不同的结果Gamma广义线性模型R和SPSS [英] Different results Gamma generalized linear model R and SPSS
本文介绍了不同的结果Gamma广义线性模型R和SPSS的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
更新：如果我将SPSS中的参数估算方法更改为混合，将比例参数方法更改为皮尔逊卡方，则SPSS和R的p值和S.E.相似。现在有人会在R中更改这些设置以及这些设置的实际含义吗？
Update: The p-values and S.E.'s are similar between SPSS and R if I change the parameter estimation method in SPSS to 'Hybrid' and the scale parameter method to 'Pearson Chi-square'. Does anyone now how to change these settings in R and what these settings actually mean?
我正在尝试执行GLM
I am trying to perform an GLM with a gamma log link function in R, to analyse a multiple imputation dataset.
但是，当我比较R和SPSS中相同分析的结果时，它们有很大不同。此示例位于非输入数据集中，以使事情更易于解释。 SPSS结果如下：
However, when I compare the results from the same analysis in R and SPSS they are very different. This example is in a non-imputation dataset to make things easier to interpret. The SPSS result is as follows:
Parameter Estimates
Parameter B Std. Error 95% Wald Confidence Interval Hypothesis Test
Lower Upper Wald Chi-Square df Sig.
(Intercept) 3,263 ,2499 2,774 3,753 170,571 1 ,000
[Comorb=1] -,631 ,1335 -,893 -,369 22,331 1 ,000
[Comorb=2] -,371 ,1473 -,660 -,083 6,358 1 ,012
[Comorb=3] 0a . . . . . .
PAIDhoog ,257 ,1283 ,006 ,509 4,023 1 ,045
PHQhoog ,039 ,1504 -,256 ,334 ,068 1 ,794
[etndich=1,00] -,085 ,1125 -,306 ,135 ,575 1 ,448
[etndich=2,00] 0a . . . . . .
Leeftijd ,009 ,0035 ,002 ,016 6,588 1 ,010
(Scale) ,613b ,0470 ,528 ,712
Dependent Variable: totaalhealthcareutilization
Model: (Intercept), Comorb, PAIDhoog, PHQhoog, etndich, Leeftijd
a Set to zero because this parameter is redundant.
b Maximum likelihood estimate.
R中的相同分析得出以下结果：
While the same analysis in R yields this result:
Call:
glm(formula = (totaalhealthcareutilization) ~ PAIDhoog + PHQhoog +
Comorb + Leeftijd + etndich, family = Gamma(link = log),
data = F)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1297 -0.7231 -0.3018 0.2075 3.1365
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.006208 0.273817 10.979 < 0.0000000000000002 ***
PAIDhoog 0.201881 0.131777 1.532 0.1264
PHQhoog 0.126989 0.157416 0.807 0.4203
Comorbgeen -0.638842 0.144459 -4.422 0.0000128 ***
Comorb1 -0.348187 0.158484 -2.197 0.0286 *
Leeftijd 0.007311 0.003534 2.069 0.0392 *
etndich 0.151836 0.118872 1.277 0.2023
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for Gamma family taken to be 0.9432289)
Null deviance: 286.49 on 381 degrees of freedom
Residual deviance: 243.01 on 375 degrees of freedom
(71 observations deleted due to missingness)
AIC: 3156
Number of Fisher Scoring iterations: 6
这怎么可能？即使我在R中使用na.omit或na.exclude，结果仍会有所不同。我在R中使用了 relevel函数，以确保对分类变量使用相同的引用类别。
How is this possible? The results keep differing, even if I use na.omit or na.exclude in R. I have used the function 'relevel' in R, to make sure that the same reference category is used for the categoric variables.
我希望您知道我在R中做错了什么。
I hope you have any idea what I am doing wrong in R.
This is what a sample of my data looks like:
verrichtingen verpleegkanders Leeftijd HbA1c BMI Type_Treat DurationDM
1 0 0 26 69 26.7 Insulin 5
2 0 0 69 75 34.5 Insulin 17
3 0 0 67 62 24.3 Insulin 1
4 6 0 38 96 NA Insulin 10
5 0 0 29 NA 19.1 Insulin 25
6 0 0 50 86 37.9 Both 9
7 1 0 29 44 29.1 Both 33
451 4 0 68 113 37.9 Both 11
452 21 1 57 62 21.5 Insulin 1
453 0 0 37 54 25.4 Both 14
Socstatus PAID1 PAID2 PAID3 PAID4 PAID5 PAIDtot PHQ1 PHQ2 PHQ3
1 wel achterstandsw 0 1 2 1 0 4 0 0 0
2 geen achterstandsw 2 1 1 2 0 6 0 0 0
3 <NA> 0 0 0 0 0 0 0 0 0
4 geen achterstandsw 0 0 1 1 0 2 1 0 3
5 geen achterstandsw 0 0 0 0 0 0 1 1 3
6 wel achterstandsw 0 1 0 2 0 3 2 0 3
7 geen achterstandsw 1 1 2 3 0 7 1 1 3
451 geen achterstandsw 0 0 1 0 0 1 0 0 0
452 wel achterstandsw 1 0 4 1 0 6 2 0 3
453 wel achterstandsw 1 1 2 3 2 9 1 0 1
PHQ4 PHQ5 PHQ6 PHQ7 PHQ8 PHQ9 Geslacht Etnicit HAPOH Bedrijfsarts MW
1 1 1 0 0 0 0 vrouw Overigwest NA NA NA
2 1 0 0 1 1 0 man Mar NA NA NA
3 0 0 0 0 0 0 man Overigwest NA NA NA
4 3 1 1 1 1 0 vrouw Overignietwest NA NA NA
5 0 0 0 3 0 0 man Overigwest NA NA NA
6 1 1 1 0 0 0 man Turk NA NA NA
7 3 0 0 2 0 0 vrouw Overigwest NA NA NA
451 0 0 0 0 0 0 man 4 NA NA NA
452 3 0 0 1 0 0 vrouw Mar NA NA NA
453 2 2 0 0 0 0 vrouw Mar NA NA NA
FysioErgo Diet Psychiat Psychol Dvk VPtot Internist Specialist ICUopname
1 NA 5 0 0 5 5 2 3 0
2 NA 2 0 0 2 2 3 8 0
3 NA 0 0 0 1 1 2 3 0
4 NA 0 1 2 11 11 6 25 0
5 NA 0 0 0 4 4 2 6 0
6 NA 1 0 0 2 2 2 0 0
7 NA 3 0 0 4 4 2 3 0
451 NA 0 0 0 1 1 3 7 0
452 NA 2 0 0 4 5 0 25 4
453 NA 1 0 0 2 2 0 5 0
Opnamegewoon SEH Comorb DMtype PAIDtotaal PHQtotaal PAIDhoog PHQhoog
1 0 0 geen DM1 4 2 0 0
2 0 0 geen DM2 6 3 0 0
3 0 0 geen DM1 0 0 0 0
4 1 0 geen DM2 2 NA 0 NA
5 0 0 geen DM1 0 8 0 0
6 0 0 geen DM2 3 8 0 0
7 0 0 geen DM2 7 10 0 0
451 18 2 <NA> DM2 1 0 0 0
452 34 3 <NA> DM1 6 9 0 0
453 0 0 <NA> DM2 9 6 1 0
interactPHQPAID paidtotaalimp PHQtotaalimp GADtotaalimp PAIDhoogimp
1 0 4 2 1 0
2 0 6 3 0 0
3 0 0 0 0 0
4 0 2 11 2 0
5 0 0 8 0 0
6 0 3 8 0 0
7 0 7 10 3 0
451 0 1 0 0 0
452 0 6 9 0 0
453 0 9 6 1 1
PHQhoogimp GADimphoog kostenopnames kosteninternist kostenspecialist
1 0 0 0 160 240
2 0 0 0 240 640
3 0 0 0 160 240
4 0 0 443 480 2000
5 0 0 0 160 480
6 0 0 0 160 0
7 0 1 0 160 240
451 0 0 7974 240 560
452 0 0 15062 0 2000
453 0 0 0 0 400
kostenhuisarts kostenMW kostenfysioergo kostendvk kostendietist
1 NA NA NA 240 240
2 NA NA NA 96 96
3 NA NA NA 48 0
4 NA NA NA 528 0
5 NA NA NA 192 0
6 NA NA NA 96 48
7 NA NA NA 192 144
451 NA NA NA 48 0
452 NA NA NA 192 96
453 NA NA NA 96 48
totaalkosten jaarHAPOH jaarbedrijfsarts jaarMW jaarfysioergo
1 NA NA NA NA NA
2 NA NA NA NA NA
3 NA NA NA NA NA
4 NA NA NA NA NA
5 NA NA NA NA NA
6 NA NA NA NA NA
7 NA NA NA NA NA
451 NA NA NA NA NA
452 NA NA NA NA NA
453 NA NA NA NA NA
totaalverbruikjaar kostenHAjaar kostenMWjaar kostenjaarfysioergo
1 NA NA NA NA
2 NA NA NA NA
3 NA NA NA NA
4 NA NA NA NA
5 NA NA NA NA
6 NA NA NA NA
7 NA NA NA NA
451 NA NA NA NA
452 NA NA NA NA
453 NA NA NA NA
kostenopnameICU kostenpsycholoog kostenpsychiater kostenvpanders
1 0 0 0 0
2 0 0 0 0
3 0 0 0 0
4 0 188 94 0
5 0 0 0 0
6 0 0 0 0
7 0 0 0 0
451 0 0 0 0
452 8060 0 0 48
453 0 0 0 0
kostenverrichtingen totaalutilization kostenseh totaalkostennieuw hypoangst
1 0 NA 0 880 1
2 0 NA 0 1072 1
3 0 NA 0 448 0
4 876 NA 0 4609 0
5 0 NA 0 832 0
6 0 NA 0 304 1
7 146 NA 0 882 5
451 584 NA 518 9924 0
452 3066 NA 777 29301 0
453 0 NA 0 544 3
contactprimarycare contactsecondarycare totaalhealthcareutilization
1 NA 15 15
2 NA 15 15
3 NA 6 6
4 NA 52 52
5 NA 12 12
6 NA 5 5
7 NA 13 13
451 NA 35 35
452 NA 94 94
453 NA 8 8
kostenprimarycare kostensecondarycare totaalkostenhealthcare etndich
1 NA 880 NA 1
2 NA 1072 NA 2
3 NA 448 NA 1
4 NA 4609 NA 2
5 NA 832 NA 1
6 NA 304 NA 2
7 NA 882 NA 1
451 NA 9924 NA 1
452 NA 29301 NA 2
453 NA 544 NA 2
推荐答案
下面的代码再现了SPSS的输出。
The following reproduces your SPSS output.
注意，这是正确设置分类变量的引用级别以匹配SPSS编码的问题。在R中，第一级将用作参考级。
Note, it's all a matter of setting the reference levels of the categorical variables correctly, to match the SPSS encoding. In R the first level will be used as the reference level.
df <- within(F, {
Comorb <- relevel(Comorb, ref = "2 of meer"); # Reference level = "2 of meer"
etndich <- factor(etndich, levels = 2:1); # Reference level = 2
PAIDhoog <- factor(PAIDhoog, levels = 1:0); # Reference level = 1
PHQhoog <- factor(PHQhoog, levels = 1:0); # Reference level = 1
})
fit <- glm(formula = totaalhealthcareutilization ~ PAIDhoog + PHQhoog +
Comorb + Leeftijd + etndich, family = Gamma(link = log),
data = df)
summary(fit)
#
#Call:
#glm(formula = totaalhealthcareutilization ~ PAIDhoog + PHQhoog +
# Comorb + Leeftijd + etndich, family = Gamma(link = log),
# data = df)
#
#Deviance Residuals:
# Min 1Q Median 3Q Max
#-2.1297 -0.7231 -0.3018 0.2075 3.1365
#
#Coefficients:
# Estimate Std. Error t value Pr(>|t|)
#(Intercept) 3.638751 0.267741 13.591 < 2e-16 ***
#PAIDhoog0 -0.201881 0.131777 -1.532 0.1264
#PHQhoog0 -0.126989 0.157416 -0.807 0.4203
#Comorbgeen -0.638842 0.144459 -4.422 1.28e-05 ***
#Comorb1 -0.348187 0.158484 -2.197 0.0286 *
#Leeftijd 0.007311 0.003534 2.069 0.0392 *
#etndich1 -0.151836 0.118872 -1.277 0.2023
#---
#Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
#(Dispersion parameter for Gamma family taken to be 0.9432289)
#
# Null deviance: 286.49 on 381 degrees of freedom
#Residual deviance: 243.01 on 375 degrees of freedom
# (71 observations deleted due to missingness)
#AIC: 3156
#
#Number of Fisher Scoring iterations: 6
与SPSS输出进行比较
Compare with SPSS output
Parameter Estimates
Parameter B Std. Error 95% Wald Confidence Interval Hypothesis Test
Lower Upper Wald Chi-Square df Sig.
