如何使用 'par' 来操纵图边距？
How to use 'par' for manipulating plot margins?
我正在绘制箱线图，但很难获得我喜欢的外边距。我正在使用以下代码：
tiff("Boxplot.tif", height=10, width=10, units="in", res=300, compression="lzw");
boxplot(Betadisp, col=c("darkgreen", "blue", "red"), boxwex=0.4, outline=T,
ylim=c(0, 0.46), xlim=c(0.75,2.25), names = c("OG", "LIL","HIL"),
cex.lab=1.5, ylab="Distance to centroid", xlab='x', las = 1, notch=F, axis=T, at=at.x)
text(1, 0.36, cex=1.8, bquote(paste("ab")))
text(1.5, 0.235, cex=1.8, bquote(paste("a")))
text(2, 0.39, cex=1.8, bquote(paste("b")))
text(2.2, 0.45, cex=1.8, bquote(paste("NRI")))
par(mai = par("mai")*.75, oma = c(0,0,0,0) + 0.1, mar = c(0,0,0,0) + 0.1)
dev.off()
我希望缩小外边距，尤其是顶部和右侧。使用 par() 似乎没有任何改变。任何关于如何调整代码的建议将不胜感激。
图形是这样的：
> dput(PMPhyloSOBetadisp)
structure(list(eig = structure(4.15892930122748, .Names = "PCoA1"),
vectors = structure(c(0.187894881623694, -0.0462669264321831,
0.078270716100596, -0.0708373862452924, 0.0330432406942923,
0.00808151460353643, 0.189307120491109, 0.191686534306445,
0.279993576281652, 0.28571659518387, -0.292963094442439,
0.130307798194384, -0.108034670599994, -0.279412265592119,
-0.317212249248107, -0.201985621091697, 0.0132092286292795,
0.396467849399822, 0.0211368138831204, 0.30188342895443,
0.206873272795936, 0.138385483231689, 0.124786446212896,
-0.281430210350172, -0.0305849438602823, -0.00255636044610587,
0.177458408517268, -0.12231080332995, -0.281871351530724,
-0.152724144457444, 0.176186794362196, -0.155165330924759,
0.0320858194688813, -0.223473307376246, 0.127613754254968,
0.166962002449328, 0.158685218038405, 0.100008083268373,
-0.0940881894843899, 0.121856979176153, -0.140265149171084,
0.0613648415652045, -0.190745874749306, -0.00219528996378689,
-0.1430862908501, -0.26172388196876, -0.0727143694332558,
0.331841908277, 0.220415829691309, 0.123101542527033, 0.180278291830266,
0.215427947847535, 0.190756247591512, 0.165126573796417,
0.139890849869231, 0.0933688471441757, 0.0729113372604114,
0.19610126202653, -0.0522909285621633, -0.12468483037786,
-0.0921365696698343, 0.14270372033243, -0.0720884766538631,
0.0606904684333239, -0.15285729454955, -0.144265372972496,
-0.0887902944817819, -0.0159417136106468, -0.085279046400012,
-0.0691969457493266, 0.147813990005166, -0.2912450223718,
0.0852462637551566, -0.158567373346935, -0.100576512229591,
0.0057226914258182, -0.325235562525615, -0.0981544032193745,
0.0829599052753697, -0.0819102728840672, -0.100848420365388,
0.0284072979563995, -0.196807045981046, 0.0738952152156015,
0.0448146573968611, -0.0534264885898359, -0.117434245302843,
0.0826195634628877, 0.199034727378092, -0.0467567273886303,
0.0532651574812087, 0.00569859196113407, -0.120110959638929,
-0.0205367406051655, -0.104116321893903, -0.153130831169889,
-0.0666402710314291, 0.246990006965615, 0.0540941400034081,
-0.0185000753890068, 0.149547217533089, 0.090513237877524,
0.00387509228547038, 0.0229945137667384, 0.23535371290394,
0.33598374482058, 0.269258915330889, 0.00689306033364249,
0.00226287725342899, -0.186909704550723, 0.0841922617367422,
-0.119109532559289, -0.209836298377522, 0.145071133844632,
-0.332068338669535, -0.0975072972715105, -0.0443222416741678,
