "Initiating More Than Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	3.8316078	0.0592510	64.667	0
Married: True	-0.6435315	0.0482471	-13.338	0
Initiating More Than Partner	-0.0818772	0.0208914	-3.919	1e-04

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$InitNum, "Initiating More Than Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	5.0115369	0.0730855	68.571	0
Married: True	-0.1413439	0.0489341	-2.888	0.0039
Initiating More Than Partner	-0.0916778	0.0218405	-4.198	0
Age (years)	-0.0333608	0.0014293	-23.34	0
Duration	-0.0031523	0.0010779	-2.925	0.0035
Male	0.3242687	0.0464120	6.987	0

---

Unreciprocated Wanting Predicts More Wanting And Less Liking, Slight Relationship To Marriage

Graphs

sex_data$IdoNum =  as.numeric(sex_data$i_do)

contsmall(sex_data, sex_data$IdoNum, "Wanting Sex But Partner Doesn't, ")

Want - Only Unreciprocated Wanting

reg_just_mod_i(sex_data$want, sex_data, sex_data$IdoNum, "Wanting Unreciprocated")

	Estimate	Std. Error	t_value	p_value
Intercept	25.608438	0.3178940	80.557	0
Married: True	-5.163224	0.2743014	-18.823	0
Wanting Unreciprocated	1.609025	0.1060707	15.169	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$IdoNum, "Wanting Unreciprocated")

	Estimate	Std. Error	t_value	p_value
Intercept	31.3000838	0.4145978	75.495	0
Married: True	-2.5843066	0.2804288	-9.216	0
Wanting Unreciprocated	1.1333319	0.1029135	11.012	0
Age (years)	-0.1689724	0.0078802	-21.443	0
Duration	-0.0184619	0.0049194	-3.753	2e-04
Male	3.8166747	0.2494155	15.302	0

Like - Only Unreciprocated Wanting

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$IdoNum,"Wanting Unreciprocated")

	Estimate	Std. Error	t_value	p_value
Intercept	40.298068	0.3660930	110.076	0
Married: True	-3.873351	0.3154206	-12.28	0
Wanting Unreciprocated	-0.857799	0.1225959	-6.997	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$IdoNum, "Wanting Unreciprocated")

	Estimate	Std. Error	t_value	p_value
Intercept	43.3239290	0.5026739	86.187	0
Married: True	-2.5297734	0.3385024	-7.473	0
Wanting Unreciprocated	-1.1822918	0.1251488	-9.447	0
Age (years)	-0.0944112	0.0095501	-9.886	0
Duration	-0.0077867	0.0058452	-1.332	0.1829
Male	2.6657806	0.3026689	8.808	0

Frequency - Only Unreciprocated Wanting

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$IdoNum, "Wanting Unreciprocated")

	Estimate	Std. Error	t_value	p_value
Intercept	3.7492218	0.0559672	66.99	0
Married: True	-0.6472979	0.0482933	-13.403	0
Wanting Unreciprocated	-0.0417347	0.0185793	-2.246	0.0247

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$IdoNum, "Wanting Unreciprocated")

	Estimate	Std. Error	t_value	p_value
Intercept	5.0484524	0.0736695	68.528	0
Married: True	-0.1302306	0.0489606	-2.66	0.0078
Wanting Unreciprocated	-0.0902839	0.0178239	-5.065	0
Age (years)	-0.0340074	0.0014345	-23.707	0
Duration	-0.0031844	0.0010767	-2.958	0.0031
Male	0.2944787	0.0428882	6.866	0

---

Partner Unreciprocated Wanting Predicts Lower WLF, Unrelated To Marriage

Graphs

sex_data$TheydoNum = as.numeric(sex_data$they_do)

contsmall(sex_data, sex_data$TheydoNum, "Not Wanting Sex But Partner Does, ")

Want - Only Partner Unreciprocated Wanting

reg_just_mod_i(sex_data$want, sex_data, sex_data$TheydoNum, "Partner Wanting Unreciprocated")

	Estimate	Std. Error	t_value	p_value
Intercept	31.120626	0.3259845	95.467	0
Married: True	-5.143193	0.2781674	-18.49	0
Partner Wanting Unreciprocated	-1.058403	0.1100998	-9.613	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$TheydoNum, "Partner Wanting Unreciprocated")

