CFA_DYADS.qmd

---
title: "Dyadic analsysis CFA"
format: 
  html:
    toc: true
    code-fold: true
editor_options:
  chunk_output_type: console
---

```{r libraries}
#| message: false
#| warning: false
library(tidyverse)
library(lavaan)
library(kableExtra)
library(reactable)
```

```{r inport-data}

dyads <- tibble(readRDS("dyads.Rds"))

```

```{r number-question}
#| eval: false

dyads %>% select(VARNAME) %>% unique %>% view
dyads %>% select(FACTORNAME) %>% unique %>% view

```

```{r factor-names}
#| echo: false

factors <- c("CRCDO", "CRPAD",  "CRLNFD","CPNA", 
             "FRPFM",  "FRFC", "FRPFD", "FPATT",
             "PRPRD", "PRFAD", "PRRAI","PPIPP",
             "SRLCS")

dicc_factors <- 
c("CPNA" = "Community Cohesion",
"CRCDO" = "Community Disorganization",
"CRLNFD" = "Laws and Norms Favorable to Drug Use",
"CRPAD" = "Drug Availability",
"FPATT" = "Family Cohesion",
"FRFC" = "Family Conflict",
"FRPFD" = "Favorable attitudes of parents towards drug use",
"FRPFM" = "Poor Family Management",
"PPIPP" = "Interaction with prosocial peers",
"PRFAD" = "Favorable attitudes towards drug use",
"PRPRD" = "Perceived Risks of Drug Use",
"PRRAI" = "Rewards for antisocial involvement",
"SRLCS" = "Commitment to the school")

```

```{r df-completed-data}
cfa_complete  <- dyads %>% filter(FACTORNAME %in% factors) %>% 
   select(ID, MEMBER, VARNAME, answer) %>% 
  pivot_wider(names_from = VARNAME , values_from = answer,
              id_cols = c("ID", "MEMBER"))
```

```{r df-by-group}
cfa_parent  <- dyads %>% 
  filter(FACTORNAME %in% factors) %>% 
  filter(MEMBER=="Parent") %>% 
  select(ID, MEMBER, VARNAME, answer) %>% 
  pivot_wider(names_from = VARNAME , values_from = answer,
              id_cols = c("ID", "MEMBER"))

cfa_child  <- dyads %>% 
  filter(FACTORNAME %in% factors) %>% 
  filter(MEMBER=="Student") %>% 
   select(ID, MEMBER, VARNAME, answer) %>% 
  pivot_wider(names_from = VARNAME , values_from = answer,
              id_cols = c("ID", "MEMBER"))
```