-0.0980041604145747, -0.219892017644063, 0.282116111212308,
0.128980339196711, -0.141574399557034, -0.0657539413882544,
-0.45247717974156, -0.183785397552269, 0.128383205531913,
-0.0101083206716934, -0.0178247844444956, -0.0133953258963639,
0.178986768698698, -0.138538624428617, 0.0432527997337566,
-0.603532150600179, -0.286840743894003, 0.331764689135947,
0.0383952829464595, 0.174327620102299, 0.050025120352419,
-0.00996422334957243), .Dim = c(139L, 1L), .Dimnames = list(
c("H01", "H02", "H03", "H04", "H05", "H06", "H07", "H08",
"H09", "H10", "H11", "H12", "H13", "H14", "H15", "H16",
"H17", "H18", "H19", "H20", "H21", "H22", "H23", "H24",
"H25", "H26", "H27", "H28", "H29", "H30", "H31", "H32",
"H33", "H34", "H35", "H36", "H37", "H38", "H39", "H40",
"H41", "H42", "H43", "H44", "H45", "H46", "H47", "H48",
"H49", "L01", "L02", "L03", "L04", "L05", "L06", "L07",
"L08", "L09", "L10", "L11", "L12", "L13", "L14", "L15",
"L16", "L17", "L18", "L19", "L20", "L21", "L22", "L23",
"L24", "L25", "L26", "L27", "L28", "L29", "L30", "L31",
"L32", "L33", "L34", "L35", "L36", "L37", "L38", "L39",
"L40", "L41", "L42", "L43", "L44", "L45", "O01", "O02",
"O03", "O04", "O05", "O06", "O07", "O08", "O09", "O10",
"O11", "O12", "O13", "O14", "O15", "O16", "O17", "O18",
"O19", "O20", "O21", "O22", "O23", "O24", "O25", "O26",
"O27", "O28", "O29", "O30", "O31", "O32", "O33", "O34",
"O35", "O36", "O37", "O38", "O39", "O40", "O41", "O42",
"O43", "O44", "O45"), "PCoA1")), distances = structure(c(0.171774954519456,
0.0623868535364207, 0.0621507889963585, 0.0869573133495299,
0.0169233135900547, 0.0080384125007011, 0.173187193386872,
0.175566607202207, 0.263873649177415, 0.269596668079633,
0.309083021546677, 0.114187871090147, 0.124154597704232,
0.295532192696357, 0.333332176352344, 0.218105548195935,
0.00291069847495801, 0.380347922295584, 0.0050168867788829,
0.285763501850193, 0.190753345691698, 0.122265556127451,
0.108666519108659, 0.29755013745441, 0.0467048709645198,
0.0186762875503434, 0.16133848141303, 0.138430730434188,
0.297991278634962, 0.168844071561681, 0.160066867257958,
0.171285258028997, 0.0159658923646438, 0.239593234480484,
0.11149382715073, 0.150842075345091, 0.142565290934167, 0.0838881561641354,
0.110208116588627, 0.105737052071915, 0.156385076275321,
0.045244914460967, 0.206865801853543, 0.0183152170680244,
0.159206217954337, 0.277843809072997, 0.0888342965374933,
0.315721981172762, 0.204295902587072, 0.12797501136046, 0.185151760663692,
0.220301416680961, 0.195629716424938, 0.170000042629844,
0.144764318702658, 0.0982423159776023, 0.077784806093838,
0.200974730859957, 0.0474174597287366, 0.119811361544433,
0.0872631008364077, 0.147577189165856, 0.0672150078204365,
0.0655639372667505, 0.147983825716124, 0.13939190413907,
0.0839168256483553, 0.0110682447772202, 0.0804055775665854,
0.0643234769159, 0.152687458838593, 0.286371553538374, 0.0901197325885831,
0.153693904513509, 0.0957030433961639, 0.0105961602592448,
0.320362093692189, 0.0932809343859479, 0.0878333741087963,
0.0770368040506406, 0.0959749515319617, 0.0332807667898261,
0.191933577147619, 0.0787686840490281, 0.0496881262302877,
0.0485530197564093, 0.112560776469416, 0.0874930322963143,
