我一直无法绘制出在受试者和物品上都计算出的增长曲线模型结果的图形（均包含为随机效应），而在数据集上对物品进行了平均计算得到的模型，因此受试者是唯一的随机效应，似乎工作正常。我似乎无法弄清楚为什么会这样或如何解决它。
Graph of Subjects and Items Model
> dput(head(new.df.subjitems, 20))
structure(list(Item.No = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1),
Subject_No = c(330, 322, 141, 330, 330,
330, 137, 330, 330, 330, 137, 330, 137, 330, 330, 137, 137, 330,
137, 141),
Bin.No = c(35, 17, 19, 44, 42, 34, 31, 23, 36, 32,
33, 28, 23, 33, 37, 7, 4, 30, 28, 31),
TargetFix = c(1, 1, 1,
0, 0.02, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0),
Condition.E = structure(c(5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L), .Label = c("First", "Second", "Max Entropy, Unrelated",
"Third", "Fourth", "Max Entropy, Competitive"), class = "factor"),
ot1 = c(-0.00489995957550024, 0.220498180897511, -0.171498585142509,
0.142098827689507, 0.210698261746511, -0.20089834259551,
-0.0538995553305028, -0.151898746840508, -0.112699070236506,
-0.230298100048512, 0.0146998787265008, -0.161698665991508,
-0.151898746840508, 0.0146998787265008, -0.122498989387506,
0.0538995553305028, 0.181298504293509, -0.0440996361795023,
-0.161698665991508, -0.0538995553305028),
ot2 = c(-0.158018948215706,
0.226392531578272, 0.0744512352170155, 0.00151941296361255,
0.192965446378795, 0.161057774142931, -0.135227753761518,
0.024310607417801, -0.0577376926172773, 0.26133902974136,
-0.156499535252094, 0.0486212148356019, 0.024310607417801,
-0.156499535252094, -0.0395047370539266, -0.135227753761518,
0.101800668562042, -0.142824818579581, 0.0486212148356019,
-0.135227753761518),
ot3 = c(0.0112384799617412, 0.177762495548696,
0.0718614343707494, -0.14311051566666, 0.113069194230467,
-0.05612035827049, 0.113717568074414, 0.12506411034348, 0.167352493276442,
-0.250560469916257, -0.0335353360396831, 0.101434485808536,
0.12506411034348, -0.0335353360396831, 0.16389449944206,
-0.113717568074414, -0.035984748339037, 0.0957432042894495,
0.101434485808536, 0.113717568074414),
ot4 = c(0.158779933129858,
0.0903598052498265, -0.175158477576023, -0.166766788637329,
-0.00086703192354873, -0.0706803112875428, 0.0864589810947309,
-0.178854426512889, -0.0950686572656207, 0.205524262921174,
0.153690832709029, -0.182263550144233, -0.178854426512889,
0.153690832709029, -0.123325047363878, 0.0864589810947308,
-0.15558879673071, 0.109609880754701, -0.182263550144233,
0.0864589810947309)),
.Names = c("Item.No", "Subject_No",
"Bin.No", "TargetFix", "Condition.E", "ot1", "ot2", "ot3", "ot4"
), row.names = c(1L, 5L, 8L, 22L, 29L, 59L, 61L, 74L, 78L, 86L,
90L, 98L, 101L, 111L, 115L, 120L, 126L, 133L, 140L, 145L), class = "data.frame")
> dput(head(df.subjonly, 20))
structure(list(Subject_No = c(103, 103, 103, 103, 103, 103, 103,
103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103, 103
),
Bin.No = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20),
TargetFix = c(0.16667, 0.16667, 0.16667,
0.16667, 0.32667, 0.39, 0.5, 0.5, 0.5, 0.5, 0.62667, 0.59, 0.66667,
0.66667, 0.76667, 0.76333, 0.66667, 0.40667, 0.33333, 0.48333
),
Condition.E = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("First",
"Second", "Max Entropy, Unrelated", "NaN, Unrelated", "Third",
"Fourth", "Max Entropy, Competitive", "NaN, Competitive", "Low Entropy, NaN",
"High Entropy, NaN", "Max Entropy, NaN", "NaN, NaN"), class = "factor"),
ot1 = c(-0.240098019199512, -0.230298100048512, -0.220498180897511,
-0.210698261746511, -0.20089834259551, -0.19109842344451,
-0.181298504293509, -0.171498585142509, -0.161698665991508,
-0.151898746840508, -0.142098827689507, -0.132298908538507,
-0.122498989387506, -0.112699070236506, -0.102899151085505,
-0.0930992319345048, -0.0832993127835043, -0.0734993936325038,
-0.0636994744815033, -0.0538995553305028),
ot2 = c(0.297804940868062,
0.26133902974136, 0.226392531578271, 0.192965446378795, 0.161057774142931,
0.13066951487068, 0.101800668562042, 0.0744512352170154,
