R语言 从线型图例中删除形状并从形状图例中删除线型

qmelpv7a  于 11个月前  发布在  其他
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我想对下图做两个调整:
1.我想删除linetype(型号)相关图例中的shape
1.我想删除与shape相关的图例中的linetype(标准误差(SE))
代码:

dgmedia2 <- structure(list(Parametros = structure(c(1L, 2L, 3L, 4L, 1L, 2L, 
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 5L, 1L, 
2L, 3L, 4L, 5L), .Label = c("Intercept", "Bare.ground", "Canopy.cover", 
"Shrub.cover", "Feral.mammal.dung", "Volume.lying.CWD"), class = "factor"), 
    Estimativas = c(-9.08718574009237, 0.246931091933284, 0.499326289121633, 
    -0.205561450009113, 1.07892606385103, -0.0296414533141213, 
    -0.0447905575590813, 0.182172232483717, -0.742126805654274, 
    0.222679274844953, 0.0590787053410465, -0.163485077690949, 
    0.131647768264277, 0.773377624910276, -7.22777909465642, 
    0.182391810011366, 0.422549145766675, -0.431294756177376, 
    1.10872202002585, -0.0291155842687582, -0.0464319202556404, 
    0.176542050008272, -0.78336973563058, 0.267174066310438, 
    0.0580762736413778, -0.165656899124107, 0.125965696446778, 
    0.73599525103876, -10.0274652596287, 0.282857360828957, 0.528018638631668, 
    -0.111429079805171, 0.987829625275738, -0.0259550802866814, 
    -0.0440101069770179, 0.177162265315325, -0.840395852786189, 
    -0.149771436010034, 0.0765372814155488, -0.148146161391884, 
    0.139214265328571, 0.87042464596807), ErroPadrao = c(6.04094497876376, 
    0.238714529427829, 0.312371860848067, 1.06699389843587, 0.657421168133384, 
    0.0401112312237702, 0.0572090823007306, 0.118716588061632, 
    1.30215431010075, 0.547252531459708, 0.0278076736099682, 
    0.0732591806388275, 0.0894031462569832, 0.807639175284003, 
    2.71519349846099, 0.131802564275492, 0.142804294834212, 0.890621281696227, 
    0.621662162048757, 0.0358381692477308, 0.053776574993421, 
    0.114266540855619, 0.96074807615628, 0.317046948689855, 0.0232030068948484, 
    0.0693821532984276, 0.0845515467581205, 0.653857443548544, 
    1.80370372228358, NaN, 0.346330990416065, 0.977284520368054, 
    NaN, 0.00173065248814043, 0.0640512617941663, 0.012960579747033, 
    0.386189704956875, 0.0997213546041854, 0.00866799939995657, 
    0.0462597890457268, 0.00656045805722545, 0.639150600868637
    ), Distribuição = c("Poisson", "Poisson", "Poisson", "Poisson", 
    "Poisson", "Poisson", "Poisson", "Poisson", "Poisson", "Poisson", 
    "Poisson", "Poisson", "Poisson", "Poisson", "NB", "NB", "NB", 
    "NB", "NB", "NB", "NB", "NB", "NB", "NB", "NB", "NB", "NB", 
    "NB", "COM-Poisson", "COM-Poisson", "COM-Poisson", "COM-Poisson", 
    "COM-Poisson", "COM-Poisson", "COM-Poisson", "COM-Poisson", 
    "COM-Poisson", "COM-Poisson", "COM-Poisson", "COM-Poisson", 
    "COM-Poisson", "COM-Poisson"), Tipo = c("Média", "Média", 
    "Média", "Média", "Média", "Média", "Média", "Média", 
    "Média", "Média", "Média", "Média", "Média", "Média", 
    "Média", "Média", "Média", "Média", "Média", "Média", 
    "Média", "Média", "Média", "Média", "Média", "Média", 
    "Média", "Média", "Média", "Média", "Média", "Média", 
    "Média", "Média", "Média", "Média", "Média", "Média", 
    "Média", "Média", "Média", "Média"), Response = c(9, 
    9, 9, 9, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 9, 9, 9, 
    9, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 9, 9, 9, 9, 10, 
    10, 10, 10, 10, 11, 11, 11, 11, 11), Names = structure(c(9L, 
    9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 
    11L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 
    11L, 11L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 
    11L, 11L, 11L), .Label = c("1.Amblyopone", "2.Aphaenogaster", 
    "3.Camponotus", "4.Camponotus", "5.Camponotus", "6.Camponotus", 
    "7.Camponotus", "8.Cardiocondyla", "9.Crematogaster", "10.Heteroponera", 
    "11.Iridomyrmex", "12.Iridomyrmex", "13.Iridomyrmex", "14.Iridomyrmex", 
    "15.Iridomyrmex", "16.Iridomyrmex", "17.Iridomyrmex", "18.Melophorus", 
    "19.Melophorus", "20.Melophorus", "21.Meranoplus", "22.Monomorium", 
    "23.Monomorium", "24.Monomorium", "25.Myrmecia", "26.Notoncus", 
    "27.Notoncus", "28.Nylanderia", "29.Ochetellus", "30.Paraparatrechina", 
    "31.Pheidole", "32.Pheidole", "33.Pheidole", "34.Pheidole", 
    "35.Polyrhachis", "36.Rhytidoponera", "37.Rhytidoponera", 
    "38.Solenopsis", "39.Stigmacros", "40.Tapinoma", "41.Tetramorium"
    ), class = "factor"), NAN = 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, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("1", "16"), class = "factor")), row.names = c(NA, 
-42L), class = "data.frame")