0.203908196211518, 0.0418832585552037, 0.0581386263146353,
0.0105720607945607, 0.115237490805503, 0.0156632717717389,
0.0914369812138262, 0.140451490489812, 0.0539609303513526,
0.259669347645691, 0.0667734806834846, 0.0058207347089303,
0.162226558213165, 0.103192578557601, 0.0165544329655469,
0.0356738544468149, 0.248033053584017, 0.348663085500656,
0.281938256010966, 0.019572401013719, 0.0149422179335055,
0.174230363870647, 0.0968716024168187, 0.106430191879212,
0.197156957697445, 0.157750474524708, 0.319388997989459,
0.084827956591434, 0.0316429009940913, 0.0853248197344982,
0.207212676963987, 0.294795451892385, 0.141659679876788,
0.128895058876958, 0.0530746007081779, 0.439797839061484,
0.171106056872192, 0.141062546211989, 0.00257102000838311,
0.00514544376441906, 0.000715985216287402, 0.191666109378775,
0.12585928374854, 0.0559321404138331, 0.590852809920102,
0.274161403213926, 0.344444029816024, 0.051074623626536,
0.187006960782375, 0.0627044610324955, 0.00271511733050407
), .Names = c("H01", "H02", "H03", "H04", "H05", "H06", "H07",
"H08", "H09", "H10", "H11", "H12", "H13", "H14", "H15", "H16",
"H17", "H18", "H19", "H20", "H21", "H22", "H23", "H24", "H25",
"H26", "H27", "H28", "H29", "H30", "H31", "H32", "H33", "H34",
"H35", "H36", "H37", "H38", "H39", "H40", "H41", "H42", "H43",
"H44", "H45", "H46", "H47", "H48", "H49", "L01", "L02", "L03",
"L04", "L05", "L06", "L07", "L08", "L09", "L10", "L11", "L12",
"L13", "L14", "L15", "L16", "L17", "L18", "L19", "L20", "L21",
"L22", "L23", "L24", "L25", "L26", "L27", "L28", "L29", "L30",
"L31", "L32", "L33", "L34", "L35", "L36", "L37", "L38", "L39",
"L40", "L41", "L42", "L43", "L44", "L45", "O01", "O02", "O03",
"O04", "O05", "O06", "O07", "O08", "O09", "O10", "O11", "O12",
"O13", "O14", "O15", "O16", "O17", "O18", "O19", "O20", "O21",
"O22", "O23", "O24", "O25", "O26", "O27", "O28", "O29", "O30",
"O31", "O32", "O33", "O34", "O35", "O36", "O37", "O38", "O39",
"O40", "O41", "O42", "O43", "O44", "O45")), group = structure(c(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, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 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, 1L, 1L,
1L, 1L, 1L), .Label = c("1", "2", "3"), class = "factor"),
centroids = structure(c(-0.0126793406800765, -0.0048734688334266,
0.0161199271042375), .Dim = c(3L, 1L), .Dimnames = list(c("1",
"2", "3"), "PCoA1")), call = betadisper(d = PMPhyloSOVegdist,
group = Predictors$class, type = "centroid")), .Names = c("eig",
"vectors", "distances", "group", "centroids", "call"), class = "betadisper", method = "gower", type = "centroid", bias.adjust = FALSE)
在 R 基本图形中，par 中有一个选项可以使用 "pretty" 边距。对于漂亮的标签，此默认选项将轴值上的数据范围扩展 4%。这有时会产生忽略 xlim 和 ylim 的副作用。
可以尝试的一件事是切换此选项以准确使用数据范围。 X轴和Y轴的选项分别为xaxs="i"和yaxs="i"。
这是一个示例，其中我使用了您的 boxplot 代码，无论是否打开这些选项：
Betadisp<-data.frame(distances=
c(0.171774954519456,
0.0623868535364207, 0.0621507889963585, 0.0869573133495299,
0.0169233135900547, 0.0080384125007011, 0.173187193386872,
0.175566607202207, 0.263873649177415, 0.269596668079633,
0.309083021546677, 0.114187871090147, 0.124154597704232,
0.295532192696357, 0.333332176352344, 0.218105548195935,
0.00291069847495801, 0.380347922295584, 0.0050168867788829,