(Intercept) 3,639 ,2177 3,212 4,065 279,350 1 ,000
[PAIDhoog=0] -,202 ,1056 -,409 ,005 3,657 1 ,056
[PAIDhoog=1] 0a . . . . . .
[PHQhoog=0] -,127 ,1260 -,374 ,120 1,015 1 ,314
[PHQhoog=1] 0a . . . . . .
[Comorb=1] -,639 ,1148 -,864 -,414 30,940 1 ,000
[Comorb=2] -,348 ,1250 -,593 -,103 7,758 1 ,005
[Comorb=3] 0a . . . . . .
[etndich=1,00] -,152 ,0936 -,335 ,032 2,633 1 ,105
[etndich=2,00] 0a . . . . . .
Leeftijd ,007 ,0028 ,002 ,013 6,599 1 ,010
(Scale) ,581b ,0387 ,510 ,662
Dependent Variable: totaalhealthcareutilization
Model: (Intercept), PAIDhoog, PHQhoog, Comorb, etndich, Leeftijd
a Set to zero because this parameter is redundant.
b Maximum likelihood estimate.
关于SPSS和 glm 输出
Further comments on differences in the SPSS and glm output
-
首先要注意的是参数估计SPSS和R中的值是相同的：两个参数集都对应于给定模型和数据的（唯一）最大似然（ML）估计值。
The first thing to note is that parameter estimates from SPSS and R are identical: Both parameter sets correspond to the (unique) set of maximum likelihood (ML) estimates given the model and data.
在R中，标准误差简单地作为估计协方差矩阵对角元素的平方根给出。
In R, the standard errors are simply given as the square root of the diagonal elements of the estimated covariance matrix
sqrt(diag(vcov(fit)))
#(Intercept) PAIDhoog0 PHQhoog0 Comorbgeen Comorb1 Leeftijd
#0.267740656 0.131776659 0.157416176 0.144458874 0.158484265 0.003534017
# etndich1
#0.118871533
请注意，这些值与 summary（fit）。
我不知道SPSS，但似乎SPSS的se对应于对角线的缩放平方根协方差矩阵的元素。
I don’t know SPSS, but it seems that SPSS' se's correspond to scaled square roots of the diagonal elements of the variance-covariance matrix.
置信区间基于参数和方差-协方差估计；如前所述，参数估计是相同的，但是SPSS使用缩放的方差-协方差矩阵，因此SPSS和R输出中参数的置信区间将根据所述缩放因子而不同。
Confidence intervals are based on parameter and variance-covariance estimates; as explained in the previous points, parameter estimates are identical, but SPSS uses a scaled variance-covariance matrix, so confidence intervals for the parameters in the SPSS and R output will be different according to said scaling factor.
SPSS的文档非常分散，因此我不确定 SPSS如何缩放其方差-协方差矩阵。
SPSS' documentation is regrettably diffuse, so I'm not sure how SPSS scales its variance-covariance matrix.
