r - 控制交互作用项在效果图中的显示方式

Question

我正在尝试使用效应图从线性混合效应模型中绘制交互项。请参见下面的示例:

library(nlme)
fitA <- lme(PEE ~ Pupper*max_depth,
  random=~1 + Pupper|ref, data=m4,
  cor=corAR1(), method="ML")

Pupper 是连续变量，max_depth 是阶乘变量(5 个级别 - 400,500,600,700,800)。

当我在带有交互项的效果图中绘制模型输出时，我能够显示 PEE 和 Pupper 之间的关系如何根据不同的因素水平变化最大深度:

library(effects)
plot(effect("Pupper*max_depth",fitA),
  xlab=expression(paste("d"[-5]," P"[upper]," (m"^" -1",")")),
  ylab=expression(paste("PEE rate (h"^" -1",")")),      
        factor.names=FALSE, layout=c(5,1), alternating=FALSE, 
     main="A", ticks.x=list(Pupper=list(at=seq(0.4,1.0,0.2))), 
   ##layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), TRUE)
        rotx=45, more=FALSE, grid=FALSE, lwd=1)

但是，有时当我绘制一个相似的模型时，效果图会改变我希望显示交互的方式(见下文)。在下面的效果图中，该图“决定”显示 PEE 和 max_depth 之间的关系，以及它如何根据连续变量 Pupper 的任意划分而变化(在代码中标记为 Plower，在下图中标记为“k150”):

fitB <- lme(PEE ~ Plower*max_depth,
   random=~1 + Plower|ref, data=m4, 
   cor=corAR1(), method="ML")

plot(effect("Plower*max_depth",fitB),
xlab=expression(paste("d"[-5]," P"[lower]," (m"^" -1",")")), 
ylab=expression(paste("PEE rate (h"^" -1",")")),        
factor.names=FALSE, layout=c(5,1), alternating=FALSE,
  main="A", ticks.x=list(Plower=list(at=seq(0.4,1.0,0.2))), 
 ##layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), TRUE)
rotx=45, more=FALSE, grid=FALSE, lwd=1)

但是，我对 max_depth 因素如何影响 PEE 和 Plower 之间的关系感兴趣(类似于上面的第一张图)。我不明白为什么 effect 函数以两种不同的方式显示相同的交互项。我很想知道如何控制交互项在效果图中的表示方式，因为这个问题一直浮出水面。

下面是我的数据集的一个子集:

structure(list(ref = c("2012-3corrige", "2011-28", "2011-26", 
"2011-21", "2011-21", "2013-7", "2012-1corrige", "2012-6corrige", 
"2013-4", "2011-21", "2013-10", "2013-4", "2013-13", "2011-26", 
"2013-11", "2012-3corrige", "2013-1", "2012-14corrige", "2013-1", 
"2011-27", "2012-6corrige", "2011-18", "2011-26", "2010-18", 
"2012-14corrige", "2011-21", "2013-6", "2013-11", "2011-27", 
"2011-18", "2012-16corrige", "2013-5", "2013-13", "2011-21", 
"2012-14corrige", "2013-5", "2013-18", "2012-16corrige", "2011-28", 
"2010-18", "2011-21", "2013-2", "2012-2corrige", "2013-4", "2013-5", 
"2013-11", "2011-21", "2013-6", "2011-28", "2013-6", "2010-18", 
"2011-21", "2013-18", "2011-16", "2012-11corrige", "2011-28", 
"2011-27", "2012-3corrige", "2012-2corrige", "2013-3", "2012-1corrige", 
"2012-14corrige", "2012-14corrige", "2013-10", "2012-6corrige", 
"2010-18", "2012-11corrige", "2013-7", "2013-2", "2012-16corrige", 
"2013-1", "2013-18", "2012-16corrige", "2013-6", "2012-4corrige", 
"2013-4", "2013-10", "2013-3", "2013-2", "2011-16", "2012-1corrige", 
"2011-21", "2011-21", "2013-18", "2013-3", "2011-26", "2010-18", 
"2013-13", "2012-6corrige", "2013-3", "2012-16corrige", "2012-15corrige", 
"2011-28", "2012-6corrige", "2012-6corrige", "2012-11corrige", 
"2013-1", "2013-11", "2012-11corrige", "2013-6"), Pupper = c(0.861958207287982, 
0.824829924556841, 0.958739109455748, 0.935401831656677, 0.955566680038604, 
0.948368978826279, 0.745071680369673, 0.827539122942233, 0.726448658429027, 
0.943103302931338, 0.858445846226439, 0.784802718309937, 0.881010495586365, 
0.911770168408684, 0.90971638692581, 1.02155421458351, 0.851778844536538, 
1.1553118943962, 0.887452083213511, 0.8218157295485, 0.871777265131409, 
0.829892474962871, 1.01579427707254, 0.715539162683171, 1.12624787680155, 
0.713105471394893, 0.802478037082636, 0.773243110590944, 0.762028205952159, 
0.785089166910358, 0.844285844170484, 0.887514023676371, 0.870367623478723, 
0.820303824472643, 0.636099278958915, 0.953776661488422, 0.816485694234068, 
0.861493535070196, 0.787945463425822, 0.918041865421543, 0.877275056321815, 
0.624152855209897, 0.971197595182818, 0.769613695304339, 0.941443459091764, 
0.929070549770906, 1.031203743205, 0.692597025693873, 0.846978945035432, 
0.72446749179426, 0.541564092852052, 0.744921803502444, 0.917786983273715, 
0.702051561892398, 0.975310403563878, 0.808819367281032, 0.858040403089116, 
0.741495941398947, 0.698143566897239, 0.979366380200314, 0.992046903013047, 
0.995331870590213, 0.804437082665078, 0.8307554779262, 0.878549524310762, 
0.654725061889849, 0.93024953667308, 0.654611447094126, 0.689696271315618, 
0.77302453480932, 0.916283427766758, 0.894114399839305, 0.840205756601608, 
0.767235548359607, 0.831544386468135, 0.685089269122402, 0.860269828471148, 
0.895228365651283, 0.785946885397904, 0.812567650516969, 0.797256286689962, 
0.800979891549511, 0.684467773772683, 0.846228645225391, 0.801015938251751, 
0.964375424821682, 0.783654311543043, 0.951249150678552, 0.847095453102345, 
0.782862048298847, 0.897798965949478, 0.79591714811698, 0.954852044385237, 
0.885914708711347, 0.789575506205708, 1.10814372714012, 0.875651148193922, 
0.851523408695002, 0.963324355206144, 0.795071091161036), Plower = c(0.705132769215998, 
0.667302197075824, 0.629978835623335, 0.632452896796802, 0.641619045851976, 
0.634150350206216, 0.521875889886134, 0.69048678481199, 0.620155894379255, 
0.72673011955379, 0.644805071164551, 0.691418831100224, 0.561990510002912, 
0.702502669034076, 0.5885329988032, 1.06019049650942, 0.610499795249761, 
0.863589408611907, 0.671649710290516, 0.7008237216939, 0.613070958372683, 
0.52121652570373, 0.743727100487806, 0.619214556245787, 1.0217109832694, 
0.653199816289013, 0.653255947797901, 0.629436452185155, 0.621227279933305, 
0.581484689776476, 0.605084016204913, 0.670828674932066, 0.694246594037978, 
0.732994239783339, 0.728155423409921, 0.657673931367209, 0.681945582710071, 
0.656113353702447, 0.55299186250794, 0.589741939797023, 0.760512984767519, 
0.550684422635445, 0.888934443277143, 0.615143614667881, 0.736486026117717, 
0.616589579139919, 0.640405340389975, 0.618517043688639, 0.612475849864031, 
0.681245183469212, 0.642477842246546, 0.683125578173995, 0.636702442275825, 
0.568741300299764, 0.681781639762194, 0.58956858049858, 0.697614984548545, 
0.773372843818268, 0.599073358520381, 0.653548263966276, 0.846172099647715, 
0.946825538132655, 0.635504629303462, 0.61980005655224, 0.594418483337567, 