	Estimate	Std. Error	t_value	p_value
Intercept	36.5774948	0.4605310	79.425	0
Married: True	-2.3910836	0.2805863	-8.522	0
Partner Wanting Unreciprocated	-1.0963910	0.1071773	-10.23	0
Age (years)	-0.1878379	0.0079150	-23.732	0
Duration	-0.0162207	0.0049244	-3.294	0.001
Male	3.8904459	0.2492389	15.609	0

Like - Only Partner Unreciprocated Wanting

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$TheydoNum,"Partner Wanting Unreciprocated")

	Estimate	Std. Error	t_value	p_value
Intercept	40.916311	0.3677122	111.273	0
Married: True	-4.078153	0.3142361	-12.978	0
Partner Wanting Unreciprocated	-1.158019	0.1243766	-9.311	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$TheydoNum, "Partner Wanting Unreciprocated")

	Estimate	Std. Error	t_value	p_value
Intercept	44.3243914	0.5543891	79.952	0
Married: True	-2.6442461	0.3378308	-7.827	0
Partner Wanting Unreciprocated	-1.2719444	0.1291760	-9.847	0
Age (years)	-0.0962585	0.0095587	-10.07	0
Duration	-0.0094991	0.0058364	-1.628	0.1037
Male	1.1283810	0.3018810	3.738	2e-04

Frequency - Only Partner Unreciprocated Wanting

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$TheydoNum, "Partner Wanting Unreciprocated")

	Estimate	Std. Error	t_value	p_value
Intercept	3.4221096	0.0564381	60.635	0
Married: True	-0.6390921	0.0481294	-13.279	0
Partner Wanting Unreciprocated	0.1176811	0.0190498	6.178	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$TheydoNum, "Partner Wanting Unreciprocated")

	Estimate	Std. Error	t_value	p_value
Intercept	4.7061428	0.0814074	57.81	0
Married: True	-0.1470794	0.0489827	-3.003	0.0027
Partner Wanting Unreciprocated	0.0593652	0.0186932	3.176	0.0015
Age (years)	-0.0328296	0.0014387	-22.819	0
Duration	-0.0032356	0.0010784	-3	0.0027
Male	0.2724440	0.0429522	6.343	0

---

Very Frustrated Indiviudals Are Similar, But Doesn’t Account For Marriage

Graphs

sex_data$FrustNum =  as.numeric(sex_data$frustration)

contsmall(sex_data, sex_data$FrustNum, "Frustration With Unreciprocated Desire", "")

Want - Only Frustration

reg_just_mod_i(sex_data$want, sex_data, sex_data$FrustNum, "Frustration With Unreciprocated Desire")

	Estimate	Std. Error	t_value	p_value
Intercept	23.829517	0.3347157	71.193	0
Married: True	-4.663560	0.2698580	-17.282	0
Frustration With Unreciprocated Desire	1.342334	0.0647436	20.733	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$FrustNum, "Frustration With Unreciprocated Desire")

	Estimate	Std. Error	t_value	p_value
Intercept	29.3794878	0.4596227	63.921	0
Married: True	-2.5239274	0.2780876	-9.076	0
Frustration With Unreciprocated Desire	0.9264577	0.0635664	14.575	0
Age (years)	-0.1507377	0.0080125	-18.813	0
Duration	-0.0170432	0.0048763	-3.495	5e-04
Male	3.8958162	0.2428676	16.041	0

Like - Only Partner Frustration

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$FrustNum,"Frustration With Unreciprocated Desire")

	Estimate	Std. Error	t_value	p_value
Intercept	39.0663055	0.3953944	98.803	0
Married: True	-3.9889263	0.3172953	-12.572	0
Frustration With Unreciprocated Desire	-0.1414659	0.0765132	-1.849	0.0645

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$FrustNum, "Frustration With Unreciprocated Desire")

	Estimate	Std. Error	t_value	p_value
Intercept	42.6907937	0.5642977	75.653	0
Married: True	-2.6539782	0.3404731	-7.795	0
Frustration With Unreciprocated Desire	-0.3886498	0.0781904	-4.971	0
Age (years)	-0.0963474	0.0098236	-9.808	0
Duration	-0.0094703	0.0058817	-1.61	0.1074
Male	2.1667409	0.2984582	7.26	0

Frequency - Only Frustration

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$FrustNum, "Frustration With Unreciprocated Desire")

