Characteristics & Health of Older Australian Cannabis Users
Daphne Ly
08/10/2024
#Loading packages
library(tidyverse) # For general data wrangling using dplyr and ggplot for figures## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(tidyr) # For the drop_na function which allows me to drop NA values
library(knitr) #For knitting the file
library(conflicted) # Resolves conflicts by letting me choose which package I want a function from
library(haven) #reading in SPSS file into r
library(Hmisc) #For correlation matrix
library(jtools)
library(extrafont) #For Times New Roman font in plots and figures## Registering fonts with Rlibrary(lmtest) #For stepwise regression## Loading required package: zoo
##
## Attaching package: 'zoo'
##
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric#Reading the data
HOCUS <- read_sav("HOCUS_2024Analyses_renamed.sav")
View(HOCUS) #reading SPSS into Rstudio
rstudioapi::writeRStudioPreference("data_viewer_max_columns", 500L)## NULLHOCUS_AUS <- dplyr::filter(HOCUS, Countries == 9|PostcodeZipCode == "Australia", Age >= 50, CannabisUseYearsTotal != 0)#Descriptives - Demographics
#AGE, GENDER, COUNTRY OF BIRTH
HOCUS_AUS %>%
dplyr::summarise(MeanAge = mean(Age),SDAge = sd(Age), MinAge = min(Age), MaxAge = max(Age))## # A tibble: 1 × 4
## MeanAge SDAge MinAge MaxAge
## <dbl> <dbl> <dbl> <dbl>
## 1 56.4 4.73 50 69HOCUS_AUS %>%
group_by(GenderMale, GenderFemale) %>%
dplyr::summarise(
MeanAge = mean(Age), .groups = "drop",
SDAge = sd(Age),
Total = n())## # A tibble: 2 × 5
## GenderMale GenderFemale MeanAge SDAge Total
## <dbl+lbl> <dbl+lbl> <dbl> <dbl> <int>
## 1 1 [Male] NA 55.3 4.99 43
## 2 NA 1 [Female] 57.6 4.15 37HOCUS_AUS %>%
count(CountryOfBirth)## # A tibble: 7 × 2
## CountryOfBirth n
## <dbl+lbl> <int>
## 1 9 [Australia] 68
## 2 10 [Austria] 1
## 3 48 [Denmark] 1
## 4 61 [France] 1
## 5 122 [Netherlands] 1
## 6 123 [New Zealand] 4
## 7 185 [United Kingdom of Great Britain and Northern Ireland] 4#MARTIAL STATUS
HOCUS_AUS %>%
group_by(GenderMale, GenderFemale) %>%
count(MartialStatusSingle, MaritalStatusMarried, MartialStatusDefacto, MartialStatusWidowed)## # A tibble: 8 × 7
## # Groups: GenderMale, GenderFemale [2]
## GenderMale GenderFemale MartialStatusSingle MaritalStatusMarried
## <dbl+lbl> <dbl+lbl> <dbl+lbl> <dbl+lbl>
## 1 1 [Male] NA 1 [Single] NA
## 2 1 [Male] NA NA 1 [Married]
## 3 1 [Male] NA NA NA
## 4 1 [Male] NA NA NA
## 5 NA 1 [Female] 1 [Single] NA
## 6 NA 1 [Female] NA 1 [Married]
## 7 NA 1 [Female] NA NA
## 8 NA 1 [Female] NA NA
## # ℹ 3 more variables: MartialStatusDefacto <dbl+lbl>,
## # MartialStatusWidowed <dbl+lbl>, n <int>#EDUCATION
education <- HOCUS_AUS %>%
count(LevelOfEducation) %>%
mutate(across(everything(), as.numeric)) %>%
rename(count = n)
education$LevelOfEducation[education$LevelOfEducation == "1"] <- "Grade 12"
education$LevelOfEducation[education$LevelOfEducation == "2"] <- "Grade 11"
education$LevelOfEducation[education$LevelOfEducation == "3"] <- "Grade 10"
education$LevelOfEducation[education$LevelOfEducation == "4"] <- "Grade 9"
education$LevelOfEducation[education$LevelOfEducation == "5"] <- "Grade 8"
education$LevelOfEducation[education$LevelOfEducation == "6"] <- "Grade 7"
education$LevelOfEducation <- factor(education$LevelOfEducation, levels = c("Grade 7", "Grade 8", "Grade 9", "Grade 10", "Grade 11", "Grade 12"))
ggplot(education, aes(y=LevelOfEducation, x=count)) +
geom_bar(stat="identity") +
scale_x_continuous(limits = c(0,40))
education_further <- HOCUS_AUS %>%
group_by(FurtherEducationOrTraining) %>%
count(SpecifyTraining)
#EMPLOYMENT
employment <- HOCUS_AUS %>%
count(EmploymentCurrent) %>%
na.omit(employment) %>%
mutate(across(everything(), as.numeric)) %>%
rename(count = n)
employment$EmploymentCurrent[employment$EmploymentCurrent == "1"] <- "Fulltime"
employment$EmploymentCurrent[employment$EmploymentCurrent == "2"] <- "Parttime/Casual"
employment$EmploymentCurrent[employment$EmploymentCurrent == "3"] <- "Temporarily Unemployed"
employment$EmploymentCurrent[employment$EmploymentCurrent == "4"] <- "Permanently Unemployed"
employment$EmploymentCurrent[employment$EmploymentCurrent == "5"] <- "Fulltime Student"
employment$EmploymentCurrent[employment$EmploymentCurrent == "6"] <- "Home Duties"
employment$EmploymentCurrent[employment$EmploymentCurrent == "7"] <- "Other"
employment$EmploymentCurrent <- factor(employment$EmploymentCurrent, levels = c("Fulltime", "Parttime/Casual", "Temporarily Unemployed", "Permanently Unemployed", "Fulltime Student", "Home Duties", "Other"))
ggplot(employment, aes(y=EmploymentCurrent, x=count)) +
geom_bar(stat="identity") +
scale_x_continuous(limits = c(0,40))
#ACCOMODATION
accomodation <- HOCUS_AUS %>%
count(AccommodationCurrent) %>%
na.omit(accomodation) %>%
mutate(across(everything(), as.numeric))
accomodation$AccommodationCurrent[accomodation$AccommodationCurrent == "1"] <- "Privately Owned"
accomodation$AccommodationCurrent[accomodation$AccommodationCurrent == "2"] <- "Rented (Private)"
accomodation$AccommodationCurrent[accomodation$AccommodationCurrent == "3"] <- "Rented (Public)"
accomodation$AccommodationCurrent[accomodation$AccommodationCurrent == "4"] <- "Boarding House"
accomodation$AccommodationCurrent[accomodation$AccommodationCurrent == "10"] <- "Caravan"
accomodation$AccommodationCurrent[accomodation$AccommodationCurrent == "11"] <- "No usual residence/Homeless"
accomodation$AccommodationCurrent[accomodation$AccommodationCurrent == "12"] <- "Other"
ggplot(accomodation, aes(y=AccommodationCurrent, x=n)) +
geom_bar(stat="identity") +
scale_x_continuous(limits = c(0,40))
#Descrptives - Scales ##DFAQ-CU
DFAQCU <- HOCUS_AUS %>%
select(CannabisUseEver:Q78,DFAQCUCurrentFreq:DFAQCUFormOtherSpecfiy:PhysicianReccomendation) %>%
mutate(across(everything(), as.numeric))## Warning in x:y: numerical expression has 30 elements: only the first used## Warning: There were 7 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `across(everything(), as.numeric)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 6 remaining warnings.DFAQCU[DFAQCU == 0] <- NA
#CANNABIS LAST USE
last_use <- DFAQCU %>%
count(CannabisLastUse) %>%
na.omit(last_use)
last_use$CannabisLastUse[last_use$CannabisLastUse == "6"] <- "One month ago"
last_use$CannabisLastUse[last_use$CannabisLastUse == "7"] <- "Last week"
last_use$CannabisLastUse[last_use$CannabisLastUse == "8"] <- "This week"
last_use$CannabisLastUse[last_use$CannabisLastUse == "9"] <- "Yesterday"
last_use$CannabisLastUse[last_use$CannabisLastUse == "10"] <- "Today"
last_use$CannabisLastUse[last_use$CannabisLastUse == "11"] <- "Currently High"
last_use$CannabisLastUse <- factor(last_use$CannabisLastUse, levels = c("One month ago", "Last week", "This week", "Yesterday", "Today", "Currently High"))
ggplot(last_use, aes(y=CannabisLastUse, x=n)) +
geom_bar(stat="identity") +
scale_x_continuous(limits = c(0,45))
#AVERAGE CURRENT FREQUENCY
current_freq <- DFAQCU %>%
select(DFAQCUCurrentFreq, DFAQCUCurrentFreqLength)
current_freq %>%
count(DFAQCUCurrentFreq) %>%
mutate(across(everything(), as.numeric))## # A tibble: 7 × 2
## DFAQCUCurrentFreq n
## <dbl> <dbl>
## 1 7 1
## 2 8 5
## 3 9 4
## 4 10 16
## 5 11 8
## 6 12 18
## 7 13 28current_freq$DFAQCUCurrentFreq[current_freq$DFAQCUCurrentFreq == "7"] <- "2-3 times a month"
current_freq$DFAQCUCurrentFreq[current_freq$DFAQCUCurrentFreq == "8"] <- "Once a week"
current_freq$DFAQCUCurrentFreq[current_freq$DFAQCUCurrentFreq == "9"] <- "Twice a week"
current_freq$DFAQCUCurrentFreq[current_freq$DFAQCUCurrentFreq == "10"] <- "3-4 times a week"
current_freq$DFAQCUCurrentFreq[current_freq$DFAQCUCurrentFreq == "11"] <- "5-6 times a week"
current_freq$DFAQCUCurrentFreq[current_freq$DFAQCUCurrentFreq== "12"] <- "Once a day"
current_freq$DFAQCUCurrentFreq[current_freq$DFAQCUCurrentFreq== "13"] <- "More than once a day"
current_freq %>%
dplyr::filter(DFAQCUCurrentFreq == 12|DFAQCUCurrentFreq == 13) %>%
count(DFAQCUCurrentFreqLength)## # A tibble: 0 × 2
## # ℹ 2 variables: DFAQCUCurrentFreqLength <dbl>, n <int>DFAQCU %>%
count(DFAQCUCurrentFreqLength) %>%
arrange(desc(n)) ## # A tibble: 11 × 2
## DFAQCUCurrentFreqLength n
## <dbl> <int>
## 1 12 51
## 2 7 6
## 3 8 5
## 4 11 4
## 5 2 3
## 6 6 3
## 7 9 3
## 8 10 2
## 9 3 1
## 10 4 1
## 11 5 1#PAST WEEK/MONTH/LIFETIME
colMeans(DFAQCU[sapply(DFAQCU, is.numeric)], na.rm = TRUE)## CannabisUseEver CannabisLastUse Q77
## 2.0000000 8.6455696 2.3333333
## Q78 DFAQCUCurrentFreq DFAQCUCurrentFreqLength
## 1.1428571 11.3875000 10.2625000
## DFAQCUPreviousFreq DFAQCUUsePastWeek DFAQCUUsePastMonth
## 7.9113924 6.0875000 22.9610390
## DFAQCUUseLifetime DFAQCUPatternWeekly DFAQCUUseAfterWaking
## 9.0253165 3.8375000 4.2875000
## DFAQCUUseWeekday DFAQCUUseWeekend DFAQCUPrimaryMethod
## 3.7123288 4.1410256 4.5750000
## DFAQCUOtherMethod DFAQCUMethodNone DFAQCUMethodJoints
## NaN 1.0000000 1.0000000
## DFAQCUMethodBlunts DFAQCUMethodHandpipe DFAQCUMethodBong
## 1.0000000 1.0000000 1.0000000
## DFAQCUMethodHookah DFAQCUMethodVaporise DFAQCUMethodEdible
## NaN 1.0000000 1.0000000
## DFAQCUMethodOther DFAQCUMethodOtherSpecify DFAQCUForm
## 1.0000000 NaN 2.2625000
## DFAQCUFormSpecify DFAQCUFormNone DFAQCUFormMarijuana
## NaN 1.0000000 1.0000000
## DFAQCUFormConcentrate DFAQCUFormEdibles DFAQCUFormOther
## 1.0000000 1.0000000 1.0000000
## DFAQCUFormOtherSpecfiy CannabisGramsPerSession CannabisGramsPerDay
## NaN 0.7264286 1.7096154
## CannabisGramsPerWeek CannabisSessionsPerDay AverageTHCContent
## 6.7226562 3.0390625 6.5000000
## EdilbleTHCContent CurrentAge CannabisUseYearsTotal
## NaN 56.3417722 37.2911392
## CannabisAgeFirstUse CannabisRegularUse CannabisFirstRegularUse
## 18.1772152 1.9125000 21.1549296
## CannabisDailyUse CannabisFirstDailyUse CannabisFreqBefore16
## 1.9722222 23.8615385 9.5822785
## PhysicianReccomendation
## 1.3000000sapply(DFAQCU, sd, na.rm = TRUE)## CannabisUseEver CannabisLastUse Q77
## 0.0000000 1.0132200 0.5773503
## Q78 DFAQCUCurrentFreq DFAQCUCurrentFreqLength
## 0.3779645 1.6342362 2.8227994
## DFAQCUPreviousFreq DFAQCUUsePastWeek DFAQCUUsePastMonth
## 4.7856352 2.2512655 9.3632000
## DFAQCUUseLifetime DFAQCUPatternWeekly DFAQCUUseAfterWaking
## 1.7612254 0.5612768 1.8155011
## DFAQCUUseWeekday DFAQCUUseWeekend DFAQCUPrimaryMethod
## 4.5452339 5.9105329 2.1034932
## DFAQCUOtherMethod DFAQCUMethodNone DFAQCUMethodJoints
## NA 0.0000000 0.0000000
## DFAQCUMethodBlunts DFAQCUMethodHandpipe DFAQCUMethodBong
## NA 0.0000000 0.0000000
## DFAQCUMethodHookah DFAQCUMethodVaporise DFAQCUMethodEdible
## NA 0.0000000 0.0000000
## DFAQCUMethodOther DFAQCUMethodOtherSpecify DFAQCUForm
## 0.0000000 NA 0.7419398
## DFAQCUFormSpecify DFAQCUFormNone DFAQCUFormMarijuana
## NA NA 0.0000000
## DFAQCUFormConcentrate DFAQCUFormEdibles DFAQCUFormOther
## 0.0000000 0.0000000 0.0000000
## DFAQCUFormOtherSpecfiy CannabisGramsPerSession CannabisGramsPerDay
## NA 1.3609723 2.9741257
## CannabisGramsPerWeek CannabisSessionsPerDay AverageTHCContent
## 6.6940090 2.2577990 1.9115036
## EdilbleTHCContent CurrentAge CannabisUseYearsTotal
## NA 4.7200773 39.2301401
## CannabisAgeFirstUse CannabisRegularUse CannabisFirstRegularUse
## 7.4691837 0.2843491 8.3420585
## CannabisDailyUse CannabisFirstDailyUse CannabisFreqBefore16
## 0.1654888 10.0977673 4.2895448
## PhysicianReccomendation
## 0.6443523DFAQCU %>%
count(DFAQCUUseLifetime)## # A tibble: 9 × 2
## DFAQCUUseLifetime n
## <dbl> <int>
## 1 2 2
## 2 3 1
## 3 5 1
## 4 6 3
## 5 7 2
## 6 8 9
## 7 9 13
## 8 10 48
## 9 NA 1DFAQCU$DFAQCUUsePastMonth <-as.numeric(as.character(DFAQCU$DFAQCUUsePastMonth))
DFAQCU$DFAQCUUseWeekday <-as.numeric(as.character(DFAQCU$DFAQCUUseWeekday))
DFAQCU$DFAQCUUseWeekend <-as.numeric(as.character(DFAQCU$DFAQCUUseWeekend))
DFAQCU %>%
summarise(mean_month = mean(DFAQCUUsePastMonth, na.rm = TRUE),
mean_weekday = mean(DFAQCUUseWeekday, na.rm = TRUE),
sd_weekday=sd(DFAQCUUseWeekday, na.rm=TRUE),
mean_weekend = mean(DFAQCUUseWeekend, na.rm = TRUE),
sd_weekend=sd(DFAQCUUseWeekend, na.rm=TRUE))## # A tibble: 1 × 5
## mean_month mean_weekday sd_weekday mean_weekend sd_weekend
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 23.0 3.71 4.55 4.14 5.91DFAQCU %>%
count(DFAQCUPreviousFreq) %>%
arrange(desc(n)) #More than once a day (20/80) followed by never used cannabis (18/80)## # A tibble: 12 × 2
## DFAQCUPreviousFreq n
## <dbl> <int>
## 1 13 20
## 2 1 18
## 3 10 11
## 4 4 7
## 5 12 7
## 6 8 5
## 7 11 4
## 8 9 3
## 9 2 2
## 10 3 1
## 11 7 1
## 12 NA 1DFAQCU %>%
count(DFAQCUUsePastWeek) %>%
arrange(desc(n)) #7 days a week (38/80)## # A tibble: 8 × 2
## DFAQCUUsePastWeek n
## <dbl> <int>
## 1 8 38
## 2 3 9
## 3 6 9
## 4 4 7
## 5 7 6
## 6 2 5
## 7 5 4
## 8 1 2DFAQCU %>%
count(DFAQCUPatternWeekly) %>%
arrange(desc(n)) #Cannabis use both weekends and weekdays (73/80)## # A tibble: 4 × 2
## DFAQCUPatternWeekly n
## <dbl> <int>
## 1 4 73
## 2 2 4
## 3 3 2
## 4 1 1DFAQCU %>%
count(DFAQCUUseAfterWaking) %>%
arrange(desc(n)) #12-18 hours after waking, 1-3 hours after waking (both 18/80)## # A tibble: 8 × 2
## DFAQCUUseAfterWaking n
## <dbl> <int>
## 1 2 18
## 2 6 18
## 3 4 15
## 4 3 13
## 5 5 8
## 6 7 5
## 7 8 2
## 8 9 1#INGESTION METHOD
DFAQCU %>%
count(DFAQCUPrimaryMethod) %>%
arrange(desc(n)) ## # A tibble: 7 × 2
## DFAQCUPrimaryMethod n
## <dbl> <int>
## 1 5 28
## 2 2 23
## 3 4 12
## 4 7 7
## 5 9 5
## 6 8 4
## 7 6 1#AMOUNT OF CANNABIS
DFAQCU$CannabisGramsPerSession <-as.numeric(as.character(DFAQCU$CannabisGramsPerSession))
DFAQCU$CannabisGramsPerDay <-as.numeric(as.character(DFAQCU$CannabisGramsPerDay))
DFAQCU$CannabisGramsPerWeek <-as.numeric(as.character(DFAQCU$CannabisGramsPerWeek))
DFAQCU["CannabisSessionsPerDay"][DFAQCU["CannabisSessionsPerDay"] == ''] <- NA
DFAQCU$CannabisSessionsPerDay <-as.numeric(as.character(DFAQCU$CannabisSessionsPerDay))
DFAQCU %>%
summarise(meanpersession=mean(CannabisGramsPerSession, na.rm = TRUE),
sdpersession=sd(CannabisGramsPerSession, na.rm=TRUE),
meanperday=mean(CannabisGramsPerDay, na.rm = TRUE),
sdperday=sd(CannabisGramsPerDay, na.rm=TRUE),
meanperweek=mean(CannabisGramsPerWeek, na.rm=TRUE),
sdperweek=sd(CannabisGramsPerWeek, na.rm=TRUE),
meansessions=mean(CannabisSessionsPerDay, na.rm = TRUE))## # A tibble: 1 × 7
## meanpersession sdpersession meanperday sdperday meanperweek sdperweek
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.726 1.36 1.71 2.97 6.72 6.69
## # ℹ 1 more variable: meansessions <dbl>#YEARS USED
use_years <- DFAQCU %>%
select(CannabisUseYearsTotal, CannabisAgeFirstUse, CannabisRegularUse, CannabisFirstRegularUse, CannabisDailyUse, CannabisFirstDailyUse, CannabisFreqBefore16) %>%
mutate(CannabisUseYearsTotal = str_replace(CannabisUseYearsTotal, "366","36")) %>%
mutate(across(everything(), as.numeric))
use_years %>%
summarise(mean_years=mean(CannabisUseYearsTotal, na.rm = TRUE),
sd_years=sd(CannabisUseYearsTotal, na.rm = TRUE),
mean_ageonset=mean(CannabisAgeFirstUse, na.rm = TRUE),
sd_ageonset=sd(CannabisAgeFirstUse, na.rm = TRUE),
mean_regularuse=mean(CannabisFirstRegularUse, na.rm = TRUE),
sd_regularuse=sd(CannabisFirstRegularUse, na.rm = TRUE),
mean_dailyuse=mean(CannabisFirstDailyUse, na.rm=TRUE),
sd_dailyuse=sd(CannabisFirstDailyUse, na.rm=TRUE))## # A tibble: 1 × 8
## mean_years sd_years mean_ageonset sd_ageonset mean_regularuse sd_regularuse
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 33.1 11.7 18.2 7.47 21.2 8.34
## # ℹ 2 more variables: mean_dailyuse <dbl>, sd_dailyuse <dbl>use_years1 <- use_years %>%
select(CannabisUseYearsTotal) %>%
mutate(years = case_when(CannabisUseYearsTotal>=0 & CannabisUseYearsTotal <= 9 ~ '0-9',
CannabisUseYearsTotal>=10 & CannabisUseYearsTotal <= 19 ~ '10-19',
CannabisUseYearsTotal>=20 & CannabisUseYearsTotal <= 29 ~ '20-29',
CannabisUseYearsTotal>=30 & CannabisUseYearsTotal <= 39 ~ '30-39',
CannabisUseYearsTotal>=40 & CannabisUseYearsTotal <= 49 ~ '40-49',
CannabisUseYearsTotal>=50 & CannabisUseYearsTotal <= 59 ~ '40-59')) %>%
count(years)
ggplot(use_years1, aes(x=years, y=n))+
geom_col()
use_years2 <- use_years %>%
select(CannabisAgeFirstUse) %>%
mutate(age = case_when(CannabisAgeFirstUse>=0 & CannabisAgeFirstUse <= 9 ~ '0-9',
CannabisAgeFirstUse>=10 & CannabisAgeFirstUse <= 19 ~ '10-19',
CannabisAgeFirstUse>=20 & CannabisAgeFirstUse <= 29 ~ '20-29',
CannabisAgeFirstUse>=30 & CannabisAgeFirstUse <= 39 ~ '30-39',
CannabisAgeFirstUse>=40 & CannabisAgeFirstUse <= 49 ~ '40-49',
CannabisAgeFirstUse>=50 & CannabisAgeFirstUse <= 59 ~ '40-59')) %>%
count(age)
ggplot(use_years2, aes(x=age, y=n))+
geom_col()
use_years3 <- use_years %>%
select(CannabisFirstRegularUse) %>%
mutate(age = case_when(CannabisFirstRegularUse>=0 & CannabisFirstRegularUse <= 9 ~ '0-9',
CannabisFirstRegularUse>=10 & CannabisFirstRegularUse <= 19 ~ '10-19',
CannabisFirstRegularUse>=20 & CannabisFirstRegularUse <= 29 ~ '20-29',
CannabisFirstRegularUse>=30 & CannabisFirstRegularUse <= 39 ~ '30-39',
CannabisFirstRegularUse>=40 & CannabisFirstRegularUse <= 49 ~ '40-49',
CannabisFirstRegularUse>=50 & CannabisFirstRegularUse <= 59 ~ '40-59')) %>%
count(age)
ggplot(use_years3, aes(x=age, y=n))+
geom_col()
use_years4 <- use_years %>%
select(CannabisFirstDailyUse) %>%
mutate(age = case_when(CannabisFirstDailyUse>=0 & CannabisFirstDailyUse <= 9 ~ '0-9',
CannabisFirstDailyUse>=10 & CannabisFirstDailyUse <= 19 ~ '10-19',
CannabisFirstDailyUse>=20 & CannabisFirstDailyUse <= 29 ~ '20-29',
CannabisFirstDailyUse>=30 & CannabisFirstDailyUse <= 39 ~ '30-39',
CannabisFirstDailyUse>=40 & CannabisFirstDailyUse <= 49 ~ '40-49',
CannabisFirstDailyUse>=50 & CannabisFirstDailyUse <= 59 ~ '40-59')) %>%
count(age)
ggplot(use_years4, aes(x=age, y=n))+
geom_col()
#DOCTORS RECOMMENDATION
DFAQCU %>%
count(PhysicianReccomendation)## # A tibble: 3 × 2
## PhysicianReccomendation n
## <dbl> <int>
## 1 1 64
## 2 2 8
## 3 3 8##CMMQ
CMMQ <- HOCUS_AUS %>%
select(CMMQ1:CMMQ36) %>%
mutate(across(everything(), as.numeric))
CMMQ_always_response <- CMMQ %>%
select(CMMQ1:CMMQ3, CMMQ28:CMMQ31) %>%
summarise(across(everything(), ~sum(.==5, na.rm = TRUE))) %>%
pivot_longer(cols=everything(), names_to="column", values_to="count")
print(CMMQ_always_response)## # A tibble: 7 × 2
## column count
## <chr> <int>
## 1 CMMQ1 40
## 2 CMMQ2 19
## 3 CMMQ3 32
## 4 CMMQ28 29
## 5 CMMQ29 39
## 6 CMMQ30 29
## 7 CMMQ31 33CMMQ_always_response$column[CMMQ_always_response$column == "CMMQ1"] <- "Enjoy the effects"
CMMQ_always_response$column[CMMQ_always_response$column == "CMMQ2"] <- "It is fun"
CMMQ_always_response$column[CMMQ_always_response$column == "CMMQ3"] <- "To feel good"
CMMQ_always_response$column[CMMQ_always_response$column == "CMMQ28"] <- "Safer to drink than alcohol"
CMMQ_always_response$column[CMMQ_always_response$column == "CMMQ29"] <- "Not a dangerous drug"
CMMQ_always_response$column[CMMQ_always_response$column == "CMMQ30"] <- "Low health risks"
CMMQ_always_response$column[CMMQ_always_response$column == "CMMQ31"] <- "To help you sleep"
CMMQ_always_response$column <- factor(CMMQ_always_response$column, levels = CMMQ_always_response$column)
ggplot(CMMQ_always_response, aes(x = column, y = count)) +
geom_bar(stat="identity", width = 0.8) +
coord_flip() +
scale_y_continuous(expand = c(0,0), limits = c(0,40), breaks=seq(0,40, by=10)) +
scale_x_discrete(limits=c("Enjoy the effects", "Not a dangerous drug", "To help you sleep", "To feel good", "Low health risks", "Safer to drink than alcohol", "It is fun")) +
labs(x="Motivation",
y="Number of Participants") +
theme_bw() +
theme(
text=element_text(family = "Times New Roman", size=12, face="bold"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks.x = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y= element_blank())
CMMQ_never_response <- CMMQ %>%
select(CMMQ5,CMMQ6, CMMQ4,CMMQ18,CMMQ11,CMMQ16, CMMQ17) %>%
summarise(across(everything(), ~sum(.==1, na.rm = TRUE))) %>%
pivot_longer(cols=everything(), names_to="column", values_to="count")
print(CMMQ_never_response)## # A tibble: 7 × 2
## column count
## <chr> <int>
## 1 CMMQ5 65
## 2 CMMQ6 62
## 3 CMMQ4 61
## 4 CMMQ18 53
## 5 CMMQ11 51
## 6 CMMQ16 50
## 7 CMMQ17 49CMMQ_never_response$column[CMMQ_never_response$column == "CMMQ5"] <- "Didn't want to be the only one not doing it"
CMMQ_never_response$column[CMMQ_never_response$column == "CMMQ6"] <- "To be cool"
CMMQ_never_response$column[CMMQ_never_response$column == "CMMQ4"] <- "Felt pressure from others"
CMMQ_never_response$column[CMMQ_never_response$column == "CMMQ18"] <- "You had gotten drunk and weren't thinking"
CMMQ_never_response$column[CMMQ_never_response$column == "CMMQ11"] <- "Curious about marijuana"
CMMQ_never_response$column[CMMQ_never_response$column == "CMMQ16"] <- "You were drunk"
CMMQ_never_response$column[CMMQ_never_response$column == "CMMQ17"] <- "Under the influence of alcohol"
ggplot(CMMQ_never_response, aes(x = column, y = count)) +
geom_bar(stat="identity", width = 0.8) +
coord_flip() +
scale_y_continuous(expand = c(0,0), limits = c(0,70), breaks=seq(0,70, by=10)) +
scale_x_discrete(limits=c("Didn't want to be the only one not doing it", "To be cool", "Felt pressure from others", "You had gotten drunk and weren't thinking", "Curious about marijuana", "You were drunk","Under the influence of alcohol")) +
labs(x="Motivation",
y="Number of Participants") +
theme_bw() +
theme(
text=element_text(family = "Times New Roman", size=12, face="bold"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks.x = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y = element_blank())
table(CMMQ$CMMQ1) #To enjoy the effects of it = almost always or always, 40/80##
## 1 3 4 5
## 7 16 9 40table(CMMQ$CMMQ2) #Because it's fun = almost always or always 19/80, almost never or never 17/80##
## 1 2 3 4 5
## 17 5 17 9 19table(CMMQ$CMMQ3) #To feel good = almost always or always 32/80##
## 1 2 3 4 5
## 4 1 18 13 32table(CMMQ$CMMQ4) #Because you felt pressure from others who do it = almost never or never 61/80##
## 1 2 3
## 61 5 1table(CMMQ$CMMQ5) #Because you didn't want to be the only one not doing it = almost never/never 61##
## 1 2 5
## 65 3 1table(CMMQ$CMMQ6) #To be cool = almost never/never 65##
## 1 2 3
## 62 4 2table(CMMQ$CMMQ7) #To forget your problems = almost never/never 62##
## 1 2 3 4 5
## 35 6 22 3 1table(CMMQ$CMMQ8) #Because you were depressed = almost never/never 35, sometimes 22##
## 1 2 3 4 5
## 31 11 20 4 1table(CMMQ$CMMQ9) #To escape from your life = almost never/never 31, sometimes 20##
## 1 2 3 4 5
## 37 5 20 5 3table(CMMQ$CMMQ10) #Because you were experimenting = almost never/never 37, sometimes 20##
## 1 2 3 4
## 45 10 11 2table(CMMQ$CMMQ11) #Because you were curious about marijuana = almost never/never 45##
## 1 2 3 4 5
## 51 4 8 4 2table(CMMQ$CMMQ12) #To see what it felt like = almost never/never 51##
## 1 2 3 4 5
## 43 5 11 6 3table(CMMQ$CMMQ13) #Because you had nothing better to do = almost never/never 43##
## 1 2 3 4 5
## 48 3 13 3 2table(CMMQ$CMMQ14)#To relieve boredom = almost never/never 48##
## 1 2 3 4 5
## 37 5 19 5 1table(CMMQ$CMMQ15)#Because you wanted something to do = almost never/never 37##
## 1 2 3 4 5
## 46 5 12 6 1table(CMMQ$CMMQ16)#Because you were drunk = almost never/never 46##
## 1 2 3 4
## 50 5 12 1table(CMMQ$CMMQ17)#Because you were under the influence of alcohol = almost never/never 50##
## 1 2 3 4 5
## 49 7 12 1 1table(CMMQ$CMMQ18)#Because you had gotten drunk and weren't thinking about what you were doing = almost never/never 49##
## 1 2 3
## 53 5 10table(CMMQ$CMMQ19)#To celebrate = almost never/never 53##
## 1 2 3 4 5
## 18 4 27 14 7table(CMMQ$CMMQ20)#Because it was a special day = sometimes 27##
## 1 2 3 4 5
## 25 3 29 7 4table(CMMQ$CMMQ21)#Because it was a special occasion = almost never/never 25, sometimes 29##
## 1 2 3 4 5
## 26 2 27 8 6table(CMMQ$CMMQ22)#Because you want to alter your perspective = almost never/never 26, sometimes 27##
## 1 2 3 4 5
## 26 8 16 11 7table(CMMQ$CMMQ23)#To allow you to think differently = almost never/never 26##
## 1 2 3 4 5
## 18 6 26 12 7table(CMMQ$CMMQ24)# So you can look at the world differently = sometimes 26##
## 1 2 3 4 5
## 20 4 26 12 7table(CMMQ$CMMQ25)#Because it makes you more comfortable in an unfamiliar situation = sometimes 26##
## 1 2 3 4 5
## 26 7 28 4 5table(CMMQ$CMMQ26)#To make you feel more confident = almost never/never 26, sometimes 28##
## 1 2 3 4 5
## 37 8 19 2 3table(CMMQ$CMMQ27)#Because it relaxes you when you are in an insecure situation = almost never/never 37##
## 1 2 3 4 5
## 21 3 26 13 7table(CMMQ$CMMQ28)#Because it is safer than drinking alcohol = almost never/never 21, sometimes 26##
## 1 2 3 4 5
## 15 2 15 8 29table(CMMQ$CMMQ29)#Because it is not a dangerous drug = almost always or always 29, sometimes 15##
## 1 2 3 4 5
## 10 1 13 6 39table(CMMQ$CMMQ30)#Because there are low health risks = almost always or always 39, sometimes 13##
## 1 2 3 4 5
## 13 6 11 10 29table(CMMQ$CMMQ31)#To help you sleep = almost always or always 29##
## 1 2 3 4 5
## 7 5 17 9 33table(CMMQ$CMMQ32)#Because it helps make napping easier and enjoyable = almost always or always 33##
## 1 2 3 4 5
## 19 4 18 10 18table(CMMQ$CMMQ33)#Because you are having problems sleeping = almost never or never 19, sometimes 18, almost always or always 18##
## 1 2 3 4 5
## 15 3 23 8 22table(CMMQ$CMMQ34)#Because it is readily available = almost always or always 22##
## 1 2 3 4 5
## 26 7 18 8 10table(CMMQ$CMMQ35)#Because you can get it for free = almost never or never 26, sometimes 18##
## 1 2 3 4 5
## 40 5 17 2 6table(CMMQ$CMMQ36)#Because it is there = almost never or never 34 (must be some purpose behind use)##
## 1 2 3 4 5
## 34 6 15 7 6##MMPQ
MMPQ <- HOCUS_AUS %>%
select(MedicinalIntention:CannabisUnhelpfulFor) %>%
mutate(across(everything(), as.numeric))## Warning: There were 7 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `across(everything(), as.numeric)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 6 remaining warnings.#YEARS/MONTHS USED
MMPQ_medicinal <- MMPQ %>%
dplyr::filter(MedicinalIntention == 1) %>%
summarise(
average_years = mean(MedicinalCannabisYears, na.rm = TRUE),
sd_years = sd(MedicinalCannabisYears, na.rm = TRUE),
average_months = mean(MedicinalCannabisMonths, na.rm = TRUE),
sd_months = sd(MedicinalCannabisMonths, na.rm = TRUE))
#CONDITION FOR USE
condition <- MMPQ %>%
select(ConditionChronicPain:ConditionHIVAIDS) %>%
rename(ChronicPain = ConditionChronicPain,
Anxiety = ConditionAnxiety,
PTSD = ConditionPTSD,
Depression = ConditionDepression,
Nightmares = ConditionNightmares,
Appetite = ConditionAppetite,
Nausea = ConditionNausea,
Insomnia = ConditionInsomnia,
Epilepsy = ConditionEpilepsy,
Headaches = ConditionHeadaches,
Seizures = ConditionSeizures,
Stress = ConditionStress,
Cancer = ConditionCancer,
Glaucoma = ConditionGlaucoma,
MuscleSpasms = ConditionMuscleSpasms,
MultipleSclerosis = ConditionMultipleSclerosis,
HIVAIDS = ConditionHIVAIDS) %>%
summarise(across(everything(), ~sum(.==1, na.rm = TRUE))) %>%
pivot_longer(cols=everything(), names_to="column", values_to="count") %>%
arrange(desc(count)) %>%
dplyr::filter(count > 0)
condition$column <- factor(condition$column, levels = condition$column)
ggplot(condition, aes(column, count)) +
geom_bar(stat="identity") +
coord_flip() +
scale_y_continuous(expand = c(0,0), limits = c(0,50), breaks=seq(0,50, by=10)) +
labs(x="Condition",
y="Number of Participants") +
theme_bw() +
theme(
text=element_text(family = "Times New Roman", size=12, face="bold"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks.x = element_line(colour = "black", linewidth = 0.5),
axis.ticks.y= element_blank())
MMPQ %>%
count(ConditionOtherSpecify)#3## # A tibble: 1 × 2
## ConditionOtherSpecify n
## <dbl> <int>
## 1 NA 80MMPQ %>%
count(ConditionChronicPain & ConditionInsomnia)#23## # A tibble: 2 × 2
## `ConditionChronicPain & ConditionInsomnia` n
## <lgl> <int>
## 1 TRUE 23
## 2 NA 57MMPQ %>%
count(ConditionChronicPain & ConditionStress)#17## # A tibble: 2 × 2
## `ConditionChronicPain & ConditionStress` n
## <lgl> <int>
## 1 TRUE 17
## 2 NA 63MMPQ %>%
count(ConditionInsomnia & ConditionStress)#16## # A tibble: 2 × 2
## `ConditionInsomnia & ConditionStress` n
## <lgl> <int>
## 1 TRUE 16
## 2 NA 64MMPQ %>%
count(ConditionChronicPain & ConditionDepression)#15## # A tibble: 2 × 2
## `ConditionChronicPain & ConditionDepression` n
## <lgl> <int>
## 1 TRUE 15
## 2 NA 65#HELPFUL CONDITION
MMPQ %>%
dplyr::filter(MedicinalIntention == 1) %>% #53 use for medicinal purposes
count(CannabisHelpCondition) #35 rate cannabis as extremely helpful for symptoms## # A tibble: 4 × 2
## CannabisHelpCondition n
## <dbl> <int>
## 1 3 2
## 2 4 14
## 3 5 35
## 4 NA 2##PDQ
PDQ <- HOCUS_AUS %>%
select(PDQ1:PDQ20) %>%
mutate(across(everything(), as.numeric))
PDQ_recoded <- PDQ %>%
mutate(across(1:20,
.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3,'5' = 4))) %>%
dplyr::filter(!if_all(c(PDQ1:PDQ20), is.na)) %>%
rowwise() %>%
mutate(row_sum = sum(c_across(1:20), na.rm = TRUE))
mean_pdq <- mean(PDQ_recoded$row_sum, na.rm = TRUE) #mean pdq score of 17.89
PDQ_mean <- PDQ_recoded %>%
summarise_all(mean, na.rm=TRUE) %>%
mutate(row_sum=sum(across(1:20), na.rm=TRUE))
#Averages do not indicate that cannabis users in this sample suffer from perceived cognitive impairment
attention_concerntration <- PDQ_recoded %>%
select(PDQ1, PDQ5, PDQ9, PDQ13, PDQ17) %>%
mutate(row_sum=sum(across(1:5), na.rm=TRUE))
mean(attention_concerntration$row_sum, na.rm = TRUE) #mean=5.56## [1] 5.561644sd(attention_concerntration$row_sum, na.rm=TRUE) #sd=3.18## [1] 3.184102reterospective_memory <- PDQ_recoded %>%
select(PDQ2, PDQ6, PDQ10, PDQ14, PDQ18) %>%
mutate(row_sum=sum(across(1:5), na.rm=TRUE))
mean(reterospective_memory$row_sum, na.rm = TRUE) #mean=4.16## [1] 4.164384sd(reterospective_memory$row_sum, na.rm=TRUE) #sd=3.30## [1] 3.308296prospective_memory <- PDQ_recoded %>%
select(PDQ3, PDQ7, PDQ11, PDQ15, PDQ19) %>%
mutate(row_sum=sum(across(1:5), na.rm=TRUE))
mean(prospective_memory$row_sum, na.rm = TRUE) #mean=3.93## [1] 3.931507sd(prospective_memory$row_sum, na.rm=TRUE) #sd=2.66## [1] 2.663168planning_organisation <- PDQ_recoded %>%
select(PDQ4, PDQ8, PDQ12, PDQ16, PDQ20) %>%
mutate(row_sum=sum(across(1:5), na.rm=TRUE))
mean(planning_organisation$row_sum, na.rm = TRUE) #mean=4.23## [1] 4.232877sd(planning_organisation$row_sum, na.rm=TRUE) #sd=3.02## [1] 3.020856##AQoL-6D
AQoL <- HOCUS_AUS %>%
select(AQoL6D1:AQoL6D20) %>%
dplyr::filter(!if_all(c(AQoL6D1:AQoL6D20), is.na))
#utilizing 'psychometric' measure - simple psychometric score for health related quality of life (HRQoL) and to provide profile scores on the different dimensions or items of the descriptive systems. The score is derived by adding the unweighted response order of each question.