0.610786378368084, 0.809715477703094, 0.596886365511337, 0.601414998150196, 
0.680138336678131, 0.672368946338244, 0.693205917779446, 0.736742863266092, 
0.636678882954351, 0.611664395999418, 0.630585706572337, 0.6554630468205, 
0.617362130357864, 0.615793526002561, 0.688748462389895, 0.733587834625896, 
0.715468455706547, 0.695921451506322, 0.649384323802169, 0.654685675216232, 
0.675344356606317, 0.617759392212426, 0.620895052860519, 0.652138022200822, 
0.638494322605926, 0.798451637031189, 0.884450865414997, 0.895823643358446, 
0.661857655055493, 0.743487278528243, 0.88302132854573, 0.660494764046872, 
0.638155299450374, 0.515272975866573, 0.636047692132176), PEE = c(1.49625537031302, 
0.579708304983786, 1.09755665230733, 0.79999598579792, 0.366971323405136, 
0.534519852186464, 0.892172302701565, 0.764300080784289, 0.162161584516302, 
1.05547854644252, 1.75994722974226, 0.502090519778478, 0.813556191910923, 
0.72071830101183, 0.124737712452804, 0.24096278221797, 2.11763754191128, 
0.0970872140009704, 0.214668546888839, 0.997227687637828, 0.449413221941473, 
0.445533213208998, 0.719422276286327, 0.311417756794472, 0.0799998795735146, 
0.836011454943211, 0.217381544231536, 0.131863894834852, 1.1881086717854, 
0.562478645146688, 2.13755423670725, 0.260855398500945, 0.769228719926564, 
0.792077729637109, 0.46631964160662, 0.727219913477435, 0.234599414042217, 
1.24135448496734, 1.73912823566756, 0.437157161658986, 1.18491673172712, 
1.57894265236866, 0.325374033367569, 0.133629870488068, 0.260855348600893, 
0.279711624960117, 1.04650548272943, 1.13790951622142, 0.512819441159373, 
2.51301278595252, 0.948078086013639, 0.183485515251287, 0.521708195407091, 
0.834371581292816, 0.907354231373586, 0.263735732207326, 0.94736384877553, 
0.865382874911045, 0.162378076290205, 1.80685106084338, 1.07131194190618, 
1.20567188480079, 1.01009693910426, 0.352933736835024, 0.315767760495837, 
0.901500577761704, 1.08481956174672, 0.553504151294972, 1.81542854475215, 
2.23136249824668, 0.14018646847029, 0.58250584995577, 1.74754206600435, 
0.404021461283339, 1.0507621403718, 3.1578487818322, 1.23592560921063, 
0.428569941841892, 2.59927399028359, 0.462953155929056, 1.60334686111772, 
0.610996390428844, 0.93749693604517, 0.374210416022193, 0.596024133599949, 
1.07142175386991, 0.233917466116807, 0.773637607411201, 0.733915433670645, 
0.693195231932592, 0.699678270730694, 0.75104196328333, 1.1707299559812, 
0.376558572007052, 1.5725384212365, 0.17424722659426, 0.481925512179189, 
0.127383975172354, 0.449990814000021, 0.828701950628209), max_depth = structure(c(5L, 
1L, 5L, 5L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 5L, 3L, 3L, 3L, 4L, 5L, 
1L, 5L, 1L, 5L, 2L, 4L, 3L, 3L, 3L, 3L, 2L, 4L, 2L, 2L, 5L, 2L, 
2L, 4L, 5L, 4L, 2L, 1L, 5L, 2L, 1L, 4L, 4L, 4L, 3L, 4L, 1L, 2L, 
2L, 5L, 5L, 4L, 1L, 3L, 1L, 3L, 2L, 4L, 1L, 5L, 1L, 5L, 4L, 4L, 
1L, 2L, 4L, 1L, 1L, 4L, 4L, 2L, 2L, 2L, 4L, 2L, 5L, 1L, 2L, 1L, 
3L, 4L, 3L, 1L, 5L, 1L, 4L, 4L, 3L, 3L, 3L, 1L, 3L, 1L, 1L, 4L, 
1L, 4L, 2L), .Label = c("400", "500", "600", "700", "800"), class = "factor"), 
    fangle = structure(c(2L, 3L, 1L, 4L, 3L, 4L, 3L, 3L, 3L, 
    3L, 2L, 4L, 2L, 4L, 2L, 4L, 4L, 4L, 4L, 1L, 4L, 2L, 2L, 4L, 
    3L, 4L, 4L, 2L, 2L, 2L, 4L, 3L, 3L, 4L, 1L, 3L, 4L, 2L, 3L, 
    3L, 4L, 3L, 2L, 3L, 3L, 3L, 2L, 1L, 2L, 2L, 3L, 3L, 2L, 2L, 
    2L, 3L, 2L, 4L, 1L, 3L, 2L, 4L, 3L, 3L, 1L, 2L, 2L, 4L, 2L, 
    1L, 4L, 3L, 4L, 2L, 4L, 3L, 4L, 4L, 3L, 2L, 3L, 3L, 2L, 2L, 
    4L, 2L, 4L, 4L, 3L, 4L, 3L, 4L, 1L, 4L, 4L, 4L, 2L, 2L, 3L, 
    3L), .Label = c("0", "20", "40", "60"), class = "factor")), .Names = c("ref", 
"Pupper", "Plower", "PEE", "max_depth", "fangle"), row.names = c(26297L, 
18163L, 13367L, 10757L, 10813L, 43605L, 22984L, 27608L, 39808L, 
11220L, 32882L, 39987L, 35960L, 13719L, 34174L, 25877L, 31747L, 
19402L, 31394L, 14990L, 28537L, 9023L, 13684L, 1781L, 19411L, 
9964L, 41834L, 33800L, 15277L, 8673L, 21864L, 40681L, 35425L, 
11590L, 19901L, 40867L, 36845L, 21698L, 18302L, 470L, 11459L, 
37414L, 24555L, 40026L, 40578L, 33627L, 9525L, 41816L, 17695L, 
42057L, 294L, 9972L, 37137L, 8304L, 19086L, 15817L, 15351L, 26097L, 
24896L, 39059L, 23703L, 20110L, 19937L, 32121L, 28556L, 13L, 
19157L, 42865L, 37922L, 21887L, 31638L, 37008L, 21905L, 41848L, 
26621L, 39864L, 32870L, 39107L, 37721L, 7969L, 23826L, 11903L, 
12024L, 36500L, 38488L, 13287L, 462L, 36245L, 28096L, 38611L, 
21500L, 20565L, 17140L, 27772L, 27773L, 18897L, 30992L, 34564L, 
18553L, 41312L), class = "data.frame")

