Paste not working for long strings? [closed] - r
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I cannot for the life of me figure out why paste with collapse="\n" won't work for me, for just this line (it works in other parts of the code).
Perhaps a character limit with the paste function?
(I have trimmed leading and lagging white space)
Below you will notice that paste does not in fact insert \n between the two long strings:
> MM
[1] "F1_all =~ target\nF2_all =~ target\nF3_all =~ target\nF4_all =~ target\nF5_all =~ target\nF6_all =~ target"
> regsflat
[1] "F1_all ~ 1*F1_0351 + 1*F1_0354 + 1*F1_0414 + 1*F1_0415 + 1*F1_0420 + 1*F1_0430 + 1*F1_0464 + 1*F1_0484 + 1*F1_0488 + 1*F1_0496 + 1*F1_0508 + 1*F1_0517 + 1*F1_0527 + 1*F1_0592 + 1*F1_0593 + 1*F1_0596 + 1*F1_0609 + 1*F1_0640 + 1*F1_0646 + 1*F1_0647 + 1*F1_0683 + 1*F1_0686 + 1*F1_0691 + 1*F1_0696 + 1*F1_0713 + 1*F1_0715 + 1*F1_0717 + 1*F1_0757 + 1*F1_0759 + 1*F1_0764 + 1*F1_0765 + 1*F1_0771 + 1*F1_0772 + 1*F1_0775 + 1*F1_0776 + 1*F1_0778 + 1*F1_0781 + 1*F1_0793 + 1*F1_0796 + 1*F1_0797 + 1*F1_0799 + 1*F1_0842 + 1*F1_0843 + 1*F1_0845 + 1*F1_0865 + 1*F1_0879 + 1*F1_0895 + 1*F1_0936 + 1*F1_1544 + 1*F1_1545 + 1*F1_1802 + 1*F1_1803 + 1*F1_1804 + 1*F1_1805 + 1*F1_1806 + 1*F1_1807 + 1*F1_1809 + 1*F1_1815 + 1*F1_2261 + 1*F1_2262 + 1*F1_2353 + 1*F1_2354 + 1*F1_2435 + 1*F1_BBRM1WA + 1*F1_BBRM2WA + 1*F1_BUSINESSBANKWA + 1*F1_CBWACENTRAL + 1*F1_CBWASOUTH + 1*F1_R&R-WESTCOAST\nF2_all ~ 1*F2_0351 + 1*F2_0354 + 1*F2_0414 + 1*F2_0415 + 1*F2_0420 + 1*F2_0430 + 1*F2_0464 + 1*F2_0484 + 1*F2_0488 + 1*F2_0496 + 1*F2_0508 + 1*F2_0517 + 1*F2_0527 + 1*F2_0592 + 1*F2_0593 + 1*F2_0596 + 1*F2_0609 + 1*F2_0640 + 1*F2_0646 + 1*F2_0647 + 1*F2_0683 + 1*F2_0686 + 1*F2_0691 + 1*F2_0696 + 1*F2_0713 + 1*F2_0715 + 1*F2_0717 + 1*F2_0757 + 1*F2_0759 + 1*F2_0764 + 1*F2_0765 + 1*F2_0771 + 1*F2_0772 + 1*F2_0775 + 1*F2_0776 + 1*F2_0778 + 1*F2_0781 + 1*F2_0793 + 1*F2_0796 + 1*F2_0797 + 1*F2_0799 + 1*F2_0842 + 1*F2_0843 + 1*F2_0845 + 1*F2_0865 + 1*F2_0879 + 1*F2_0895 + 1*F2_0936 + 1*F2_1544 + 1*F2_1545 + 1*F2_1802 + 1*F2_1803 + 1*F2_1804 + 1*F2_1805 + 1*F2_1806 + 1*F2_1807 + 1*F2_1809 + 1*F2_1815 + 1*F2_2261 + 1*F2_2262 + 1*F2_2353 + 1*F2_2354 + 1*F2_2435 + 1*F2_BBRM1WA + 1*F2_BBRM2WA + 1*F2_BUSINESSBANKWA + 1*F2_CBWACENTRAL + 1*F2_CBWASOUTH + 1*F2_R&R-WESTCOAST\nF3_all ~ 1*F3_0351 + 1*F3_0354 + 1*F3_0414 + 1*F3_0415 + 1*F3_0420 + 1*F3_0430 + 1*F3_0464 + 1*F3_0484 + 1*F3_0488 + 1*F3_0496 + 1*F3_0508 + 1*F3_0517 + 1*F3_0527 + 1*F3_0592 + 1*F3_0593 + 1*F3_0596 + 1*F3_0609 + 1*F3_0640 + 1*F3_0646 + 1*F3_0647 + 1*F3_0683 + 1*F3_0686 + 1*F3_0691 + 1*F3_0696 + 1*F3_0713 + 1*F3_0715 + 1*F3_0717 + 1*F3_0757 + 1*F3_0759 + 1*F3_0764 + 1*F3_0765 + 1*F3_0771 + 1*F3_0772 + 1*F3_0775 + 1*F3_0776 + 1*F3_0778 + 1*F3_0781 + 1*F3_0793 + 1*F3_0796 + 1*F3_0797 + 1*F3_0799 + 1*F3_0842 + 1*F3_0843 + 1*F3_0845 + 1*F3_0865 + 1*F3_0879 + 1*F3_0895 + 1*F3_0936 + 1*F3_1544 + 1*F3_1545 + 1*F3_1802 + 1*F3_1803 + 1*F3_1804 + 