	Estimate	Std. Error	t_value	p_value
Intercept	3.2757672	0.0592372	55.299	0
Married: True	-0.6249548	0.0478917	-13.049	0
Frustration With Unreciprocated Desire	0.1035010	0.0114120	9.069	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$FrustNum, "Frustration With Unreciprocated Desire")

	Estimate	Std. Error	t_value	p_value
Intercept	4.7175565	0.0819851	57.542	0
Married: True	-0.1503541	0.0489541	-3.071	0.0021
Frustration With Unreciprocated Desire	0.0324711	0.0111016	2.925	0.0035
Age (years)	-0.0323377	0.0014686	-22.019	0
Duration	-0.0031568	0.0010770	-2.931	0.0034
Male	0.2140341	0.0421540	5.077	0

---

Importance Accounts For A Lot Of Marriage Effect On Want, Like, Freq

Graphs

sex_data$Satis2Num =  as.numeric(sex_data$satis2)

contsmall(sex_data, sex_data$Satis2Num, "Sex Life Adds TO Relationship", "")

Want - Only Satis2

reg_just_mod_i(sex_data$want, sex_data, sex_data$Satis2Num, "Sex Life Adds TO Relationship")

	Estimate	Std. Error	t_value	p_value
Intercept	12.663917	0.3602982	35.148	0
Married: True	-3.372148	0.2269714	-14.857	0
Sex Life Adds TO Relationship	4.327446	0.0815111	53.09	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$Satis2Num, "Sex Life Adds TO Relationship")

	Estimate	Std. Error	t_value	p_value
Intercept	17.4526374	0.4375596	39.886	0
Married: True	-1.7034706	0.2325226	-7.326	0
Sex Life Adds TO Relationship	3.9536934	0.0786010	50.301	0
Age (years)	-0.1255029	0.0066001	-19.015	0
Duration	-0.0120335	0.0040407	-2.978	0.0029
Male	3.0622233	0.2022959	15.137	0

Like - Only Satis2

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$Satis2Num,"Sex Life Adds TO Relationship")

	Estimate	Std. Error	t_value	p_value
Intercept	17.071048	0.3536608	48.27	0
Married: True	-1.991664	0.2213427	-8.998	0
Sex Life Adds TO Relationship	5.682211	0.0799903	71.036	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$Satis2Num, "Sex Life Adds TO Relationship")

	Estimate	Std. Error	t_value	p_value
Intercept	17.8439318	0.4522109	39.459	0
Married: True	-1.6971955	0.2398380	-7.076	0
Sex Life Adds TO Relationship	5.6533230	0.0814955	69.37	0
Age (years)	-0.0152042	0.0068033	-2.235	0.0255
Duration	-0.0048095	0.0041370	-1.163	0.2451
Male	-0.1209782	0.2086165	-0.58	0.562

Frequency - Only Satis2

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$Satis2Num, "Sex Life Adds TO Relationship")

	Estimate	Std. Error	t_value	p_value
Intercept	1.4710152	0.0654456	22.477	0
Married: True	-0.4350021	0.0419224	-10.376	0
Sex Life Adds TO Relationship	0.5934546	0.0148458	39.975	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$Satis2Num, "Sex Life Adds TO Relationship")

	Estimate	Std. Error	t_value	p_value
Intercept	2.6789570	0.0799321	33.515	0
Married: True	-0.0593518	0.0427579	-1.388	0.1652
Sex Life Adds TO Relationship	0.5447578	0.0141899	38.39	0
Age (years)	-0.0263283	0.0012619	-20.864	0
Duration	-0.0020104	0.0009345	-2.151	0.0315
Male	0.0177950	0.0366640	0.485	0.6274

---

---

Belief That Partner Is Sexually Pleased Accounts For A Very Large Portion Of Marriage Like Effect

Graphs

sex_data$Satis9Num =  as.numeric(sex_data$satis9)

contsmall(sex_data, sex_data$Satis9Num, "Partner Is Sexually Pleased", "")

Want - Only Satis9

reg_just_mod_i(sex_data$want, sex_data, sex_data$Satis9Num, "Partner Is Sexually Pleased")

	Estimate	Std. Error	t_value	p_value
Intercept	14.616535	0.4244795	34.434	0
Married: True	-3.339812	0.2509269	-13.31	0
Partner Is Sexually Pleased	3.619867	0.0934515	38.735	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$Satis9Num, "Partner Is Sexually Pleased")