# CFA models

```{r, define-models, comment=''}
#| echo: true
# Community Cohesion
cfa_CPNA   <- 'CPNA =~ OUTNH + MISSNH + CARENH + CAREOTH + PEOPLE'

# Community Disorganization
cfa_CRCOD  <- 'CRCDO =~  SAFENH + NHDELIN + NHVENTA + NHFIGHT + NHEMPTY + NHGRAF'

# Laws and Norms Favorable to Drug Use
cfa_CRLNFD <- 'CRLNFD =~ POLALC + POLMAR + POLGUN + POLCIG + AWRMAR + AWRALC + AWRCIG + AWBOR' 

# Drug Availability
cfa_CRPAD  <- ' CRPAD =~  GETCIG + GETALC + GETMAR + GETDRUG'

# Poor Family Management
cfa_FPATT  <- 'FPATT =~  CLSFAM + SHRPADRE + SHRHERMN + FAMHACER'

# Family Conflict
cfa_FRFC   <- 'FRFC =~  ARGUE + SERARG + FAMYELL + GRITAR + CONFLICT'

#Favorable attitudes of parents towards drug use
cfa_FRPFD  <- 'FRPFD =~ WRPRALC + WRPRCIG + WRPRMAR + WRBORR'

#Family Cohesion
cfa_FRPFM  <- 'FRPFM =~  CATCHAL + CATCHSK + CLRRULE + CMHOME + FAMRULE + HMWORK + PARKNOW'

# Perceived Risks of Drug Use
cfa_PPIPP  <- 'PPIPP =~ FRCLUB + FRCOM + FRLKSCH + FRREL + FRSCH'

# Favorable attitudes towards drug use
cfa_PRFAD  <- 'PRFAD =~ WRALC +  WRCIG + WRMAR + WRDRUG'

# Rewards for antisocial involvement
cfa_PRPRD  <- 'PRPRD =~ HMALC  + HMCIG + HMMARO + HMMARR'

# Interaction with prosocial peers
cfa_PRRAI  <- 'PRRAI =~ COOLCIG + COOLALC + COOLMAR + COOLGUN'

# Commitment to the school
cfa_SRLCS  <- 'SRLCS =~ ENJSCH + SCHMEJOR + SCHINT + SCHLRN +SAFESCH' 
```

```{r name-models}
#| echo: false
models <- list(cfa_CPNA, cfa_CRCOD,  cfa_CRLNFD, cfa_CRPAD, 
               cfa_FRPFM, cfa_FRFC, cfa_FRPFD, cfa_FPATT,   
               cfa_PRPRD, cfa_PRFAD, cfa_PRRAI, cfa_PPIPP, 
               cfa_SRLCS)

names(models) <- c("Community Cohesion",
                   "Community Disorganization",
                   "Laws and Norms Favorable to Drug Use",
                   "Drug Availability",
                   "Poor Family Management",
                   "Family Conflict",
                   "Favorable attitudes of parents towards drug use",
                   "Family Cohesion",
                   "Perceived Risks of Drug Use",
                   "Favorable attitudes towards drug use",
                   "Rewards for antisocial involvement",
                   "Interaction with prosocial peers",
                   "Commitment to the school")
```

# Estimate models as categorical

```{r extract-fit}
#| echo: false
# cfa_continous <-
# function(x, y){
# fit1 <- cfa(x, data=y)
# fitMeasures(fit1, c("chisq","df","pvalue" , "cfi", "tli" ,"rmsea"))
# }

cfa_categorical <-
function(x, y){
fit1 <- cfa(x, data=y, ordered = TRUE)
fitMeasures(fit1, c("chisq","df","pvalue" , "cfi", "tli" ,"rmsea"))
}
```


# Complete sample (parent-child together)
```{r}
#| echo: false
#models_fit_continous <- map_dfr(models, cfa_continous, cfa_complete, .id="names")
models_fit_categorical <- map_dfr(models, cfa_categorical,cfa_complete, .id="names")
```

```{r table-model-complete}

models_fit_categorical %>% 
  mutate(across(!is.character, as.numeric)) %>% 
  reactable(columns = list(
    chisq = colDef(format = colFormat(digits = 3)),
    pvalue =colDef(format = colFormat(digits = 3)),
      cfi=colDef(format = colFormat(digits = 3)),
      tli=colDef(format = colFormat(digits = 3)),
      rmsea=colDef(format = colFormat(digits = 3))
  ))
  


```


# Only Parents

```{r}
#| echo: false
# models_fit_continous_parent <- map_dfr(models, cfa_continous, cfa_parent, .id="names")
models_fit_categorical_parent <- map_dfr(models, cfa_categorical,cfa_parent, .id="names") 

```

```{r table-model-parent}
#| echo: false
models_fit_categorical_parent %>% 
    mutate(across(!is.character, as.numeric)) %>% 
  reactable(columns = list(
    chisq = colDef(format = colFormat(digits = 3)),
    pvalue =colDef(format = colFormat(digits = 3)),
      cfi=colDef(format = colFormat(digits = 3)),
      tli=colDef(format = colFormat(digits = 3)),
      rmsea=colDef(format = colFormat(digits = 3))
  ))
```