1*F3_1805 + 1*F3_1806 + 1*F3_1807 + 1*F3_1809 + 1*F3_1815 + 1*F3_2261 + 1*F3_2262 + 1*F3_2353 + 1*F3_2354 + 1*F3_2435 + 1*F3_BBRM1WA + 1*F3_BBRM2WA + 1*F3_BUSINESSBANKWA + 1*F3_CBWACENTRAL + 1*F3_CBWASOUTH + 1*F3_R&R-WESTCOAST\nF4_all ~ 1*F4_0351 + 1*F4_0354 + 1*F4_0414 + 1*F4_0415 + 1*F4_0420 + 1*F4_0430 + 1*F4_0464 + 1*F4_0484 + 1*F4_0488 + 1*F4_0496 + 1*F4_0508 + 1*F4_0517 + 1*F4_0527 + 1*F4_0592 + 1*F4_0593 + 1*F4_0596 + 1*F4_0609 + 1*F4_0640 + 1*F4_0646 + 1*F4_0647 + 1*F4_0683 + 1*F4_0686 + 1*F4_0691 + 1*F4_0696 + 1*F4_0713 + 1*F4_0715 + 1*F4_0717 + 1*F4_0757 + 1*F4_0759 + 1*F4_0764 + 1*F4_0765 + 1*F4_0771 + 1*F4_0772 + 1*F4_0775 + 1*F4_0776 + 1*F4_0778 + 1*F4_0781 + 1*F4_0793 + 1*F4_0796 + 1*F4_0797 + 1*F4_0799 + 1*F4_0842 + 1*F4_0843 + 1*F4_0845 + 1*F4_0865 + 1*F4_0879 + 1*F4_0895 + 1*F4_0936 + 1*F4_1544 + 1*F4_1545 + 1*F4_1802 + 1*F4_1803 + 1*F4_1804 + 1*F4_1805 + 1*F4_1806 + 1*F4_1807 + 1*F4_1809 + 1*F4_1815 + 1*F4_2261 + 1*F4_2262 + 1*F4_2353 + 1*F4_2354 + 1*F4_2435 + 1*F4_BBRM1WA + 1*F4_BBRM2WA + 1*F4_BUSINESSBANKWA + 1*F4_CBWACENTRAL + 1*F4_CBWASOUTH + 1*F4_R&R-WESTCOAST\nF5_all ~ 1*F5_0351 + 1*F5_0354 + 1*F5_0414 + 1*F5_0415 + 1*F5_0420 + 1*F5_0430 + 1*F5_0464 + 1*F5_0484 + 1*F5_0488 + 1*F5_0496 + 1*F5_0508 + 1*F5_0517 + 1*F5_0527 + 1*F5_0592 + 1*F5_0593 + 1*F5_0596 + 1*F5_0609 + 1*F5_0640 + 1*F5_0646 + 1*F5_0647 + 1*F5_0683 + 1*F5_0686 + 1*F5_0691 + 1*F5_0696 + 1*F5_0713 + 1*F5_0715 + 1*F5_0717 + 1*F5_0757 + 1*F5_0759 + 1*F5_0764 + 1*F5_0765 + 1*F5_0771 + 1*F5_0772 + 1*F5_0775 + 1*F5_0776 + 1*F5_0778 + 1*F5_0781 + 1*F5_0793 + 1*F5_0796 + 1*F5_0797 + 1*F5_0799 + 1*F5_0842 + 1*F5_0843 + 1*F5_0845 + 1*F5_0865 + 1*F5_0879 + 1*F5_0895 + 1*F5_0936 + 1*F5_1544 + 1*F5_1545 + 1*F5_1802 + 1*F5_1803 + 1*F5_1804 + 1*F5_1805 + 1*F5_1806 + 1*F5_1807 + 1*F5_1809 + 1*F5_1815 + 1*F5_2261 + 1*F5_2262 + 1*F5_2353 + 1*F5_2354 + 1*F5_2435 + 1*F5_BBRM1WA + 1*F5_BBRM2WA + 1*F5_BUSINESSBANKWA + 1*F5_CBWACENTRAL + 1*F5_CBWASOUTH + 1*F5_R&R-WESTCOAST\nF6_all ~ 1*F6_0351 + 1*F6_0354 + 1*F6_0414 + 1*F6_0415 + 1*F6_0420 + 1*F6_0430 + 1*F6_0464 + 1*F6_0484 + 1*F6_0488 + 1*F6_0496 + 1*F6_0508 + 1*F6_0517 + 1*F6_0527 + 1*F6_0592 + 1*F6_0593 + 1*F6_0596 + 1*F6_0609 + 1*F6_0640 + 1*F6_0646 + 1*F6_0647 + 1*F6_0683 + 1*F6_0686 + 1*F6_0691 + 1*F6_0696 + 1*F6_0713 + 1*F6_0715 + 1*F6_0717 + 1*F6_0757 + 1*F6_0759 + 1*F6_0764 + 1*F6_0765 + 1*F6_0771 + 1*F6_0772 + 1*F6_0775 + 1*F6_0776 + 1*F6_0778 + 1*F6_0781 + 1*F6_0793 + 1*F6_0796 + 1*F6_0797 + 1*F6_0799 + 1*F6_0842 + 1*F6_0843 + 1*F6_0845 + 1*F6_0865 + 1*F6_0879 + 1*F6_0895 + 1*F6_0936 + 1*F6_1544 + 1*F6_1545 + 1*F6_1802 + 1*F6_1803 + 1*F6_1804 + 1*F6_1805 + 1*F6_1806 + 1*F6_1807 + 1*F6_1809 + 1*F6_1815 + 1*F6_2261 + 1*F6_2262 + 1*F6_2353 + 1*F6_2354 + 1*F6_2435 + 1*F6_BBRM1WA + 1*F6_BBRM2WA + 1*F6_BUSINESSBANKWA + 1*F6_CBWACENTRAL + 1*F6_CBWASOUTH + 1*F6_R&R-WESTCOAST"
> paste(MM, regsflat, collapse="\n")
[1] "F1_all =~ target\nF2_all =~ target\nF3_all =~ target\nF4_all =~ target\nF5_all =~ target\nF6_all =~ target F1_all ~ 1*F1_0351 + 1*F1_0354 + 1*F1_0414 + 1*F1_0415 + 1*F1_0420 + 1*F1_0430 + 1*F1_0464 + 1*F1_0484 + 1*F1_0488 + 1*F1_0496 + 1*F1_0508 + 1*F1_0517 + 1*F1_0527 + 1*F1_0592 + 1*F1_0593 + 1*F1_0596 + 1*F1_0609 + 1*F1_0640 + 1*F1_0646 + 1*F1_0647 + 1*F1_0683 + 1*F1_0686 + 1*F1_0691 + 1*F1_0696 + 1*F1_0713 + 1*F1_0715 + 1*F1_0717 + 1*F1_0757 + 1*F1_0759 + 1*F1_0764 + 1*F1_0765 + 1*F1_0771 + 1*F1_0772 + 1*F1_0775 + 1*F1_0776 + 1*F1_0778 + 1*F1_0781 + 1*F1_0793 + 1*F1_0796 + 1*F1_0797 + 1*F1_0799 + 1*F1_0842 + 1*F1_0843 + 1*F1_0845 + 1*F1_0865 + 1*F1_0879 + 1*F1_0895 + 1*F1_0936 + 1*F1_1544 + 1*F1_1545 + 1*F1_1802 + 1*F1_1803 + 1*F1_1804 + 1*F1_1805 + 1*F1_1806 + 1*F1_1807 + 1*F1_1809 + 1*F1_1815 + 1*F1_2261 + 1*F1_2262 + 1*F1_2353 + 1*F1_2354 + 1*F1_2435 + 1*F1_BBRM1WA + 1*F1_BBRM2WA + 1*F1_BUSINESSBANKWA + 1*F1_CBWACENTRAL + 1*F1_CBWASOUTH + 1*F1_R&R-WESTCOAST\nF2_all ~ 1*F2_0351 + 1*F2_0354 + 1*F2_0414 + 1*F2_0415 + 1*F2_0420 + 1*F2_0430 + 1*F2_0464 + 1*F2_0484 + 1*F2_0488 + 1*F2_0496 + 1*F2_0508 + 1*F2_0517 + 1*F2_0527 + 1*F2_0592 + 1*F2_0593 + 1*F2_0596 + 1*F2_0609 + 1*F2_0640 + 1*F2_0646 + 1*F2_0647 + 1*F2_0683 + 1*F2_0686 + 1*F2_0691 + 1*F2_0696 + 1*F2_0713 + 1*F2_0715 + 1*F2_0717 + 1*F2_0757 + 1*F2_0759 + 1*F2_0764 + 1*F2_0765 + 1*F2_0771 + 1*F2_0772 + 1*F2_0775 + 1*F2_0776 + 1*F2_0778 + 1*F2_0781 + 1*F2_0793 + 1*F2_0796 + 1*F2_0797 + 1*F2_0799 + 1*F2_0842 + 1*F2_0843 + 1*F2_0845 + 1*F2_0865 + 1*F2_0879 + 1*F2_0895 + 1*F2_0936 + 1*F2_1544 + 1*F2_1545 + 1*F2_1802 + 1*F2_1803 + 1*F2_1804 + 1*F2_1805 + 1*F2_1806 + 1*F2_1807 + 1*F2_1809 + 1*F2_1815 + 1*F2_2261 + 1*F2_2262 + 1*F2_2353 + 1*F2_2354 + 1*F2_2435 + 1*F2_BBRM1WA + 1*F2_BBRM2WA + 1*F2_BUSINESSBANKWA + 1*F2_CBWACENTRAL + 1*F2_CBWASOUTH + 1*F2_R&R-WESTCOAST\nF3_all ~ 1*F3_0351 + 1*F3_0354 + 1*F3_0414 + 1*F3_0415 + 1*F3_0420 + 1*F3_0430 + 1*F3_0464 + 1*F3_0484 + 1*F3_0488 + 1*F3_0496 + 1*F3_0508 + 1*F3_0517 + 1*F3_0527 + 1*F3_0592 + 1*F3_0593 + 1*F3_0596 + 1*F3_0609 + 1*F3_0640 + 1*F3_0646 + 1*F3_0647 + 1*F3_0683 + 1*F3_0686 + 1*F3_0691 + 1*F3_0696 + 1*F3_0713 + 1*F3_0715 + 1*F3_0717 + 1*F3_0757 + 1*F3_0759 + 1*F3_0764 + 1*F3_0765 + 1*F3_0771 + 1*F3_0772 + 1*F3_0775 + 1*F3_0776 + 1*F3_0778 + 1*F3_0781 + 1*F3_0793 + 1*F3_0796 + 1*F3_0797 + 1*F3_0799 + 1*F3_0842 + 1*F3_0843 + 1*F3_0845 + 1*F3_0865 + 1*F3_0879 + 1*F3_0895 + 1*F3_0936 + 1*F3_1544 + 1*F3_1545 + 1*F3_1802 + 1*F3_1803 + 1*F3_1804 + 1*F3_1805 + 1*F3_1806 + 1*F3_1807 + 1*F3_1809 + 1*F3_1815 + 1*F3_2261 + 1*F3_2262 + 1*F3_2353 + 1*F3_2354 + 1*F3_2435 + 1*F3_BBRM1WA + 1*F3_BBRM2WA + 1*F3_BUSINESSBANKWA + 1*F3_CBWACENTRAL + 1*F3_CBWASOUTH + 1*F3_R&R-WESTCOAST\nF4_all ~ 1*F4_0351 + 1*F4_0354 + 1*F4_0414 + 1*F4_0415 + 1*F4_0420 + 1*F4_0430 + 1*F4_0464 + 1*F4_0484 + 1*F4_0488 + 1*F4_0496 + 1*F4_0508 + 1*F4_0517 + 1*F4_0527 + 1*F4_0592 + 1*F4_0593 + 1*F4_0596 + 1*F4_0609 + 1*F4_0640 + 1*F4_0646 + 1*F4_0647 + 1*F4_0683 + 1*F4_0686 + 1*F4_0691 + 1*F4_0696 + 1*F4_0713 + 1*F4_0715 + 