AQoL_mean <- AQoL %>%
mutate(across(everything(), as.numeric)) %>%
summarise_all(mean, na.rm=TRUE) %>%
mutate(sum=rowSums(., na.rm=TRUE)) #total score=38.14
AQoL_mean2 <- AQoL %>%
mutate(across(everything(), as.numeric)) %>%
rowwise() %>%
mutate(row_sum = sum(c_across(1:20), na.rm = TRUE))
sd(AQoL_mean2$row_sum) #sd=10.30## [1] 10.29585independent_living <- AQoL %>%
select(AQoL6D1:AQoL6D4) %>%
mutate(across(everything(), as.numeric)) %>%
summarise_all(mean, na.rm=TRUE) %>%
mutate(sum=rowSums(., na.rm=TRUE))
relationships <- AQoL %>%
select(AQoL6D5:AQoL6D7) %>%
mutate(across(everything(), as.numeric)) %>%
summarise_all(mean, na.rm=TRUE) %>%
mutate(sum=rowSums(., na.rm=TRUE))
mental_health <- AQoL %>%
select(AQoL6D8:AQoL6D11) %>%
mutate(across(everything(), as.numeric)) %>%
summarise_all(mean, na.rm=TRUE) %>%
mutate(sum=rowSums(., na.rm=TRUE))
coping <- AQoL %>%
select(AQoL6D12:AQoL6D14) %>%
mutate(across(everything(), as.numeric)) %>%
summarise_all(mean, na.rm=TRUE) %>%
mutate(sum=rowSums(., na.rm=TRUE))
pain <- AQoL %>%
select(AQoL6D15:AQoL6D17) %>%
mutate(across(everything(), as.numeric)) %>%
summarise_all(mean, na.rm=TRUE) %>%
mutate(sum=rowSums(., na.rm=TRUE))
senses <- AQoL %>%
select(AQoL6D18:AQoL6D20) %>%
mutate(across(everything(), as.numeric)) %>%
summarise_all(mean, na.rm=TRUE) %>%
mutate(sum=rowSums(., na.rm=TRUE))##ICD-10
ICD10 <- HOCUS_AUS %>%
select(Participant, Q53_1:Q53_7) %>%
mutate(across(everything(), as.numeric))
ICD10 %>%
rowwise() %>%
mutate(num_yes=sum(c_across(Q53_1:Q53_7) == 1, na.rm=TRUE))%>%
dplyr::filter(num_yes>3)## # A tibble: 10 × 9
## # Rowwise:
## Participant Q53_1 Q53_2 Q53_3 Q53_4 Q53_5 Q53_6 Q53_7 num_yes
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 4 1 1 1 1 2 1 1 6
## 2 6 1 2 1 2 1 2 1 4
## 3 11 1 1 1 2 2 1 1 5
## 4 22 1 2 1 2 2 1 1 4
## 5 36 1 1 1 1 1 1 1 7
## 6 37 1 1 1 1 1 1 1 7
## 7 42 1 1 1 1 1 1 1 7
## 8 54 1 2 2 1 2 1 1 4
## 9 65 1 2 1 1 2 1 1 5
## 10 75 1 1 1 1 2 2 1 5##CAST
CAST <- HOCUS_AUS %>%
select(Q127:Q132) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(1:6,.fns = ~recode(.,'2' = 0))) %>%
rename(CASTq1 = Q127,
CASTq2 = Q128,
CASTq3 = Q129,
CASTq4 = Q130,
CASTq5 = Q131,
CASTq6 = Q132)
CAST_means <- CAST %>% #14 participants considered having problematic use
mutate(sum=rowSums(across(1:6), na.rm=TRUE)) %>%
mutate(problematic_use = case_when(
sum>=4 ~ 'High risk',
sum<=3 ~'Low/moderate risk')) %>%
count(problematic_use)##AUDIT-C
AUDITC <- HOCUS_AUS %>%
select(GenderMale, GenderFemale, AUDITC1:AUDITC3) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(3:5,
.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3,'5' = 4, '6'=5)))
AUDITC_male <- AUDITC %>%
select(GenderMale, AUDITC1:AUDITC3) %>%
dplyr::filter(GenderMale == 1)
AUDITC_score_male <- AUDITC_male %>%
mutate(hazardous_drinking = rowSums(across(c(AUDITC1:AUDITC3)))) %>%
dplyr::filter(hazardous_drinking>=4)
AUDITC_female <- AUDITC %>%
select(GenderFemale, AUDITC1:AUDITC3) %>%
dplyr::filter(GenderFemale == 1)
AUDITC_score_female <- AUDITC_female %>%
mutate(hazardous_drinking = rowSums(across(c(AUDITC1:AUDITC3)))) %>%
dplyr::filter(hazardous_drinking>=3) ##ATOP
ATOP_substances <- HOCUS_AUS %>%
select(ATOPAlcohol:ATOPTobacco) %>%
summarise(across(everything(), ~sum(.==1, na.rm = TRUE))) %>%
pivot_longer(cols=everything(), names_to="column", values_to="count") %>%
arrange(desc(count))
print(ATOP_substances)## # A tibble: 9 × 2
## column count
## <chr> <int>
## 1 ATOPCannabis 68
## 2 ATOPAlcohol 44
## 3 ATOPTobacco 32
## 4 ATOPBenzodiazepine 5
## 5 ATOPCocaine 2
## 6 ATOPOtherOpiods 1
## 7 ATOPOtherSubstances 1
## 8 ATOPAmphethamine 0
## 9 ATOPHeroin 0##Mental Health Information
mental_health_info <- HOCUS_AUS %>%
select(MentalHealthInfo1:MentalConditionTreatmentYear)
#MENTAL HEALTH CONDITION
mental_health_info %>%
group_by(MentalHealthInfo1) %>%
count(MentalHealthInfo2,MentalHealthInfo3, MentalHealthInfo4,MentalHealthInfo5)## # A tibble: 9 × 6
## # Groups: MentalHealthInfo1 [3]
## MentalHealthInfo1 MentalHealthInfo2 MentalHealthInfo3 MentalHealthInfo4
## <dbl+lbl> <dbl+lbl> <dbl+lbl> <dbl+lbl>
## 1 1 [Yes] 1 [Depression] 1 [Anxiety] NA
## 2 1 [Yes] 1 [Depression] 1 [Anxiety] NA
## 3 1 [Yes] 1 [Depression] NA NA
## 4 1 [Yes] 1 [Depression] NA NA
## 5 1 [Yes] NA 1 [Anxiety] NA
## 6 1 [Yes] NA 1 [Anxiety] NA
## 7 1 [Yes] NA NA NA
## 8 2 [No] NA NA NA
## 9 NA NA NA NA
## # ℹ 2 more variables: MentalHealthInfo5 <dbl+lbl>, n <int>##DASS-21
DASS_21 <- HOCUS_AUS %>%
select(DASS21_1:DASS21_21) %>%
mutate(across(everything(), as.numeric)) %>%
dplyr::filter(!if_all(c(DASS21_1:DASS21_18), is.na))
DASS_21_recoded <- DASS_21 %>%
mutate(across(1:21,
.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3))) %>%
rowwise() %>%
mutate(row_sum = sum(c_across(1:21), na.rm = TRUE))
mean <- mean(DASS_21_recoded$row_sum, na.rm = TRUE) #score overall m=9.15
sd <- sd(DASS_21_recoded$row_sum, na.rm=TRUE) #sd=8.12
DASS_stress <- DASS_21_recoded %>%
select(DASS21_1, DASS21_6,DASS21_8, DASS21_11, DASS21_12, DASS21_14, DASS21_18)
DASS_depression <- DASS_21_recoded %>%
select(DASS21_3, DASS21_5, DASS21_10, DASS21_13, DASS21_16, DASS21_17, DASS21_21)
DASS_anxiety <- DASS_21_recoded %>%
select(DASS21_2, DASS21_4, DASS21_7, DASS21_9, DASS21_15, DASS21_18, DASS21_19)
#STRESS
stress_severity <- DASS_stress %>%
rowwise() %>%
mutate(row_sum = sum(c_across(1:7), na.rm = TRUE)) %>%
mutate(severity = case_when(
row_sum>=0 & row_sum<=7 ~ 'Normal',
row_sum>=8 & row_sum<=9 ~ 'Mild',
row_sum>=10 & row_sum<=12 ~ 'Moderate',
row_sum>=13 & row_sum<=16 ~ 'Severe',
row_sum>=17 ~ 'Extremely Severe')) %>%
count(severity) %>%
mutate(severity = factor(severity, levels=c('Normal', 'Mild', 'Moderate', 'Severe', 'Extremely Severe')))
ggplot(stress_severity, aes(x=severity, y=n))+
geom_bar(stat = "identity") +
labs(x='Stress Score', y='Number') +
scale_y_continuous(expand = c(0,0), limits = c(0,75), breaks=seq(0,75, by=5)) +
labs(x="Stress Severity",
y="Number of Participants") +
theme_bw() +
theme(
text=element_text(family = "Times New Roman", size=12, face="bold"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x= element_blank())
#ANXIETY
anxiety_severity <- DASS_anxiety %>%
rowwise() %>%
mutate(row_sum = sum(c_across(1:7), na.rm = TRUE)) %>%
mutate(severity = case_when(
row_sum>=0 & row_sum<=3 ~ 'Normal',
row_sum>=4 & row_sum<=5 ~ 'Mild',
row_sum>=6 & row_sum<=7 ~ 'Moderate',
row_sum>=8 & row_sum<=9 ~ 'Severe',
row_sum>=10 ~ 'Extremely Severe')) %>%
count(severity) %>%
mutate(severity = factor(severity, levels=c('Normal', 'Mild', 'Moderate', 'Severe', 'Extremely Severe')))
ggplot(anxiety_severity, aes(x=severity, y=n))+
geom_bar(stat = "identity") +
labs(x='Anxiety Score', y='Number')
#DEPRESSION
depression_severity <- DASS_depression %>%
rowwise() %>%
mutate(row_sum = sum(c_across(1:7), na.rm = TRUE)) %>%
mutate(severity = case_when(
row_sum>=0 & row_sum<=4 ~ 'Normal',
row_sum>=5 & row_sum<=6 ~ 'Mild',
row_sum>=7 & row_sum<=10 ~ 'Moderate',
row_sum>=11 & row_sum<=13 ~ 'Severe',
row_sum>=14 ~ 'Extremely Severe')) %>%
count(severity) %>%
mutate(severity = factor(severity, levels=c('Normal', 'Mild', 'Moderate', 'Severe', 'Extremely Severe')))
ggplot(depression_severity, aes(x=severity, y=n))+
geom_bar(stat = "identity") +
labs(x='Depression Score', y='Number')
##DASS-21 BY GENDER
DASS_21_gender <- HOCUS_AUS %>%
select(GenderMale, GenderFemale,DASS21_1:DASS21_21) %>%
mutate(across(everything(), as.numeric))
DASS_21_recoded_gender <- DASS_21_gender %>%
mutate(across(3:23,.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3)))
DASS_stress_gender <- DASS_21_recoded_gender %>%
select(GenderMale, GenderFemale,DASS21_1, DASS21_6,DASS21_8, DASS21_11, DASS21_12, DASS21_14, DASS21_18) %>%
mutate(gender=case_when(GenderMale==1 ~ 'Male',
GenderFemale==1 ~ 'Female')) %>%
select(-GenderMale, -GenderFemale) %>%
dplyr::filter(!if_all(c(DASS21_1:DASS21_18), is.na))
DASS_depression_gender <- DASS_21_recoded_gender %>%
select(GenderMale, GenderFemale, DASS21_3, DASS21_5, DASS21_10, DASS21_13, DASS21_16, DASS21_17, DASS21_21) %>%
mutate(gender=case_when(GenderMale==1 ~ 'Male',
GenderFemale==1 ~ 'Female')) %>%
select(-GenderMale, -GenderFemale) %>%
dplyr::filter(!if_all(c(DASS21_3:DASS21_21), is.na))
DASS_anxiety_gender <- DASS_21_recoded_gender %>%
select(GenderMale, GenderFemale, DASS21_2, DASS21_4, DASS21_7, DASS21_9, DASS21_15, DASS21_18, DASS21_19) %>%
mutate(gender=case_when(GenderMale==1 ~ 'Male',
GenderFemale==1 ~ 'Female')) %>%
select(-GenderMale, -GenderFemale) %>%
dplyr::filter(!if_all(c(DASS21_2:DASS21_19), is.na))
#STRESS (gender)
stress_severity_gender <- DASS_stress_gender %>%
rowwise() %>%
mutate(row_sum = sum(c_across(1:7), na.rm = TRUE))
mean_stress <- mean(stress_severity_gender$row_sum, na.rm = TRUE) #stress score overall m=3.58
mean_stress_gender <- stress_severity_gender %>%
group_by(gender) %>%
summarise(mean=mean(row_sum),
sd=sd(row_sum)) #male = 3.37, female = 3.84
stress_severity_gender <- DASS_stress_gender %>%
rowwise() %>%
mutate(row_sum = sum(c_across(1:7), na.rm = TRUE)) %>%
mutate(severity = case_when(
row_sum>=0 & row_sum<=7 ~ 'Normal',
row_sum>=8 & row_sum<=9 ~ 'Mild',
row_sum>=10 & row_sum<=12 ~ 'Moderate',
row_sum>=13 & row_sum<=16 ~ 'Severe',
row_sum>=17 ~ 'Extremely Severe')) %>%
count(severity, gender) %>%
complete(severity = c('Normal', 'Mild', 'Moderate', 'Severe', 'Extremely Severe'), fill = list(n = 0)) %>%
mutate(severity = factor(severity, levels=c('Normal', 'Mild', 'Moderate', 'Severe', 'Extremely Severe'))) %>% replace_na(list(gender = "Female"))
ggplot(stress_severity_gender, aes(x=severity, y=n, fill = gender))+
geom_bar(stat = "identity", position = "dodge", width = 0.8) +
scale_y_continuous(expand = c(0,0), limits = c(0,40), breaks=seq(0,40, by=10)) +
labs(x="Stress Severity",
y="Number of Participants") +
theme_bw() +
theme(
text=element_text(family = "Times New Roman", size=12, face="bold"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x= element_blank()) +
scale_fill_discrete(name = "Gender") +
scale_fill_manual("Gender", values = c("Female"="grey69", "Male"="grey28"))## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
#ANXIETY (gender)
anxiety_severity_gender <- DASS_anxiety_gender %>%
rowwise() %>%
mutate(row_sum = sum(c_across(1:7), na.rm = TRUE))
mean_anxiety <- mean(anxiety_severity_gender$row_sum, na.rm = TRUE) #stress score overall m=2.70
mean_anxiety_gender <- anxiety_severity_gender %>%
group_by(gender) %>%
summarise(mean=mean(row_sum),
sd=sd(row_sum)) #male = 2.45, female = 3.03
anxiety_severity_gender <- DASS_anxiety_gender %>%
rowwise() %>%
mutate(row_sum = sum(c_across(1:7), na.rm = TRUE)) %>%
mutate(severity = case_when(
row_sum>=0 & row_sum<=3 ~ 'Normal',
row_sum>=4 & row_sum<=5 ~ 'Mild',
row_sum>=6 & row_sum<=7 ~ 'Moderate',
row_sum>=8 & row_sum<=9 ~ 'Severe',
row_sum>=10 ~ 'Extremely Severe')) %>%
count(severity, gender) %>%
complete(severity = c('Normal', 'Mild', 'Moderate', 'Severe', 'Extremely Severe'), fill = list(n = 0)) %>%
mutate(severity = factor(severity, levels=c('Normal', 'Mild', 'Moderate', 'Severe', 'Extremely Severe'))) %>%
replace_na(list(gender = "Female"))
ggplot(anxiety_severity_gender, aes(x=severity, y=n, fill = gender))+
geom_bar(stat = "identity", position = "dodge") +
scale_y_continuous(expand = c(0,0), limits = c(0,40), breaks=seq(0,40, by=10)) +
labs(x="Anxiety Severity",
y="Number of Participants")+
theme_bw() +
theme(
text=element_text(family = "Times New Roman", size=12, face="bold"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x= element_blank(),
legend.position = c(.9, .55)) +
scale_fill_discrete(name = "Gender") +
scale_fill_manual("Gender", values = c("Female"="grey69", "Male"="grey28")) ## Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