Answer 1

您提供的数据子集不足以适合模型，但我认为我可以回答您的问题。水平轴上变量的选择由 plot.eff() 的 x.var 参数控制；来自 ?plot.eff:

x.var: the index (number) or quoted name of the covariate or factor to place on the horizontal axis of each panel of the effect plot. The default is the predictor with the largest number of levels or values.

反过来，评估效果的协变量值由 Effect() 的 xlevels 参数控制，在您的情况下由 调用效果()；来自 ?Effect:

xlevels: this argument is used to set the number of levels for any focal predictor that is not a factor. If xlevels=NULL, the default, then the number and values of levels for any numeric predictor is determined by grid.pretty. If xlevels=n is an integer, then each numeric predictor is represented by n equally spaced levels. More generally, xlevels can be a named list of values at which to set each numeric predictor. For example, xlevels=list(x1=c(2, 4, 7), x2=5) would use the values 2, 4 and 7 for the levels of x1, 5 equally spaced levels for the levels of x2, and use the default for any other numeric predictors. If partial residuals are computed, then the focal predictor that is to appear on the horizontal axis of an effect plot is evaluated at 100 equally spaced values along its full range, and, by default, other numeric predictors are evaluated at the quantiles specified in the quantiles argument, unless their values are given explicitly in xlevels.

奇怪的是，您显示的第二个图似乎包含一个变量 k150，它没有出现在模型中。

希望对你有帮助

约翰