0.285763501850193, 0.190753345691698, 0.122265556127451,
0.108666519108659, 0.29755013745441, 0.0467048709645198,
0.0186762875503434, 0.16133848141303, 0.138430730434188,
0.297991278634962, 0.168844071561681, 0.160066867257958,
0.171285258028997, 0.0159658923646438, 0.239593234480484,
0.11149382715073, 0.150842075345091, 0.142565290934167, 0.0838881561641354,
0.110208116588627, 0.105737052071915, 0.156385076275321,
0.045244914460967, 0.206865801853543, 0.0183152170680244,
0.159206217954337, 0.277843809072997, 0.0888342965374933,
0.315721981172762, 0.204295902587072, 0.12797501136046, 0.185151760663692,
0.220301416680961, 0.195629716424938, 0.170000042629844,
0.144764318702658, 0.0982423159776023, 0.077784806093838,
0.200974730859957, 0.0474174597287366, 0.119811361544433,
0.0872631008364077, 0.147577189165856, 0.0672150078204365,
0.0655639372667505, 0.147983825716124, 0.13939190413907,
0.0839168256483553, 0.0110682447772202, 0.0804055775665854,
0.0643234769159, 0.152687458838593, 0.286371553538374, 0.0901197325885831,
0.153693904513509, 0.0957030433961639, 0.0105961602592448,
0.320362093692189, 0.0932809343859479, 0.0878333741087963,
0.0770368040506406, 0.0959749515319617, 0.0332807667898261,
0.191933577147619, 0.0787686840490281, 0.0496881262302877,
0.0485530197564093, 0.112560776469416, 0.0874930322963143,
0.203908196211518, 0.0418832585552037, 0.0581386263146353,
0.0105720607945607, 0.115237490805503, 0.0156632717717389,
0.0914369812138262, 0.140451490489812, 0.0539609303513526,
0.259669347645691, 0.0667734806834846, 0.0058207347089303,
0.162226558213165, 0.103192578557601, 0.0165544329655469,
0.0356738544468149, 0.248033053584017, 0.348663085500656,
0.281938256010966, 0.019572401013719, 0.0149422179335055,
0.174230363870647, 0.0968716024168187, 0.106430191879212,
0.197156957697445, 0.157750474524708, 0.319388997989459,
0.084827956591434, 0.0316429009940913, 0.0853248197344982,
0.207212676963987, 0.294795451892385, 0.141659679876788,
0.128895058876958, 0.0530746007081779, 0.439797839061484,
0.171106056872192, 0.141062546211989, 0.00257102000838311,
0.00514544376441906, 0.000715985216287402, 0.191666109378775,
0.12585928374854, 0.0559321404138331, 0.590852809920102,
0.274161403213926, 0.344444029816024, 0.051074623626536,
0.187006960782375, 0.0627044610324955, 0.00271511733050407),
group=c(3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1)
)
Betadisp$group<-factor(Betadisp$group,levels=c("3","2","1"))
没有 xaxs="i" 和 yaxs="i":
boxplot(Betadisp$distances ~ Betadisp$group, col=c("darkgreen", "blue", "red"),
boxwex=0.4, outline=T, ylim=c(0, 0.46), xlim=c(0.75,3.25), names = c("OG", "LIL","HIL"),
cex.lab=1.5, ylab="Distance to centroid", xlab='x', las = 1, notch=F, axis=T)
与 xaxs="i" 和 yaxs="i":
boxplot(Betadisp$distances ~ Betadisp$group, col=c("darkgreen", "blue", "red"), boxwex=0.4,
outline=T, ylim=c(0, 0.46), xlim=c(0.75,3.25), names = c("OG", "LIL","HIL"),
cex.lab=1.5, ylab="Distance to centroid", xlab='x', las = 1, notch=F, axis=T,
xaxs="i",yaxs="i")
作为参考，请查看右上角。我没有触及任何保证金参数。
此外，Vincent Bonhomme 是正确的：您必须在提交 plot/boxplot 代码之前提交 par。这是因为 par 修改仅适用于下一组基本图形命令。在 tiff() 表达式中使用 par 的方式不允许 par 影响任何东西。将所有 par 表达式移动到 tiff().