## 3.5.0.
## ℹ Please use the `legend.position.inside` argument of `theme()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
#DEPRESSION (gender)
depression_severity_gender <- DASS_depression_gender %>%
rowwise() %>%
mutate(row_sum = sum(c_across(1:7), na.rm = TRUE))
mean_depression <- mean(depression_severity_gender$row_sum, na.rm = TRUE) #stress score overall m=3.08
mean_depression_gender <- depression_severity_gender %>%
group_by(gender) %>%
summarise(mean=mean(row_sum),
sd=sd(row_sum)) #male = 2.90, female = 3.31
depression_severity_gender <- DASS_depression_gender %>%
rowwise() %>%
mutate(row_sum = sum(c_across(1:7), na.rm = TRUE)) %>%
mutate(severity = case_when(
row_sum>=0 & row_sum<=4 ~ 'Normal',
row_sum>=5 & row_sum<=6 ~ 'Mild',
row_sum>=7 & row_sum<=10 ~ 'Moderate',
row_sum>=11 & row_sum<=13 ~ 'Severe',
row_sum>=14 ~ 'Extremely Severe')) %>%
count(severity, gender) %>%
complete(severity = c('Normal', 'Mild', 'Moderate', 'Severe', 'Extremely Severe'), fill = list(n = 0)) %>%
mutate(severity = factor(severity, levels=c('Normal', 'Mild', 'Moderate', 'Severe', 'Extremely Severe')))
ggplot(depression_severity_gender, aes(x=severity, y=n, fill=gender))+
geom_bar(stat = "identity", position = "dodge") +
scale_y_continuous(expand = c(0,0), limits = c(0,40), breaks=seq(0,40, by=10)) +
labs(x="Depression Severity",
y="Number of Participants") +
theme_bw() +
theme(
text=element_text(family = "Times New Roman", size=12, face="bold"),
axis.line = element_line(colour = "black"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks.y = element_line(colour = "black", linewidth = 0.5),
axis.ticks.x= element_blank(),
legend.position = c(.9, .55)) +
scale_fill_discrete(name = "Gender") +
scale_fill_manual("Gender", values = c("Female"="grey69", "Male"="grey28"))## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
##ISI
ISI <- HOCUS_AUS %>%
select(Participant, ISI_1:ISI_4,ISI_5:ISI_7) %>%
mutate(across(everything(), as.numeric))
ISI_recoded <- ISI %>%
mutate(
across(2:8,
.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3,'5' = 4))) %>%
rowwise() %>%
mutate(row_sum = sum(c_across(2:8), na.rm = TRUE))
mean(ISI_recoded$row_sum)## [1] 6.5sd(ISI_recoded$row_sum)## [1] 5.807023ISI_scoring <- ISI_recoded %>%
rowwise() %>%
mutate(row_sum = sum(c_across(2:8), na.rm = TRUE)) %>%
mutate(severity = case_when(
row_sum>=0 & row_sum<=7 ~ 'No Insomnia',
row_sum>=8 & row_sum<=14 ~ 'Subthreshold Insomnia',
row_sum>=15 & row_sum<=21 ~ 'Moderate Insomnia',
row_sum>=22 & row_sum<=28 ~ 'Severe Insomnia')) %>%
count(severity) %>%
mutate(severity = factor(severity, levels=c('No Insomnia', 'Subthreshold Insomnia', 'Moderate Insomnia', 'Severe Insomnia')))
ISI_scoring <- ISI_recoded %>%
rowwise() %>%
mutate(row_sum = sum(c_across(2:8), na.rm = TRUE)) %>%
rowwise() %>%
mutate(row_sum = sum(c_across(2:8), na.rm = TRUE)) %>%
mutate(severity = case_when(
row_sum>=0 & row_sum<=7 ~ 'No insomnia',
row_sum>=8 & row_sum<=14 ~ 'Subthreshold insomnia',
row_sum>=15 & row_sum<=21 ~ 'Moderate Insomnia',
row_sum>=22 & row_sum<=28 ~ 'Severe Insomnia')) %>%
count(severity) %>%
mutate(severity = factor(severity, levels=c('No insomnia', 'Subthreshold Insomnia', 'Moderate Insomnia', 'Severe Insomnia')))
ISI_scoring <- ISI_recoded %>%
rowwise() %>%
mutate(row_sum = sum(c_across(2:8), na.rm = TRUE))
ISI_scoring %>%
dplyr::filter(row_sum >= 15)## # A tibble: 9 × 9
## # Rowwise:
## Participant ISI_1 ISI_2 ISI_3 ISI_4 ISI_5 ISI_6 ISI_7 row_sum
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 3 3 2 4 2 2 2 18
## 2 29 0 4 4 3 2 3 3 19
## 3 41 3 2 0 4 4 3 3 19
## 4 42 0 3 3 3 3 3 3 18
## 5 44 4 3 4 3 2 1 2 19
## 6 48 4 4 3 2 2 1 2 18
## 7 52 3 2 2 3 2 1 2 15
## 8 58 2 3 4 4 2 2 3 20
## 9 64 4 2 3 2 3 1 2 17sleep <- HOCUS_AUS %>%
dplyr::filter(Participant == 2|Participant ==29|Participant ==41|Participant ==42|Participant ==44|Participant ==48|Participant ==58|Participant ==64|Participant ==52) %>%
select(Participant, ConditionInsomnia, CannabisMostHelpfulFor)##BPI
BPI <- HOCUS_AUS %>%
select(BPI_Q1:BPI_Q9g)
BPI_pain <- BPI %>%
select(BPI_Q3:BPI_Q6) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(1:4,
.fns = ~recode(.,'1' = 0,'2' = 1,
'3' = 2,'4' = 3,
'5' = 4,'6' = 5,
'7' = 6, '8' = 7,
'9' = 8, '10' = 9,
'11' = 10))) %>%
rowwise() %>%
mutate(row_mean = mean(c_across(1:4), na.rm=TRUE)) %>%
mutate(pain_severity = case_when(
row_mean>=1 & row_mean<=4 ~ 'Mild Pain',
row_mean>=5 & row_mean<=6 ~ 'Moderate Pain',
row_mean>=7 & row_mean<=10 ~ 'Severe Pain')) %>%
count(pain_severity)
BPI_pain <- BPI %>%
select(BPI_Q3:BPI_Q6) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(1:4,
.fns = ~recode(.,'1' = 0,'2' = 1,
'3' = 2,'4' = 3,
'5' = 4,'6' = 5,
'7' = 6, '8' = 7,
'9' = 8, '10' = 9,
'11' = 10))) %>%
rowwise() %>%
mutate(row_mean = mean(c_across(1:4), na.rm=TRUE))
mean(BPI_pain$row_mean, na.rm=TRUE) #mean = 3.28## [1] 3.285714sd(BPI_pain$row_mean, na.rm=TRUE) #sd=2.33## [1] 2.329041BPI_interference <- BPI %>%
select(BPI_Q9a:BPI_Q9g) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(1:7,
.fns = ~recode(.,'1' = 0,'2' = 1,
'3' = 2,'4' = 3,
'5' = 4,'6' = 5,
'7' = 6, '8' = 7,
'9' = 8, '10' = 9,
'11' = 10))) %>%
rowwise() %>%
mutate(row_mean = mean(c_across(1:7), na.rm=TRUE)) %>%
mutate(pain_interference = case_when(
row_mean>=1 & row_mean<=4 ~ 'Mild Interference',
row_mean>=5 & row_mean<=6 ~ 'Moderate Interference',
row_mean>=7 & row_mean<=10 ~ 'Severe Interference')) %>%
count(pain_interference)
BPI_interference <- BPI %>%
select(BPI_Q9a:BPI_Q9g) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(1:7,
.fns = ~recode(.,'1' = 0,'2' = 1,
'3' = 2,'4' = 3,
'5' = 4,'6' = 5,
'7' = 6, '8' = 7,
'9' = 8, '10' = 9,
'11' = 10))) %>%
rowwise() %>%
mutate(row_mean = mean(c_across(1:7), na.rm=TRUE))
mean(BPI_interference$row_mean, na.rm=TRUE) ## [1] 2.935034sd(BPI_interference$row_mean, na.rm=TRUE)## [1] 2.74598#Correlations - Usage Patterns
usage_pattern <- HOCUS_AUS %>%
select(CannabisUseYearsTotal, CannabisAgeFirstUse, CannabisFirstRegularUse, CannabisFirstDailyUse, CannabisGramsPerDay, CannabisGramsPerWeek) %>%
mutate(across(everything(), as.numeric))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `across(everything(), as.numeric)`.