library(ggplot2)
library(latex2exp)
  ggplot(dgmedia2) +
  geom_pointrange(aes(x = Parametros, 
                      y = Estimativas,
                      ymin = Estimativas-1.96*ErroPadrao,
                      ymax = Estimativas+1.96*ErroPadrao,
                      linetype = Distribuição,
                      shape = NAN),
                  # It controls the vertical distance between models
                  position = position_dodge(width = .6),
                  
                  size = .5) +
  theme_bw() +
  theme(axis.title.y = element_blank(),
        text = element_text(size = 20),
        legend.position = "bottom") +
  scale_shape_manual(values = c(1,20),
                     labels = c("Without SE", "With SE")) +
  scale_linetype_manual(values=c("solid", "dashed", "dotted"))+
  facet_wrap(~Names, scales = "free_x") +
  coord_flip() +
  labs(y = TeX('$\\hat{\\beta}\\pm 1.96 SE}$'),
       linetype = "Model",
       shape = "Standard Error (SE)")

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到目前为止的结果:x1c 0d1x
我想这样做,因为从图例中不清楚linetype对应于COM-Poisson或NB。我在theme上看到了legend.key.height = unit(1, 'cm')的解决方案,但这对我没有100%的帮助。我也考虑过制作手动图例的想法,但它会减慢编码过程。

vhipe2zx

vhipe2zx1#

这可以通过guide_legend来实现,它允许您通过override.aes删除shapelinetype,如下所示:

library(ggplot2)
library(latex2exp)
ggplot(dgmedia2) +
  geom_pointrange(aes(
    x = Parametros,
    y = Estimativas,
    ymin = Estimativas - 1.96 * ErroPadrao,
    ymax = Estimativas + 1.96 * ErroPadrao,
    linetype = Distribuição,
    shape = NAN
  ),
  # It controls the vertical distance between models
  position = position_dodge(width = .6),

  size = .5
  ) +
  theme_bw() +
  theme(
    axis.title.y = element_blank(),
    text = element_text(size = 20),
    legend.position = "bottom"
  ) +
  scale_shape_manual(
    values = c(1, 20),
    labels = c("Without SE", "With SE")
  ) +
  scale_linetype_manual(values = c("solid", "dashed", "dotted")) +
  guides(linetype = guide_legend(override.aes = list(shape = NA)),
         shape = guide_legend(override.aes = list(linetype = "blank"))) +
  facet_wrap(~Names, scales = "free_x") +
  coord_flip() +
  labs(
    y = TeX("$\\hat{\\beta}\\pm 1.96 SE}$"),
    linetype = "Model",
    shape = "Standard Error (SE)"
  )
#> Warning: Removed 1 rows containing missing values (geom_segment).

#> Warning: Removed 1 rows containing missing values (geom_segment).

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的数据

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