1*F4_0717 + 1*F4_0757 + 1*F4_0759 + 1*F4_0764 + 1*F4_0765 + 1*F4_0771 + 1*F4_0772 + 1*F4_0775 + 1*F4_0776 + 1*F4_0778 + 1*F4_0781 + 1*F4_0793 + 1*F4_0796 + 1*F4_0797 + 1*F4_0799 + 1*F4_0842 + 1*F4_0843 + 1*F4_0845 + 1*F4_0865 + 1*F4_0879 + 1*F4_0895 + 1*F4_0936 + 1*F4_1544 + 1*F4_1545 + 1*F4_1802 + 1*F4_1803 + 1*F4_1804 + 1*F4_1805 + 1*F4_1806 + 1*F4_1807 + 1*F4_1809 + 1*F4_1815 + 1*F4_2261 + 1*F4_2262 + 1*F4_2353 + 1*F4_2354 + 1*F4_2435 + 1*F4_BBRM1WA + 1*F4_BBRM2WA + 1*F4_BUSINESSBANKWA + 1*F4_CBWACENTRAL + 1*F4_CBWASOUTH + 1*F4_R&R-WESTCOAST\nF5_all ~ 1*F5_0351 + 1*F5_0354 + 1*F5_0414 + 1*F5_0415 + 1*F5_0420 + 1*F5_0430 + 1*F5_0464 + 1*F5_0484 + 1*F5_0488 + 1*F5_0496 + 1*F5_0508 + 1*F5_0517 + 1*F5_0527 + 1*F5_0592 + 1*F5_0593 + 1*F5_0596 + 1*F5_0609 + 1*F5_0640 + 1*F5_0646 + 1*F5_0647 + 1*F5_0683 + 1*F5_0686 + 1*F5_0691 + 1*F5_0696 + 1*F5_0713 + 1*F5_0715 + 1*F5_0717 + 1*F5_0757 + 1*F5_0759 + 1*F5_0764 + 1*F5_0765 + 1*F5_0771 + 1*F5_0772 + 1*F5_0775 + 1*F5_0776 + 1*F5_0778 + 1*F5_0781 + 1*F5_0793 + 1*F5_0796 + 1*F5_0797 + 1*F5_0799 + 1*F5_0842 + 1*F5_0843 + 1*F5_0845 + 1*F5_0865 + 1*F5_0879 + 1*F5_0895 + 1*F5_0936 + 1*F5_1544 + 1*F5_1545 + 1*F5_1802 + 1*F5_1803 + 1*F5_1804 + 1*F5_1805 + 1*F5_1806 + 1*F5_1807 + 1*F5_1809 + 1*F5_1815 + 1*F5_2261 + 1*F5_2262 + 1*F5_2353 + 1*F5_2354 + 1*F5_2435 + 1*F5_BBRM1WA + 1*F5_BBRM2WA + 1*F5_BUSINESSBANKWA + 1*F5_CBWACENTRAL + 1*F5_CBWASOUTH + 1*F5_R&R-WESTCOAST\nF6_all ~ 1*F6_0351 + 1*F6_0354 + 1*F6_0414 + 1*F6_0415 + 1*F6_0420 + 1*F6_0430 + 1*F6_0464 + 1*F6_0484 + 1*F6_0488 + 1*F6_0496 + 1*F6_0508 + 1*F6_0517 + 1*F6_0527 + 1*F6_0592 + 1*F6_0593 + 1*F6_0596 + 1*F6_0609 + 1*F6_0640 + 1*F6_0646 + 1*F6_0647 + 1*F6_0683 + 1*F6_0686 + 1*F6_0691 + 1*F6_0696 + 1*F6_0713 + 1*F6_0715 + 1*F6_0717 + 1*F6_0757 + 1*F6_0759 + 1*F6_0764 + 1*F6_0765 + 1*F6_0771 + 1*F6_0772 + 1*F6_0775 + 1*F6_0776 + 1*F6_0778 + 1*F6_0781 + 1*F6_0793 + 1*F6_0796 + 1*F6_0797 + 1*F6_0799 + 1*F6_0842 + 1*F6_0843 + 1*F6_0845 + 1*F6_0865 + 1*F6_0879 + 1*F6_0895 + 1*F6_0936 + 1*F6_1544 + 1*F6_1545 + 1*F6_1802 + 1*F6_1803 + 1*F6_1804 + 1*F6_1805 + 1*F6_1806 + 1*F6_1807 + 1*F6_1809 + 1*F6_1815 + 1*F6_2261 + 1*F6_2262 + 1*F6_2353 + 1*F6_2354 + 1*F6_2435 + 1*F6_BBRM1WA + 1*F6_BBRM2WA + 1*F6_BUSINESSBANKWA + 1*F6_CBWACENTRAL + 1*F6_CBWASOUTH + 1*F6_R&R-WESTCOAST"
>
Try this:
paste(MM, regsflat, sep="\n")
Related
Problem with Lavaan not computing standard errors, the information matrix could not be converted
I am trying to run a CFA in R. The code looks like this: item.model1 <- ' Reflective =~ IES_EFPR_3 + IES_EFPR_10 + IES_EFPR_16 + IES_EFPR_17 + IES_EFPR_23 + IES_EFPR_24 + IES_EFPR_25 + IES_EFPR_26 + IES_RHSC_11 + IES_RHSC_12 + IES_RHSC_13 + IES_RHSC_35 + IES_RHSC_36 + IES_RHSC_37 + IES_BFCC_31 + IES_BFCC_32 + IES_BFCC_33 + SREBQ_A + SREBQ_B + SREBQ_C + SREBQ_D + SREBQ_E Reactive =~ BES_1 + BES_2 + BES_3 + BES_4 + BES_5 + BES_6 + BES_7 + BES_8 + BES_9 + BES_10 + BES_11 + BES_12 + BES_13 + BES_14 + BES_15 + BES_16 + PFS_1 + PFS_2 + PFS_3 + PFS_4 + PFS_5 + PFS_6 + PFS_7 + PFS_8 + PFS_9 + PFS_10 + PFS_11 + PFS_12 + PFS_13 + PFS_14 + PFS_15 + AEBQ_153 + AEBQ_155 + AEBQ_154 + AEBQ_156 + AEBQ_157 + AEBQ_146 + AEBQ_145 + AEBQ_144 + AEBQ_147 + AEBQ_148 + AEBQ_149 + AEBQ_150 + AEBQ_151 + AEBQ_152 + DEBQ_11 + DEBQ_12 + DEBQ_13 + DEBQ_14 + DEBQ_15 + DEBQ_16 + DEBQ_17 + DEBQ_18 + DEBQ_19 + DEBQ_20 + TFEQ_D_16 + TFEQ_D_25 + TFEQ_D_31 + TFEQ_D_1 + TFEQ_D_2 + TFEQ_D_7 + TFEQ_D_9 + TFEQ_D_11 + TFEQ_D_13 + TFEQ_D_15 + TFEQ_D_20 + TFEQ_D_27 + TFEQ_D_36 + TFEQ_D_45 + TFEQ_D_49 + TFEQ_D_51 + TFEQ_H_3 + TFEQ_H_5 + TFEQ_H_8 + TFEQ_H_12 + TFEQ_H_17 + TFEQ_H_19 + TFEQ_H_22 + TFEQ_H_24 + TFEQ_H_26 + TFEQ_H_29 + TFEQ_H_34 + TFEQ_H_39 + TFEQ_H_41 + TFEQ_H_47 + PNEES_1 + PNEES_2 + PNEES_4 + PNEES_6 + PNEES_7 + PNEES_8 + PNEES_11 + PNEES_12 + PNEES_13 + PNEES_15 + PNEES_16 + PNEES_18 IES.EFPR =~ IES_EFPR_3 + IES_EFPR_10 + IES_EFPR_16 + IES_EFPR_17 + IES_EFPR_23 + IES_EFPR_24 + IES_EFPR_25 + IES_EFPR_26 IES.RHSC =~ IES_RHSC_11 + IES_RHSC_12 + IES_RHSC_13 + IES_RHSC_35 + IES_RHSC_36 + IES_RHSC_37 IES.BFCC =~ IES_BFCC_31 + IES_BFCC_32 + IES_BFCC_33 SREBQ. =~ SREBQ_A + SREBQ_B + SREBQ_C + SREBQ_D + SREBQ_E BES. =~ BES_1 + BES_2 + BES_3 + BES_4 + BES_5 + BES_6 + BES_7 + BES_8 + BES_9 + BES_10 + BES_11 + BES_12 + BES_13 + BES_14 + BES_15 + BES_16 PFS. =~ PFS_1 + PFS_2 + PFS_3 + PFS_4 + PFS_5 + PFS_6 + PFS_7 + PFS_8 + PFS_9 + PFS_10 + PFS_11 + PFS_12 + PFS_13 + PFS_14 + PFS_15 AEBQ.EOE =~ AEBQ_153 + AEBQ_155 + AEBQ_154 + AEBQ_156 + AEBQ_157 AEBQ.H =~ AEBQ_146 + AEBQ_145 + AEBQ_144 + AEBQ_147 + AEBQ_148 AEBQ.FR =~ AEBQ_149 + AEBQ_150 + AEBQ_151 + AEBQ_152 DEBQ.EX =~ DEBQ_11 + DEBQ_12 + DEBQ_13 + DEBQ_14 + DEBQ_15 + DEBQ_16 + DEBQ_17 + DEBQ_18 + DEBQ_19 + DEBQ_20 TFEQ.D =~ TFEQ_D_16 + TFEQ_D_25 + TFEQ_D_31 + TFEQ_D_1 + TFEQ_D_2 + TFEQ_D_7 + TFEQ_D_9 + TFEQ_D_11 + TFEQ_D_13 + TFEQ_D_15 + TFEQ_D_20 + TFEQ_D_27 + TFEQ_D_36 + TFEQ_D_45 + TFEQ_D_49 + TFEQ_D_51 TFEQ.H =~ TFEQ_H_3 + TFEQ_H_5 + TFEQ_H_8 + TFEQ_H_12 + TFEQ_H_17 + TFEQ_H_19 + TFEQ_H_22 + TFEQ_H_24 + TFEQ_H_26 + TFEQ_H_29 + TFEQ_H_34 + TFEQ_H_39 + TFEQ_H_41 + TFEQ_H_47 PNEES.N =~ PNEES_1 + PNEES_2 + PNEES_4 + PNEES_6 + PNEES_7 + PNEES_8 + PNEES_11 + PNEES_12 + PNEES_13 + PNEES_15 + PNEES_16 + PNEES_18 ' ### calculate model item.cfa.1 <- cfa(item.model1, data = item.dat, missing="pairwise", std.lv = TRUE, ordered =ALL) summary(item.cfa.1, fit.measures=TRUE, standardized=TRUE) When I run the code I get this error message: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING: Could not compute standard errors! The information matrix could not be inverted. This may be a symptom that the model is not identified. I understand this could be because my model is not identified. However, when I check the df's it says there are 7021 df's. I am also not sure how to test my model to see if it under identified. Any advice would be very helpful.
Why glm make an input error on this function
