我正在使用RMarkdown和knitr编译一个简短的协议（在一个html文件中），关于建模。基础是Zuur，A.F。＆＃34; leno，E.N，Walker，NJ，Saveliev，AA＆amp; Smith，G M. 2009：R。＆＃34;
中的混合效应模型和生态学扩展我下载了他们网站1中共享的代码和数据集，并且我将其与其他来源和评论混合以生成对我有用的内容。在本网站中，您可以免费下载我使用的两个数据集。
主要问题是我试图将使用mgcv包装制作的东西与使用gam包装的东西混合。
我从这两个主题中清楚地了解到这就是问题所在：
R Package conflict between gam and mgcv?
Are there known compatibility issues with R package mgcv? Are there general rules for compatibility?
但我想找到解决方案。显然，正如在theese中回答的两个主题指定包或分离未使用的一个不起作用。我刚试过我的代码。
这些是导致我出现问题的部分：
---
title: "error"
author: "Simone Marini"
date: "29 marzo 2016"
output: html_document
---
```{r setup, include = FALSE, cache = FALSE}
knitr::opts_chunk$set(error = TRUE) # to allow rendering to html even if there are errors
```
```{r}
Loyn <- read.table(file = "zuur_data/Loyn.txt", header = TRUE, dec = ".")
Loyn$fGRAZE <- factor(Loyn$GRAZE) # Transform in factor Graze data (from 1 to 5 -> 5 classes)
Loyn$L.AREA<-log10(Loyn$AREA)
Loyn$L.DIST<-log10(Loyn$DIST)
Loyn$L.LDIST<-log10(Loyn$LDIST)
```
```{r}
library(mgcv)
AM1<-mgcv::gam(ABUND~s(L.AREA)+s(L.DIST)+s(L.LDIST)+
s(YR.ISOL)+s(ALT)+fGRAZE, data = Loyn)
# The anova command does not apply a sequential F-test as it did for the linear regression model.
# Instead, it gives the Wald test (approximate!) that shows the significance of each term in the model.
anova(AM1)
AM2<-mgcv:::gam(ABUND ~ s(L.AREA, bs = "cs") + s(L.DIST, bs = "cs") +
s(L.LDIST,bs = "cs") + s(YR.ISOL, bs = "cs") +
s(ALT, bs = "cs") + fGRAZE, data = Loyn)
anova(AM2)
AM3 <- mgcv:::gam(ABUND ~ s(L.AREA, bs = "cs") + fGRAZE, data = Loyn)
#Model plot
plot(AM3)
E.AM3 <- resid(AM3) # Residuals
Fit.AM3 <- fitted(AM3) # Fitted values
plot(x = Fit.AM3, y = E.AM3, xlab = "Fitted values",
ylab = "Residuals") # Graph
M3<-lm(ABUND ~ L.AREA + fGRAZE, data = Loyn)
AM3<-mgcv:::gam(ABUND ~ s(L.AREA, bs = "cs") + fGRAZE, data = Loyn)
anova(M3, AM3)
```
```{r}
rm(list=ls())
detach("package:mgcv")
ISIT <- read.table(file = "zuur_data/ISIT.txt", header = TRUE, dec = ".")
ISIT$fStation<-factor(ISIT$Station)
op <- par(mfrow=c(2,2),mar=c(5,4,1,2))
Sources16<-ISIT$Sources[ISIT$Station==16]
Depth16<-ISIT$SampleDepth[ISIT$Station==16]
plot(Depth16,Sources16,type="p")
library(gam)
M2<-gam:::gam(Sources16~gam::lo(Depth16,span=0.5))
plot(M2,se=T)
P2 <- predict(M2, se = TRUE)
plot(Depth16, Sources16, type = "p")
I1 <- order(Depth16)
lines(Depth16[I1], P2$fit[I1], lty = 1)
lines(Depth16[I1], P2$fit[I1] + 2 * P2$se[I1], lty = 2)
lines(Depth16[I1], P2$fit[I1] - 2 * P2$se[I1], lty = 2)
par(op)
```
有没有人知道如何分离＆＃34;完全mgcv或gam？或者是一个可以＆＃34; reaload＆＃34;我必须编译gam部分时的整个环境？
我的sessionInfo是否有用。
R version 3.2.3 (2015-12-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
locale:
[1] LC_COLLATE=Italian_Italy.1252 LC_CTYPE=Italian_Italy.1252 LC_MONETARY=Italian_Italy.1252
[4] LC_NUMERIC=C LC_TIME=Italian_Italy.1252
attached base packages:
[1] splines stats graphics grDevices utils datasets methods base
other attached packages:
[1] lattice_0.20-33 gam_1.12 foreach_1.4.3 mgcv_1.8-11 nlme_3.1-124
loaded via a namespace (and not attached):
[1] Matrix_1.2-3 htmltools_0.3 tools_3.2.3 yaml_2.1.13 codetools_0.2-14 rmarkdown_0.9.2
[7] grid_3.2.3 iterators_1.0.8 knitr_1.12.3 digest_0.6.9