facto_summarize: Subset and summarize the output of factor analyses
In factoextra: Extract and Visualize the Results of Multivariate Data Analyses
Description
Usage
Arguments
Details
Value
Author(s)
References
Examples
View source: R/facto_summarize.R
Description
Subset and summarize the results of Principal Component Analysis
(PCA), Correspondence Analysis (CA), Multiple Correspondence Analysis
(MCA), Factor Analysis of Mixed Data (FAMD), Multiple Factor Analysis
(MFA) and Hierarchical Multiple Factor Analysis (HMFA) functions from several packages.
Usage
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facto_summarize(
X,
element,
node.level = 1,
group.names,
result = c("coord", "cos2", "contrib"),
axes = 1:2,
select = NULL
)
Arguments
X
an object of class PCA, CA, MCA, FAMD, MFA and HMFA [FactoMineR]; prcomp
and princomp [stats]; dudi, pca, coa and acm [ade4]; ca [ca package]; expoOutput [ExPosition].
element
the element to subset from the output. Possible values are
"row" or "col" for CA; "var" or "ind" for PCA and MCA; "mca.cor" for MCA;
'quanti.var', 'quali.var' , 'group' or 'ind' for FAMD, MFA and HMFA.
node.level
a single number indicating the HMFA node level.
group.names
a vector containing the name of the groups (by default,
NULL and the group are named group.1, group.2 and so on).
result
the result to be extracted for the element. Possible values are
the combination of c("cos2", "contrib", "coord")
axes
a numeric vector specifying the axes of interest. Default values
are 1:2 for axes 1 and 2.
select
a selection of variables. Allowed values are NULL or a list
containing the arguments name, cos2 or contrib. Default is list(name =
NULL, cos2 = NULL, contrib = NULL):
name: is a character
vector containing variable names to be selected
cos2: if cos2 is in
[0, 1], ex: 0.6, then variables with a cos2 > 0.6 are selected. if cos2 >
1, ex: 5, then the top 5 variables with the highest cos2 are selected
-
contrib: if contrib > 1, ex: 5, then the top 5 variables with the highest
cos2 are selected.
Details
If length(axes) > 1, then the columns contrib and cos2 correspond to
the total contributions and total cos2 of the axes. In this case, the
column coord is calculated as x^2 + y^2 + ...+; x, y, ... are the
coordinates of the points on the specified axes.
Value
A data frame containing the (total) coord, cos2 and the contribution
for the axes.
Author(s)
Alboukadel Kassambara alboukadel.kassambara@gmail.com
References
http://www.sthda.com/english/
Examples
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# Principal component analysis
# +++++++++++++++++++++++++++++
data(decathlon2)
decathlon2.active <- decathlon2[1:23, 1:10]
res.pca <- prcomp(decathlon2.active, scale = TRUE)
# Summarize variables on axes 1:2
facto_summarize(res.pca, "var", axes = 1:2)[,-1]
# Select the top 5 contributing variables
facto_summarize(res.pca, "var", axes = 1:2,
select = list(contrib = 5))[,-1]
# Select variables with cos2 >= 0.6
facto_summarize(res.pca, "var", axes = 1:2,
select = list(cos2 = 0.6))[,-1]
# Select by names
facto_summarize(res.pca, "var", axes = 1:2,
select = list(name = c("X100m", "Discus", "Javeline")))[,-1]
# Summarize individuals on axes 1:2
facto_summarize(res.pca, "ind", axes = 1:2)[,-1]
# Correspondence Analysis
# ++++++++++++++++++++++++++
# Install and load FactoMineR to compute CA
# install.packages("FactoMineR")
library("FactoMineR")
data("housetasks")