	Estimate	Std. Error	t_value	p_value
Intercept	19.2123412	0.5006374	38.376	0
Married: True	-1.5284964	0.2532365	-6.036	0
Partner Is Sexually Pleased	3.3201639	0.0882444	37.625	0
Age (years)	-0.1407840	0.0071493	-19.692	0
Duration	-0.0113235	0.0043911	-2.579	0.0099
Male	4.2116975	0.2177424	19.343	0

Like - Only Satis9

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$Satis9Num,"Partner Is Sexually Pleased")

	Estimate	Std. Error	t_value	p_value
Intercept	14.368420	0.3671552	39.134	0
Married: True	-1.352923	0.2156987	-6.272	0
Partner Is Sexually Pleased	6.080568	0.0807743	75.279	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$Satis9Num,"Partner Is Sexually Pleased")

	Estimate	Std. Error	t_value	p_value
Intercept	14.7698562	0.4595837	32.137	0
Married: True	-1.0831210	0.2319604	-4.669	0
Partner Is Sexually Pleased	6.0322123	0.0811975	74.291	0
Age (years)	-0.0231803	0.0065437	-3.542	4e-04
Duration	-0.0022536	0.0039915	-0.565	0.5724
Male	1.4273118	0.1993687	7.159	0

Frequency - Only Satis9

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$Satis9Num, "Partner Is Sexually Pleased")

	Estimate	Std. Error	t_value	p_value
Intercept	1.5681412	0.0728509	21.525	0
Married: True	-0.4134790	0.0438456	-9.43	0
Partner Is Sexually Pleased	0.5386635	0.0160846	33.489	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$Satis9Num, "Partner Is Sexually Pleased")

	Estimate	Std. Error	t_value	p_value
Intercept	2.8068062	0.0871164	32.219	0
Married: True	-0.0290623	0.0445982	-0.652	0.5147
Partner Is Sexually Pleased	0.4841241	0.0152116	31.826	0
Age (years)	-0.0283415	0.0013093	-21.647	0
Duration	-0.0018218	0.0009730	-1.872	0.0612
Male	0.1825889	0.0377636	4.835	0

---

---

Belief That Partner Is Lucky Has Positive, Unrelated Effect

Graphs

sex_data$PLuckyNuM =  as.numeric(sex_data$PartnerLucky)

contsmall(sex_data, sex_data$PLuckyNuM, "Partner Is Lucky To Be With Me", "")

Want - Only Partner Lucky

reg_just_mod_i(sex_data$want, sex_data, sex_data$PLuckyNuM, "Partner Is Lucky To Be With Me")

	Estimate	Std. Error	t_value	p_value
Intercept	18.127828	0.7042133	25.742	0
Married: True	-4.740036	0.3757265	-12.616	0
Partner Is Lucky To Be With Me	2.564097	0.1598204	16.044	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$PLuckyNuM, "Partner Is Lucky To Be With Me")

	Estimate	Std. Error	t_value	p_value
Intercept	22.9499233	0.7818718	29.353	0
Married: True	-2.2752571	0.3899386	-5.835	0
Partner Is Lucky To Be With Me	2.5429259	0.1483818	17.138	0
Age (years)	-0.1728813	0.0107446	-16.09	0
Duration	-0.0084972	0.0050420	-1.685	0.0921
Male	4.6118880	0.3493840	13.2	0

Like - Only Partner Lucky

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$PLuckyNuM,"Partner Is Lucky To Be With Me")

	Estimate	Std. Error	t_value	p_value
Intercept	18.435402	0.6754787	27.292	0
Married: True	-3.243428	0.3603954	-9	0
Partner Is Lucky To Be With Me	4.912480	0.1532991	32.045	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$PLuckyNuM,"Partner Is Lucky To Be With Me")

	Estimate	Std. Error	t_value	p_value
Intercept	19.7278235	0.7978311	24.727	0
Married: True	-2.4150828	0.3978978	-6.07	0
Partner Is Lucky To Be With Me	4.9311615	0.1514105	32.568	0
Age (years)	-0.0631778	0.0109639	-5.762	0
Duration	-0.0038898	0.0051449	-0.756	0.4497
Male	2.6033603	0.3565155	7.302	0

Frequency - Only Partner Lucky

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$PLuckyNuM, "Partner Is Lucky To Be With Me")

