我正在尝试使用PCA进行探索性分析，以确定量表的析因结构。我使用的软件包是：
library(GPArotation) # required for `principal` to work
library(psych)
功能是：
principal()
我想根据参与者的性别应用一个调整权重。
以下是我的数据集示例：
GPS_01 GPS_03 GPS_04 GPS_05 GPS_07 GPS_08 GPS_10 GPS_11 GPS_12 GPS_13 GPS_14 GPS_15 GPS_17 GPS_18 GPS_19 gender_pscore
1 1 1 2 2 4 1 3 2 1 1 3 1 2 2 4 0.62
2 1 1 1 1 2 1 1 1 1 1 3 2 3 2 1 2.78
3 1 1 1 1 1 1 1 1 1 1 2 1 2 2 1 0.62
4 1 1 2 2 1 1 1 1 1 1 3 1 1 4 1 0.62
5 4 4 4 4 5 5 4 5 4 4 5 2 5 5 4 0.62
6 1 1 1 1 1 1 1 1 1 1 2 2 3 2 2 0.62
7 1 1 1 1 1 1 2 1 1 1 3 2 4 3 2 0.62
8 1 3 1 1 1 1 3 1 2 1 4 1 4 3 2 0.62
9 3 3 3 5 3 1 4 2 3 1 2 1 5 2 3 0.62
10 1 2 1 1 2 2 1 2 1 2 4 2 2 3 2 0.62
11 1 4 1 1 3 4 1 2 3 1 2 2 3 2 3 0.62
12 1 1 1 1 5 2 1 5 1 3 5 4 5 4 5 0.62
13 1 2 1 1 1 4 1 4 1 3 5 1 4 2 5 0.62
14 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0.62
15 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0.62
16 1 1 1 1 1 1 2 2 1 1 3 1 1 1 4 0.62
17 2 2 1 2 2 2 4 4 1 4 3 1 2 3 4 0.62
18 1 1 2 2 1 1 1 1 2 1 2 1 2 2 1 0.62
19 1 2 1 1 3 3 1 3 1 1 4 1 3 3 4 0.62
20 1 1 1 2 1 1 2 1 1 1 3 1 2 1 1 2.78
或者原始数据的更小子集(如果更容易的话(
data<-structure(list(GPS_01 = c(1L, 1L, 1L, 1L, 4L, 1L), GPS_03 = c(1L,
1L, 1L, 1L, 4L, 1L), GPS_04 = c(2L, 1L, 1L, 2L, 4L, 1L), GPS_05 = c(2L,
1L, 1L, 2L, 4L, 1L), GPS_07 = c(4L, 2L, 1L, 1L, 5L, 1L), GPS_08 = c(1L,
1L, 1L, 1L, 5L, 1L), GPS_10 = c(3L, 1L, 1L, 1L, 4L, 1L), GPS_11 = c(2L,
1L, 1L, 1L, 5L, 1L), GPS_12 = c(1L, 1L, 1L, 1L, 4L, 1L), GPS_13 = c(1L,
1L, 1L, 1L, 4L, 1L), GPS_14 = c(3L, 3L, 2L, 3L, 5L, 2L), GPS_15 = c(1L,
2L, 1L, 1L, 2L, 2L), GPS_17 = c(2L, 3L, 2L, 1L, 5L, 3L), GPS_18 = c(2L,
2L, 2L, 4L, 5L, 2L), GPS_19 = c(4L, 1L, 1L, 1L, 4L, 2L), gender_pscore = c(0.62,
2.78, 0.62, 0.62, 0.62, 0.62)), row.names = c(NA, 6L), class = "data.frame")
这里是我使用的代码：
pc <- principal(data[,1:15], nfactors = 3, rotate ="oblimin",weights ="gender_pscore")
我总是遇到同样的问题：
Error in (function (L, Tmat = diag(ncol(L)), gam = 0, normalize = FALSE, :
unused argument (weights = "gender_pscore")
Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), :
'data' must be of a vector type, was 'NULL'
In addition: Warning message:
In data[,1:20], nfactors = 3, rotate = "oblimin", :
The requested transformaton failed, Promax was used instead as an oblique transformation
我对使用R很陌生，所以不知道如何解决这个问题。当我卸下weights ="gender_pscore"时，问题就消失了。但在这种情况下，我不能再根据参与者的性别对我的析因分析应用调整权重。
weight似乎是principal()函数的正确参数。我认为问题来自于你告诉principal()使用列"；gender_pscore"；。
从?principal我们可以看出，weight是长度为n.obs的向量，它包含每个观测的权重。如果使用weight = "gender_pscore"，则将长度为1的字符向量输入到weight参数中。