I'm trying to run a glm in R but it results me with an error I can't figure it out how to solve: > GLM.3 <- glm(log(Total_Pass + 1) ~ Total_Pass + Total_Buzz + dm_plant + dm_cdeagua + dm_cultivo + dm_humed + dm_bnativ + dm_snaspe + Cultivos + BosqNat + Plantac + Pastizal + Matorral + Humedal + C_agua + Sup_imper + Tie_desnud + hielo + alt_media + pend_media + Temp_media + PP_media + CA _100 + PLAND _100 + PD _100 + ED _100 + AREA_MN _100 + ENN_MN_100 + CA _210 + PLAND _210 + PD _210 + ED _210 + AREA_MN _210 + ENN_MN_210 + CA _600 + PLAND _600 + PD _600 + ED _600 + AREA_MN _600 + ENN_MN_600 + SHDI + SIDI + MSIDI + SHEI + SIEI + MSIEI, family=gaussian(identity), data=bats_araucania_500) Error: unexpected input in "Total_Pass + Total_Buzz + dm_plant + dm_cdeagua + dm_cultivo + dm_humed + dm_bnativ + dm_snaspe + Cultivos + BosqNat + Plantac + Pastizal + Matorral + Humedal + C_agua + Sup_imper + Tie_desnud" Any help is useful
R can not handle column names with space: CA _210. Try to wrap these columns between two ` (backticks) or rename your columns without spaces. FYI : If you are using all columns as predictors, you can write your code this way: glm(log(y+1) ~ . , nextargs...)
How can i opptimize that code of asp.net
I want to optimize that code that is written bellow if you have an answer please quickly reply me, In This code I want to put different conditions and return a different url in each condition if it is possible with optimize way then reply me:
if (Offer1_Rb_Yes.Checked == true || DropDownList1.SelectedIndex > 0)
{
int offerid = MyOffers[0].OfferId;
DAL.offers Offer = new DAL.offers();
Offer = obj.GetOffer(offerid);
if (Offer.CampId == "WINE-MAKERS-CHOICE")
{
url1 = url1 + "WINE-MAKERS-CHOICE&sid=TPF34" + "&email=" + Email + "&firstname=" + FirstName + "&lastname=" + LastName + "&dob=" + DOB + "&county=" + State + "&postcode=" + PostCode + "&phone1=" + Phone1 + "&c1=" + Gender;
}
else if (Offer.CampId == "LETS-INSURE-CA")
{
url1 = url1 + "LETS-INSURE-CA&sid=TPF34" + "&email=" + Email + "&firstname=" + FirstName + "&lastname=" + LastName + "&dob=" + DOB + "&county=" + State + "&postcode=" + PostCode + "&phone1=" + Phone1 + "&c1=" + Age;
}
else if (Offer.CampId == "SOLAR-BROKER")
{
url1 = url1 + "SOLAR-BROKER&sid=TPF34" + "&email=" + Email + "&firstname=" + FirstName + "&lastname=" + LastName + "&dob=" + DOB + "&county=" + State + "&postcode=" + PostCode + "&phone1=" + Phone1 + "&c1=" + Age;
}
else if (Offer.CampId == "WENATEX-AU")
{
url1 = url1 + "WENATEX-AU&sid=TPF34" + "&email=" + Email + "&firstname=" + FirstName + "&lastname=" + LastName + "&dob=" + DOB + "&street1=" + street1 + "&towncity=" + suburb + "&county=" + State + "&postcode=" + PostCode + "&phone1=" + Phone1 + "&source=" + Source;
}
else if (Offer.CampId == "MODERN-SOLAR-COMP")
{
url1 = url1 + "MODERN-SOLAR-COMP&sid=TPF34" + "&email=" + Email + "&firstname=" + FirstName + "&lastname=" + LastName + "&dob=" + DOB + "&street1=" + street1 + "&towncity=" + suburb + "&county=" + State + "&postcode=" + PostCode + "&phone1=" + Phone1 + "&phone2=" + Phone2 + "&gender=" + Gender + "&solar_pv=" + Solar_pv;