	Estimate	Std. Error	t_value	p_value
Intercept	2.3040453	0.1209948	19.043	0
Married: True	-0.6601568	0.0648768	-10.176	0
Partner Is Lucky To Be With Me	0.3291528	0.0274849	11.976	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$PLuckyNuM, "Partner Is Lucky To Be With Me")

	Estimate	Std. Error	t_value	p_value
Intercept	3.4101431	0.1386268	24.599	0
Married: True	-0.2009122	0.0690989	-2.908	0.0037
Partner Is Lucky To Be With Me	0.3098280	0.0260415	11.897	0
Age (years)	-0.0288850	0.0019617	-14.724	0
Duration	-0.0014540	0.0011674	-1.246	0.2131
Male	0.2394457	0.0612726	3.908	1e-04

---

---

Belief That Ther Are Lucky To Be With Partner Has Positive, Unrelated Effect

Graphs

sex_data$Satis22Num =  as.numeric(sex_data$satis22)

contsmall(sex_data, sex_data$Satis22Num, "Lucky To Be With Partner", "")

Want - Only Satis22

reg_just_mod_i(sex_data$want, sex_data, sex_data$Satis22Num, "Lucky To Be With Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	16.221597	0.4885928	33.201	0
Married: True	-4.706754	0.2623980	-17.937	0
Lucky To Be With Partner	3.112898	0.1058147	29.418	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$Satis22Num, "Lucky To Be With Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	20.9911034	0.5386432	38.97	0
Married: True	-2.4306077	0.2645141	-9.189	0
Lucky To Be With Partner	2.9508234	0.0980005	30.11	0
Age (years)	-0.1601768	0.0074474	-21.508	0
Duration	-0.0158831	0.0045701	-3.475	5e-04
Male	4.3343557	0.2282834	18.987	0

Like - Only Satis2

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$Satis22Num,"Lucky To Be With Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	15.529762	0.4541060	34.199	0
Married: True	-3.604512	0.2438769	-14.78	0
Lucky To Be With Partner	5.576174	0.0983459	56.7	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$Satis22Num,"Lucky To Be With Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	17.3421302	0.5348059	32.427	0
Married: True	-2.6678846	0.2626297	-10.158	0
Lucky To Be With Partner	5.5136596	0.0973024	56.665	0
Age (years)	-0.0596718	0.0073943	-8.07	0
Duration	-0.0107211	0.0045376	-2.363	0.0182
Male	1.6167529	0.2266572	7.133	0

Frequency - Only Satis22

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$Satis22Num, "Lucky To Be With Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	2.2892605	0.0854083	26.804	0
Married: True	-0.6177296	0.0467202	-13.222	0
Lucky To Be With Partner	0.3431301	0.0184967	18.551	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$Satis22Num, "Lucky To Be With Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	3.5076597	0.0959424	36.56	0
Married: True	-0.1617874	0.0475644	-3.401	7e-04
Lucky To Be With Partner	0.3174340	0.0172247	18.429	0
Age (years)	-0.0308375	0.0013947	-22.111	0
Duration	-0.0028252	0.0010347	-2.731	0.0063
Male	0.2094392	0.0404073	5.183	0

---

---

Sexual Attraction To Partner Decreases Marriage Effects

Graphs

sex_data$Satis23Num =  as.numeric(sex_data$satis23)

contsmall(sex_data, sex_data$Satis23Num, "Sexual Attraction To Partner", "")

Want - Only Attraction To

reg_just_mod_i(sex_data$want, sex_data, sex_data$Satis23Num, "Sexual Attraction To Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	11.113315	0.4169079	26.657	0
Married: True	-3.517980	0.2342962	-15.015	0
Sexual Attraction To Partner	4.382228	0.0900014	48.691	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$Satis23Num, "Sexual Attraction To Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	16.0889127	0.4865806	33.065	0
Married: True	-1.7685708	0.2396736	-7.379	0
Sexual Attraction To Partner	3.9996142	0.0863412	46.323	0
Age (years)	-0.1281127	0.0067951	-18.854	0
Duration	-0.0142868	0.0041333	-3.456	6e-04
Male	3.2177052	0.2082244	15.453	0