# Only Child

```{r}
#| echo: false
#models_fit_continous_child <- map_dfr(models, cfa_continous, cfa_child, .id="names")
models_fit_categorical_child <- map_dfr(models, cfa_categorical,cfa_child, .id="names")

```

```{r table-model-complete-child}
#| echo: false
models_fit_categorical_child %>% 
  mutate(across(!is.character, as.numeric)) %>% 
  reactable(columns = list(
    chisq = colDef(format = colFormat(digits = 3)),
    pvalue =colDef(format = colFormat(digits = 3)),
      cfi=colDef(format = colFormat(digits = 3)),
      tli=colDef(format = colFormat(digits = 3)),
      rmsea=colDef(format = colFormat(digits = 3))
  ))
```


## Visual comparison

```{r tables-to-fit-comparison}
tab_parent <- models_fit_categorical_parent %>% 
    mutate(across(!is.character, as.numeric)) %>% 
    mutate(Member="Parent")
tab_student <- models_fit_categorical_child %>% 
  mutate(across(!is.character, as.numeric)) %>% 
  mutate(Member="Student")

to_cfa_fit_table <- bind_rows(tab_parent, tab_student)
```

```{r}
library(ggrepel)

to_cfa_fit_table %>% pivot_wider(names_from = Member, values_from = cfi, id_cols = names) %>% 
  ggplot(aes(Parent, Student, label=names)) +
  geom_point() +
  geom_label_repel(box.padding = 2.3, max.overlaps = Inf, size=4) +
  geom_abline(intercept = 0, slope = 1, size = 0.5) 
  

```

# Estimate multigroup analysis

```{r}
#| echo: false
cfa_dyadic <- 
  cfa_complete %>% 
  mutate(MEMBER= if_else(MEMBER=="Parent", "P", "S")) %>% 
  pivot_wider(names_from = MEMBER, 
              values_from = -c(ID, MEMBER))
```


```{r}
cfa_CPNA   <- 'CPNA_P =~ OUTNH_P + MISSNH_P + CARENH_P + CAREOTH_P + PEOPLE_P
               CPNA_S =~ OUTNH_S + MISSNH_S + CARENH_S + CAREOTH_S + PEOPLE_S
               CPNA_P ~~ CPNA_S
               OUTNH_P ~~ OUTNH_S
               MISSNH_P ~~ MISSNH_S
               CARENH_P ~~ CARENH_S
               CAREOTH_P ~~ CAREOTH_S
               PEOPLE_P ~~ PEOPLE_S
                '

cfa(cfa_CPNA, data=cfa_dyadic, estimator = "WLSMV",
            ordered = TRUE,
            std.lv=T,
    mimic="Mplus") %>% 
  fitmeasures(.,c("chisq","df","pvalue" , "cfi", "tli" ,"rmsea"))

summary(cfa(cfa_CPNA, data=cfa_dyadic, 
            estimator = "WLSMV",
            ordered = TRUE,
            std.lv=T,
            mimic="Mplus"))

```


```{r}

# Community Cohesion
to_CPNA   <- cfa_dyadic %>% select(OUTNH_P, MISSNH_P, CARENH_P, CAREOTH_P, PEOPLE_P,
                                   OUTNH_S, MISSNH_S, CARENH_S, CAREOTH_S, PEOPLE_S)

# Community Disorganization
to_CRCOD  <- cfa_dyadic %>% select(SAFENH_P, NHDELIN_P, NHVENTA_P, NHFIGHT_P, NHEMPTY_P, NHGRAF_P,
                                    SAFENH_S, NHDELIN_S, NHVENTA_S, NHFIGHT_S, NHEMPTY_S, NHGRAF_S)

# Laws and Norms Favorable to Drug Use
to_CRLNFD <- cfa_dyadic %>% select(POLALC_P, POLMAR_P, POLGUN_P, POLCIG_P, AWRMAR_P, AWRALC_P, AWRCIG_P,AWBOR_P,
                                    POLALC_S, POLMAR_S, POLGUN_S, POLCIG_S, AWRMAR_S, AWRALC_S, AWRCIG_S,AWBOR_S)

# Drug Availability
to_CRPAD  <- cfa_dyadic %>% select(GETCIG_P , GETALC_P, GETMAR_P, GETDRUG_P,
                                    GETCIG_S , GETALC_S, GETMAR_S, GETDRUG_S)

# Poor Family Management
to_FPATT  <- cfa_dyadic %>% select(CLSFAM_P, SHRPADRE_P ,SHRHERMN_P , FAMHACER_P,
                                    CLSFAM_S, SHRPADRE_S ,SHRHERMN_S , FAMHACER_S)

# Family Conflict
to_FRFC   <- cfa_dyadic %>% select(ARGUE_P, SERARG_P ,FAMYELL_P ,GRITAR_P ,CONFLICT_P,
                                    ARGUE_S, SERARG_S ,FAMYELL_S ,GRITAR_S ,CONFLICT_S)

#Favorable attitudes of parents towards drug use
to_FRPFD  <- cfa_dyadic %>% select( WRPRALC_P,  WRPRCIG_P ,WRPRMAR_P , WRBORR_P,
                                     WRPRALC_S,  WRPRCIG_S ,WRPRMAR_S , WRBORR_S)

#Family Cohesion
to_FRPFM  <- cfa_dyadic %>% 
  select(CATCHAL_P, CATCHSK_P, CLRRULE_P, CMHOME_P, FAMRULE_P, HMWORK_P, PARKNOW_P,
         CATCHAL_S, CATCHSK_S, CLRRULE_S, CMHOME_S, FAMRULE_S, HMWORK_S, PARKNOW_S)


to_PPIPP  <- cfa_dyadic %>% select(FRCLUB_P, FRCOM_P, FRLKSCH_P, FRREL_P, FRSCH_P,
                                    FRCLUB_S, FRCOM_S, FRLKSCH_S, FRREL_S, FRSCH_S)


to_PRFAD  <- cfa_dyadic %>% select( WRALC_P,  WRCIG_P,  WRMAR_P,  WRDRUG_P,
                                     WRALC_S,  WRCIG_S,  WRMAR_S,  WRDRUG_S)

# Perceived Risks of Drug Use
to_PRPRD  <- cfa_dyadic %>% select( HMALC_P, HMCIG_P,  HMMARO_P, HMMARR_P,
                                     HMALC_S, HMCIG_S,  HMMARO_S, HMMARR_S)


to_PRRAI  <- cfa_dyadic %>% select( COOLCIG_P,  COOLALC_P, COOLMAR_P,  COOLGUN_P,
                                     COOLCIG_S,  COOLALC_S, COOLMAR_S,  COOLGUN_S)


tp_SRLCS  <- cfa_dyadic %>% select(ENJSCH_P,  SCHMEJOR_P, SCHINT_P, SCHLRN_P  , SAFESCH_P,
                                    ENJSCH_S,  SCHMEJOR_S, SCHINT_S, SCHLRN_S  , SAFESCH_S) 

library(MplusAutomation)

to_CPNA[is.na(to_CPNA)] <- 999
prepareMplusData(to_CPNA, "to_CPNA.dta")

to_CRCOD[is.na(to_CRCOD)] <- 999
prepareMplusData(to_CRCOD, "to_CRCOD.dta")

to_CRLNFD
to_CRLNFD[is.na(to_CRLNFD)] <- 999
prepareMplusData(to_CRLNFD, "to_CRLNFD")

to_CRPAD
to_CRPAD[is.na(to_CRPAD)] <- 999
prepareMplusData(to_CRPAD, "to_CRPAD")

to_FPATT
to_FPATT[is.na(to_FPATT)] <- 999
prepareMplusData(to_FPATT, "to_FPATT.dat")

to_FRFC
to_FRFC[is.na(to_FRFC)] <- 999
prepareMplusData(to_FRFC, "to_FRFC.dta")

to_FRPFD
to_FRPFD[is.na(to_FRPFD)] <- 999
prepareMplusData(to_FRPFD, "to_FRPFD.dta")

to_FRPFM
to_FRPFM[is.na(to_FRPFM)] <- 999
prepareMplusData(to_FRPFM, "to_FRPFM.dta")

to_PPIPP
to_PPIPP[is.na(to_PPIPP)] <- 999
prepareMplusData(to_PPIPP, "to_PPIPP.dta")

to_PRFAD
to_PRFAD[is.na(to_PRFAD)] <- 999
prepareMplusData(to_PRFAD, "to_PRFAD.dta")

to_PRPRD
to_PRPRD[is.na(to_PRPRD)] <- 999
prepareMplusData(to_PRPRD, "to_PRPRD.dta")

to_PRRAI
to_PRRAI[is.na(to_PRRAI)] <- 999
prepareMplusData(to_PRRAI, "to_PRRAI.dta")

tp_SRLCS
tp_SRLCS[is.na(tp_SRLCS)] <- 999
prepareMplusData(tp_SRLCS, "tp_SRLCS.dta")
```