0.0486212148356019, 0.0243106074178009, 0.0015194129636125,
-0.0197523685269634, -0.0395047370539266, -0.0577376926172774,
-0.0744512352170155, -0.0896453648531411, -0.103320081525654,
-0.115475385234555, -0.126111275979843, -0.135227753761518
),
ot3 = c(-0.331823325024233, -0.250560469916256, -0.177762495548696,
-0.113069194230468, -0.0561203582704902, -0.00655577997768254,
0.0359847483390369, 0.0718614343707493, 0.101434485808536,
0.12506411034348, 0.14311051566666, 0.15593390946916, 0.16389449944206,
0.167352493276442, 0.166668098663387, 0.162201523293977,
0.154312974859294, 0.143362661050417, 0.12971078955843, 0.113717568074414
),
ot4 = c(0.347265133901292, 0.205524262921176, 0.0903598052498273,
-0.000867031923547791, -0.0706803112875423, -0.121489365408538,
-0.15558879673071, -0.175158477576023, -0.182263550144233,
-0.178854426512889, -0.16676678863733, -0.147721588350685,
-0.123325047363878, -0.0950686572656211, -0.0643291795224186,
-0.0323686454785664, -0.000334356356151326, 0.0307411167449481,
0.0599399328470624, 0.0864589810947308)), .Names = c("Subject_No",
"Bin.No", "TargetFix", "Condition.E", "ot1", "ot2", "ot3", "ot4"
), row.names = 3:22, class = "data.frame")
>
# create 4th-order polynormial in the range of Bin.no
t <- poly ((unique(new.df.subjitems$Bin.No)), 4)
# create variables ot1, ot2, ot3, ot4 corresponding to the orthogonal
polynomial time terms and populate their values
# with the Bin.No-appropriate orthogonal polynomial values:
new.df.subjitems[,paste("ot", 1:4, sep="")] <- t[new.df.subjitems$Bin.No, 1:4]
Model.subjitems.2 <- lmer(TargetFix ~ (ot1+ot2+ot3+ot4)*Condition.E +
(1+ot1+ot2+ot3+ot4|Subject_No) +(1+ot1+ot2+ot3+ot4|Item.No),
control = lmerControl(optimizer="bobyqa"),
data=new.df.subjitems, REML=F)
# create 4th-order polynormial in the range of Bin.no
t <- poly ((unique(df.subjonly$Bin.No)), 4)
# create variables ot1, ot2, ot3, ot4 corresponding to the orthogonal
polynomial time terms and populate their values
# with the Bin.No-appropriate orthogonal polynomial values:
df.subjonly[,paste("ot", 1:4, sep="")] <- t[df.subjonly$Bin.No, 1:4]
Model.subj.2 <- lmer(TargetFix ~ (ot1+ot2+ot3+ot4)*Condition.E +
(1+ot1+ot2+ot3+ot4|Subject_No),
control = lmerControl(optimizer="bobyqa"), data=df.subjonly,
REML=F)
# Graph Subject Items
ggplot(data=new.df.subjitems, aes(Bin.No, TargetFix, color=Condition.E,
lty=Condition.E, shape=Condition.E)) +
stat_summary(fun.data=mean_se,geom="ribbon",linetype=0,alpha=0.25) +
stat_summary(fun.data=mean_se,geom="point",size=1,alpha=0.40)
+stat_summary(aes(y=fitted(Model.subjitems.2)),
fun.y=mean,geom="line",size=2.0,alpha=0.9) +
theme_bw(base_size=10) +
labs(y="Fixation Proportion", x="Bins") +
ggtitle("Subjects and Items, Target Fixations") +
theme(text=element_text(color = "black", size=20, family = "Georgia")) +
theme(axis.text = element_text(color = "black", size=10, family = "Georgia"))
+
scale_color_viridis(begin=0, end = .8, discrete=TRUE) +
scale_shape_manual(values=c(3,2,16,7)) + #16,3,2
theme(legend.key.width=unit(4,"line")) +
ggsave("Testing_SubjectItems_v3.png",width=10,height=5)
# Graph Subject Only
ggplot(data=df.subjonly, aes(Bin.No, TargetFix, color=Condition.E,
lty=Condition.E, shape=Condition.E)) +
stat_summary(fun.data=mean_se,geom="ribbon",linetype=0,alpha=0.25) +
stat_summary(fun.data=mean_se,geom="point",size=1,alpha=0.40) +
stat_summary(aes(y=fitted(Model.subj.2)),
fun.y=mean,geom="line",size=2.0,alpha=0.9) +
theme_bw(base_size=10) +
labs(y="Fixation Proportion", x="Bins") +
ggtitle("Subj Only, Target Fixations") +
theme(text=element_text(color = "black", size=20, family = "Georgia")) +
theme(axis.text = element_text(color = "black", size=10, family = "Georgia"))
+
scale_color_viridis(begin=0, end = .8, discrete=TRUE) +
scale_shape_manual(values=c(3,2,16,7)) + #16,3,2
theme(legend.key.width=unit(4,"line")) +
ggsave("Testing_SubjectOnly_v3.png",width=10,height=5)