Я провожу научное исследование. Хочу посмотреть, являются ли стратегии цифрового поведения медиатором или модератором для влияния особенностей цифрового поведения на показатели благополучия пользователя. Решил сделать анализ через R. Написал код. Но столкнулся с двумя проблемами: не понимаю, правильно ли я интерпретирую полученные результаты и не знаю, как сделать красивые сводные графики, чтобы визуализировать результаты. Оговорюсь заранее: у меня есть только базовые навыки работы в R, только начал его учить. Код писал с очень большой помощью дипсика
Вот сам код. Это для удовлетворенности и тревожности.
data <- read.csv("vediations для R.csv", stringsAsFactors = FALSE)
data$strategy <- as.factor(data$strategy)
med_model <- glm(strategy ~ Preference + Regulation,
data = data,
family = binomial(link = "logit"))
data$Satisfaction_adj <- data$Satisfaction + 0.01
out_model_satisfaction <- glm(Satisfaction_adj ~ Preference + Regulation + strategy,
data = data,
family = Gamma(link = "identity"))
out_model_anxiety <- glm(Anxiety ~ Preference + Regulation + strategy,
data = data,
family = quasipoisson(link = "log"))
set.seed(123)
med_results_sat <- mediate(med_model, out_model_satisfaction,
treat = "Preference",
mediator = "strategy",
robustSE = TRUE,
sims = 1000)
summary(med_results_sat)
med_results_anx <- mediate(med_model, out_model_anxiety,
treat = "Preference",
mediator = "strategy",
robustSE = TRUE,
sims = 1000)
summary(med_results_anx)
plot(med_results_sat, main = "Медиационный анализ: Satisfaction")
plot(med_results_anx, main = "Медиационный анализ: Anxiety")
problem_use_vars <- c("Preference", "Regulation", "Absorption",
"Compulsiveness", "Negativeconsequences",
"Problemsolving")
med_formula <- as.formula(paste("strategy ~", paste(problem_use_vars, collapse = " + ")))
med_model_full <- glm(med_formula,
data = data,
family = binomial(link = "logit"))
data$Satisfaction_adj <- data$Satisfaction + 0.01
out_formula_sat <- as.formula(paste("Satisfaction_adj ~",
paste(problem_use_vars, collapse = " + "),
"+ strategy"))
out_model_sat_full <- glm(out_formula_sat,
data = data,
family = Gamma(link = "identity"))
out_formula_anx <- as.formula(paste("Anxiety ~",
paste(problem_use_vars, collapse = " + "),
"+ strategy"))
out_model_anx_full <- glm(out_formula_anx,
data = data,
family = quasipoisson(link = "log"))
summary(med_model_full)
summary(out_model_sat_full)
summary(out_model_anx_full)
model_interaction <- glm(Satisfaction_adj ~ Preference * strategy +
Regulation * strategy + Absorption * strategy + Compulsiveness * strategy + Negativeconsequences * strategy,
data = data, family = Gamma(link = "identity"))
summary(model_interaction)
model_anxiety_interaction <- glm(Anxiety ~ Preference*strategy +
Regulation*strategy + Absorption*strategy+ Compulsiveness * strategy + Negativeconsequences * strategy,,
data = data, family = quasipoisson(link = "log"))
summary(model_anxiety_interaction)
<code>
А это для глобального самоотношения и чувствительности:
med_model <- glm(strategy ~ Preference + Regulation,
data = data,
family = binomial(link = "logit"))
data$GSE_adj <- data$GSE + 0.01
data$Sens_adj <- data$Sens + 0.01
out_model_GSE <- glm(GSE_adj ~ Preference + Regulation + strategy,
data = data,
family = Gamma(link = "log"))
out_model_Sens <- glm(Sens_adj ~ Preference + Regulation + strategy,
data = data,
family = Gamma(link = "log"))
med_results_GSE <- mediate(med_model, out_model_GSE,
treat = "Preference",
mediator = "strategy",
robustSE = TRUE,
sims = 1000)
summary(med_results_GSE)
med_results_Sens <- mediate(med_model, out_model_Sens,
treat = "Preference",
mediator = "strategy",
robustSE = TRUE,
sims = 1000)
summary(med_results_Sens)
plot(med_results_GSE, main = "Медиационный анализ: GSE")
plot(med_results_Sens, main = "Медиационный анализ: Sens")
problem_use_vars <- c("Preference", "Regulation", "Absorption",
"Compulsiveness", "Negativeconsequences",
"Problemsolving")
med_formula <- as.formula(paste("strategy ~", paste(problem_use_vars, collapse = " + ")))
med_model_full <- glm(med_formula,
data = data,
family = binomial(link = "logit"))
out_formula_GSE <- as.formula(paste("GSE_adj ~",
paste(problem_use_vars, collapse = " + "),
"+ strategy"))
out_model_GSE_full <- glm(out_formula_GSE,
data = data,
family = Gamma(link = "log"))
out_formula_Sens <- as.formula(paste("Sens_adj ~",
paste(problem_use_vars, collapse = " + "),
"+ strategy"))
out_model_Sens_full <- glm(out_formula_Sens,
data = data,
family = Gamma(link = "log"))
summary(med_model_full)
summary(out_model_GSE_full)
summary(out_model_Sens_full)
model_interaction_GSE <- glm(GSE_adj ~ Preference * strategy +
Regulation * strategy + Absorption * strategy + Compulsiveness * strategy + Negativeconsequences * strategy,
data = data, family = Gamma(link = "log"))
summary(model_interaction_GSE)
model_interaction_Sens <- glm(Sens_adj ~ Preference * strategy +
Regulation * strategy + Absorption * strategy + Compulsiveness * strategy + Negativeconsequences * strategy,
data = data, family = Gamma(link = "log"))
summary(model_interaction_Sens)
Простой
