我正在尝试从 GLM 生成具有 95% 置信区间的预测值图。我一直在改编我的主管的一些代码，它曾经工作过，所以我可以继续制作下面的图表。但是，从那时起，代码已停止工作。即使在数据框未发生任何更改的同一项目中，它现在也只会给出错误消息。为什么它现在这样做，我该如何解决它？
#Reordering factor levels for the models
river$SpeciesF<-river$Species
river$SpeciesF<-as.factor(river$SpeciesF)
river$SpeciesF<-relevel(river$SpeciesF,"UP")
View(river)
print(levels(river$SpeciesF))
#Models
massvlength<-glm(river$Mass~river$Length*river$SpeciesF,family=gaussian())
shellvlength<-glm(river$Log.shell~river$Log.length*river$SpeciesF,family=gaussian())
massvshell<-glm(river$Log.mass~river$Log.shell*river$SpeciesF,family=gaussian())
#Predicting values for massvlength model
pframe <- with(river,expand.grid(Length=seq(min(Length),max(Length),length=100), SpeciesF=levels(SpeciesF)))
pd7 <- cbind(pframe, predict(massvlength,newdata=pframe,type="response", se.fit = TRUE)[1:2])
pd7 <- transform(pd7, Fitted = fit, Upper = fit + (1.96 * se.fit), Lower = fit - (1.96 * se.fit))
这曾经有效 - 我知道它有效，因为 pd7 存在，我用它来绘制图表(链接如下(。我现在需要将代码用于其他两个模型，但不能。它给出错误：
pd7.2 <- cbind(pframe2, predict(shellvlength,newdata=pframe2,type="response", se.fit = TRUE)[1:2])
data.frame(...， check.names = FALSE( 中的错误： 参数表示不同的行数：300、95另外： 警告消息：'newdata' 有 300 行，但找到的变量有 95 行
抱歉，对此有点陌生，所以不确定我应该如何在帖子中添加数据 -数据：
dput(river)
structure(list(Species = c("AA", "AA", "AA", "AA", "AA", "AA",
"AA", "AA", "AA", "AA", "AA", "AA", "AA", "AA", "AA", "AA", "AA",
"AA", "AA", "AA", "AA", "AA", "AA", "AA", "AA", "AC", "AC", "AC",
"AC", "AC", "AC", "AC", "AC", "AC", "AC", "AC", "AC", "AC", "AC",
"AC", "AC", "AC", "AC", "AC", "AC", "AC", "AC", "AC", "AC", "AC",
"AC", "AC", "AC", "AC", "AC", "UP", "UP", "UP", "UP", "UP", "UP",
"UP", "UP", "UP", "UP", "UP", "UP", "UP", "UP", "UP", "UP", "UP",
"UP", "UP", "UP", "UP", "UP", "UP", "UP", "UP", "UP", "UP", "UP",
"UP", "UP", "UP", "UP", "UP", "UP", "UP", "UP", "UP", "UP", "UP",
"UP"), Mass = c(0.5117, 0.788, 0.722, 0.588, 1.58, 0.73, 0.784,
0.3176, 0.4838, 0.5254, 0.737, 0.5622, 0.6244, 0.919, 0.961,
0.91, 0.39, 0.37, 0.8, 0.37, 0.47, 0.51, 0.99, 0.56, 0.82, 1.755,
2.906, 0.5928, 1.522, 1.793, 0.2909, 1.714, 2.272, 0.924, 2.49,
0.804, 2.777, 1.788, 1.37, 1.07, 1.52, 1.51, 1.28, 2.59, 1.91,
1.55, 0.81, 1.8, 1.03, 2.34, 1.37, 1.78, 0.95, 2.59, 1.03, 1.882,
1.162, 1.294, 1.087, 1.206, 1.399, 1.112, 0.983, 1.511, 0.717,
0.873, 1.189, 0.91, 1.123, 1.674, 2.365, 2.74, 1.47, 1.64, 0.96,
1.12, 1.24, 1.49, 1.13, 1.46, 0.84, 1.12, 1.58, 0.96, 1.02, 0.91,
1.2, 1.4, 1.82, 1.14, 1.43, 0.82, 0.89, 2.07, 2.08), Length = c(55.42,
59.23, 56.69, 60.56, 74.69, 61.81, 74.47, 46.83, 53.58, 59.18,
65.86, 60.47, 58.4, 62.44, 59.01, 72.9, 59.61, 54.17, 69.68,
50.55, 57.37, 53.63, 75.84, 61.39, 64.26, 93.82, 105.79, 70.43,
92.2, 83.49, 56.36, 106.66, 97.73, 81.09, 100.34, 77.39, 114.36,
98.63, 93.61, 84.09, 95.75, 91.4, 87.88, 115.41, 109.77, 93.52,
