Question

我想在我的数据集上绘制交互效果。由于保密原因，我无法共享完整的数据集，但我添加了effect()函数的输出，可以将其替换为绘图函数以重现我的结果

交互效应介于两个连续变量varP和varL之间。该模型是二项式概率glm。我正在使用effects()包。

调用基数R plot()会生成以下图表：

plot(effect("varL:varP", hx.x), multiline = TRUE)

调用ggplot会产生错误，因为ggplot2无法处理连续变量（Error: A continuous variable can not be mapped to linetype）。所以我决定使用cut()将连续变换为分类变量并重做回归。现在调用ggplot会产生以下图：

ggplot(data.frame(effect("varL:varP_range", hx.x1))) + 
 geom_line(aes(varL,fit,linetype=varP_range)) + 
 theme_bw() + 
 geom_point(aes(varL,fit, shape = varP_range), size = 4)

effect()调用会产生以下数据框：

structure(list(varL = c(0, 4900000, 9800000, 1.5e+07, 2e+07, 
0, 4900000, 9800000, 1.5e+07, 2e+07, 0, 4900000, 9800000, 1.5e+07, 
2e+07, 0, 4900000, 9800000, 1.5e+07, 2e+07, 0, 4900000, 9800000, 
1.5e+07, 2e+07, 0, 4900000, 9800000, 1.5e+07, 2e+07, 0, 4900000, 
9800000, 1.5e+07, 2e+07, 0, 4900000, 9800000, 1.5e+07, 2e+07), 
    varP_range = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 
    5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 
    8L, 8L), .Label = c("(0,0.1]", "(0.1,0.2]", "(0.2,0.3]", 
    "(0.3,0.4]", "(0.4,0.5]", "(0.5,0.6]", "(0.6,0.7]", "(0.7,0.8]", 
    NA), class = "factor"), fit = c(0.0493753018091432, 0.0674980435061065, 
    0.0903776022141578, 0.120475525568566, 0.155489425305936, 
    0.0137978311572348, 0.0146948327184384, 0.0156415523658014, 
    0.0167030042368772, 0.0177811545669692, 0.0283241226002015, 
    0.0320688611660079, 0.0362132772188478, 0.0410808356583781, 
    0.0462488565292175, 0.0376893470992477, 0.0434689290038736, 
    0.0499436899670642, 0.0576329205782917, 0.0658761148111993, 
    0.0461027343009466, 0.0491516310196363, 0.0523594626652533, 
    0.0559432088894986, 0.0595689358953948, 0.0271243286792884, 
    0.0288090021916312, 0.0305797680715237, 0.0325565879397662, 
    0.0345556259933517, 0.0585874566843392, 0.0695486360371181, 
    0.0820248510196267, 0.0970343941431089, 0.113280038210583, 
    0.0209075509863267, 0.0315251673839061, 0.046253681947139, 
    0.0674584535698224, 0.0942769896468577), se = c(0.026293040668674, 
    0.0305781859107515, 0.0540612604209606, 0.0833019722980397, 
    0.112487542810938, 0.0338534463222995, 0.0398771851800222, 
    0.0731653210576088, 0.113913900015802, 0.15437353282528, 
    0.0409108254231718, 0.0511475671899819, 0.0972355260310069, 
    0.152187250282481, 0.206403037601197, 0.0130040830190118, 
    0.0163131582968098, 0.03036212612247, 0.0472104967827754, 
    0.0638665398985353, 0.00832820196917705, 0.0121268883535081, 
    0.0232740805148284, 0.0361535949146859, 0.0487814483071773, 
    0.0113362224665359, 0.0147098330114556, 0.027542020584634, 
    0.0427882020481342, 0.057830481511906, 0.01601220706873, 
    0.0215340145342622, 0.040911260287911, 0.0636748713064177, 
    0.0860732173940343, 0.0419361989774607, 0.0474273262485864, 
    0.085507397635156, 0.132979171779259, 0.180309829530109), 
    lower = c(0.0443332267166309, 0.0600183728153565, 0.0743144400165266, 
    0.0907939516369112, 0.108668209187463, 0.0116238722892657, 
    0.012023490450893, 0.0108135425488573, 0.00937570172867644, 
    0.00809811278621186, 0.0235056239743921, 0.025500658500369, 
    0.0234608868503482, 0.0208470526400479, 0.0184481609874694, 
    0.0356438216686479, 0.0406013691901053, 0.0441057429680943, 
    0.0477084891588961, 0.0512971927819841, 0.0445466712620454, 
    0.0467806376034436, 0.0476567944606461, 0.048393808464277, 
    0.0490625016612481, 0.0257635103540086, 0.0269634191042765, 
    0.0270321095726769, 0.0269061995593561, 0.0267392100653416, 
    0.0550074959164803, 0.0640838876027606, 0.0705422656571929, 
    0.0773047979652944, 0.0841065921246788, 0.017106637503393, 
    0.025481438787035, 0.0321633667203674, 0.0395762352261805, 
    0.0476311065775831), upper = c(0.0548650233126245, 0.0756784493915628, 
    0.108885802402636, 0.156404820719125, 0.213976690522108, 
    0.0163138248731072, 0.0178616891841985, 0.0222128708020368, 
    0.0284614557616305, 0.0359832219741959, 0.0339378335208811, 
    0.039975201853988, 0.0541535055907615, 0.0749340625856128, 
    0.100655675600276, 0.0398297048294564, 0.0464977933470156, 
    0.0563819985882714, 0.0691131684577491, 0.0834778881744663, 
    0.0477021593328273, 0.0516176358055639, 0.0574233019439704, 
    0.0643924091986213, 0.0717620710757599, 0.0285446055381808, 
    0.0307584171780848, 0.0345046440925246, 0.0391516285886827, 
    0.0441582201173176, 0.062347826961093, 0.0753653653841884, 
    0.0948621519669373, 0.120230399782042, 0.149038245196167, 
    0.0254001895885684, 0.0387079476679479, 0.0649209421145592, 
    0.108535107340218, 0.16815843130232)), .Names = c("varL", 
"varP_range", "fit", "se", "lower", "upper"), row.names = c("1", 
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", 
"14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", 
"25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", 
"36", "37", "38", "39", "40"), class = "data.frame")