Like - Only Attraction To

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$Satis23Num,"Sexual Attraction To Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	12.466098	0.3692949	33.756	0
Married: True	-1.944504	0.2075384	-9.369	0
Sexual Attraction To Partner	6.363514	0.0797228	79.821	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$Satis23Num, "Sexual Attraction To Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	13.1683193	0.4566078	28.839	0
Married: True	-1.6228675	0.2249100	-7.216	0
Sexual Attraction To Partner	6.3347497	0.0810227	78.185	0
Age (years)	-0.0127822	0.0063765	-2.005	0.0451
Duration	-0.0080658	0.0038787	-2.079	0.0376
Male	-0.1082689	0.1953980	-0.554	0.5795

Frequency - Only Attraction to

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$Satis23Num, "Sexual Attraction To Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	1.8494335	0.0792466	23.338	0
Married: True	-0.4987895	0.0453279	-11.004	0
Sexual Attraction To Partner	0.4549570	0.0171408	26.542	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$Satis23Num, "Sexual Attraction To Partner")

	Estimate	Std. Error	t_value	p_value
Intercept	3.1191465	0.0934761	33.368	0
Married: True	-0.0992315	0.0464045	-2.138	0.0325
Sexual Attraction To Partner	0.3998997	0.0163919	24.396	0
Age (years)	-0.0277435	0.0013689	-20.267	0
Duration	-0.0025180	0.0010080	-2.498	0.0125
Male	0.0943254	0.0397445	2.373	0.0177

---

---

Belief That Partner Is Sexually Attracted Reduces Marriage Effect

Graphs

sex_data$Satis21Num =  as.numeric(sex_data$satis21)

contsmall(sex_data, sex_data$Satis21Num, "Partner Sexual Attraction Belief", "")

Want - Only Partner Sexual Attraction

reg_just_mod_i(sex_data$want, sex_data, sex_data$Satis21Num, "Partner Sexual Attraction Belief")

	Estimate	Std. Error	t_value	p_value
Intercept	16.841134	0.4668292	36.076	0
Married: True	-3.618780	0.2655335	-13.628	0
Partner Sexual Attraction Belief	3.053214	0.1029592	29.655	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$Satis21Num, "Partner Sexual Attraction Belif")

	Estimate	Std. Error	t_value	p_value
Intercept	20.7450116	0.5550085	37.378	0
Married: True	-1.7675501	0.2666382	-6.629	0
Partner Sexual Attraction Belif	2.8569398	0.0974504	29.317	0
Age (years)	-0.1412421	0.0075476	-18.714	0
Duration	-0.0122728	0.0045908	-2.673	0.0075
Male	4.8386517	0.2295121	21.082	0

Like - Only Partner Sexual Attraction

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$Satis21Num,"Partner Sexual Attraction Belief")

	Estimate	Std. Error	t_value	p_value
Intercept	15.562854	0.4167388	37.344	0
Married: True	-1.542520	0.2370420	-6.507	0
Partner Sexual Attraction Belief	5.738337	0.0919118	62.433	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$Satis21Num,"Partner Sexual Attraction Belief")

	Estimate	Std. Error	t_value	p_value
Intercept	14.8765907	0.5250826	28.332	0
Married: True	-1.3235843	0.2522612	-5.247	0
Partner Sexual Attraction Belief	5.7838679	0.0921959	62.735	0
Age (years)	-0.0192046	0.0071406	-2.689	0.0072
Duration	-0.0034816	0.0043433	-0.802	0.4228
Male	2.6138234	0.2171369	12.038	0

Frequency - Only Partner Sexual Attraction

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$Satis21Num, "Partner Sexual Attraction Belief")

	Estimate	Std. Error	t_value	p_value
Intercept	1.7498286	0.0780685	22.414	0
Married: True	-0.4346514	0.0451372	-9.63	0
Partner Sexual Attraction Belief	0.4909396	0.0172624	28.44	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$Satis21Num, "Partner Sexual Attraction Belief")

	Estimate	Std. Error	t_value	p_value
Intercept	2.9385681	0.0948766	30.973	0
Married: True	-0.0646439	0.0460886	-1.403	0.1608
Partner Sexual Attraction Belief	0.4311782	0.0165150	26.108	0
Age (years)	-0.0277529	0.0013559	-20.469	0
Duration	-0.0018881	0.0010000	-1.888	0.0591
Male	0.2841097	0.0390648	7.273	0

---

---

Emotional Proximity Has Positiv, Largely Unrelated Effect

Graphs

sex_data$EmotNum =  as.numeric(sex_data$emotion)

contsmall(sex_data, sex_data$EmotNum, "Emotional Proximity", "")

