我有一些分类数据显示植物家族，果实类型，果实颜色和种子传播，其中响应变量（Dispersal）为0表示否，或者1表示是。
test1.3
FAMILY FRUIT_TYPE COLOUR_RF Dispersal
3 Erythroxylaceae D R 1
4 Lamiaceae D G 1
8 Clusiaceae D Y 1
12 Clusiaceae D Y 1
16 Myrtaceae D R 1
19 Rubiaceae D R 0
22 Anacardiaceae D Br 1
25 Melastomataceae D R 1
29 Moraceae F Y 1
32 Moraceae F Br 1
35 Fabaceae C Br 1
37 Lauraceae D PG 1
39 Sapindaceae D Br 1
41 Myrtaceae D R 1
43 Moraceae D G 1
45 Clusiaceae D Y 1
51 Moraceae F Y 1
52 Lauraceae D R 0
54 Rubiaceae D Y 0
57 Euphorbiaceae D PY 0
75 Dichapetalaceae D Y 1
83 Moraceae F R 1
86 Myrtaceae D Y 1
91 Solanaceae D Y 1
94 Myrsinaceae D Y 1
98 Connaraceae D O 1
101 Ochnaceae C R 1
104 Proteaceae D Br 0
107 Clusiaceae D R 1
114 Clusiaceae D P 1
116 Clusiaceae D P 1
119 Smilacaceae D R 1
124 Apocynaceae D Y 1
129 Apocynaceae D Br 1
138 Icacinaceae D Y 0
141 Moraceae D Y 1
144 Myrsinaceae D R 0
147 Pittosporaceae D O 1
150 Sapindaceae C O 1
154 Fabaceae C Y 1
158 Aphloiaceae C W 1
169 Celastraceae D O 1
176 Oleaceae D P 0
179 Rubiaceae D Y 0
182 Meliaceae D R 0
186 Apocynaceae C PY 1
188 Salicaceae D R 1
192 Burseraceae D Br 0
195 Araceae D Y 0
198 Rubiaceae D P 1
202 Rutaceae D O 1
206 Torrecilliaceae D PY 0
214 Arecaceae D PY 1
220 Rutaceae D PY 0
223 Rubiaceae D DR 0
230 Rubiaceae D B 0
237 Clusiaceae D Y 1
244 Myrsinaceae D R 1
247 Melastomataceae D R 0
250 Aquifoliacea D R 1
260 Marsaceae D W 1
263 Vitaceae D DR 0
266 Lamiaceae D B 0
278 Hypericaceae D O 1
281 Cannelaceae D Y 0
283 Rubiaceae D R 1
289 Sapotaceae D Br 1
293 Lauraceae D R 0
343 Sapindaceae D O 0
344 Lauraceae D P 0
362 Clusiaceae D Gr 1
366 Anacardiaceae D Br 1
370 Lauraceae D P 1
374 Lauraceae D R 0
399 Lauraceae D R 0
405 Lauraceae D R 0
我正在执行二项式GLM并使用MuMIn包来“挖掘”全局模型。
Global<-glm(Dispersal~FAMILY+COLOUR_RF+FRUIT_TYPE+COLOUR_RF*FRUIT_TYPE+COLOUR_RF*FAMILY, family=binomial(link="logit"), na.action="na.fail", data=test1.3)
当我尝试根据AICc权重绘制最重要的模型时（正如我之前多次使用其他迭代），我收到警告错误：
library(MuMIn)
x<-dredge(Global)
Fixed term is "(Intercept)"
Warning messages:
1: glm.fit: fitted probabilities numerically 0 or 1 occurred
2: glm.fit: fitted probabilities numerically 0 or 1 occurred
3: glm.fit: fitted probabilities numerically 0 or 1 occurred
4: glm.fit: fitted probabilities numerically 0 or 1 occurred
5: glm.fit: algorithm did not converge
6: glm.fit: fitted probabilities numerically 0 or 1 occurred
7: glm.fit: fitted probabilities numerically 0 or 1 occurred
8: glm.fit: fitted probabilities numerically 0 or 1 occurred
9: glm.fit: fitted probabilities numerically 0 or 1 occurred
par(mfrow=c(1,1))
par(mar = c(5,5,14,5))
plot(subset(x, delta <2), labAsExpr = T, xlab=c("Predictor Variables for 'P. edwardsi' Dispersal"), ylab=c(expression(paste("Model Number by Cumulative Akaike Weight ", (omega) ))))
"Error in pal[, 1L + ((i - 1L)%%npal)] : incorrect number of dimensions"
有谁知道为什么会这样？我在许多其他迭代中使用了相同的代码（即更改响应变量）并且没有先前的问题。
答案 0 :(得分：1)
问题的原因是subset(x, delta < 2)只选择了一个案例（请参阅x$delta）。显然，函数plot.model.selection无法处理这个问题。您可以通过增加子集范围或在绘图函数中插入一些代码来修复错误来解决此问题。通过添加第pal <- as.matrix(pal)行
plot0 = function (x, ylab = NULL, xlab = NULL, labels = attr(x, "terms"),
labAsExpr = FALSE, col = c("SlateGray", "SlateGray2"), col2 = "white",
border = par("col"), par.lab = NULL, par.vlab = NULL, axes = TRUE,
ann = TRUE, ...)