78.2, 95.7, 79.73, 102.7, 91.37, 101.74, 73.64, 108.02, 87.11,
80.15, 82.09, 73.66, 65.66, 64.1, 78.47, 63.91, 62.87, 74.88,
56.63, 55.81, 73.97, 62.68, 69.8, 90.08, 85.91, 97.02, 76.69,
79.49, 67.22, 69.1, 66.71, 74.88, 68.32, 77.15, 64.54, 67.3,
77.22, 61.48, 64.14, 70.32, 75.64, 80.19, 92.69, 71.29, 71.86,
59.36, 64.09, 87.87, 79.53), Width = c(17.36, 18.59, 19.3, 19.51,
26.31, 20.71, 25.07, 14.1, 16.37, 21.42, 21.97, 17.73, 20.41,
20.92, 20.51, 23.85, 17.55, 16.67, 23.34, 14.59, 17.64, 18.62,
24.6, 19.85, 19.87, 27.18, 32.2, 17.99, 24.79, 25.96, 13.53,
34.76, 28.99, 24.39, 27.77, 20.01, 34.49, 33.42, 26.04, 24.18,
28, 26.3, 23.94, 33.8, 33.37, 26.47, 21.72, 27.77, 20.77, 30.01,
27.89, 30.8, 20.45, 32.16, 22.7, 26.17, 24.87, 21.59, 22.16,
22.66, 26.61, 19.31, 22.15, 23.42, 19.03, 17.75, 23.68, 18.32,
21.08, 30.52, 26.47, 27.76, 24.63, 25.98, 19.96, 23.2, 21.2,
23.03, 21.74, 21.82, 18.83, 21.55, 22.35, 18.51, 19.95, 22.31,
21.76, 25.21, 28.28, 20.94, 22.49, 19.52, 20.34, 29.12, 26.63
), Height = c(34.02, 35.22, 35.94, 35.95, 43.32, 36.68, 41.85,
29.98, 30.45, 33.08, 37.42, 38.55, 34.3, 37.61, 39.56, 41.06,
35.13, 32.38, 43.8, 32.74, 35.44, 33.77, 43.52, 35.47, 38.17,
53.47, 59.19, 40.84, 55.06, 49.6, 32.72, 58.04, 56.39, 45.01,
55.64, 43.65, 64.48, 53.25, 50.66, 47.47, 53.14, 53.6, 51.86,
65.52, 58.58, 51.74, 44.37, 49.88, 45.82, 58.78, 48.61, 59.25,
40.99, 56.79, 48.47, 34.83, 36.78, 33.66, 31.63, 30.76, 34.77,
28.83, 30.19, 32.78, 28.1, 25.35, 32.75, 29.71, 29.85, 42.24,
39.9, 43.25, 35.82, 38.47, 30.69, 33.7, 30.38, 32.37, 31.72,
34.15, 31.27, 30.05, 34.7, 28.39, 29.07, 31.49, 34.01, 34.58,
40.15, 30.76, 32.74, 28.18, 28.72, 39.87, 37.76), Shell = c(2.56,
4.21, 2.93, 4.63, 7.16, 4.62, 8.26, 1.7, 2.49, 4.2, 4.85, 3.3,
3.82, 4.41, 3.41, 6.6, 4.11, 2.74, 8.23, 1.89, 2.87, 3.04, 9.21,
3.87, 5.38, 8.65, 14.99, 3.76, 9.17, 7.51, 1.79, 17.29, 9.2,
6.45, 10.97, 5.07, 18.35, 14.16, 8.52, 6.4, 12.65, 7.77, 7.09,
24.59, 14.55, 7.43, 4.6, 9.88, 4.88, 15.64, 8.75, 13.08, 4.67,
13.23, 5.82, 18.61, 15.06, 9.99, 9.87, 10.34, 13.46, 7.23, 8.99,
10.46, 6.15, 5.56, 10.7, 5.73, 8.79, 26.88, 20.04, 17.2, 12.52,
18.8, 9.28, 11.45, 9.16, 10.12, 11.52, 14.4, 9.92, 7.82, 11.79,
6.62, 8.7, 11.88, 11.02, 16.5, 22.17, 8.26, 12.4, 7.36, 7.39,
19.04, 17.3), Log.mass = c(-0.290984583027883, -0.103473782510445,
-0.141462802430361, -0.230622673923862, 0.198657086954423, -0.136677139879544,
-0.105683937315562, -0.498119506244941, -0.315334135974139, -0.279509931549949,
-0.132532512140949, -0.250109158728578, -0.20453710560962, -0.0366844886138888,
-0.0172766123314546, -0.0409586076789064, -0.408935392973501,
-0.431798275933005, -0.0969100130080564, -0.431798275933005,
-0.327902142064283, -0.292429823902064, -0.00436480540245004,
-0.2518119729938, -0.0861861476162833, 0.244277120801843, 0.463295609962003,