Want - Only Emotional Proximity

reg_just_mod_i(sex_data$want, sex_data, sex_data$EmotNum, "Emotional Proximity")

	Estimate	Std. Error	t_value	p_value
Intercept	15.422532	0.5484573	28.12	0
Married: True	-4.890454	0.2657303	-18.404	0
Emotional Proximity	2.303690	0.0846465	27.215	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$EmotNum, "Emotional Proximity")

	Estimate	Std. Error	t_value	p_value
Intercept	20.2200879	0.5835692	34.649	0
Married: True	-2.5167980	0.2676349	-9.404	0
Emotional Proximity	2.2124643	0.0782225	28.284	0
Age (years)	-0.1636021	0.0075269	-21.736	0
Duration	-0.0179953	0.0046176	-3.897	1e-04
Male	4.2930686	0.2309471	18.589	0

Like - Only Emotional Proximity

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$EmotNum,"Emotional Proximity")

	Estimate	Std. Error	t_value	p_value
Intercept	15.003781	0.5354774	28.019	0
Married: True	-3.938998	0.2594414	-15.183	0
Emotional Proximity	3.972394	0.0826433	48.067	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$EmotNum,"Emotional Proximity")

	Estimate	Std. Error	t_value	p_value
Intercept	17.1002657	0.6086301	28.096	0
Married: True	-2.8249047	0.2791283	-10.12	0
Emotional Proximity	3.9362337	0.0815817	48.249	0
Age (years)	-0.0674999	0.0078501	-8.599	0
Duration	-0.0142132	0.0048159	-2.951	0.0032
Male	1.5905415	0.2408650	6.603	0

Frequency - Only Emotional Proximity

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$EmotNum, "Emotional Proximity")

	Estimate	Std. Error	t_value	p_value
Intercept	2.2343211	0.0951211	23.489	0
Married: True	-0.6325176	0.0470125	-13.454	0
Emotional Proximity	0.2481978	0.0146997	16.885	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$EmotNum, "Emotional Proximity")

	Estimate	Std. Error	t_value	p_value
Intercept	3.4275364	0.1030599	33.258	0
Married: True	-0.1630333	0.0477688	-3.413	6e-04
Emotional Proximity	0.2366541	0.0136512	17.336	0
Age (years)	-0.0309942	0.0013997	-22.144	0
Duration	-0.0033258	0.0010380	-3.204	0.0014
Male	0.2005798	0.0405752	4.943	0

---

---

Partner Work Power Has A Small ANd Unrelated Positive Effect

Graphs

sex_data$WPNum =  as.numeric(sex_data$workpower)
sex_data$WPNum =  ifelse(sex_data$workpower == "Not applicable (my partner does not work)", NA, sex_data$WPNum)

contsmall(sex_data, sex_data$WPNum, "Partner Work Power", "")

Want - Only Partner Work Power

reg_just_mod_i(sex_data$want, sex_data, sex_data$WPNum, "Partner Work Power")

	Estimate	Std. Error	t_value	p_value
Intercept	25.2608724	0.4324477	58.414	0
Married: True	-4.5416555	0.3005030	-15.114	0
Partner Work Power	0.8507393	0.0772669	11.01	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$WPNum, "Partner Work Power")

	Estimate	Std. Error	t_value	p_value
Intercept	29.3892562	0.5235945	56.13	0
Married: True	-2.4426162	0.3032917	-8.054	0
Partner Work Power	0.8279658	0.0724102	11.434	0
Age (years)	-0.1608716	0.0087291	-18.429	0
Duration	-0.0136752	0.0050000	-2.735	0.0063
Male	4.3376282	0.2662168	16.294	0

Like - Only Partner Work Power

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$WPNum,"Partner Work Power")

	Estimate	Std. Error	t_value	p_value
Intercept	32.487080	0.4743244	68.491	0
Married: True	-3.577460	0.3296027	-10.854	0
Partner Work Power	1.300749	0.0847491	15.348	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$WPNum,"Partner Work Power")

	Estimate	Std. Error	t_value	p_value
Intercept	34.4855008	0.6078150	56.737	0
Married: True	-2.5631179	0.3520764	-7.28	0
Partner Work Power	1.2883544	0.0840575	15.327	0
Age (years)	-0.0714282	0.0101331	-7.049	0
Duration	-0.0102757	0.0058043	-1.77	0.0767
Male	1.6549009	0.3090380	5.355	0