res.ca <- CA(housetasks, graph = FALSE)
# Summarize row variables on axes 1:2
facto_summarize(res.ca, "row", axes = 1:2)[,-1]
# Summarize column variables on axes 1:2
facto_summarize(res.ca, "col", axes = 1:2)[,-1]
# Multiple Correspondence Analysis
# +++++++++++++++++++++++++++++++++
library(FactoMineR)
data(poison)
res.mca <- MCA(poison, quanti.sup = 1:2,
quali.sup = 3:4, graph=FALSE)
# Summarize variables on axes 1:2
res <- facto_summarize(res.mca, "var", axes = 1:2)
head(res)
# Summarize individuals on axes 1:2
res <- facto_summarize(res.mca, "ind", axes = 1:2)
head(res)
# Multiple factor Analysis
# +++++++++++++++++++++++++++++++++
library(FactoMineR)
data(poison)
res.mfa <- MFA(poison, group=c(2,2,5,6), type=c("s","n","n","n"),
name.group=c("desc","desc2","symptom","eat"),
num.group.sup=1:2, graph=FALSE)
# Summarize categorcial variables on axes 1:2
res <- facto_summarize(res.mfa, "quali.var", axes = 1:2)
head(res)
# Summarize individuals on axes 1:2
res <- facto_summarize(res.mfa, "ind", axes = 1:2)
head(res)
Example output
Loading required package: ggplot2
Welcome! Related Books: `Practical Guide To Cluster Analysis in R` at https://goo.gl/13EFCZ
Dim.1 Dim.2 coord cos2 contrib
X100m -0.850625692 0.17939806 0.7557477 0.7557477 12.674495
Long.jump 0.794180641 -0.28085695 0.7096035 0.7096035 11.900620
Shot.put 0.733912733 -0.08540412 0.5459218 0.5459218 9.155546
High.jump 0.610083985 0.46521415 0.5886267 0.5886267 9.871741
X400m -0.701603377 -0.29017826 0.5764507 0.5764507 9.667541
X110m.hurdle -0.764125197 0.02474081 0.5844994 0.5844994 9.802524
Discus 0.743209016 -0.04966086 0.5548258 0.5548258 9.304874
Pole.vault -0.217268042 -0.80745110 0.6991827 0.6991827 11.725854
Javeline 0.428226639 -0.38610928 0.3324584 0.3324584 5.575594
X1500m 0.004278487 -0.78448019 0.6154275 0.6154275 10.321212
Dim.1 Dim.2 coord cos2 contrib
X100m -0.850625692 0.1793981 0.7557477 0.7557477 12.674495
Long.jump 0.794180641 -0.2808570 0.7096035 0.7096035 11.900620
Pole.vault -0.217268042 -0.8074511 0.6991827 0.6991827 11.725854
X1500m 0.004278487 -0.7844802 0.6154275 0.6154275 10.321212
High.jump 0.610083985 0.4652142 0.5886267 0.5886267 9.871741
Dim.1 Dim.2 coord cos2 contrib
X100m -0.850625692 0.1793981 0.7557477 0.7557477 12.67450
Long.jump 0.794180641 -0.2808570 0.7096035 0.7096035 11.90062
Pole.vault -0.217268042 -0.8074511 0.6991827 0.6991827 11.72585
X1500m 0.004278487 -0.7844802 0.6154275 0.6154275 10.32121
Dim.1 Dim.2 coord cos2 contrib
X100m -0.8506257 0.17939806 0.7557477 0.7557477 12.674495
Discus 0.7432090 -0.04966086 0.5548258 0.5548258 9.304874
Javeline 0.4282266 -0.38610928 0.3324584 0.3324584 5.575594
Dim.1 Dim.2 coord cos2 contrib
SEBRLE 0.1912074 -1.5541282 2.4518746 0.5050034 1.7878218
CLAY 0.7901217 -2.4204156 6.4827039 0.5057178 4.7269626
BERNARD -1.3292592 -1.6118687 4.3650507 0.4871654 3.1828434
YURKOV -0.8694134 0.4328779 0.9432630 0.1199355 0.6877946
ZSIVOCZKY -0.1057450 2.0233632 4.1051806 0.5779938 2.9933552
McMULLEN 0.1185550 0.9916237 0.9973729 0.1543704 0.7272497
MARTINEAU -2.3923532 1.2849234 7.3743818 0.5205607 5.3771432
HERNU -1.8910497 -1.1784614 4.9648401 0.5543447 3.6201890
BARRAS -1.7744575 0.4125321 3.3188820 0.6495490 2.4200135
NOOL -2.7770058 1.5726757 10.1850700 0.6469840 7.4265994
BOURGUIGNON -4.4137335 -1.2635770 21.0776704 0.9301572 15.3691054
Sebrle 3.4514485 -1.2169193 13.3933893 0.7593400 9.7659944