选择数据中的列应该可以解决您的问题：
psych::principal(df[,1:15],
nfactors = 3,
rotate ="oblimin",
weight = df[,"gender_pscore"])
警告：这不是一般规则，它们是使用带引号或不带引号的列名的函数(例如：大多数tidyverse函数(！
上面代码的输出：
> psych::principal(df[,1:15],
+ nfactors = 3,
+ rotate ="oblimin",
+ weight = df[,"gender_pscore"])
Principal Components Analysis
Call: psych::principal(r = df[, 1:15], nfactors = 3, rotate = "oblimin",
weight = df[, "gender_pscore"])
Standardized loadings (pattern matrix) based upon correlation matrix
TC1 TC3 TC2 h2 u2 com
GPS_01 0.83 0.13 0.22 0.99 0.01288 1.2
GPS_03 0.83 0.13 0.22 0.99 0.01288 1.2
GPS_04 0.81 0.29 -0.17 0.99 0.00837 1.4
GPS_05 0.81 0.29 -0.17 0.99 0.00837 1.4
GPS_07 0.08 0.88 0.15 0.94 0.05795 1.1
GPS_08 0.83 0.13 0.22 0.99 0.01288 1.2
GPS_10 0.28 0.81 -0.07 1.00 0.00011 1.3
GPS_11 0.63 0.43 0.11 0.99 0.00559 1.8
GPS_12 0.83 0.13 0.22 0.99 0.01288 1.2
GPS_13 0.83 0.13 0.22 0.99 0.01288 1.2
GPS_14 0.69 0.19 0.23 0.83 0.17091 1.4
GPS_15 -0.05 -0.20 1.00 0.95 0.05006 1.1
GPS_17 0.25 0.20 0.81 0.99 0.01386 1.3
GPS_18 1.17 -0.28 -0.23 0.97 0.03065 1.2
GPS_19 -0.01 0.99 -0.13 0.95 0.05381 1.0
TC1 TC3 TC2
SS loadings 8.23 3.99 2.31
Proportion Var 0.55 0.27 0.15
Cumulative Var 0.55 0.81 0.97
Proportion Explained 0.57 0.27 0.16
Cumulative Proportion 0.57 0.84 1.00
With component correlations of
TC1 TC3 TC2
TC1 1.00 0.65 0.24
TC3 0.65 1.00 0.19
TC2 0.24 0.19 1.00
Mean item complexity = 1.3
Test of the hypothesis that 3 components are sufficient.
The root mean square of the residuals (RMSR) is 0.02
with the empirical chi square 0.52 with prob < 1
Fit based upon off diagonal values = 1Warning messages:
1: In cor.smooth(r) : Matrix was not positive definite, smoothing was done
2: In psych::principal(df[, 1:15], nfactors = 3, rotate = "oblimin", :
The matrix is not positive semi-definite, scores found from Structure loadings
df的dput：
df <- structure(list(GPS_01 = c(1L, 1L, 1L, 1L, 4L, 1L), GPS_03 = c(1L,
1L, 1L, 1L, 4L, 1L), GPS_04 = c(2L, 1L, 1L, 2L, 4L, 1L), GPS_05 = c(2L,
1L, 1L, 2L, 4L, 1L), GPS_07 = c(4L, 2L, 1L, 1L, 5L, 1L), GPS_08 = c(1L,
1L, 1L, 1L, 5L, 1L), GPS_10 = c(3L, 1L, 1L, 1L, 4L, 1L), GPS_11 = c(2L,
1L, 1L, 1L, 5L, 1L), GPS_12 = c(1L, 1L, 1L, 1L, 4L, 1L), GPS_13 = c(1L,
1L, 1L, 1L, 4L, 1L), GPS_14 = c(3L, 3L, 2L, 3L, 5L, 2L), GPS_15 = c(1L,
2L, 1L, 1L, 2L, 2L), GPS_17 = c(2L, 3L, 2L, 1L, 5L, 3L), GPS_18 = c(2L,
2L, 2L, 4L, 5L, 2L), GPS_19 = c(4L, 1L, 1L, 1L, 4L, 2L), gender_pscore = c(0.62,
2.78, 0.62, 0.62, 0.62, 0.62)), row.names = c(NA, 6L), class = "data.frame")