About step function
0
spam <- read.csv("spam.csv")
names(spam) <-c ("w_make","w_address","w_all","w_3d","w_our","w_over","w_remove","w_internet","w_order","w_mail","w_receive","w_will","w_people","w_report", "w_addresses","w_free", "w_business", "w_email","w_you", "w_credit", "w_your", "w_font", "w_000", "w_money", "w_hp", "w_hpl", "w_george", "w_650", "w_lab", "w_labs", "w_telnet", "w_857", "w_data", "w_415", "w_85", "w_technology", "w_1999", "w_parts", "w_pm", "w_direct", "w_cs", "w_meeting", "w_original", "w_project", "w_re", "w_edu", "w_table", "w_conference", "c_semicolon", "c_roundparen", "c_squareparen", "c_exclaim", "c_dollar", "c_hash", "caps_avg", "caps_long", "caps_total", "spam")
yspam <- spam$spam
nspam <- nrow(spam)
null <- glm(yspam ~ 1, family=binomial(link=logit), data=spam)
full <- glm(yspam ~ . + .^2, family=binomial(link=logit), data=spam)
fwd <- step(null, scope=formula(yspam ~ .),
direction="forward", k=log(nspam))
I am trying to generate a glm model to predict if email is spam or not based on a dataset with 58 covariates including if the email is spam or not. I am trying to generate the best possible model using the step function in R to get the model with the lowest BIC value but I keep getting the error:
glm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurred
Any suggestions?
r
edited Nov 16 '18 at 7:06
abenci
3,690845105
asked Nov 14 '18 at 0:42
Cody KimCody Kim
1
add a comment |
0
spam <- read.csv("spam.csv")
names(spam) <-c ("w_make","w_address","w_all","w_3d","w_our","w_over","w_remove","w_internet","w_order","w_mail","w_receive","w_will","w_people","w_report", "w_addresses","w_free", "w_business", "w_email","w_you", "w_credit", "w_your", "w_font", "w_000", "w_money", "w_hp", "w_hpl", "w_george", "w_650", "w_lab", "w_labs", "w_telnet", "w_857", "w_data", "w_415", "w_85", "w_technology", "w_1999", "w_parts", "w_pm", "w_direct", "w_cs", "w_meeting", "w_original", "w_project", "w_re", "w_edu", "w_table", "w_conference", "c_semicolon", "c_roundparen", "c_squareparen", "c_exclaim", "c_dollar", "c_hash", "caps_avg", "caps_long", "caps_total", "spam")
yspam <- spam$spam
nspam <- nrow(spam)
null <- glm(yspam ~ 1, family=binomial(link=logit), data=spam)
full <- glm(yspam ~ . + .^2, family=binomial(link=logit), data=spam)
fwd <- step(null, scope=formula(yspam ~ .),
direction="forward", k=log(nspam))
I am trying to generate a glm model to predict if email is spam or not based on a dataset with 58 covariates including if the email is spam or not. I am trying to generate the best possible model using the step function in R to get the model with the lowest BIC value but I keep getting the error:
glm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurred
Any suggestions?
r
edited Nov 16 '18 at 7:06
abenci
3,690845105
asked Nov 14 '18 at 0:42
Cody KimCody Kim
1
3
Looks like you have a variable in your predictors that identifies spam perfectly. This is probably because you have bothspam$yspamandspam$spamin your dataset, which are exactly the same variable.
– thelatemail
Nov 14 '18 at 0:54
add a comment |
0
0
0
spam <- read.csv("spam.csv")
names(spam) <-c ("w_make","w_address","w_all","w_3d","w_our","w_over","w_remove","w_internet","w_order","w_mail","w_receive","w_will","w_people","w_report", "w_addresses","w_free", "w_business", "w_email","w_you", "w_credit", "w_your", "w_font", "w_000", "w_money", "w_hp", "w_hpl", "w_george", "w_650", "w_lab", "w_labs", "w_telnet", "w_857", "w_data", "w_415", "w_85", "w_technology", "w_1999", "w_parts", "w_pm", "w_direct", "w_cs", "w_meeting", "w_original", "w_project", "w_re", "w_edu", "w_table", "w_conference", "c_semicolon", "c_roundparen", "c_squareparen", "c_exclaim", "c_dollar", "c_hash", "caps_avg", "caps_long", "caps_total", "spam")
yspam <- spam$spam
nspam <- nrow(spam)
null <- glm(yspam ~ 1, family=binomial(link=logit), data=spam)
full <- glm(yspam ~ . + .^2, family=binomial(link=logit), data=spam)
fwd <- step(null, scope=formula(yspam ~ .),
direction="forward", k=log(nspam))
I am trying to generate a glm model to predict if email is spam or not based on a dataset with 58 covariates including if the email is spam or not. I am trying to generate the best possible model using the step function in R to get the model with the lowest BIC value but I keep getting the error:
glm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurred
Any suggestions?
