利用R中的glm函数对我的数据应用对数正态分布

我想在我的数据上实现一个具有特定分布类型的混合效应模型。下面是一个名为“a”的数据示例：

首先，我看到什么分布最适合我的数据：

# gamma distribution
fit_g  <- fitdist(magnitudedata$FirstSteeringTime, "gamma")

# log normal distribution - can only be used on positive values (any zeros and it will fail)
fit_ln <- fitdist(magnitudedata$FirstSteeringTime, "lnorm")

# weibull distribution
fit_w <- fitdist(magnitudedata$FirstSteeringTime, "weibull")

par(mfrow=c(2,2))
plot.legend <- c("Weibull", "gamma", "lognormal")
denscomp(list(fit_w, fit_g, fit_ln), legendtext = plot.legend)
cdfcomp (list(fit_w, fit_g, fit_ln), legendtext = plot.legend)
qqcomp  (list(fit_w, fit_g, fit_ln), legendtext = plot.legend)
ppcomp  (list(fit_w, fit_g, fit_ln), legendtext = plot.legend)

#伽马分布
可能重复：@MrFlick谢谢你！

# gamma distribution
fit_g  <- fitdist(magnitudedata$FirstSteeringTime, "gamma")

# log normal distribution - can only be used on positive values (any zeros and it will fail)
fit_ln <- fitdist(magnitudedata$FirstSteeringTime, "lnorm")

# weibull distribution
fit_w <- fitdist(magnitudedata$FirstSteeringTime, "weibull")

par(mfrow=c(2,2))
plot.legend <- c("Weibull", "gamma", "lognormal")
denscomp(list(fit_w, fit_g, fit_ln), legendtext = plot.legend)
cdfcomp (list(fit_w, fit_g, fit_ln), legendtext = plot.legend)
qqcomp  (list(fit_w, fit_g, fit_ln), legendtext = plot.legend)
ppcomp  (list(fit_w, fit_g, fit_ln), legendtext = plot.legend)



# model 3 - applying gamma distribution
m3 <- glmer(FirstSteeringTime ~ error_rate + (1 + error_rate | pNum), family = Gamma, data = a)