Frequency - Only Partner Work Power

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$WPNum, "Partner Work Power")

	Estimate	Std. Error	t_value	p_value
Intercept	3.3481543	0.0717875	46.64	0
Married: True	-0.5717298	0.0503803	-11.348	0
Partner Work Power	0.0894081	0.0128017	6.984	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$WPNum, "Partner Work Power")

	Estimate	Std. Error	t_value	p_value
Intercept	4.4776459	0.0882072	50.763	0
Married: True	-0.1659542	0.0509915	-3.255	0.0011
Partner Work Power	0.0802235	0.0119886	6.692	0
Age (years)	-0.0302376	0.0015253	-19.824	0
Duration	-0.0024153	0.0010715	-2.254	0.0242
Male	0.1779924	0.0441335	4.033	1e-04

---

---

Partner Physical Attractiveness Reduces Marriage Effect, Less Than Attraction To Partner

Graphs

sex_data$PAttracNum =  as.numeric(sex_data$PhysicalAttractive_PrimaryPartner)

contsmall(sex_data, sex_data$PAttracNum, "Partner Physical Attractiveness", "")

Want - Only Partner Physical Attractiveness

reg_just_mod_i(sex_data$want, sex_data, sex_data$PAttracNum, "Partner Physical Attractiveness")

	Estimate	Std. Error	t_value	p_value
Intercept	10.639096	0.7112652	14.958	0
Married: True	-3.982559	0.3468968	-11.481	0
Partner Physical Attractiveness	2.432250	0.0899434	27.042	0

Want – Full Controls

reg_full_control(sex_data$want, sex_data, sex_data$PAttracNum, "Partner Physical Attractiveness")

	Estimate	Std. Error	t_value	p_value
Intercept	16.0195886	0.8094368	19.791	0
Married: True	-2.0154397	0.3646536	-5.527	0
Partner Physical Attractiveness	2.2255667	0.0857366	25.958	0
Age (years)	-0.1410848	0.0101339	-13.922	0
Duration	-0.0087690	0.0046993	-1.866	0.0622
Male	3.7500118	0.3264691	11.487	0

Like - Only Partner Physical Attractiveness

reg_just_mod_i(sex_data$totlike, sex_data, sex_data$PAttracNum,"Partner Physical Attractiveness")

	Estimate	Std. Error	t_value	p_value
Intercept	15.112688	0.7296525	20.712	0
Married: True	-2.444246	0.3558646	-6.868	0
Partner Physical Attractiveness	3.140848	0.0922685	34.04	0

Like – Full Controls

reg_full_control(sex_data$totlike,sex_data, sex_data$PAttracNum,"Partner Physical Attractiveness")

	Estimate	Std. Error	t_value	p_value
Intercept	15.8581475	0.8791610	18.038	0
Married: True	-2.1173712	0.3960645	-5.346	0
Partner Physical Attractiveness	3.0999806	0.0931218	33.29	0
Age (years)	-0.0246886	0.0110068	-2.243	0.025
Duration	-0.0036312	0.0051041	-0.711	0.4769
Male	1.2584230	0.3545908	3.549	4e-04

Frequency - Only Partner Physical Attractiveness

reg_just_mod_i(sex_data$freq_num, sex_data, sex_data$PAttracNum, "Partner Physical Attractiveness")

	Estimate	Std. Error	t_value	p_value
Intercept	1.9712684	0.1310729	15.039	0
Married: True	-0.5973298	0.0647256	-9.229	0
Partner Physical Attractiveness	0.2270554	0.0166671	13.623	0

Frequency – Full Controls

reg_full_control(sex_data$freq_num, sex_data, sex_data$PAttracNum, "Partner Physical Attractiveness")

	Estimate	Std. Error	t_value	p_value
Intercept	3.1817834	0.1516085	20.987	0
Married: True	-0.1743550	0.0691916	-2.52	0.0118
Partner Physical Attractiveness	0.1949393	0.0160373	12.155	0
Age (years)	-0.0267014	0.0019757	-13.515	0
Duration	-0.0014411	0.0011654	-1.237	0.2164
Male	0.1509480	0.0613573	2.46	0.014

---

---

Security Scale Not Yet Included For Time Purposes