r
edited Nov 16 '18 at 7:06
abenci
3,690845105
asked Nov 14 '18 at 0:42
Cody KimCody Kim
1
spam <- read.csv("spam.csv")
names(spam) <-c ("w_make","w_address","w_all","w_3d","w_our","w_over","w_remove","w_internet","w_order","w_mail","w_receive","w_will","w_people","w_report", "w_addresses","w_free", "w_business", "w_email","w_you", "w_credit", "w_your", "w_font", "w_000", "w_money", "w_hp", "w_hpl", "w_george", "w_650", "w_lab", "w_labs", "w_telnet", "w_857", "w_data", "w_415", "w_85", "w_technology", "w_1999", "w_parts", "w_pm", "w_direct", "w_cs", "w_meeting", "w_original", "w_project", "w_re", "w_edu", "w_table", "w_conference", "c_semicolon", "c_roundparen", "c_squareparen", "c_exclaim", "c_dollar", "c_hash", "caps_avg", "caps_long", "caps_total", "spam")
yspam <- spam$spam
nspam <- nrow(spam)
null <- glm(yspam ~ 1, family=binomial(link=logit), data=spam)
full <- glm(yspam ~ . + .^2, family=binomial(link=logit), data=spam)
fwd <- step(null, scope=formula(yspam ~ .),
direction="forward", k=log(nspam))
I am trying to generate a glm model to predict if email is spam or not based on a dataset with 58 covariates including if the email is spam or not. I am trying to generate the best possible model using the step function in R to get the model with the lowest BIC value but I keep getting the error:
glm.fit: algorithm did not convergeglm.fit: fitted probabilities numerically 0 or 1 occurred
Any suggestions?
r
r
edited Nov 16 '18 at 7:06
abenci
3,690845105
asked Nov 14 '18 at 0:42
Cody KimCody Kim
1
edited Nov 16 '18 at 7:06
abenci
3,690845105
asked Nov 14 '18 at 0:42
Cody KimCody Kim
1
edited Nov 16 '18 at 7:06
abenci
3,690845105
edited Nov 16 '18 at 7:06
abenci
3,690845105
edited Nov 16 '18 at 7:06
abenci
3,690845105
3,690845105
asked Nov 14 '18 at 0:42
Cody KimCody Kim
1
asked Nov 14 '18 at 0:42
Cody KimCody Kim
1
asked Nov 14 '18 at 0:42
Cody KimCody Kim
1
1
3
Looks like you have a variable in your predictors that identifies spam perfectly. This is probably because you have bothspam$yspamandspam$spamin your dataset, which are exactly the same variable.
– thelatemail
Nov 14 '18 at 0:54
add a comment |
3
Looks like you have a variable in your predictors that identifies spam perfectly. This is probably because you have bothspam$yspamandspam$spamin your dataset, which are exactly the same variable.
– thelatemail
Nov 14 '18 at 0:54
3
3
Looks like you have a variable in your predictors that identifies spam perfectly. This is probably because you have both
spam$yspam and spam$spam in your dataset, which are exactly the same variable.– thelatemail
Nov 14 '18 at 0:54
Looks like you have a variable in your predictors that identifies spam perfectly. This is probably because you have both
spam$yspam and spam$spam in your dataset, which are exactly the same variable.– thelatemail
Nov 14 '18 at 0:54
add a comment |
0
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3
Looks like you have a variable in your predictors that identifies spam perfectly. This is probably because you have both
spam$yspamandspam$spamin your dataset, which are exactly the same variable.– thelatemail
Nov 14 '18 at 0:54