-0.227091805028728, 0.182414652434554, 0.253580289562183, -0.536256278752941,
0.234010817587179, 0.356408327038981, -0.0343280287798933, 0.396199347095736,
-0.0947439512515487, 0.443575879750258, 0.252367514459899, 0.136720567156407,
0.0293837776852096, 0.181843587944773, 0.17897694729317, 0.107209969647868,
0.413299764081252, 0.281033367247728, 0.190331698170291, -0.0915149811213503,
0.255272505103306, 0.0128372247051722, 0.369215857410143, 0.136720567156407,
0.250420002308894, -0.0222763947111523, 0.413299764081252, 0.0128372247051721,
0.274619619091238, 0.0652061280543119, 0.111934276332682, 0.0362295440862946,
0.0813473078041325, 0.145817714491828, 0.0461047872460386, -0.00744648216786434,
0.179264464339025, -0.1444808443322, -0.0589857562944303, 0.0751818546186916,
-0.0409586076789063, 0.0503797562614578, 0.223755453657241, 0.37383114507383,
0.437750562820388, 0.167317334748176, 0.214843848047698, -0.0177287669604316,
0.0492180226701817, 0.0934216851622351, 0.173186268412274, 0.0530784434834197,
0.164352855784437, -0.0757207139381184, 0.0492180226701817, 0.198657086954423,
-0.0177287669604316, 0.00860017176191757, -0.0409586076789064,
0.0791812460476248, 0.146128035678238, 0.260071387985075, 0.0569048513364726,
0.155336037465062, -0.0861861476162833, -0.0506099933550873,
0.315970345456918, 0.318063334962762), Log.shell = c(0.40823996531185,
0.624282095835668, 0.46686762035411, 0.665580991017953, 0.854913022307856,
0.664641975556125, 0.916980047320382, 0.230448921378274, 0.396199347095736,
0.6232492903979, 0.685741738602264, 0.518513939877887, 0.582063362911709,
0.644438589467838, 0.532754378992498, 0.819543935541869, 0.613841821876069,
0.437750562820388, 0.91539983521227, 0.276461804173244, 0.457881896733992,
0.482873583608754, 0.964259630196849, 0.587710965018911, 0.730782275666389,
0.937016107464814, 1.17580163284828, 0.575187844927661, 0.962369335670021,
0.875639937004168, 0.252853030979893, 1.23779499327392, 0.963787827345555,
0.809559714635268, 1.04020662757471, 0.705007959333336, 1.26363606858811,
1.15106325335375, 0.9304395947667, 0.806179973983887, 1.10209052551184,
0.890421018800914, 0.850646235183066, 1.39075852873872, 1.16286299332193,
0.870988813760575, 0.662757831681574, 0.994756944587628, 0.688419822002711,
1.19423674872383, 0.942008053022313, 1.11660774398825, 0.669316880566112,
1.1215598441875, 0.764922984649889, 1.26974637313077, 1.17782497186468,
0.999565488225982, 0.994317152669637, 1.01452053875792, 1.12904505988796,
0.859138297294531, 0.953759691733229, 1.01953168453126, 0.788875115775417,
0.745074791582057, 1.02938377768521, 0.75815462196739, 0.943988875073772,
1.42942926438179, 1.30189771719521, 1.23552844690755, 1.09760432887441,
1.27415784926368, 0.967547976218862, 1.05880548667591, 0.96189547366785,