## Caused by warning:
## ! NAs introduced by coercioncor.test(usage_pattern$CannabisAgeFirstUse,usage_pattern$CannabisFirstRegularUse, method = "pearson")##
## Pearson's product-moment correlation
##
## data: usage_pattern$CannabisAgeFirstUse and usage_pattern$CannabisFirstRegularUse
## t = 4.9146, df = 69, p-value = 5.767e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3130944 0.6636640
## sample estimates:
## cor
## 0.5092028cor.test(usage_pattern$CannabisAgeFirstUse,usage_pattern$CannabisGramsPerDay, method = "pearson")##
## Pearson's product-moment correlation
##
## data: usage_pattern$CannabisAgeFirstUse and usage_pattern$CannabisGramsPerDay
## t = -1.4184, df = 63, p-value = 0.161
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.40254559 0.07102845
## sample estimates:
## cor
## -0.1759182cor.test(usage_pattern$CannabisAgeFirstUse,usage_pattern$CannabisGramsPerWeek, method = "pearson")##
## Pearson's product-moment correlation
##
## data: usage_pattern$CannabisAgeFirstUse and usage_pattern$CannabisGramsPerWeek
## t = -1.9837, df = 62, p-value = 0.05172
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.462347070 0.001602962
## sample estimates:
## cor
## -0.2443026cor.test(usage_pattern$CannabisFirstRegularUse,usage_pattern$CannabisGramsPerDay, method = "pearson")##
## Pearson's product-moment correlation
##
## data: usage_pattern$CannabisFirstRegularUse and usage_pattern$CannabisGramsPerDay
## t = -1.7193, df = 59, p-value = 0.0908
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.44573312 0.03533428
## sample estimates:
## cor
## -0.21843cor.test(usage_pattern$CannabisFirstRegularUse,usage_pattern$CannabisGramsPerWeek, method = "pearson")##
## Pearson's product-moment correlation
##
## data: usage_pattern$CannabisFirstRegularUse and usage_pattern$CannabisGramsPerWeek
## t = -1.7938, df = 58, p-value = 0.07806
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.45660765 0.02618423
## sample estimates:
## cor
## -0.2292648cor.test(usage_pattern$CannabisGramsPerDay,usage_pattern$CannabisGramsPerWeek, method = "pearson")##
## Pearson's product-moment correlation
##
## data: usage_pattern$CannabisGramsPerDay and usage_pattern$CannabisGramsPerWeek
## t = 4.3841, df = 62, p-value = 4.59e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2733367 0.6540602
## sample estimates:
## cor
## 0.4864613ggplot(usage_pattern, aes(CannabisFirstRegularUse, CannabisGramsPerWeek)) +
geom_point() +
stat_smooth(method = lm)## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 20 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 20 rows containing missing values or values outside the scale range
## (`geom_point()`).
#Correlations - Scales
scales <- HOCUS_AUS %>%
select(DASS21_1:DASS21_21,PDQ1:PDQ20,AQoL6D1:AQoL6D20,ISI_1:ISI_4,ISI_5:ISI_7,BPI_Q3:BPI_Q6,BPI_Q3:BPI_Q6) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(1:21,
.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3))) %>%
rowwise() %>%
mutate(DASS = sum(c_across(1:21), na.rm = TRUE)) %>%
mutate(across(22:41,
.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3,'5' = 4))) %>%
rowwise() %>%
mutate(PDQ = sum(c_across(22:41), na.rm = TRUE)) %>%
rowwise() %>%
mutate(AQOL = sum(c_across(42:61), na.rm = TRUE)) %>%
rowwise() %>%
mutate(across(62:68,
.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3,'5' = 4))) %>%
rowwise() %>%
mutate(ISI = sum(c_across(62:68), na.rm = TRUE)) %>%
mutate(across(69:72,
.fns = ~recode(.,'1' = 0,'2' = 1,
'3' = 2,'4' = 3,
'5' = 4,'6' = 5,
'7' = 6, '8' = 7,
'9' = 8, '10' = 9,
'11' = 10))) %>%
rowwise() %>%
mutate(BPI = mean(c_across(69:72), na.rm=TRUE)) %>%
dplyr::filter(!if_all(c(DASS21_1:DASS21_21), is.na)) %>%
dplyr::filter(!if_all(c(PDQ1:PDQ20), is.na)) %>%
dplyr::filter(!if_all(c(AQoL6D1:AQoL6D20), is.na)) %>%
dplyr::filter(!if_all(c(ISI_1:ISI_4,ISI_5:ISI_7), is.na))
scales <- scales[,-c(1:72)]
scales## # A tibble: 72 × 5
## # Rowwise:
## DASS PDQ AQOL ISI BPI
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 20 28 54 18 4
## 2 8 12 30 1 3
## 3 14 24 41 8 5.25
## 4 5 15 26 1 0
## 5 18 42 43 7 2.25
## 6 14 23 37 5 2.5
## 7 3 14 39 2 5.25
## 8 9 18 49 6 4
## 9 25 46 46 13 0
## 10 3 5 25 8 0
## # ℹ 62 more rowsmatrix <- rcorr(as.matrix(scales))
matrix## DASS PDQ AQOL ISI BPI
## DASS 1.00 0.65 0.56 0.44 0.07
## PDQ 0.65 1.00 0.37 0.19 -0.03
## AQOL 0.56 0.37 1.00 0.48 0.49
## ISI 0.44 0.19 0.48 1.00 0.38
## BPI 0.07 -0.03 0.49 0.38 1.00
##
## n
## DASS PDQ AQOL ISI BPI
## DASS 72 72 72 72 70
## PDQ 72 72 72 72 70
## AQOL 72 72 72 72 70
## ISI 72 72 72 72 70
## BPI 70 70 70 70 70
##
## P
## DASS PDQ AQOL ISI BPI
## DASS 0.0000 0.0000 0.0000 0.5374
## PDQ 0.0000 0.0013 0.1060 0.7751
## AQOL 0.0000 0.0013 0.0000 0.0000
## ISI 0.0000 0.1060 0.0000 0.0013
## BPI 0.5374 0.7751 0.0000 0.0013ggplot(scales, aes(DASS, PDQ)) +
geom_point() +
stat_smooth(method = lm)## `geom_smooth()` using formula = 'y ~ x'
cor.test(scales$DASS,scales$PDQ, method = "pearson")##
## Pearson's product-moment correlation
##
## data: scales$DASS and scales$PDQ
## t = 7.216, df = 70, p-value = 5.036e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4965845 0.7685052
## sample estimates:
## cor
## 0.6531166cor.test(scales$DASS,scales$AQOL, method = "pearson")##
## Pearson's product-moment correlation
##
## data: scales$DASS and scales$AQOL
## t = 5.6755, df = 70, p-value = 2.899e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3789991 0.7017774
## sample estimates:
## cor
## 0.5613775cor.test(scales$DASS,scales$ISI, method = "pearson")##
## Pearson's product-moment correlation
##
## data: scales$DASS and scales$ISI
## t = 4.1557, df = 70, p-value = 9.037e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2376763 0.6133123
## sample estimates:
## cor
## 0.4448504cor.test(scales$DASS,scales$BPI, method = "pearson")##
## Pearson's product-moment correlation
##
## data: scales$DASS and scales$BPI
## t = 0.61993, df = 68, p-value = 0.5374
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1628777 0.3045744
## sample estimates:
## cor
## 0.07496542cor.test(scales$PDQ,scales$AQOL, method = "pearson")##
## Pearson's product-moment correlation
##
## data: scales$PDQ and scales$AQOL
## t = 3.3484, df = 70, p-value = 0.001312
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1530614 0.5554146
## sample estimates:
## cor
## 0.3715548cor.test(scales$AQOL,scales$ISI, method = "pearson")##
## Pearson's product-moment correlation
##
## data: scales$AQOL and scales$ISI
## t = 4.5284, df = 70, p-value = 2.378e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2746096 0.6373787
## sample estimates:
## cor
## 0.475996cor.test(scales$AQOL,scales$BPI, method = "pearson")##
## Pearson's product-moment correlation
##
## data: scales$AQOL and scales$BPI
## t = 4.6043, df = 68, p-value = 1.867e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2852035 0.6482258
## sample estimates:
## cor
## 0.487507cor.test(scales$ISI,scales$BPI, method = "pearson")##
## Pearson's product-moment correlation
##
## data: scales$ISI and scales$BPI
## t = 3.3536, df = 68, p-value = 0.001307
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1555096 0.5619490
## sample estimates:
## cor
## 0.3767181#Correlations - Usage Patterns & Scales
frequency_and_distress <- HOCUS_AUS %>%
select(DFAQCUCurrentFreq, CannabisUseYearsTotal, CannabisAgeFirstUse, CannabisFirstRegularUse, CannabisFirstDailyUse, CannabisFreqBefore16, DASS21_1:DASS21_21) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(7:27,
.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3))) %>%
rowwise() %>%
mutate(total = sum(c_across(7:27), na.rm = TRUE)) %>%
dplyr::filter(!if_all(c(DASS21_1:DASS21_21), is.na))
cor.test(frequency_and_distress$CannabisAgeFirstUse,frequency_and_distress$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_and_distress$CannabisAgeFirstUse and frequency_and_distress$total
## t = -0.18781, df = 69, p-value = 0.8516
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.254565 0.211817
## sample estimates:
## cor
## -0.02260371cor.test(frequency_and_distress$CannabisFirstRegularUse,frequency_and_distress$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_and_distress$CannabisFirstRegularUse and frequency_and_distress$total
## t = -1.6095, df = 61, p-value = 0.1127
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.42818290 0.04835327
## sample estimates:
## cor
## -0.2018297frequency_and_PDQ <- HOCUS_AUS %>%
select(DFAQCUCurrentFreq, CannabisUseYearsTotal, CannabisAgeFirstUse, CannabisFirstRegularUse, CannabisFirstDailyUse, CannabisFreqBefore16, PDQ1:PDQ20) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(7:26,
.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3,'5' = 4))) %>%
dplyr::filter(!if_all(c(PDQ1:PDQ20), is.na)) %>%
rowwise() %>%
mutate(total = sum(c_across(7:26), na.rm = TRUE)) %>%
dplyr::filter(!if_all(c(PDQ1:PDQ20), is.na))
cor.test(frequency_and_PDQ$DFAQCUCurrentFreq,frequency_and_PDQ$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_and_PDQ$DFAQCUCurrentFreq and frequency_and_PDQ$total
## t = -0.27374, df = 71, p-value = 0.7851
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2605899 0.1990852
## sample estimates:
## cor
## -0.03246928cor.test(frequency_and_PDQ$CannabisUseYearsTotal,frequency_and_PDQ$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_and_PDQ$CannabisUseYearsTotal and frequency_and_PDQ$total
## t = 0.36694, df = 70, p-value = 0.7148
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1897789 0.2727163
## sample estimates:
## cor
## 0.04381602cor.test(frequency_and_PDQ$CannabisAgeFirstUse,frequency_and_PDQ$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_and_PDQ$CannabisAgeFirstUse and frequency_and_PDQ$total
## t = -0.96467, df = 70, p-value = 0.338
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3372601 0.1203204
## sample estimates:
## cor
## -0.1145409cor.test(frequency_and_PDQ$CannabisFirstRegularUse,frequency_and_PDQ$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_and_PDQ$CannabisFirstRegularUse and frequency_and_PDQ$total
## t = -0.83577, df = 62, p-value = 0.4065
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3424741 0.1439949
## sample estimates:
## cor
## -0.1055504cor.test(frequency_and_PDQ$CannabisFirstDailyUse,frequency_and_PDQ$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_and_PDQ$CannabisFirstDailyUse and frequency_and_PDQ$total
## t = 0.44144, df = 56, p-value = 0.6606
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2024885 0.3124308
## sample estimates:
## cor
## 0.05888717cor.test(frequency_and_PDQ$CannabisFreqBefore16,frequency_and_PDQ$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_and_PDQ$CannabisFreqBefore16 and frequency_and_PDQ$total
## t = -0.39379, df = 70, p-value = 0.6949
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2756811 0.1866867
## sample estimates:
## cor
## -0.04701526frequency_and_AQoL <- HOCUS_AUS %>%
select(DFAQCUCurrentFreq, CannabisUseYearsTotal, CannabisAgeFirstUse, CannabisFirstRegularUse, CannabisFirstDailyUse, CannabisFreqBefore16, AQoL6D1:AQoL6D20) %>%
mutate(across(everything(), as.numeric)) %>%
dplyr::filter(!if_all(c(AQoL6D1:AQoL6D20), is.na)) %>%
rowwise() %>%
mutate(total = sum(c_across(7:26), na.rm = TRUE))
cor.test(frequency_and_AQoL$DFAQCUCurrentFreq,frequency_and_AQoL$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_and_AQoL$DFAQCUCurrentFreq and frequency_and_AQoL$total
## t = -0.2128, df = 70, p-value = 0.8321
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2555893 0.2074644
## sample estimates:
## cor
## -0.02542625cor.test(frequency_and_AQoL$CannabisUseYearsTotal,frequency_and_AQoL$total, use= "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_and_AQoL$CannabisUseYearsTotal and frequency_and_AQoL$total
## t = -0.9461, df = 69, p-value = 0.3474
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3375572 0.1233960
## sample estimates:
## cor
## -0.1131656cor.test(frequency_and_AQoL$CannabisAgeFirstUse,frequency_and_AQoL$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_and_AQoL$CannabisAgeFirstUse and frequency_and_AQoL$total
## t = 0.45887, df = 69, p-value = 0.6478
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1804689 0.2847961
## sample estimates:
## cor
## 0.05515722cor.test(frequency_and_AQoL$CannabisFirstRegularUse,frequency_and_AQoL$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_and_AQoL$CannabisFirstRegularUse and frequency_and_AQoL$total
## t = 0.60953, df = 61, p-value = 0.5444
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1733000 0.3194134
## sample estimates:
## cor
## 0.07780587cor.test(frequency_and_AQoL$CannabisFirstDailyUse,frequency_and_AQoL$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_and_AQoL$CannabisFirstDailyUse and frequency_and_AQoL$total
## t = 1.3555, df = 55, p-value = 0.1808
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08474626 0.42065256
## sample estimates:
## cor
## 0.1797916cor.test(frequency_and_AQoL$CannabisFreqBefore16,frequency_and_AQoL$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_and_AQoL$CannabisFreqBefore16 and frequency_and_AQoL$total
## t = 0.84418, df = 69, p-value = 0.4015
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1353913 0.3267034
## sample estimates:
## cor
## 0.1011061frequency_ISI <- HOCUS_AUS %>%
select(DFAQCUCurrentFreq, CannabisUseYearsTotal, CannabisAgeFirstUse, CannabisRegularUse, CannabisFirstRegularUse, CannabisDailyUse, CannabisFirstDailyUse, CannabisFreqBefore16, ISI_1:ISI_4,ISI_5:ISI_7) %>%
mutate(across(everything(), as.numeric)) %>%
dplyr::filter(!if_all(c(ISI_1:ISI_4,ISI_5:ISI_7), is.na)) %>%
rowwise() %>%
mutate(across(9:15,
.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3,'5' = 4))) %>%
rowwise() %>%
mutate(total = sum(c_across(9:15), na.rm = TRUE))
cor.test(frequency_ISI$DFAQCUCurrentFreq,frequency_ISI$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_ISI$DFAQCUCurrentFreq and frequency_ISI$total
## t = -1.3283, df = 70, p-value = 0.1884
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.37485333 0.07768899
## sample estimates:
## cor
## -0.1568017cor.test(frequency_ISI$CannabisUseYearsTotal,frequency_ISI$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_ISI$CannabisUseYearsTotal and frequency_ISI$total
## t = 1.2867, df = 69, p-value = 0.2025
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08319471 0.37306043
## sample estimates:
## cor
## 0.1530803cor.test(frequency_ISI$CannabisAgeFirstUse,frequency_ISI$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_ISI$CannabisAgeFirstUse and frequency_ISI$total
## t = 1.4893, df = 69, p-value = 0.141
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.05926354 0.39357899