{
if (is.null(xlab))
xlab <- NA
if (is.null(ylab))
ylab <- expression("Cumulative Akaike weight" ~ ~(omega))
op <- par(..., no.readonly = TRUE)
on.exit(par(op))
cumweight <- cumsum(weight <- Weights(x))
stdweight <- weight/max(weight)
n <- nrow(x)
m <- length(attr(x, "terms"))
plot.new()
plot.window(xlim = c(0, m), ylim = c(1, 0), xaxs = "i",
yaxs = "i")
pal <- if (is.na(col2))
rbind(col)
else vapply(col, function(x) grDevices::rgb((grDevices::colorRamp(c(col2,
x)))(stdweight), maxColorValue = 255), character(n))
pal <- as.matrix(pal) # the only new line
npal <- ncol(pal)
for (i in 1L:m) rect(i - 1, c(0, cumweight), i,
c(cumweight, 1),
col = ifelse(is.na(x[, i]), NA, pal[, 1L + ((i - 1L)%%npal)]),
border = border)
if (ann) {
labCommonArg <- list(col = par("col.axis"), font = par("font.axis"),
cex = par("cex.axis"))
if (labAsExpr) {
labels <- gsub(":", "%*%", labels, perl = TRUE)
labels <- gsub("\\B_?(\\d+)(?![\\w\\._])", "[\\1]",
labels, perl = TRUE)
labels <- parse(text = labels)
}
arg <- c(list(side = 3L, padj = 0.5, line = 1, las = 2),
labCommonArg)
for (i in names(par.lab)) arg[i] <- par.lab[i]
if (is.expression(labels)) {
if (length(labels) != m)
stop("length of 'labels' is not equal to number of terms")
for (i in 1L:m) do.call("mtext", c(list(text = as.expression(labels[[i]]),
at = i - 0.5), arg))
}
else if (!is.null(labels) && !is.na(labels)) {
if (length(labels) != m)
stop("length of 'labels' is not equal to number of terms")
do.call("mtext", c(list(text = labels, at = 1L:m -
0.5), arg))
}
arg <- c(list(side = 4, las = 2, line = 1, adj = 1),
labCommonArg)
for (i in names(par.vlab)) arg[i] <- par.vlab[i]
ss <- weight > -(1.2 * strheight("I", cex = arg$cex))
arg[["at"]] <- (c(0, cumweight[-n]) + cumweight)[ss]/2
arg[["text"]] <- rownames(x)[ss]
arg$line <- arg$line + max(strwidth(arg[["text"]], cex = arg$cex,
units = "in"))/par("mai")[4L] * par("mar")[4L]
do.call(mtext, arg)
title(ylab = ylab, xlab = xlab)
}
if (axes) {
axis(2L, col = border, col.ticks = border)
box(col = border)
}
invisible(x)
}
您还需要通过Weights将隐私环境中的Weights = MuMIn:::Weights功能移出。那么这对我有用：
plot0(subset(x, delta < 2), labAsExpr = T,
xlab=c("Predictor Variables for 'P. edwardsi' Dispersal"),
ylab=c(expression(paste("Model Number by Cumulative Akaike Weight ", (omega) ))))
答案 1 :(得分：1)
这是您要发送到plot()的对象的str（）调用顶部的输出：
> xsub <- subset(x, delta <2)
> str(xsub)
Classes ‘model.selection’ and 'data.frame': 1 obs. of 11 variables:
$ (Intercept) : num 17.6
$ COLOUR_RF : Factor w/ 1 level "+": NA
$ FAMILY : Factor w/ 1 level "+": NA
$ FRUIT_TYPE : Factor w/ 1 level "+": 1
$ COLOUR_RF:FAMILY : Factor w/ 1 level "+": NA
$ COLOUR_RF:FRUIT_TYPE: Factor w/ 1 level "+": NA
$ df : int 3
$ logLik : num -44.3
$ AICc : num 94.8
$ delta : num 0
$ weight : num 1
我怀疑你只是做了一个限制性的电话，它没有提供绘图功能所需的尽可能多的信息来完成任何有用的工作。使用subset(x, delta <5)成功。