样本数据
Sample data
F <- structure(list(HbA1c = c(69, 75, 62, 96, NA, 86, 44, 49, NA, 63, 43, 75, 48, 56, 79, 78, 67, 66, 75, 67, 65, 66, 34, 62, 79, 60, 91, 51, 84, 72, 65, NA, NA, 62, 61, 69, 63, NA, 85, 38, 42, 80, 59, 96, 59, 49, 62, 98, 71, 78, 50, 43, 44, 69, 56, 38, 59, 74, 115, 69, 67, 51, NA, 107, 71, 86, 78, 41, 60, 59, 74, 73, 49, 34, 71, 57, 55, 74, 67, 61, 48, 59, 70, NA, 55, 72, 69, 82, 40, 58, NA, 53, 46, 69, 60, 39, 76, 69, 61, 86, 58, 63, 66, 103, 73, 54, 59, 46, 58, 70, 57, 53, 49, 53, 58, 71, 60, 76, 64, 97, 60, 49, 53, 44, 53, 73, 59, 75, 61, 55, 68, 56, 51, 91, 92, 76, 51, 55, 61, 83, 52, 62, 71, 75, 54, 64, 90, 65, NA, 69, 70, 70, 59, 62, 60, 63, 58, 58, 63, 60, 49, 62, 95, 42, 99, 67, 117, 68, 55, 55, 70, 60, 61, 91, 33, 89, 60, 47, 62, 72, 40, 88, 59, 56, 57, 59, 74, 41, 53, 76, 48, 73, 65, 96, 58, 55, 67, 45, 45, 69, 72, 44, 59, 43, 90, 69, 69, 71, 93, 42, 87, 54, 83, 60, 48, NA, 53, 56, 57, 77, 63, NA, 63, 60, 68, 51, 48, 65, 61, 79, 63, 62, 53, 67, 53, 53, 63, 55, 61, 51, 53, 46, NA, 78, 76, 73, 51, 49, 68, 86, 71, 55, 57, 113, 63, 68, 94, NA, 38, 50, NA, 42, 60, 57, 49, 60, 81, 69, 55, 82, 64, 55, 74, 71, 56, 60, NA, 47, 49, 98, 55, 80, 71, 69, 35, 53, 90, 64, 82, 132, 64, 70, 65, 34, 65, 54, NA, 68, 58, 76, 82, 66, 74, 66, NA, 54, 53, 78, 62, 88, 69, 49, 83, 54, 55, 56, 66, 84, 47, 82, 53, 62, 163, 41, 55, 89, 76, 81, 45, 50, 89, 72, 90, 47, 38, 83, NA, 53, 74, 55, 47, 49, 56, 74, 107, 86, 48, 59, 86, 44, 55, 64, 81, 66, 63, 98, 51, NA, 60, 50, 55, 52, 79, 58, 50, 89, NA, 36, 50, 70, NA, 86, 57, 60, 78, 53, 70, 79, 49, 78, 83, 66, 57, 62, 80, 70, NA, 67, 80, 46, 79, 47, 145, 87, 53, 65, 73, 75, 53, 50, 71, NA, 65, 106, 123, 51, 55, 43, 48, 86, 61, 64, 55, 71, 61, 96, 80, 69, 66, 74, 88, 48, 68, 55, 52, 58, 69, 66, 44, 45, 64, 84, 72, 49, NA, 71, 70, 104, 78, 73, 47, 75, 45, 57, 88, 86, 55, 72, 47, 53, 113, 62, 54), BMI = c(26.7, 34.5, 24.3, NA, 19.1, 37.9, 29.1, 27.1, NA, 21.1, 48.5, 26.2, 26.9, NA, 25.5, 25.3, 44.3, 25.2, 26.7, NA, 25.5, 25.9, 31.2, 33, 21.8, 23.7, 32, 23.6, 32.4, 29.7, NA, 22.9, 24.4, 33.9, 35.4, 41.2, 20.4, NA, 30.1, 21, NA, NA, 29.5, 16.6, 38.1, 23.9, 19.1, 35.4, 24.2, NA, 26.1, 20, 28.7, 30.7, 25.4, 29.6, 25.4, 26.2, 18.3, 31, NA, NA, 31.5, 32, 35.6, 24.3, 33.3, 35.5, NA, 24.1, NA, 33.4, 28.4, NA, 25.9, 26.7, 35.5, 31.6, 25, 25.5, 22.2, 22.3, 23.4, 35.3, 26.1, 32.6, 20.9, 35.9, 29.1, 32.8, 32.2, 28.9, 28.9, 28.8, 19.7, 29.4, 28.8, 28.2, 20.9, 33.5, 17.6, 38.6, 27.1, NA, 29, 25.6, 22.5, 30.6, 35.6, 32.5, 23.4, 27.2, 23.6, 26.6, 23.5, 30.3, 30.6, 26.4, 38.1, 34.7, NA, 24.6, 22.2, 39.8, 23, 35.8, 31.4, 22.8, 29.3, 27, 31.1, NA, NA, 32.4, 36, NA, 52.8, 22, 27.1, 23.3, 22.7, 25, 42.6, 30.2, 25.3, 30.5, 25.3, 28.4, 30.1, 32.4, NA, 32, 18.8, 23.1, 28.5, 25.1, 22.8, 23.6, 18.5, NA, 27.1, 25.3, 19.8, 20.8, 32.7, 