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我正在尝试使用参数从gamm模型（来自mgcv包）生成预测。newdata我想对lme模型的一部分进行预测，以便预测包括随机效应。但是，我认为，由于模型系数的命名方式，我遇到了问题。

我的问题是，应该如何newdata构造/命名论点以允许预测。谢谢。

阿米韦

mod <- gamm(outcome ~ s(time) + predvar, data=d, 
                        random=list(groupvar=~1), 
                        correlation = corARMA(form=~1|groupvar, p = 1))     
# okay
pred <- predict(mod$lme)

# Not okay
pred <- predict(mod$lme, newdata=d)

产生错误

predict.lme(mod$lme, newdata = d) 中的错误：无法评估“newdata”上所需级别的组

如果我在nlme没有样条项的情况下运行模型，则newdata执行没有问题

mod2 <- lme(outcome ~ time + predvar, data=d, 
                        random=list(groupvar=~1), 
                        correlation = corARMA(form=~1|groupvar, p = 1))     
# okay
pred2 <- predict(mod2, newdata=d)

d <- structure(list(time = c(0, 1, 2, 3, 4, 5, 6, 3, 4, 5, 6, 7, 8, 
9, 10, 11, 12, 13, 14, 15, 16, 17, 5, 6, 7, 8, 9, 10, 11, 12, 
13, 14), outcome = c(-1.85, -1.57, -1.38, -1.22, -1.27, -1.63, 
-2.07, -1.36, -0.33, 0.08, 0.3, 0.44, 0.78, 1.03, 1.13, 1.14, 
1.05, 0.94, 0.73, 0.51, 0.08, 0.01, 0.42, 0.59, 0.71, 0.79, 0.87, 
0.75, 0.6, 0.38, 0.01, -0.63), predvar = c(-1.83, -1.77, -1.7, 
-1.84, -1.84, -1.72, -1.69, 0.01, -0.07, 0.16, -0.04, 0.04, 0.25, 
0.19, 0.17, 0.22, 0.34, 0.54, 0.7, 0.81, 0.92, 1.12, 0.58, 0.63, 
0.63, 0.68, 0.62, 0.56, 0.61, 0.73, 0.92, 1.07), groupvar = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("a", 
"b", "c"), class = "factor")), .Names = c("time", "outcome", 
"predvar", "groupvar"), row.names = c(NA, -32L), class = "data.frame")

信息：我没有将随机效果指定为样条曲线 (s(. , bs="re"))，因为我的 RE 比上面的示例更复杂。

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1 回答 1

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如果您需要随机效应，一种对新数据进行预测的方法是gam对模型的一部分进行预测，然后添加随机效应。

使用上面的例子，

library(mgcv)

mod <- gamm(outcome ~ s(time) + predvar, data=d, 
                        random=list(groupvar=~1), 
                        correlation = corARMA(form=~1|groupvar, p = 1))     
# For comparison: predict with RE: we cant use the newdata arg here
pred <- predict(mod$lme)

# Extract the random effects from the model and match with the relevant observation
re <- coef(mod$lme)[ncol(coef(mod$lme))]
pred_ref <- re[[1]][match(d$groupvar,   gsub(".*/", "", rownames(re)) )]

# Predict on gam part of model and adjust for RE
pred2 <- as.vector(predict(mod$gam, data=d) - pred_ref)

# Compare
all.equal(pred, pred2, check.attributes = F, use.names = F)
于 2016-04-04T02:06:36.580 回答