Clay 3.3162243 -1.6232908 13.6324164 0.8523470 9.9402847
Karpov 4.0703560 0.7983510 17.2051623 0.8138146 12.5454070
Macey 1.8484623 2.0638828 7.6764252 0.8165181 5.5973828
Warners 1.3873514 -0.2819083 2.0042163 0.2662078 1.4614049
Zsivoczky 0.4715533 0.9267436 1.0812163 0.2190667 0.7883854
Hernu 0.2763118 1.1657260 1.4352654 0.4666709 1.0465457
Bernard 1.3672590 1.4780354 4.0539857 0.6274807 2.9560256
Schwarzl -0.7102777 -0.6584251 0.9380181 0.2170229 0.6839702
Pogorelov -0.2143524 -0.8610557 0.7873639 0.1337231 0.5741185
Schoenbeck -0.4953166 -1.3000530 1.9354762 0.5291161 1.4112821
Barras -0.3158867 0.8193681 0.7711485 0.1466237 0.5622947
Dim.1 Dim.2 coord cos2 contrib
Laundry -0.9918368 0.4953220 1.2290841 0.9245395 12.5556235
Main_meal -0.8755855 0.4901092 1.0068569 0.9739621 8.9413526
Dinner -0.6925740 0.3081043 0.5745869 0.9303433 3.6018327
Breakfeast -0.5086002 0.4528038 0.4637054 0.9051733 3.7680288
Tidying -0.3938084 -0.4343444 0.3437401 0.9748275 2.4340753
Dishes -0.1889641 -0.4419662 0.2310416 0.7642703 1.5153488
Shopping -0.1176813 -0.4033171 0.1765136 0.8113088 1.2294289
Official 0.2266324 0.2536132 0.1156819 0.1194711 0.6445857
Driving 0.7417696 0.6534143 0.9771724 0.7672477 7.8836989
Finances 0.2707669 -0.6178684 0.4550760 0.9973464 2.9847396
Insurance 0.6470759 -0.4737832 0.6431778 0.8848140 5.1890738
Repairs 1.5287787 0.8642647 3.0841176 0.9326072 29.5364916
Holidays 0.2524863 -1.4350066 2.1229933 0.9921522 19.7157199
Dim.1 Dim.2 coord cos2 contrib
Wife -0.83762154 0.3652207 0.83499601 0.9543242 29.079014
Alternating -0.06218462 0.2915938 0.08889388 0.1098815 1.310538
Husband 1.16091847 0.6019199 1.71003929 0.9795683 37.815957
Jointly 0.14942609 -1.0265791 1.07619274 0.9979998 31.794491
name Dim.1 Dim.2 coord cos2 contrib
Nausea_n Nausea_n 0.2673909 0.12139029 0.08623348 0.3090033 1.319847
Nausea_y Nausea_y -0.9581506 -0.43498187 1.10726185 0.3090033 4.729453
Vomit_n Vomit_n 0.4790279 -0.40919465 0.39690803 0.5953620 4.662117
Vomit_y Vomit_y -0.7185419 0.61379197 0.89304306 0.5953620 6.993176
Abdo_n Abdo_n 1.3180221 -0.03574501 1.73845988 0.8457372 11.138239
Abdo_y Abdo_y -0.6411999 0.01738946 0.41143974 0.8457372 5.418603
name Dim.1 Dim.2 coord cos2 contrib
1 1 -0.4525811 -0.26415072 0.2746052 0.46457063 1.0751792
2 2 0.8361700 -0.03193457 0.7002000 0.55670644 2.7415373
3 3 -0.4481892 0.13538726 0.2192032 0.59815656 0.8582602
4 4 0.8803694 -0.08536230 0.7823370 0.75476958 3.0631335
5 5 -0.4481892 0.13538726 0.2192032 0.59815656 0.8582602
6 6 -0.3594324 -0.43604390 0.3193260 0.06143111 1.2502771
name Dim.1 Dim.2 coord cos2 contrib
Nausea_n Nausea_n 0.2995559 -0.008263233 0.0898020 0.5679634 0.6601587
Nausea_y Nausea_y -1.0734086 0.029609918 1.1530827 0.5679634 2.3655688
Vomit_n Vomit_n 0.4923055 -0.335833158 0.3551486 0.7818420 2.4633487
Vomit_y Vomit_y -0.7384582 0.503749737 0.7990843 0.7818420 3.6950230
Abdo_n Abdo_n 1.4594717 -0.253368918 2.1942534 0.9439283 6.8916745
Abdo_y Abdo_y -0.7100132 0.123260555 0.5193120 0.9439283 3.3527065
name Dim.1 Dim.2 coord cos2 contrib
1 1 -0.8980293 -0.19968268 0.8463298 0.37413632 0.6692610
2 2 1.6550439 -0.41095346 2.9080530 0.42074506 2.2996311
3 3 -0.8673037 0.09906989 0.7620305 0.51312594 0.6025988
4 4 1.7839172 -0.56856945 3.5056316 0.66185891 2.7721845
5 5 -0.8673037 0.09906989 0.7620305 0.51312594 0.6025988
6 6 -1.1229099 -1.07440938 2.4152822 0.06519297 1.9099576
factoextra documentation built on April 2, 2020, 1:09 a.m.