下的下一行
我正在绘制箱线图，但很难获得我喜欢的外边距。我正在使用以下代码：
tiff("Boxplot.tif", height=10, width=10, units="in", res=300, compression="lzw");
boxplot(Betadisp, col=c("darkgreen", "blue", "red"), boxwex=0.4, outline=T,
ylim=c(0, 0.46), xlim=c(0.75,2.25), names = c("OG", "LIL","HIL"),
cex.lab=1.5, ylab="Distance to centroid", xlab='x', las = 1, notch=F, axis=T, at=at.x)
text(1, 0.36, cex=1.8, bquote(paste("ab")))
text(1.5, 0.235, cex=1.8, bquote(paste("a")))
text(2, 0.39, cex=1.8, bquote(paste("b")))
text(2.2, 0.45, cex=1.8, bquote(paste("NRI")))
par(mai = par("mai")*.75, oma = c(0,0,0,0) + 0.1, mar = c(0,0,0,0) + 0.1)
dev.off()
我希望缩小外边距，尤其是顶部和右侧。使用 par() 似乎没有任何改变。任何关于如何调整代码的建议将不胜感激。
图形是这样的：
> dput(PMPhyloSOBetadisp)
structure(list(eig = structure(4.15892930122748, .Names = "PCoA1"),
vectors = structure(c(0.187894881623694, -0.0462669264321831,
0.078270716100596, -0.0708373862452924, 0.0330432406942923,
0.00808151460353643, 0.189307120491109, 0.191686534306445,
0.279993576281652, 0.28571659518387, -0.292963094442439,
0.130307798194384, -0.108034670599994, -0.279412265592119,
-0.317212249248107, -0.201985621091697, 0.0132092286292795,
0.396467849399822, 0.0211368138831204, 0.30188342895443,
0.206873272795936, 0.138385483231689, 0.124786446212896,
-0.281430210350172, -0.0305849438602823, -0.00255636044610587,
0.177458408517268, -0.12231080332995, -0.281871351530724,
-0.152724144457444, 0.176186794362196, -0.155165330924759,
0.0320858194688813, -0.223473307376246, 0.127613754254968,
0.166962002449328, 0.158685218038405, 0.100008083268373,
-0.0940881894843899, 0.121856979176153, -0.140265149171084,
0.0613648415652045, -0.190745874749306, -0.00219528996378689,
-0.1430862908501, -0.26172388196876, -0.0727143694332558,
0.331841908277, 0.220415829691309, 0.123101542527033, 0.180278291830266,
0.215427947847535, 0.190756247591512, 0.165126573796417,
0.139890849869231, 0.0933688471441757, 0.0729113372604114,
0.19610126202653, -0.0522909285621633, -0.12468483037786,
-0.0921365696698343, 0.14270372033243, -0.0720884766538631,
0.0606904684333239, -0.15285729454955, -0.144265372972496,
-0.0887902944817819, -0.0159417136106468, -0.085279046400012,
-0.0691969457493266, 0.147813990005166, -0.2912450223718,
0.0852462637551566, -0.158567373346935, -0.100576512229591,
0.0057226914258182, -0.325235562525615, -0.0981544032193745,
0.0829599052753697, -0.0819102728840672, -0.100848420365388,
0.0284072979563995, -0.196807045981046, 0.0738952152156015,
0.0448146573968611, -0.0534264885898359, -0.117434245302843,
0.0826195634628877, 0.199034727378092, -0.0467567273886303,
0.0532651574812087, 0.00569859196113407, -0.120110959638929,
-0.0205367406051655, -0.104116321893903, -0.153130831169889,
-0.0666402710314291, 0.246990006965615, 0.0540941400034081,
-0.0185000753890068, 0.149547217533089, 0.090513237877524,
0.00387509228547038, 0.0229945137667384, 0.23535371290394,
0.33598374482058, 0.269258915330889, 0.00689306033364249,
0.00226287725342899, -0.186909704550723, 0.0841922617367422,
-0.119109532559289, -0.209836298377522, 0.145071133844632,
-0.332068338669535, -0.0975072972715105, -0.0443222416741678,
-0.0980041604145747, -0.219892017644063, 0.282116111212308,