30.1, 34.8, 37.5, NA, 28.1, 46, 23.5, 26.3, 22.2, 28.2, 29.3, 24.2, 29.7, 28.9, 28, 31.3, 28.6, 29.1, 28.4, 23.1, 34.9, 22.7, 26.9, 28.9, 35.9, 23, 25.8, 22.8, 19.2, 27.9, 29.2, 35, 25.1, 20.5, 23.9, 34.3, 23.1, 25.1, 20.5, 24.6, 24.4, 23.7, 22.4, 40.1, 21.9, 50, 34.2, 30.5, 20.7, 29.3, 32.6, 32.1, 23.9, NA, 34, 22.6, 30.2, 28.6, 27.5, 33, 24, 28.8, NA, 32.8, 21.8, NA, 37.8, 26.4, 36.2, 20.8, 24.4, 31, 31.9, 27.6, 25.4, 22.7, NA, 27.7, 32.4, 34, 26.2, 26.7, 23.7, 32, 24.1, 35.8, 23.5, 38.9, 35.3, NA, 23.9, 30.2, 24.4, 24.4, 27.9, NA, 25.7, 25.6, 25.8, 47.9, 25.6, 36.1, NA, 24.2, 24.8, 21.4, 22.3, 24.3, 24.7, 22.5, 25.9, 30.1, 27.4, 27.8, 22.6, 24.4, NA, 33.8, 41.9, 21.4, 32.5, 41.1, 27.2, NA, 37.8, 29, 23.2, 28.7, 25.2, 32.6, 29, 24.4, 23.1, 22.8, 23.1, 39.8, 26.6, 25.3, 53.5, 25, 22.9, 22.2, 30.2, 27.4, 27.4, NA, 25.2, 22.4, 20.2, 23.9, 23.3, 31.2, 24, 23.5, 38.8, 30, 30.6, 28.9, 23.1, 34.4, 28.7, 30.8, 21.6, 24.1, 25.5, 39.2, 29.3, 36.2, 28.3, NA, NA, NA, 29.5, 33.1, 23.4, 23.5, 25.1, 34.4, 24.5, 29.7, 22.2, 25.5, 23.3, 37.5, 26.8, 44.5, 32.4, 26.1, 21.4, 26.5, 32.7, 26.9, NA, 27.4, 36.3, 25.1, 37.7, NA, 27.6, 24.2, 46.9, 30.8, 29.3, 25.4, 35.7, 36.8, 35, 22.3, 28.3, 20.4, 25, 35, NA, 39.4, 25.2, 22.5, 34.5, NA, 21.6, 30.1, 25, NA, 28.3, 19.7, 22.3, 33.2, NA, 24.6, 23.9, 22.8, 24.1, 31.7, 28.4, 34.5, 30.1, 33.3, 28, 38, 35.9, 30.6, 33.5, 29.5, 21.4, 24.4, 27.5, 31.7, 23.8, NA, 21.8, 28.7, 33.5, 23.5, 27.3, 28.7, NA, 25.6, 26.7, 44.8, 26.2, 27.1, 39.7, 24.1, 21.3, 29.5, 30, NA, 27, NA, 23.6, 22.3, 32.6, 51.9, 27.7, 28.7, 35.2, 27.2, 29.6, 22.8, 19.6, 25.7, 28.3, 31.2, 21.7, 36.2, 26.9, 37.9, 21.5, 25.4), Comorb = 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, 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, 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, 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, 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, 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, 2L, 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, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 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, 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, NA, NA, NA), .Label = c("2 of meer", "geen", "1"), class = "factor"), PAIDhoog = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, NA, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1), PHQhoog = c(0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 0, 1, NA, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, NA, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, NA, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, NA, 0, NA, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, NA, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, NA, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, NA, NA, 1, 0, 1, NA, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, NA, 0, 0, NA, NA, 0, NA, 0, 0, NA, 0, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 1, 0, 0, NA, 0, 0, 0, 0, 1, 1, 0, 1, NA, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, NA, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, NA, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, NA, NA, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, NA, 0, NA, 0, 0, 0, 0, NA, 0, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, 1, 1, 1, 0, 1, NA, NA, 0, 1, 0, 0, 1, 1, NA, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, NA, 0, 0, 0, 0, 0, 0, 1, NA, 0, 0, NA, 0, 0, 0, 0, 0, 1, NA, 0, 1, 1, 0, 0, NA, 0, 0, 1, 1, 0, 0, 0, NA, 0, 1, 0, 0, 0, 0), totaalhealthcareutilization = c(15, 15, 6, 52, 12, 5, 13, 15, 13, 8, 10, 4, 9, 8, 6, 5, 8, 42, 15, 21, 6.3, 9, 5, 5, 14, 24, 8, 15, 25, 12, 29, 21, 6, 11, 8, 7, 29, 7, 7, 19, 14, 25, 16, 7, 20, 13, 17, 12, 5, NA, 9, 11, 14, 57, 12, 10, 37, 8, 12, 57, 8, 11, 14, 11, 49, 10, 10, 11, 19, 20, 21, 5, 1, 2, 2, 3, 3, 6, 4, 3, 4, 6, 5, 4, 4, 5, 7, 6, 6, 8, 5, 7, 8, 5, 6, 6, 6, 8, 7, 6, 6, 9, 11, 7, 9, 7, 7, 7, 7, 8, 10, 10, 10, 9, 9, 9, 11, 8, 10, 9, 9, 11, 13, 8, 12, 12, 9, 11, 7, 8, 10, 10, 9, 10, 10, 12, 12, 16, 9, 5, 10, 7, 13, 13, 13, 15, 16, 11, 11, 17, 13, 12, 22, 19, 15, 14, 11, 12, 19, 13, 15, 13, 14, 11, 17, 12, 17, 10, 13, 15, 12, 13, 13, 20, 16, 21, 17, 25, 22, 18, 18, 17, 15, 19, 10, 15, 20, 33, 22, 26, 23, 27, 20, 21, 21, 13, 24, 45, 27, 27, 19, 19, 25, 43, 16, 16, 13, 24, 29, 17, 24, 25, 32, 27, 29, 22, 35, 56, 26, 45, 23, 54, 26, 33, 23, 39, 35, 24, 36, 37, 37, 74, 53, 36, 60, 33, 35, 26, 44, 78, 22, 26, 77, 62, 121, 51, 28, 68, 63, 43, 64, 81, 120, 95, 98, 23, 11, 21, 10, 7, 41, 7, 33, 6, 40, 20, 2, 31, 23, 23, 13, 68, 9, 8, 41, 19, 27, 29, 46, NA, 35, 16, 12, 9, 14, 20, 7, 2, 4, 6, 6, 6, 4, 9, 6, 8, 9, 12, 9, 7, 8, 12, 11, 11, 14, 12, 14, 12, 16, 15, 22, 23, 19, 11, 12, 13, 17, 18, 19, 27, 15, 9, 17, 18, 19, 17, 19, 12, 16, 54, 21, 30, 23, 25, 24, 37, 35, 27, 47, 22, 27, 27, 30, 32, 32, 31, 39, 28, 36, 54, 50, 45, 42, 88, 56, 63, 82, 60, 70, 139, 122, 71, 130, 84, 33, 111, 111, 246, 157, 54, 24, 41, 22, 7, 33, 15, 9, 6, 16, 67, 3, 22, 48, 15, 57, 25, 48, 74, 40, 25, 18, 21, 3, 6, 7, 7, 14, 9, 11, 16, 14, 14, 14, 28, 18, 22, 21, 26, 39, 24, 22, 18, 22, 19, 19, 45, 15, 13, 22, 31, 29, 46, 37, 23, 35, 68, 39, 51, 35, 50, 80, 69, 51, 41, 90, 43, 32, 48, 34, 53, 25, 66, 39, 83, 70, 237, 81, 126, 95, 170, 35, 94, 8), etndich = c(1, 2, 1, 2, 1, 2, 1, 1, NA, 1, 1, 1, 1, 1, NA, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2, 2, NA, 2, 1, NA, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, NA, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, NA, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 