}
else if (Offer.CampId == "CAREERS-AUSTRALIA-3")
{
url1 = url1 + "CAREERS-AUSTRALIA-3&sid=TPF34" + "&firstname=" + FirstName + "&lastname=" + LastName + "&county=" + State + "&postcode=" + PostCode + "&phone1=" + Phone1;
}
else if (Offer.CampId == "GOOD-LIFE")
{
url1 = url1 + "GOOD-LIFE&sid=TPF34" + "&email=" + Email + "&firstname=" + FirstName + "&lastname=" + LastName + "&postcode=" + PostCode + "&phone1=" + Phone1;
}
else if (Offer.CampId == "GRYPHON-SOLAR")
{
url1 = url1 + "GRYPHON-SOLAR&sid=TPF34" + "&email=" + Email + "&firstname=" + FirstName + "&lastname=" + LastName + "&county=VIC&postcode=" + PostCode + "&phone1=" + Phone1 + "&source=" + Source + "&age=" + Age;
}
else if (Offer.CampId == "GRYPHON-SOLAR-WA")
{
url1 = url1 + "GRYPHON-SOLAR-WA&sid=TPF34" + "&email=" + Email + "&firstname=" + FirstName + "&lastname=" + LastName + "&county=WA&postcode=" + PostCode + "&phone1=" + Phone1 + "&source=" + Source + "&age=" + Age;
}
else if (Offer.CampId == "ACQUIRE-LEARNING")
{
url1 = url1 + "ACQUIRE-LEARNING&sid=TPF34" + "&email=" + Email + "&firstname=" + FirstName + "&lastname=" + LastName + "&county=" + State + "&postcode=" + PostCode + "&phone1=" + Phone1 + "&industry=" + Industry;
}
else if (Offer.CampId == "KOGAN")
{
url1 = url1 + "KOGAN&sid=TPF34" + "&email=" + Email + "&firstname=" + FirstName + "&lastname=" + LastName + "&dob=" + DOB + "&towncity=" + suburb + "&county=" + State + "&postcode=" + PostCode + "&phone1=" + Phone1 + "&ipaddress=" + ipaddress;
}
}
I would write it as below:
if (offer5_Rb_Yes.Checked == true)
{
int offerid = MyOffers[4].OfferId;
DAL.offers Offer = new DAL.offers();
Offer = obj.GetOffer(offerid);
url5=url5+Offer.CampId+"&sid=TPF34" + "&email=" + Email + "&firstname=" + FirstName + "&lastname=" + LastName + "&dob=" + DOB + "&county=" + State + "&postcode=" + PostCode + "&phone1=" + Phone1 + "&c1=" + Age;
return url5;
}
I see that only thing gets changed in your url is Offer.CampId and rest remains same! so you can do it as above!
UPDATE
I just noticed that in the first condition your url has small change at the end! Not sure whether its intentional or by mistake! If it's intentional then you may try using ternary operators as your url changes only for one condition and remains same for other 2.
url5= Offer.CampId=="WINE-MAKERS-CHOICE"?
url5+Offer.CampId+"&sid=TPF34" + "&email=" + Email + "&firstname=" + FirstName + "&lastname=" + LastName + "&dob=" + DOB + "&county=" + State + "&postcode=" + PostCode + "&phone1=" + Phone1 + "&c1=" + Gender
:url5+Offer.CampId+"&sid=TPF34" + "&email=" + Email + "&firstname=" + FirstName + "&lastname=" + LastName + "&dob=" + DOB + "&county=" + State + "&postcode=" + PostCode + "&phone1=" + Phone1 + "&c1=" + Age;
Random Forest in R (multi-label-classification)
I'm fairly new to R, trying to implement Random Forest algorithm.