0.128980339196711, -0.141574399557034, -0.0657539413882544,
-0.45247717974156, -0.183785397552269, 0.128383205531913,
-0.0101083206716934, -0.0178247844444956, -0.0133953258963639,
0.178986768698698, -0.138538624428617, 0.0432527997337566,
-0.603532150600179, -0.286840743894003, 0.331764689135947,
0.0383952829464595, 0.174327620102299, 0.050025120352419,
-0.00996422334957243), .Dim = c(139L, 1L), .Dimnames = list(
c("H01", "H02", "H03", "H04", "H05", "H06", "H07", "H08",
"H09", "H10", "H11", "H12", "H13", "H14", "H15", "H16",
"H17", "H18", "H19", "H20", "H21", "H22", "H23", "H24",
"H25", "H26", "H27", "H28", "H29", "H30", "H31", "H32",
"H33", "H34", "H35", "H36", "H37", "H38", "H39", "H40",
"H41", "H42", "H43", "H44", "H45", "H46", "H47", "H48",
"H49", "L01", "L02", "L03", "L04", "L05", "L06", "L07",
"L08", "L09", "L10", "L11", "L12", "L13", "L14", "L15",
"L16", "L17", "L18", "L19", "L20", "L21", "L22", "L23",
"L24", "L25", "L26", "L27", "L28", "L29", "L30", "L31",
"L32", "L33", "L34", "L35", "L36", "L37", "L38", "L39",
"L40", "L41", "L42", "L43", "L44", "L45", "O01", "O02",
"O03", "O04", "O05", "O06", "O07", "O08", "O09", "O10",
"O11", "O12", "O13", "O14", "O15", "O16", "O17", "O18",
"O19", "O20", "O21", "O22", "O23", "O24", "O25", "O26",
"O27", "O28", "O29", "O30", "O31", "O32", "O33", "O34",
"O35", "O36", "O37", "O38", "O39", "O40", "O41", "O42",
"O43", "O44", "O45"), "PCoA1")), distances = structure(c(0.171774954519456,
0.0623868535364207, 0.0621507889963585, 0.0869573133495299,
0.0169233135900547, 0.0080384125007011, 0.173187193386872,
0.175566607202207, 0.263873649177415, 0.269596668079633,
0.309083021546677, 0.114187871090147, 0.124154597704232,
0.295532192696357, 0.333332176352344, 0.218105548195935,
0.00291069847495801, 0.380347922295584, 0.0050168867788829,
0.285763501850193, 0.190753345691698, 0.122265556127451,
0.108666519108659, 0.29755013745441, 0.0467048709645198,
0.0186762875503434, 0.16133848141303, 0.138430730434188,
0.297991278634962, 0.168844071561681, 0.160066867257958,
0.171285258028997, 0.0159658923646438, 0.239593234480484,
0.11149382715073, 0.150842075345091, 0.142565290934167, 0.0838881561641354,
0.110208116588627, 0.105737052071915, 0.156385076275321,
0.045244914460967, 0.206865801853543, 0.0183152170680244,
0.159206217954337, 0.277843809072997, 0.0888342965374933,
0.315721981172762, 0.204295902587072, 0.12797501136046, 0.185151760663692,
0.220301416680961, 0.195629716424938, 0.170000042629844,
0.144764318702658, 0.0982423159776023, 0.077784806093838,
0.200974730859957, 0.0474174597287366, 0.119811361544433,
0.0872631008364077, 0.147577189165856, 0.0672150078204365,
0.0655639372667505, 0.147983825716124, 0.13939190413907,
0.0839168256483553, 0.0110682447772202, 0.0804055775665854,
0.0643234769159, 0.152687458838593, 0.286371553538374, 0.0901197325885831,
0.153693904513509, 0.0957030433961639, 0.0105961602592448,
0.320362093692189, 0.0932809343859479, 0.0878333741087963,
0.0770368040506406, 0.0959749515319617, 0.0332807667898261,
0.191933577147619, 0.0787686840490281, 0.0496881262302877,
0.0485530197564093, 0.112560776469416, 0.0874930322963143,
0.203908196211518, 0.0418832585552037, 0.0581386263146353,
0.0105720607945607, 0.115237490805503, 0.0156632717717389,
0.0914369812138262, 0.140451490489812, 0.0539609303513526,
0.259669347645691, 0.0667734806834846, 0.0058207347089303,
0.162226558213165, 0.103192578557601, 0.0165544329655469,
0.0356738544468149, 0.248033053584017, 0.348663085500656,
0.281938256010966, 0.019572401013719, 0.0149422179335055,
0.174230363870647, 0.0968716024168187, 0.106430191879212,
0.197156957697445, 0.157750474524708, 0.319388997989459,
0.084827956591434, 0.0316429009940913, 0.0853248197344982,
0.207212676963987, 0.294795451892385, 0.141659679876788,
0.128895058876958, 0.0530746007081779, 0.439797839061484,
0.171106056872192, 0.141062546211989, 0.00257102000838311,
0.00514544376441906, 0.000715985216287402, 0.191666109378775,
0.12585928374854, 0.0559321404138331, 0.590852809920102,
0.274161403213926, 0.344444029816024, 0.051074623626536,
0.187006960782375, 0.0627044610324955, 0.00271511733050407
), .Names = c("H01", "H02", "H03", "H04", "H05", "H06", "H07",
"H08", "H09", "H10", "H11", "H12", "H13", "H14", "H15", "H16",
"H17", "H18", "H19", "H20", "H21", "H22", "H23", "H24", "H25",
"H26", "H27", "H28", "H29", "H30", "H31", "H32", "H33", "H34",
"H35", "H36", "H37", "H38", "H39", "H40", "H41", "H42", "H43",
"H44", "H45", "H46", "H47", "H48", "H49", "L01", "L02", "L03",
"L04", "L05", "L06", "L07", "L08", "L09", "L10", "L11", "L12",
"L13", "L14", "L15", "L16", "L17", "L18", "L19", "L20", "L21",
"L22", "L23", "L24", "L25", "L26", "L27", "L28", "L29", "L30",
"L31", "L32", "L33", "L34", "L35", "L36", "L37", "L38", "L39",
"L40", "L41", "L42", "L43", "L44", "L45", "O01", "O02", "O03",
"O04", "O05", "O06", "O07", "O08", "O09", "O10", "O11", "O12",
"O13", "O14", "O15", "O16", "O17", "O18", "O19", "O20", "O21",
"O22", "O23", "O24", "O25", "O26", "O27", "O28", "O29", "O30",
"O31", "O32", "O33", "O34", "O35", "O36", "O37", "O38", "O39",
"O40", "O41", "O42", "O43", "O44", "O45")), group = structure(c(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, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 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, 1L, 1L,
1L, 1L, 1L), .Label = c("1", "2", "3"), class = "factor"),
centroids = structure(c(-0.0126793406800765, -0.0048734688334266,
0.0161199271042375), .Dim = c(3L, 1L), .Dimnames = list(c("1",
"2", "3"), "PCoA1")), call = betadisper(d = PMPhyloSOVegdist,
group = Predictors$class, type = "centroid")), .Names = c("eig",
"vectors", "distances", "group", "centroids", "call"), class = "betadisper", method = "gower", type = "centroid", bias.adjust = FALSE)
在 R 基本图形中，par 中有一个选项可以使用 "pretty" 边距。对于漂亮的标签，此默认选项将轴值上的数据范围扩展 4%。这有时会产生忽略 xlim 和 ylim 的副作用。
可以尝试的一件事是切换此选项以准确使用数据范围。 X轴和Y轴的选项分别为xaxs="i"和yaxs="i"。
这是一个示例，其中我使用了您的 boxplot 代码，无论是否打开这些选项：
Betadisp<-data.frame(distances=
c(0.171774954519456,
0.0623868535364207, 0.0621507889963585, 0.0869573133495299,
0.0169233135900547, 0.0080384125007011, 0.173187193386872,
0.175566607202207, 0.263873649177415, 0.269596668079633,
0.309083021546677, 0.114187871090147, 0.124154597704232,
0.295532192696357, 0.333332176352344, 0.218105548195935,
0.00291069847495801, 0.380347922295584, 0.0050168867788829,
0.285763501850193, 0.190753345691698, 0.122265556127451,
0.108666519108659, 0.29755013745441, 0.0467048709645198,
0.0186762875503434, 0.16133848141303, 0.138430730434188,
0.297991278634962, 0.168844071561681, 0.160066867257958,
0.171285258028997, 0.0159658923646438, 0.239593234480484,
0.11149382715073, 0.150842075345091, 0.142565290934167, 0.0838881561641354,
0.110208116588627, 0.105737052071915, 0.156385076275321,
0.045244914460967, 0.206865801853543, 0.0183152170680244,
0.159206217954337, 0.277843809072997, 0.0888342965374933,
0.315721981172762, 0.204295902587072, 0.12797501136046, 0.185151760663692,
0.220301416680961, 0.195629716424938, 0.170000042629844,
0.144764318702658, 0.0982423159776023, 0.077784806093838,
0.200974730859957, 0.0474174597287366, 0.119811361544433,
0.0872631008364077, 0.147577189165856, 0.0672150078204365,
0.0655639372667505, 0.147983825716124, 0.13939190413907,
0.0839168256483553, 0.0110682447772202, 0.0804055775665854,
0.0643234769159, 0.152687458838593, 0.286371553538374, 0.0901197325885831,
0.153693904513509, 0.0957030433961639, 0.0105961602592448,
0.320362093692189, 0.0932809343859479, 0.0878333741087963,
0.0770368040506406, 0.0959749515319617, 0.0332807667898261,
0.191933577147619, 0.0787686840490281, 0.0496881262302877,
0.0485530197564093, 0.112560776469416, 0.0874930322963143,
0.203908196211518, 0.0418832585552037, 0.0581386263146353,
0.0105720607945607, 0.115237490805503, 0.0156632717717389,
0.0914369812138262, 0.140451490489812, 0.0539609303513526,
0.259669347645691, 0.0667734806834846, 0.0058207347089303,
0.162226558213165, 0.103192578557601, 0.0165544329655469,
0.0356738544468149, 0.248033053584017, 0.348663085500656,
0.281938256010966, 0.019572401013719, 0.0149422179335055,
0.174230363870647, 0.0968716024168187, 0.106430191879212,
0.197156957697445, 0.157750474524708, 0.319388997989459,
0.084827956591434, 0.0316429009940913, 0.0853248197344982,
0.207212676963987, 0.294795451892385, 0.141659679876788,
0.128895058876958, 0.0530746007081779, 0.439797839061484,
0.171106056872192, 0.141062546211989, 0.00257102000838311,
0.00514544376441906, 0.000715985216287402, 0.191666109378775,
0.12585928374854, 0.0559321404138331, 0.590852809920102,
0.274161403213926, 0.344444029816024, 0.051074623626536,
0.187006960782375, 0.0627044610324955, 0.00271511733050407),
group=c(3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1)
)
Betadisp$group<-factor(Betadisp$group,levels=c("3","2","1"))
没有 xaxs="i" 和 yaxs="i":
boxplot(Betadisp$distances ~ Betadisp$group, col=c("darkgreen", "blue", "red"),
boxwex=0.4, outline=T, ylim=c(0, 0.46), xlim=c(0.75,3.25), names = c("OG", "LIL","HIL"),
cex.lab=1.5, ylab="Distance to centroid", xlab='x', las = 1, notch=F, axis=T)
与 xaxs="i" 和 yaxs="i":
boxplot(Betadisp$distances ~ Betadisp$group, col=c("darkgreen", "blue", "red"), boxwex=0.4,
outline=T, ylim=c(0, 0.46), xlim=c(0.75,3.25), names = c("OG", "LIL","HIL"),
cex.lab=1.5, ylab="Distance to centroid", xlab='x', las = 1, notch=F, axis=T,
xaxs="i",yaxs="i")
作为参考，请查看右上角。我没有触及任何保证金参数。
此外，Vincent Bonhomme 是正确的：您必须在提交 plot/boxplot 代码之前提交 par。这是因为 par 修改仅适用于下一组基本图形命令。在 tiff() 表达式中使用 par 的方式不允许 par 影响任何东西。将所有 par 表达式移动到 tiff().