## sample estimates:
## cor
## 0.1764803cor.test(frequency_ISI$CannabisFirstRegularUse,frequency_ISI$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_ISI$CannabisFirstRegularUse and frequency_ISI$total
## t = 0.55017, df = 61, p-value = 0.5842
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1806424 0.3125904
## sample estimates:
## cor
## 0.07026756cor.test(frequency_ISI$CannabisFirstDailyUse,frequency_ISI$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_ISI$CannabisFirstDailyUse and frequency_ISI$total
## t = 0.28736, df = 55, p-value = 0.7749
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2241099 0.2962972
## sample estimates:
## cor
## 0.03871881cor.test(frequency_ISI$CannabisFreqBefore16,frequency_ISI$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_ISI$CannabisFreqBefore16 and frequency_ISI$total
## t = 0.53234, df = 69, p-value = 0.5962
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1719136 0.2928888
## sample estimates:
## cor
## 0.06395522frequency_pain <- HOCUS_AUS %>%
select(DFAQCUCurrentFreq, CannabisUseYearsTotal, CannabisAgeFirstUse, CannabisRegularUse, CannabisFirstRegularUse, CannabisDailyUse, CannabisFirstDailyUse, CannabisFreqBefore16, BPI_Q3:BPI_Q6) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(9:12,
.fns = ~recode(.,'1' = 0,'2' = 1,
'3' = 2,'4' = 3,
'5' = 4,'6' = 5,
'7' = 6, '8' = 7,
'9' = 8, '10' = 9,
'11' = 10))) %>%
rowwise() %>%
mutate(total = mean(c_across(9:12), na.rm=TRUE)) %>%
dplyr::filter(!if_all(c(BPI_Q3:BPI_Q6), is.na))
cor.test(frequency_pain$DFAQCUCurrentFreq,frequency_pain$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_pain$DFAQCUCurrentFreq and frequency_pain$total
## t = 0.3146, df = 68, p-value = 0.754
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1986306 0.2706721
## sample estimates:
## cor
## 0.03812276cor.test(frequency_pain$CannabisUseYearsTotal,frequency_pain$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_pain$CannabisUseYearsTotal and frequency_pain$total
## t = -0.7918, df = 67, p-value = 0.4313
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3255465 0.1436702
## sample estimates:
## cor
## -0.09628454cor.test(frequency_pain$CannabisAgeFirstUse,frequency_pain$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_pain$CannabisAgeFirstUse and frequency_pain$total
## t = -0.15984, df = 67, p-value = 0.8735
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2550262 0.2181649
## sample estimates:
## cor
## -0.01952392cor.test(frequency_pain$CannabisFirstRegularUse,frequency_pain$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_pain$CannabisFirstRegularUse and frequency_pain$total
## t = 0.62861, df = 59, p-value = 0.532
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1738258 0.3266762
## sample estimates:
## cor
## 0.08156532cor.test(frequency_pain$CannabisFirstDailyUse,frequency_pain$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_pain$CannabisFirstDailyUse and frequency_pain$total
## t = 0.79086, df = 53, p-value = 0.4326
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1619397 0.3628970
## sample estimates:
## cor
## 0.1079973cor.test(frequency_pain$CannabisFreqBefore16,frequency_pain$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_pain$CannabisFreqBefore16 and frequency_pain$total
## t = -0.4504, df = 67, p-value = 0.6539
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2878787 0.1841336
## sample estimates:
## cor
## -0.05494146frequency_interference <- HOCUS_AUS %>%
select(DFAQCUCurrentFreq, CannabisUseYearsTotal, CannabisAgeFirstUse, CannabisRegularUse, CannabisFirstRegularUse, CannabisDailyUse, CannabisFirstDailyUse, CannabisFreqBefore16, BPI_Q9a:BPI_Q9g) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(9:15,
.fns = ~recode(.,'1' = 0,'2' = 1,
'3' = 2,'4' = 3,
'5' = 4,'6' = 5,
'7' = 6, '8' = 7,
'9' = 8, '10' = 9,
'11' = 10))) %>%
rowwise() %>%
mutate(total = mean(c_across(9:15), na.rm=TRUE)) %>%
dplyr::filter(!if_all(c(BPI_Q9a:BPI_Q9g), is.na))
cor.test(frequency_interference$DFAQCUCurrentFreq,frequency_interference$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_interference$DFAQCUCurrentFreq and frequency_interference$total
## t = -0.1059, df = 68, p-value = 0.916
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2470696 0.2228053
## sample estimates:
## cor
## -0.01284103cor.test(frequency_interference$CannabisUseYearsTotal,frequency_interference$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_interference$CannabisUseYearsTotal and frequency_interference$total
## t = -1.2091, df = 67, p-value = 0.2309
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.37001621 0.09379141
## sample estimates:
## cor
## -0.1461332cor.test(frequency_interference$CannabisAgeFirstUse,frequency_interference$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_interference$CannabisAgeFirstUse and frequency_interference$total
## t = -0.0064389, df = 67, p-value = 0.9949
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2374231 0.2359380
## sample estimates:
## cor
## -0.0007866409cor.test(frequency_interference$CannabisFirstRegularUse,frequency_interference$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_interference$CannabisFirstRegularUse and frequency_interference$total
## t = 1.2565, df = 59, p-value = 0.2139
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09421267 0.39711478
## sample estimates:
## cor
## 0.1614381cor.test(frequency_interference$CannabisFirstDailyUse,frequency_interference$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_interference$CannabisFirstDailyUse and frequency_interference$total
## t = 1.1241, df = 53, p-value = 0.266
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1174523 0.4016370
## sample estimates:
## cor
## 0.1525998cor.test(frequency_interference$CannabisFreqBefore16,frequency_interference$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: frequency_interference$CannabisFreqBefore16 and frequency_interference$total
## t = 0.25471, df = 67, p-value = 0.7997
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2071027 0.2658264
## sample estimates:
## cor
## 0.03110261total_BPI <- HOCUS_AUS %>%
select(DFAQCUCurrentFreq, CannabisUseYearsTotal, CannabisAgeFirstUse, CannabisRegularUse, CannabisFirstRegularUse, CannabisDailyUse, CannabisFirstDailyUse, CannabisFreqBefore16, BPI_Q3:BPI_Q6,BPI_Q9a:BPI_Q9g) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(9:19,
.fns = ~recode(.,'1' = 0,'2' = 1,
'3' = 2,'4' = 3,
'5' = 4,'6' = 5,
'7' = 6, '8' = 7,
'9' = 8, '10' = 9,
'11' = 10))) %>%
rowwise() %>%
mutate(total = mean(c_across(9:19), na.rm=TRUE)) %>%
dplyr::filter(!if_all(c(BPI_Q3:BPI_Q6,BPI_Q9a:BPI_Q9g), is.na))
cor.test(total_BPI$DFAQCUCurrentFreq,total_BPI$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: total_BPI$DFAQCUCurrentFreq and total_BPI$total
## t = 0.037915, df = 68, p-value = 0.9699
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2306254 0.2393133
## sample estimates:
## cor
## 0.004597812cor.test(total_BPI$CannabisUseYearsTotal,total_BPI$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: total_BPI$CannabisUseYearsTotal and total_BPI$total
## t = -1.1205, df = 67, p-value = 0.2665
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3607306 0.1044024
## sample estimates:
## cor
## -0.1356297cor.test(total_BPI$CannabisAgeFirstUse,total_BPI$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: total_BPI$CannabisAgeFirstUse and total_BPI$total
## t = -0.056006, df = 67, p-value = 0.9555
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2431290 0.2302115
## sample estimates:
## cor
## -0.006842017cor.test(total_BPI$CannabisFirstRegularUse,total_BPI$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: total_BPI$CannabisFirstRegularUse and total_BPI$total
## t = 1.0952, df = 59, p-value = 0.2779
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1147447 0.3794864
## sample estimates:
## cor
## 0.1411547cor.test(total_BPI$CannabisFirstDailyUse,total_BPI$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: total_BPI$CannabisFirstDailyUse and total_BPI$total
## t = 1.0572, df = 53, p-value = 0.2952
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1264029 0.3939892
## sample estimates:
## cor
## 0.1437136cor.test(total_BPI$CannabisFreqBefore16,total_BPI$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: total_BPI$CannabisFreqBefore16 and total_BPI$total
## t = 0.033263, df = 67, p-value = 0.9736
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.232841 0.240513
## sample estimates:
## cor
## 0.004063671amount_DASS <- HOCUS_AUS %>%
select(CannabisGramsPerDay, CannabisGramsPerWeek,DASS21_1:DASS21_21) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(3:23,
.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3))) %>%
rowwise() %>%
mutate(total = sum(c_across(3:23), na.rm = TRUE)) %>%
dplyr::filter(!if_all(c(DASS21_1:DASS21_21), is.na)) %>%
dplyr::filter(!if_all(c(CannabisGramsPerDay:CannabisGramsPerWeek), is.na))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `across(everything(), as.numeric)`.
## Caused by warning:
## ! NAs introduced by coercioncor.test(amount_DASS$CannabisGramsPerDay,amount_DASS$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: amount_DASS$CannabisGramsPerDay and amount_DASS$total
## t = 0.40622, df = 57, p-value = 0.6861
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2051784 0.3056045
## sample estimates:
## cor
## 0.05372687cor.test(amount_DASS$CannabisGramsPerWeek,amount_DASS$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: amount_DASS$CannabisGramsPerWeek and amount_DASS$total
## t = -0.28154, df = 56, p-value = 0.7793
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2930461 0.2228640
## sample estimates:
## cor
## -0.03759603amount_PDQ <- HOCUS_AUS %>%
select(Age, LevelOfEducation, CannabisGramsPerDay, CannabisGramsPerWeek, GenderMale, GenderFemale, PDQ1:PDQ20, DASS21_1:DASS21_21) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(Gender = case_when(GenderMale == 1 ~ 1,
GenderFemale == 1 ~ 2)) %>%
mutate(across(5:24,
.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3,'5' = 4))) %>%
dplyr::filter(!if_all(c(PDQ1:PDQ20), is.na)) %>%
rowwise() %>%
mutate(PDQ = sum(c_across(5:24), na.rm = TRUE)) %>%
dplyr::filter(!if_all(c(PDQ1:PDQ20), is.na)) %>%
mutate(across(25:45,
.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3))) %>%
rowwise() %>%
mutate(DASS = sum(c_across(25:45), na.rm = TRUE)) %>%
dplyr::filter(!if_all(c(DASS21_1:DASS21_21), is.na)) %>%
dplyr::filter(!if_all(c(CannabisGramsPerDay:CannabisGramsPerWeek), is.na))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `across(everything(), as.numeric)`.
## Caused by warning:
## ! NAs introduced by coercionamount_PDQ <- amount_PDQ[,-c(5:6)]
subdomains <- amount_PDQ %>%
rowwise() %>%
mutate(attention = sum(PDQ1, PDQ5, PDQ9, PDQ13, PDQ17)) %>%
mutate(r_memory = sum(PDQ2, PDQ6, PDQ10, PDQ14, PDQ18)) %>%
mutate(p_memory = sum(PDQ3, PDQ7, PDQ11, PDQ15, PDQ19)) %>%
mutate(planning = sum(PDQ4, PDQ8, PDQ12, PDQ16, PDQ20))
cor.test(subdomains$CannabisGramsPerDay,subdomains$attention, method = "pearson") ##
## Pearson's product-moment correlation
##
## data: subdomains$CannabisGramsPerDay and subdomains$attention
## t = 3.374, df = 56, p-value = 0.00135
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1708718 0.6050830
## sample estimates:
## cor
## 0.4110267cor.test(subdomains$CannabisGramsPerDay,subdomains$r_memory, method = "pearson") ##
## Pearson's product-moment correlation
##
## data: subdomains$CannabisGramsPerDay and subdomains$r_memory
## t = 0.9701, df = 56, p-value = 0.3362
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1341921 0.3744223
## sample estimates:
## cor
## 0.1285597cor.test(subdomains$CannabisGramsPerDay,subdomains$p_memory, method = "pearson") ##
## Pearson's product-moment correlation
##
## data: subdomains$CannabisGramsPerDay and subdomains$p_memory
## t = 2.1399, df = 56, p-value = 0.03674
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.01790831 0.49787229
## sample estimates:
## cor
## 0.2749324cor.test(subdomains$CannabisGramsPerDay,subdomains$planning, method = "pearson") ##
## Pearson's product-moment correlation
##
## data: subdomains$CannabisGramsPerDay and subdomains$planning
## t = 0.41501, df = 55, p-value = 0.6798
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2077196 0.3118991
## sample estimates:
## cor
## 0.05587218cor.test(amount_PDQ$Age,amount_PDQ$PDQ, method = "pearson") ##
## Pearson's product-moment correlation
##
## data: amount_PDQ$Age and amount_PDQ$PDQ
## t = -0.23373, df = 57, p-value = 0.816
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2847693 0.2269370
## sample estimates:
## cor
## -0.03094351cor.test(amount_PDQ$LevelOfEducation,amount_PDQ$PDQ, method = "pearson") ##
## Pearson's product-moment correlation
##
## data: amount_PDQ$LevelOfEducation and amount_PDQ$PDQ
## t = -1.6192, df = 57, p-value = 0.1109
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.44203973 0.04902003
## sample estimates:
## cor
## -0.2096946with(amount_PDQ, shapiro.test(PDQ[Gender == "1"])) #p=.3##
## Shapiro-Wilk normality test
##
## data: PDQ[Gender == "1"]
## W = 0.96554, p-value = 0.3328with(amount_PDQ, shapiro.test(PDQ[Gender == "2"])) #p=.1##
## Shapiro-Wilk normality test
##
## data: PDQ[Gender == "2"]
## W = 0.93236, p-value = 0.1101res.ftest <- var.test(PDQ ~ Gender, data = amount_PDQ)
res.ftest #p=.17##
## F test to compare two variances
##
## data: PDQ by Gender
## F = 1.7484, num df = 34, denom df = 23, p-value = 0.164
## alternative hypothesis: true ratio of variances is not equal to 1
## 95 percent confidence interval:
## 0.7912144 3.6506008
## sample estimates:
## ratio of variances
## 1.74844t_test <- t.test(PDQ ~ Gender, data = amount_PDQ)
t_test##
## Welch Two Sample t-test
##
## data: PDQ by Gender
## t = 1.308, df = 56.385, p-value = 0.1962
## alternative hypothesis: true difference in means between group 1 and group 2 is not equal to 0
## 95 percent confidence interval:
## -1.727373 8.229754
## sample estimates:
## mean in group 1 mean in group 2
## 18.54286 15.29167amount_AQoL <- HOCUS_AUS %>%
select(CannabisGramsPerDay, CannabisGramsPerWeek, AQoL6D1:AQoL6D20) %>%
mutate(across(everything(), as.numeric)) %>%
dplyr::filter(!if_all(c(AQoL6D1:AQoL6D20), is.na)) %>%
rowwise() %>%
mutate(total = sum(c_across(3:22), na.rm = TRUE))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `across(everything(), as.numeric)`.