My training and test set have 60 features in the format:
Train: feature1,feature2 .. feature60,Label
Test: FileName,feature1,feature2 ... feature60
Train-sample
mov-mov,or-or,push-push,or-mov,sub-sub,mov-or,sub-mov,xor-or,call-sub,mul-imul,mov-push,push-mov,push-call,or-jz,mov-mul,cmp-or,mov-sub,sub-or,or-sub,or-push,jnz-or,jmp-sub,or-in,mov-call,retn-sub,mul-mul,or-jmp,imul-mul,pop-pop,nop-nop,nop-mul,sub-push,imul-mov,test-or,mul-mov,lea-push,std-mov,in-call,or-call,mov-std,mov-cmp,std-mul,call-or,jz-mov,push-or,pop-retn,add-mov,mov-add,mov-xor,in-inc,mov-pop,in-or,in-push,push-lea,lea-mov,mov-lea,sub-add,std-std,sub-cmp,or-cmp,Label
687,1346,1390,1337,750,2770,1518,418,1523,0,441,532,612,512,0,411,354,310,412,495,134,236,318,237,226,0,0,0,200,0,0,386,39,365,0,0,0,125,528,0,125,0,41,260,169,143,149,61,89,0,127,126,107,44,45,40,79,0,273,157,9
812,873,83,533,88,484,264,106,199,0,188,137,128,51,38,92,131,102,52,58,37,26,428,95,107,0,34,0,58,0,0,39,0,26,0,27,0,152,152,0,45,0,124,0,0,73,84,88,22,23,59,319,105,56,86,47,0,0,43,41,2
Test-sample
FileName,mov-mov,or-or,push-push,or-mov,sub-sub,mov-or,xor-or,sub-mov,call-sub,mul-imul,push-mov,mov-push,push-call,mov-mul,or-jz,cmp-or,mov-sub,sub-or,or-sub,or-push,jmp-sub,jnz-or,or-in,mul-mul,or-jmp,mov-call,retn-sub,imul-mul,nop-mul,pop-pop,nop-nop,imul-mov,sub-push,mul-mov,test-or,lea-push,std-mov,or-call,mov-std,in-call,std-mul,mov-cmp,call-or,push-or,jz-mov,pop-retn,in-or,add-mov,mov-add,in-inc,mov-xor,in-push,push-lea,mov-pop,lea-mov,mov-lea,mov-nop,or-cmp,sub-add,sub-cmp
Ig2DB5tSiEy1cJvV0zdw,166,360,291,194,41,201,62,61,41,18,85,56,121,18,15,0,57,131,113,123,0,9,54,0,0,18,15,0,0,15,0,8,25,0,0,11,0,70,0,43,0,0,63,37,0,14,51,43,56,36,26,0,20,14,17,14,0,9,18,0
k4HCwy5WRFXczJU6eQdT,3,88,106,23,104,0,12,43,59,0,65,87,99,0,2,2,47,22,4,53,1,5,0,0,0,0,46,0,0,0,0,0,4,0,0,6,0,44,0,21,0,0,0,0,0,0,0,2,1,1,3,0,1,2,9,2,0,0,44,2
So what I have so far in R is this,
library(randomForest);
dat <- read.csv("train-sample.csv", sep=",", h=T);
test <- read.csv("test-sample.csv", sep=",", h=T);
attach(dat);
#If I do this, I get Error: unexpected 'in' ...
rfmodel = randomForest (Label ~ mov-mov + or-or + push-push + or-mov + sub-sub + mov-or + sub-mov + xor-or + call-sub + mul-imul + mov-push + push-mov + push-call + or-jz + mov-mul + cmp-or + mov-sub + sub-or + or-sub + or-push + jnz-or + jmp-sub + or-in + mov-call + retn-sub + mul-mul + or-jmp + imul-mul + pop-pop + nop-nop + nop-mul + sub-push + imul-mov + test-or + mul-mov + lea-push + std-mov + in-call + or-call + mov-std + mov-cmp + std-mul + call-or + jz-mov + push-or + pop-retn + add-mov + mov-add + mov-xor + in-inc + mov-pop + in-or + in-push + push-lea + lea-mov + mov-lea + sub-add + std-std + sub-cmp + or-cmp, data=dat);
#If I do this, I get Error in terms.formula(formula, data = data) : invalid model formula in ExtractVars
rfmodel = randomForest (Label ~ 'mov-mov' + 'or-or' + 'push-push' + or-mov + sub-sub + mov-or + sub-mov + xor-or + call-sub + mul-imul + mov-push + push-mov + push-call + or-jz + mov-mul + cmp-or + mov-sub + sub-or + or-sub + or-push + jnz-or + jmp-sub + 'or-in' + mov-call + retn-sub + mul-mul + or-jmp + imul-mul + pop-pop + nop-nop + nop-mul + sub-push + imul-mov + test-or + mul-mov + lea-push + 'std-mov' + 'in-call' + 'or-call' + 'mov-std' + 'mov-cmp' + 'std-mul' + 'call-or' + 'jz-mov' + 'push-or' + 'pop-retn' + 'add-mov' + 'mov-add' + 'mov-xor' + 'in-inc' + 'mov-pop' + 'in-or' + 'in-push' + 'push-lea' + 'lea-mov' + 'mov-lea' + 'sub-add' + 'std-std' + 'sub-cmp' + 'or-cmp', data=dat);
#I even tried this and got Error in na.fail.default(list(Label = c(9L, 2L, 9L, 1L, 8L, 6L, 2L, 2L, : missing values in object
rfmodel <- randomForest(Label~., dat);
So I'm kinda stuck. I want to end up using something like,
predicted <- predict(rfmodel, test, type="response");
prop.table(table(test$FileName, predicted),1);
To get an output in form of:
FileName, Label1, Label2, Label3 .. Label9
name1, 0.98, 0, 0.02, 0, 0 .. 0
(basically the fileName with probabilities of each label)
Any help is appreciated. Thank you.
inserting custom text to ggplot2
I have a ggplot graph that I would like to insert custom string below 0 as "Within" and above 0 as "Breached".
I am doing this:
ggplot(z, aes(Date, Breach1/60, group=Jobs, label=c("Within SLA", "Breached SLA"))) +
geom_line(size=1) +
theme_bw() + ylab("Hours") + xlab("Date") + opts(title="Jobs") +
geom_hline(yintercept=0, color="red", size=2) + geom_text(hjust=0, vjust=3)
This seems to put text all over the place. I like to put one text above the zero and one text below the zero value. Any ideas?
You are after annotate:
ggplot(z, aes(Date, Breach1/60, group=Jobs)) +
geom_line(size=1) +
theme_bw() + ylab("Hours") + xlab("Date") + opts(title="Jobs") +
geom_hline(yintercept=0, color="red", size=2) +
annotate("text", label = "Within SLA", x = 1, y = 2) +
annotate("text", label = "Breached", x = 1, y = -2)