1.00518051250378, 1.06145247908719, 1.15836249209525, 0.996511672154179,
0.893206753059848, 1.07151380509509, 0.8208579894397, 0.939519252618618,
1.07481644064517, 1.04218159451577, 1.21748394421391, 1.34576569311449,
0.916980047320382, 1.09342168516224, 0.866877814337499, 0.868644438394826,
1.27966694404846, 1.2380461031288), Log.length = c(1.74366652144621,
1.77254173264094, 1.75350645699097, 1.78218586649202, 1.87326245943873,
1.79105874359183, 1.87198135384337, 1.67052415778208, 1.72900270927219,
1.77217496082461, 1.81862172637589, 1.78153996860594, 1.7664128471124,
1.79546289439038, 1.770925614639, 1.86272752831797, 1.77531912182948,
1.7337588355872, 1.84310814199961, 1.70372115992702, 1.75868484988244,
1.72940779696307, 1.87989832433001, 1.7880976333803, 1.8079407212155,
1.97229542861114, 2.02444461713135, 1.84775768839233, 1.96473092105363,
1.92163446105371, 1.75097098443732, 2.02800157934686, 1.99002789877029,
1.90896730041839, 2.00147409669173, 1.8886848466597, 2.05827414668595,
1.99400903312361, 1.97132224524281, 1.92474435247995, 1.98113878264066,
1.96094619573383, 1.94389004824847, 2.06224344102648, 2.04048366420627,
1.97090449815378, 1.89320675305985, 1.98091193777684, 1.90162176409336,
2.01157044359728, 1.96080362491177, 2.00749173329534, 1.86711377983198,
2.03350417294517, 1.94006801373935, 1.90390352669016, 1.91429025566595,
1.86723171451889, 1.81730087839332, 1.80685802951882, 1.89470365260923,
1.80556881754856, 1.79844346035019, 1.87436583573005, 1.75304656162653,
1.74671202251666, 1.86905561870191, 1.79712898779655, 1.84385542262316,
1.95462837750727, 1.93404371903553, 1.98686127029004, 1.88473873776963,
1.90031249698373, 1.82749850813346, 1.8394780473742, 1.82419094065258,
1.87436583573005, 1.83454785768095, 1.88733593039917, 1.80982896106789,
1.82801506422398, 1.88772979728803, 1.78873385882771, 1.80712895559242,
1.84707886206572, 1.878751520173, 1.9041202134762, 1.9670328821587,
1.85302861471299, 1.85648721286863, 1.77349389227097, 1.80679027158407,
1.94384062640126, 1.90053098245276), SpeciesF = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("UP",
"AA", "AC"), class = "factor")), .Names = c("Species", "Mass",
"Length", "Width", "Height", "Shell", "Log.mass", "Log.shell",
"Log.length", "SpeciesF"), row.names = c(NA, -95L), class = c("tbl_df",
"tbl", "data.frame"))
帮助文件中有一个注释predict.glm：
变量首先在新数据中查找，然后在 通常的方式(这将包括中使用的公式的环境 适合(。如果找到的变量不是 如果提供，则与 newdata 中的长度相同。
这意味着您得到的错误是因为您的 newdata 缺少一个变量，并且 predict 正在尝试使用不同长度的原始数据中的变量。因此，pframe2可能缺少您正在使用的模型规范中的变量。
顺便说一句，既然你只是在做线性模型，为什么不直接使用lm并让predict.lm使用interval="confidence"为你计算置信区间呢？
不知道为什么这有效，但对于其他有类似问题的人 - 一旦我更改了模型公式
massvlength<-glm(river$Mass~river$Length*river$SpeciesF，family=gaussian(((
自
massvlength<-glm(Mass~Length*SpeciesF，family=gaussian((，data=river(
它工作得很好 - 即使框架等没有变化。