## To ACE model

# sex-table

```{r}
library(table1)
library(gt)
dyads %>% 
  group_by(ID, MEMBER) %>% 
  mutate(n=row_number()) %>% 
  filter(n==1) %>% 
  mutate(GENDER= case_when(GENDER=="f" ~ "Femenino",
                           GENDER=="m" ~ "Masculino",
                           TRUE ~ GENDER)) %>% 
  select(MEMBER, GENDER) %>% arrange(ID) %>% 
  pivot_wider(names_from = MEMBER, 
              values_from = GENDER, 
              id_cols = ID) %>% 
  ungroup() %>% 
  count(Parent, Student)
  
```


```{r}
same_dyad <- dyads %>% 
  group_by(ID, MEMBER) %>% 
  mutate(n=row_number()) %>% 
  filter(n==1) %>% 
  mutate(GENDER= case_when(GENDER=="f" ~ "Femenino",
                           GENDER=="m" ~ "Masculino",
                           TRUE ~ GENDER)) %>% 
  select(MEMBER, GENDER) %>% arrange(ID) %>% 
  pivot_wider(names_from = MEMBER, 
              values_from = GENDER, 
              id_cols = ID) %>% 
  mutate(same=case_when(Parent==Student ~ 1,
                        TRUE ~ 0))

```

```{r}

# Perceived Risks of Drug Use
to_PRPRD  <- cfa_dyadic %>% select(ID, HMALC_P, HMCIG_P,  HMMARO_P, HMMARR_P,
                                     HMALC_S, HMCIG_S,  HMMARO_S, HMMARR_S)

to_mplus_ace <- to_PRPRD %>% left_join(., same_dyad) %>% 
  select(-ID, -Parent, - Student)

to_mplus_ace[is.na(to_mplus_ace)] <- 999
prepareMplusData(to_mplus_ace, "to_mplus_ace.dta")
```


# Read models

```{r}

results <- readModels("ace_dyadic_CAT.out")

```

```{r}
dyads %>% group_by(MEMBER) %>% 
  summarise(age=mean(AGE, na.rm=T),
            sd=sd(AGE, na.rm = T),
            n=n())

```


# Invariance
```{r}

to_invariance <- 
  cfa_complete %>% 
  mutate(parent= if_else(MEMBER=="Parent", 1, 0)) %>% 
  select(HMALC,HMCIG, HMMARO, HMMARR, parent)

to_invariance[is.na(to_invariance)] <- 999
prepareMplusData(to_invariance, "to_invariance_ace.dta")

```

# CFA in mplus

```{r}

```