## Caused by warning:
## ! NAs introduced by coercioncor.test(amount_AQoL$CannabisGramsPerDay,amount_AQoL$total, method = "pearson") ##
## Pearson's product-moment correlation
##
## data: amount_AQoL$CannabisGramsPerDay and amount_AQoL$total
## t = 0.53387, df = 57, p-value = 0.5955
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1889586 0.3208240
## sample estimates:
## cor
## 0.07053686cor.test(amount_AQoL$CannabisGramsPerWeek,amount_AQoL$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: amount_AQoL$CannabisGramsPerWeek and amount_AQoL$total
## t = -0.24813, df = 56, p-value = 0.8049
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2889615 0.2271005
## sample estimates:
## cor
## -0.03313919amount_ISI <- HOCUS_AUS %>%
select(CannabisGramsPerDay, CannabisGramsPerWeek, ISI_1:ISI_4,ISI_5:ISI_7) %>%
mutate(across(everything(), as.numeric)) %>%
rowwise() %>%
mutate(across(3:9,
.fns = ~recode(.,'1' = 0,'2' = 1,'3' = 2,'4' = 3,'5' = 4))) %>%
rowwise() %>%
mutate(total = sum(c_across(3:9), na.rm = TRUE)) %>%
dplyr::filter(!if_all(c(ISI_1:ISI_4,ISI_5:ISI_7), is.na)) ## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `across(everything(), as.numeric)`.
## Caused by warning:
## ! NAs introduced by coercioncor.test(amount_ISI$CannabisGramsPerDay,amount_ISI$total, method = "pearson") ##
## Pearson's product-moment correlation
##
## data: amount_ISI$CannabisGramsPerDay and amount_ISI$total
## t = -0.50062, df = 57, p-value = 0.6186
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3168768 0.1931921
## sample estimates:
## cor
## -0.0661635cor.test(amount_ISI$CannabisGramsPerWeek,amount_ISI$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: amount_ISI$CannabisGramsPerWeek and amount_ISI$total
## t = -0.73768, df = 56, p-value = 0.4638
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3475894 0.1643593
## sample estimates:
## cor
## -0.09810119amount_pain <- HOCUS_AUS %>%
select(CannabisGramsPerDay, CannabisGramsPerWeek, BPI_Q3:BPI_Q6) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(3:6,
.fns = ~recode(.,'1' = 0,'2' = 1,
'3' = 2,'4' = 3,
'5' = 4,'6' = 5,
'7' = 6, '8' = 7,
'9' = 8, '10' = 9,
'11' = 10))) %>%
rowwise() %>%
mutate(total = mean(c_across(3:6), na.rm=TRUE)) %>%
dplyr::filter(!if_all(c(BPI_Q3:BPI_Q6), is.na))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `across(everything(), as.numeric)`.
## Caused by warning:
## ! NAs introduced by coercioncor.test(amount_pain$CannabisGramsPerDay,amount_pain$total, method = "pearson") ##
## Pearson's product-moment correlation
##
## data: amount_pain$CannabisGramsPerDay and amount_pain$total
## t = -0.091659, df = 56, p-value = 0.9273
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2696901 0.2468291
## sample estimates:
## cor
## -0.01224753cor.test(amount_pain$CannabisGramsPerWeek,amount_pain$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: amount_pain$CannabisGramsPerWeek and amount_pain$total
## t = 0.54307, df = 55, p-value = 0.5893
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1911737 0.3273699
## sample estimates:
## cor
## 0.07303199amount_interference <- HOCUS_AUS %>%
select(CannabisGramsPerDay, CannabisGramsPerWeek, BPI_Q9a:BPI_Q9g) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(3:9,
.fns = ~recode(.,'1' = 0,'2' = 1,
'3' = 2,'4' = 3,
'5' = 4,'6' = 5,
'7' = 6, '8' = 7,
'9' = 8, '10' = 9,
'11' = 10))) %>%
rowwise() %>%
mutate(total = mean(c_across(3:9), na.rm=TRUE)) %>%
dplyr::filter(!if_all(c(BPI_Q9a:BPI_Q9g), is.na))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `across(everything(), as.numeric)`.
## Caused by warning:
## ! NAs introduced by coercioncor.test(amount_interference$CannabisGramsPerDay,amount_interference$total, method = "pearson") ##
## Pearson's product-moment correlation
##
## data: amount_interference$CannabisGramsPerDay and amount_interference$total
## t = -0.36599, df = 56, p-value = 0.7157
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3033181 0.2121227
## sample estimates:
## cor
## -0.04884953cor.test(amount_interference$CannabisGramsPerWeek,amount_interference$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: amount_interference$CannabisGramsPerWeek and amount_interference$total
## t = 0.44919, df = 55, p-value = 0.6551
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2033120 0.3160474
## sample estimates:
## cor
## 0.06045858amount_BPI <- HOCUS_AUS %>%
select(CannabisGramsPerDay, CannabisGramsPerWeek, BPI_Q3:BPI_Q6,BPI_Q9a:BPI_Q9g) %>%
mutate(across(everything(), as.numeric)) %>%
mutate(across(3:13,
.fns = ~recode(.,'1' = 0,'2' = 1,
'3' = 2,'4' = 3,
'5' = 4,'6' = 5,
'7' = 6, '8' = 7,
'9' = 8, '10' = 9,
'11' = 10))) %>%
rowwise() %>%
mutate(total = mean(c_across(3:13), na.rm=TRUE)) %>%
dplyr::filter(!if_all(c(BPI_Q3:BPI_Q6,BPI_Q9a:BPI_Q9g), is.na))## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `across(everything(), as.numeric)`.
## Caused by warning:
## ! NAs introduced by coercioncor.test(amount_BPI$CannabisGramsPerDay,amount_BPI$total, method = "pearson") ##
## Pearson's product-moment correlation
##
## data: amount_BPI$CannabisGramsPerDay and amount_BPI$total
## t = -0.28556, df = 56, p-value = 0.7763
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2935366 0.2223538
## sample estimates:
## cor
## -0.03813201cor.test(amount_BPI$CannabisGramsPerWeek,amount_BPI$total, method = "pearson")##
## Pearson's product-moment correlation
##
## data: amount_BPI$CannabisGramsPerWeek and amount_BPI$total
## t = 0.5008, df = 55, p-value = 0.6185
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1966451 0.3222845
## sample estimates:
## cor
## 0.06737482#Regression Analyses
model_PDQ <- amount_PDQ %>%
select(CannabisGramsPerDay, DASS, PDQ)
#Checking assumptions
#Linearity
ggplot(amount_PDQ, aes(CannabisGramsPerDay, PDQ)) +
geom_point() +
stat_smooth(method = lm)## `geom_smooth()` using formula = 'y ~ x'
ggplot(amount_PDQ, aes(DASS, PDQ)) +
geom_point() +
stat_smooth(method = lm)## `geom_smooth()` using formula = 'y ~ x'
#Independence
model <- lm(PDQ ~ CannabisGramsPerDay, data = amount_PDQ)
dw_resut <- dwtest(model)
print(dw_resut) #p-value = .21##
## Durbin-Watson test
##
## data: model
## DW = 1.7917, p-value = 0.2086
## alternative hypothesis: true autocorrelation is greater than 0model <- lm(PDQ ~ DASS, data = amount_PDQ)
dw_resut <- dwtest(model)
print(dw_resut) #p-value = .70##
## Durbin-Watson test
##
## data: model
## DW = 2.1357, p-value = 0.6976
## alternative hypothesis: true autocorrelation is greater than 0#Normality of residuals
plot(lm(PDQ ~ CannabisGramsPerDay + DASS, data = amount_PDQ))
model <- lm(PDQ ~ CannabisGramsPerDay, data = amount_PDQ)
model##
## Call:
## lm(formula = PDQ ~ CannabisGramsPerDay, data = amount_PDQ)
##
## Coefficients:
## (Intercept) CannabisGramsPerDay
## 15.424 1.041summary(model)##
## Call:
## lm(formula = PDQ ~ CannabisGramsPerDay, data = amount_PDQ)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.466 -6.075 -1.466 4.815 28.055
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.425 1.416 10.890 1.5e-15 ***
## CannabisGramsPerDay 1.041 0.404 2.578 0.0126 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.472 on 57 degrees of freedom
## Multiple R-squared: 0.1044, Adjusted R-squared: 0.08869
## F-statistic: 6.645 on 1 and 57 DF, p-value: 0.01255model <- lm(scale(PDQ) ~ scale(CannabisGramsPerDay), data=model_PDQ)
options(scipen=999)
summary(model)##
## Call:
## lm(formula = scale(PDQ) ~ scale(CannabisGramsPerDay), data = model_PDQ)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6595 -0.6123 -0.1477 0.4853 2.8275
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.00000000000000001256 0.12428148613004143253
## scale(CannabisGramsPerDay) 0.32311734140732306653 0.12534829956941972995
## t value Pr(>|t|)
## (Intercept) 0.000 1.0000
## scale(CannabisGramsPerDay) 2.578 0.0126 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9546 on 57 degrees of freedom
## Multiple R-squared: 0.1044, Adjusted R-squared: 0.08869
## F-statistic: 6.645 on 1 and 57 DF, p-value: 0.01255#Empty model
intercept_model <- lm(PDQ ~ 1, data = model_PDQ)
step(intercept_model)## Start: AIC=271.78
## PDQ ~ 1##
## Call:
## lm(formula = PDQ ~ 1, data = model_PDQ)
##
## Coefficients:
## (Intercept)
## 17.22#Initialize model
forward_model <- lm(PDQ ~ ., data = model_PDQ)
# Forward step-wise regression
forward_model <- step(forward_model, direction = "forward", scope = formula(~ .))## Start: AIC=239.96
## PDQ ~ CannabisGramsPerDay + DASSsummary(forward_model)##
## Call:
## lm(formula = PDQ ~ CannabisGramsPerDay + DASS, data = model_PDQ)
##
## Residuals:
## Min 1Q Median 3Q Max
## -18.1601 -3.6017 -0.5123 2.7302 26.6906
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.1756 1.6433 4.975 0.00000655 ***
## CannabisGramsPerDay 0.9344 0.3184 2.934 0.00484 **
## DASS 0.8167 0.1361 6.003 0.00000015 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.454 on 56 degrees of freedom
## Multiple R-squared: 0.4551, Adjusted R-squared: 0.4356
## F-statistic: 23.38 on 2 and 56 DF, p-value: 0.00000004147#Obtaining coefficients
forward_model$coefficients## (Intercept) CannabisGramsPerDay DASS
## 8.1755984 0.9343683 0.8166997#Obtaining standarised beta vales
forward_model <- lm(scale(PDQ) ~ scale(CannabisGramsPerDay) + scale(DASS), data=model_PDQ)
options(scipen=999)
summary(forward_model)##
## Call:
## lm(formula = scale(PDQ) ~ scale(CannabisGramsPerDay) + scale(DASS),
## data = model_PDQ)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.83024 -0.36300 -0.05163 0.27516 2.68998
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -0.00000000000000001424 0.09780642012210835623
## scale(CannabisGramsPerDay) 0.28992859812021815058 0.09880078849108286931
## scale(DASS) 0.59309280906196282235 0.09880078849108286931
## t value Pr(>|t|)
## (Intercept) 0.000 1.00000
## scale(CannabisGramsPerDay) 2.934 0.00484 **
## scale(DASS) 6.003 0.00000015 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7513 on 56 degrees of freedom
## Multiple R-squared: 0.4551, Adjusted R-squared: 0.4356
## F-statistic: 23.38 on 2 and 56 DF, p-value: 0.00000004147