1, 1, 2, 1, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, NA, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 1, 1, 2, NA, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, NA, NA, 2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, NA, 2, 1, 1, 1, 1, NA, 1, 1, 2, 1, 1, 1, 2, 1, NA, 1, 1, 1, 1, 1, 1, 1, NA, NA, 2, 1, 1, 2, 2, NA, 2, NA, 2, 2, 1, NA, 1, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, NA, 1, 1, NA, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 2), Leeftijd = c(26, 69, 67, 38, 29, 50, 29, 23, 52, 39, 50, 29, 36, 52, 43, 53, 47, 33, 52, 55, 43, 64, 35, 24, 51, 39, 50, 51, 46, 51, 30, 32, 28, 25, 52, 48, 60, 31, 61, 47, 46, 56, 38, 72, 88, 34, 56, 27, 27, 56, 52, 49, 34, 25, 22, 60, 61, 42, 45, 51, 42, 61, 69, 57, 35, 50, 42, 50, 51, 46, 28, 34, 52, 33, 30, 64, 65, 35, 31, 57, 75, 43, 46, 35, 65, 29, 29, 75, 49, 31, 57, 29, 40, 75, 30, 34, 58, 47, 37, 43, 34, 47, 46, 42, 49, 57, 46, 36, 51, 80, 45, 47, 48, 23, 51, 53, 44, 64, 44, 33, 40, 42, 29, 60, 28, 47, 47, 39, 25, 41, 39, 27, 57, 66, 42, 22, 59, 27, 43, 53, 65, 52, 41, 50, 55, 29, 55, 39, 41, 25, 74, 68, 55, 29, 77, 45, 18, 34, 49, 74, 44, 33, 48, 82, 61, 54, 46, 30, 33, 65, 51, 44, 50, 57, 27, 56, 85, 52, 31, 62, 62, 34, 48, 28, 28, 63, 30, 40, 44, 37, 73, 70, 39, 59, 56, 61, 40, 43, 33, 58, 44, 62, 26, 72, 67, 59, 48, 37, 52, 37, 57, 53, 59, 44, 71, 81, 33, 61, 50, 33, 48, 50, 63, 46, 60, 58, 40, 63, 39, 71, 38, 40, 56, 36, 52, 61, 83, 59, 43, 69, 50, 57, 38, 50, 27, 43, 46, 30, 50, 34, 68, 53, 48, 84, 41, 57, 61, 72, 27, 80, 71, 69, 61, 43, 67, 60, 58, 67, 72, 40, 79, 52, 80, 33, 25, 80, 67, 56, 66, 54, 50, 65, 39, 36, 69, 39, 34, 41, 36, 61, 33, 42, 43, 45, 48, 67, 69, 66, 37, 28, 64, 65, 68, 62, 84, 82, 59, 61, 74, 52, 41, 30, 33, 55, 55, 26, 53, 33, 64, 65, 74, 67, 70, 58, 51, 62, 67, 52, 40, 57, 57, 57, 59, 56, 61, 58, 45, 63, 61, 50, 70, 32, 50, 74, 70, 49, 42, 71, 51, 67, 46, 45, 75, 54, 75, 45, 46, 64, 60, 55, 61, 65, 68, 71, 43, 78, 53, 63, 85, 75, 66, 67, 54, 63, 68, 84, 58, 72, 70, 58, 29, 63, 83, 64, 75, 59, 76, 61, 62, 65, 61, 72, 20, 43, 67, 33, 62, 63, 51, 34, 68, 68, 60, 67, 44, 64, 69, 53, 69, 47, 41, 38, 57, 71, 70, 68, 25, 60, 71, 48, 64, 62, 72, 60, 45, 67, 59, 73, 27, 64, 66, 57, 72, 71, 77, 58, 56, 65, 74, 44, 22, 63, 42, 80, 52, 66, 60, 56, 54, 42, 68, 57, 37)), .Names = c("HbA1c", "BMI", "Comorb", "PAIDhoog", "PHQhoog", "totaalhealthcareutilization", "etndich", "Leeftijd"), row.names = c(NA, -453L), variable.labels = structure(c("HbA1c", "BMI level", "", "", "", "", "", ""), .Names = c("HbA1c", "BMI", "Comorb", "PAIDhoog", "PHQhoog", "totaalhealthcareutilization", "etndich", "Leeftijd")), codepage = 65001L, class = "data.frame")
这篇关于不同的结果Gamma广义线性模型R和SPSS的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
查看全文