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Description Usage Arguments Details Value Author(s) References Examples
View source: R/facto_summarize.R
Description
Subset and summarize the results of Principal Component Analysis (PCA), Correspondence Analysis (CA), Multiple Correspondence Analysis (MCA), Factor Analysis of Mixed Data (FAMD), Multiple Factor Analysis (MFA) and Hierarchical Multiple Factor Analysis (HMFA) functions from several packages.
Usage
1 2 3 4 5 6 7 8 9 | facto_summarize(
X,
element,
node.level = 1,
group.names,
result = c("coord", "cos2", "contrib"),
axes = 1:2,
select = NULL
)
|
Arguments
X |
an object of class PCA, CA, MCA, FAMD, MFA and HMFA [FactoMineR]; prcomp and princomp [stats]; dudi, pca, coa and acm [ade4]; ca [ca package]; expoOutput [ExPosition]. |
element |
the element to subset from the output. Possible values are "row" or "col" for CA; "var" or "ind" for PCA and MCA; "mca.cor" for MCA; 'quanti.var', 'quali.var' , 'group' or 'ind' for FAMD, MFA and HMFA. |
node.level |
a single number indicating the HMFA node level. |
group.names |
a vector containing the name of the groups (by default, NULL and the group are named group.1, group.2 and so on). |
result |
the result to be extracted for the element. Possible values are the combination of c("cos2", "contrib", "coord") |
axes |
a numeric vector specifying the axes of interest. Default values are 1:2 for axes 1 and 2. |
select |
a selection of variables. Allowed values are NULL or a list containing the arguments name, cos2 or contrib. Default is list(name = NULL, cos2 = NULL, contrib = NULL):
|
Details
If length(axes) > 1, then the columns contrib and cos2 correspond to the total contributions and total cos2 of the axes. In this case, the column coord is calculated as x^2 + y^2 + ...+; x, y, ... are the coordinates of the points on the specified axes.
Value
A data frame containing the (total) coord, cos2 and the contribution for the axes.
Author(s)
Alboukadel Kassambara alboukadel.kassambara@gmail.com
References
http://www.sthda.com/english/
Examples
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 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | # Principal component analysis
# +++++++++++++++++++++++++++++
data(decathlon2)
decathlon2.active <- decathlon2[1:23, 1:10]
res.pca <- prcomp(decathlon2.active, scale = TRUE)
# Summarize variables on axes 1:2
facto_summarize(res.pca, "var", axes = 1:2)[,-1]
# Select the top 5 contributing variables
facto_summarize(res.pca, "var", axes = 1:2,
select = list(contrib = 5))[,-1]
# Select variables with cos2 >= 0.6
facto_summarize(res.pca, "var", axes = 1:2,
select = list(cos2 = 0.6))[,-1]
# Select by names
facto_summarize(res.pca, "var", axes = 1:2,
select = list(name = c("X100m", "Discus", "Javeline")))[,-1]
# Summarize individuals on axes 1:2
facto_summarize(res.pca, "ind", axes = 1:2)[,-1]
# Correspondence Analysis
# ++++++++++++++++++++++++++
# Install and load FactoMineR to compute CA
# install.packages("FactoMineR")
library("FactoMineR")
data("housetasks")
res.ca <- CA(housetasks, graph = FALSE)
# Summarize row variables on axes 1:2
facto_summarize(res.ca, "row", axes = 1:2)[,-1]
# Summarize column variables on axes 1:2
facto_summarize(res.ca, "col", axes = 1:2)[,-1]
# Multiple Correspondence Analysis
# +++++++++++++++++++++++++++++++++
library(FactoMineR)
data(poison)
res.mca <- MCA(poison, quanti.sup = 1:2,
quali.sup = 3:4, graph=FALSE)
# Summarize variables on axes 1:2
res <- facto_summarize(res.mca, "var", axes = 1:2)
head(res)
# Summarize individuals on axes 1:2
res <- facto_summarize(res.mca, "ind", axes = 1:2)
head(res)
# Multiple factor Analysis
# +++++++++++++++++++++++++++++++++
library(FactoMineR)
data(poison)
res.mfa <- MFA(poison, group=c(2,2,5,6), type=c("s","n","n","n"),
name.group=c("desc","desc2","symptom","eat"),
num.group.sup=1:2, graph=FALSE)
# Summarize categorcial variables on axes 1:2
res <- facto_summarize(res.mfa, "quali.var", axes = 1:2)
head(res)
# Summarize individuals on axes 1:2
res <- facto_summarize(res.mfa, "ind", axes = 1:2)
head(res)
|
Example output
Loading required package: ggplot2
Welcome! Related Books: `Practical Guide To Cluster Analysis in R` at https://goo.gl/13EFCZ
Dim.1 Dim.2 coord cos2 contrib
X100m -0.850625692 0.17939806 0.7557477 0.7557477 12.674495
Long.jump 0.794180641 -0.28085695 0.7096035 0.7096035 11.900620
Shot.put 0.733912733 -0.08540412 0.5459218 0.5459218 9.155546
High.jump 0.610083985 0.46521415 0.5886267 0.5886267 9.871741
X400m -0.701603377 -0.29017826 0.5764507 0.5764507 9.667541
X110m.hurdle -0.764125197 0.02474081 0.5844994 0.5844994 9.802524
Discus 0.743209016 -0.04966086 0.5548258 0.5548258 9.304874
Pole.vault -0.217268042 -0.80745110 0.6991827 0.6991827 11.725854
Javeline 0.428226639 -0.38610928 0.3324584 0.3324584 5.575594
X1500m 0.004278487 -0.78448019 0.6154275 0.6154275 10.321212
Dim.1 Dim.2 coord cos2 contrib
X100m -0.850625692 0.1793981 0.7557477 0.7557477 12.674495
Long.jump 0.794180641 -0.2808570 0.7096035 0.7096035 11.900620
Pole.vault -0.217268042 -0.8074511 0.6991827 0.6991827 11.725854
X1500m 0.004278487 -0.7844802 0.6154275 0.6154275 10.321212
High.jump 0.610083985 0.4652142 0.5886267 0.5886267 9.871741
Dim.1 Dim.2 coord cos2 contrib
X100m -0.850625692 0.1793981 0.7557477 0.7557477 12.67450
Long.jump 0.794180641 -0.2808570 0.7096035 0.7096035 11.90062
Pole.vault -0.217268042 -0.8074511 0.6991827 0.6991827 11.72585
X1500m 0.004278487 -0.7844802 0.6154275 0.6154275 10.32121
Dim.1 Dim.2 coord cos2 contrib
X100m -0.8506257 0.17939806 0.7557477 0.7557477 12.674495
Discus 0.7432090 -0.04966086 0.5548258 0.5548258 9.304874
Javeline 0.4282266 -0.38610928 0.3324584 0.3324584 5.575594
Dim.1 Dim.2 coord cos2 contrib
SEBRLE 0.1912074 -1.5541282 2.4518746 0.5050034 1.7878218
CLAY 0.7901217 -2.4204156 6.4827039 0.5057178 4.7269626
BERNARD -1.3292592 -1.6118687 4.3650507 0.4871654 3.1828434
YURKOV -0.8694134 0.4328779 0.9432630 0.1199355 0.6877946
ZSIVOCZKY -0.1057450 2.0233632 4.1051806 0.5779938 2.9933552
McMULLEN 0.1185550 0.9916237 0.9973729 0.1543704 0.7272497
MARTINEAU -2.3923532 1.2849234 7.3743818 0.5205607 5.3771432
HERNU -1.8910497 -1.1784614 4.9648401 0.5543447 3.6201890
BARRAS -1.7744575 0.4125321 3.3188820 0.6495490 2.4200135
NOOL -2.7770058 1.5726757 10.1850700 0.6469840 7.4265994
BOURGUIGNON -4.4137335 -1.2635770 21.0776704 0.9301572 15.3691054
Sebrle 3.4514485 -1.2169193 13.3933893 0.7593400 9.7659944
Clay 3.3162243 -1.6232908 13.6324164 0.8523470 9.9402847
Karpov 4.0703560 0.7983510 17.2051623 0.8138146 12.5454070
Macey 1.8484623 2.0638828 7.6764252 0.8165181 5.5973828
Warners 1.3873514 -0.2819083 2.0042163 0.2662078 1.4614049
Zsivoczky 0.4715533 0.9267436 1.0812163 0.2190667 0.7883854
Hernu 0.2763118 1.1657260 1.4352654 0.4666709 1.0465457
Bernard 1.3672590 1.4780354 4.0539857 0.6274807 2.9560256
Schwarzl -0.7102777 -0.6584251 0.9380181 0.2170229 0.6839702
Pogorelov -0.2143524 -0.8610557 0.7873639 0.1337231 0.5741185
Schoenbeck -0.4953166 -1.3000530 1.9354762 0.5291161 1.4112821
Barras -0.3158867 0.8193681 0.7711485 0.1466237 0.5622947
Dim.1 Dim.2 coord cos2 contrib
Laundry -0.9918368 0.4953220 1.2290841 0.9245395 12.5556235
Main_meal -0.8755855 0.4901092 1.0068569 0.9739621 8.9413526
Dinner -0.6925740 0.3081043 0.5745869 0.9303433 3.6018327
Breakfeast -0.5086002 0.4528038 0.4637054 0.9051733 3.7680288
Tidying -0.3938084 -0.4343444 0.3437401 0.9748275 2.4340753
Dishes -0.1889641 -0.4419662 0.2310416 0.7642703 1.5153488
Shopping -0.1176813 -0.4033171 0.1765136 0.8113088 1.2294289
Official 0.2266324 0.2536132 0.1156819 0.1194711 0.6445857
Driving 0.7417696 0.6534143 0.9771724 0.7672477 7.8836989
Finances 0.2707669 -0.6178684 0.4550760 0.9973464 2.9847396
Insurance 0.6470759 -0.4737832 0.6431778 0.8848140 5.1890738
Repairs 1.5287787 0.8642647 3.0841176 0.9326072 29.5364916
Holidays 0.2524863 -1.4350066 2.1229933 0.9921522 19.7157199
Dim.1 Dim.2 coord cos2 contrib
Wife -0.83762154 0.3652207 0.83499601 0.9543242 29.079014
Alternating -0.06218462 0.2915938 0.08889388 0.1098815 1.310538
Husband 1.16091847 0.6019199 1.71003929 0.9795683 37.815957
Jointly 0.14942609 -1.0265791 1.07619274 0.9979998 31.794491
name Dim.1 Dim.2 coord cos2 contrib
Nausea_n Nausea_n 0.2673909 0.12139029 0.08623348 0.3090033 1.319847
Nausea_y Nausea_y -0.9581506 -0.43498187 1.10726185 0.3090033 4.729453
Vomit_n Vomit_n 0.4790279 -0.40919465 0.39690803 0.5953620 4.662117
Vomit_y Vomit_y -0.7185419 0.61379197 0.89304306 0.5953620 6.993176
Abdo_n Abdo_n 1.3180221 -0.03574501 1.73845988 0.8457372 11.138239
Abdo_y Abdo_y -0.6411999 0.01738946 0.41143974 0.8457372 5.418603
name Dim.1 Dim.2 coord cos2 contrib
1 1 -0.4525811 -0.26415072 0.2746052 0.46457063 1.0751792
2 2 0.8361700 -0.03193457 0.7002000 0.55670644 2.7415373
3 3 -0.4481892 0.13538726 0.2192032 0.59815656 0.8582602
4 4 0.8803694 -0.08536230 0.7823370 0.75476958 3.0631335
5 5 -0.4481892 0.13538726 0.2192032 0.59815656 0.8582602
6 6 -0.3594324 -0.43604390 0.3193260 0.06143111 1.2502771
name Dim.1 Dim.2 coord cos2 contrib
Nausea_n Nausea_n 0.2995559 -0.008263233 0.0898020 0.5679634 0.6601587
Nausea_y Nausea_y -1.0734086 0.029609918 1.1530827 0.5679634 2.3655688
Vomit_n Vomit_n 0.4923055 -0.335833158 0.3551486 0.7818420 2.4633487
Vomit_y Vomit_y -0.7384582 0.503749737 0.7990843 0.7818420 3.6950230
Abdo_n Abdo_n 1.4594717 -0.253368918 2.1942534 0.9439283 6.8916745
Abdo_y Abdo_y -0.7100132 0.123260555 0.5193120 0.9439283 3.3527065
name Dim.1 Dim.2 coord cos2 contrib
1 1 -0.8980293 -0.19968268 0.8463298 0.37413632 0.6692610
2 2 1.6550439 -0.41095346 2.9080530 0.42074506 2.2996311
3 3 -0.8673037 0.09906989 0.7620305 0.51312594 0.6025988
4 4 1.7839172 -0.56856945 3.5056316 0.66185891 2.7721845
5 5 -0.8673037 0.09906989 0.7620305 0.51312594 0.6025988
6 6 -1.1229099 -1.07440938 2.4152822 0.06519297 1.9099576
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