我的问题是：

1）effect()包中的effects如何将连续变换为分类变量？如何使用ggplot()复制？

2）基础R图中的线条在同一坐标处相交的原因是什么？

Answer 1

回答您的第一个问题：要定义因素，error_reporting()使用

php.ini

其中nice定义如此（from here）：

Effects

使用它的一个例子：

nice(seq(min(var), max(var), length.out=5))

这可以直接与ggplot一起使用：

nice <- function (x, direction = c("round", "down", "up"), lead.digits = 1) {
  direction <- match.arg(direction)
  if (length(x) > 1){
    result <- sapply(x, nice, direction = direction, lead.digits = lead.digits)
    if (anyDuplicated(result)) result <- nice(x, direction=direction, lead.digits = lead.digits + 1)
    return(result)
  }
  if (x == 0) 
    return(0)
  power.10 <- floor(log(abs(x), 10))
  if (lead.digits > 1) 
    power.10 <- power.10 - lead.digits + 1
  lead.digit <- switch(direction, round = round(abs(x)/10^power.10), 
                       down = floor(abs(x)/10^power.10), up = ceiling(abs(x)/10^power.10))
  sign(x) * lead.digit * 10^power.10
}

关于问题2，一种直观的思考方式可能是因为Yhat的平均值没有变化（用固定的z预测），所有部分效果的标准正常CDF共享相同的交集。 E.g。

library(effects)

set.seed(123)
x = rnorm(100)
z = rexp(100)
y = factor(sample(1:2, 100, replace=T))

test = glm(y~x+z+x*z, family = binomial(link = "probit"))

preddat <- matrix('', 25, 100)
preddat <- expand.grid(nice(seq(min(x), max(x), length.out=5)), nice(seq(min(z), max(z), length.out=5)))
colnames(preddat) <- c("x", "z")
predicts <- predict(test, preddat, type = "response")
dim(predicts) <- c(5,5)

effectspred <- pnorm(effect("x:z", test)$fit)
dim(effectspred) <- c(5,5)

all.equal(effectspred, predicts)

[1] TRUE

使用效果包比较涉及来自ggplot2与基础R的连续变量的交互效应图

1 个答案: